Earl T. Campbell; Barbara M. Terhal; Christophe Vuillot
2017-09-13 (online)
Nature (Nature). 549, 7671, 172-179. doi:10.1038/nature23460
Superconducting qubits 2013-superconducting-qubit-outlook (ref. 3).
Gottesman-Knill theorem says you need T in addition to S, H, and CNOT or you get no quantum 1997-gottesman-thesis (ref. 9) (ref. 10).
Trivial codes can't have transversal implementations of all gates for universal compuation (ref. 11) (ref. 12).
Surface code first as a topological memory 2002-surface-code (ref. 13). Logical qubit can be two holes in a code sheet (ref. 17) or two pairs of latice defects or twists (ref. 18) (ref. 19).
Show References| Num | Entry | Why |
|---|---|---|
| 13 | 2002-surface-code | "Seminal paper on using the surface code as a quantum memory" |
| 3 | 2013-superconducting-qubit-outlook | |
| 9 | 1997-gottesman-thesis |
Leonie Mueck
2017-09-13 (online)
Nature (Nature). 549, 7671, 171-171. doi:10.1038/549171a
Nature ran a featurette where everyone mused about quantum computing and how to make it useful. Includes 2017-fault-tolerant-computation, 2017-quantum-programming-language,
Frederic T. Chong; Diana Franklin; Margaret Martonosi
2017-09-13 (online)
Nature (Nature). 549, 7671, 180-187. doi:10.1038/nature23459
Quantum/classical co-processor model described by 2015-quipper (ref. 19).
Show References| Num | Entry | Why |
|---|---|---|
| 18 | 2016-h2-vqe | "This paper is a good example of the emerging importance of classical-quantum co-processing" |
| 19 | 2015-quipper | "This paper offers another perspective on quantum programming language design issues." |
| 47 | 2016-quil | "QUIL - A new language with an emphasis on the classical-quantum interface. Open source." |
Will Zeng; Blake Johnson; Robert Smith; Nick Rubin; Matt Reagor; Colm Ryan; Chad Rigetti
2017-09-13 (online)
Nature (Nature). 549, 7671, 149-151. doi:10.1038/549149a
A comment that argues for good quantum software.
Eyob A. Sete; Matthew J. Reagor; Nicolas Didier; Chad T. Rigetti
2017-08-07 (online)
Physical Review Applied (Physical Review Applied). 8, 2, doi:10.1103/PhysRevApplied.8.024004
Improve fluxonimum (ref. 10) (ref. 11) (ref. 12) (ref. 13) (ref. 14) (ref. 15) with "sweet spots". I think this is just simulations of how it would behave w.r.t noise though.
Static qubit-qubit couplings with 2q-gates in hundreds of nanoseconds, 100us coherence, and fidelity of 99.1% (ref. 1) (ref. 2) (ref. 3).
Frequency tinable qubits: 20us coherence, 50ns 2q-gates and 99.44% fidelity (ref. 4) (ref. 5). Fluctuations from flux noise ruin coherence (ref. 6) (ref. 7) (ref. 8). Also not anharmonic enough means leaks to higher levels (ref. 9).
M. Reagor; C. B. Osborn; N. Tezak; A. Staley; G. Prawiroatmodjo; M. Scheer; N. Alidoust; E. A. Sete; N. Didier; M. P. da Silva; E. Acala; J. Angeles; A. Bestwick; M. Block; B. Bloom; A. Bradley; C. Bui; S. Caldwell; L. Capelluto; R. Chilcott; J. Cordova; G. Crossman; M. Curtis; S. Deshpande; T. El Bouayadi; D. Girshovich; S. Hong; A. Hudson; P. Karalekas; K. Kuang; M. Lenihan; R. Manenti; T. Manning; J. Marshall; Y. Mohan; W. O'Brien; J. Otterbach; A. Papageorge; J. -P. Paquette; M. Pelstring; A. Polloreno; V. Rawat; C. A. Ryan; R. Renzas; N. Rubin; D. Russell; M. Rust; D. Scarabelli; M. Selvanayagam; R. Sinclair; R. Smith; M. Suska; T. -W. To; M. Vahidpour; N. Vodrahalli; T. Whyland; K. Yadav; W. Zeng; C. T. Rigetti
2017-06-20 (online)
Eight qubits in a ring, alternating fixed and tunable. Do 2q gates.
Eva-Maria Strauch; Steffen M Bernard; David La; Alan J Bohn; Peter S Lee; Caitlin E Anderson; Travis Nieusma; Carly A Holstein; Natalie K Garcia; Kathryn A Hooper; Rashmi Ravichandran; Jorgen W Nelson; William Sheffler; Jesse D Bloom; Kelly K Lee; Andrew B Ward; Paul Yager; Deborah H Fuller; Ian A Wilson; David Baker
2017-06-12 (online)
Nature Biotechnology (Nat. Biotechnol.). 35, 7, 667-671. doi:10.1038/nbt.3907
Uses computation to design an antibody for influenza A
Mohammad M. Sultan; Vijay S. Pande
2017-05-09 (online) – 2017-06-13 (print)
Journal of Chemical Theory and Computation (J. Chem. Theory Comput.). 13, 6, 2440-2447. doi:10.1021/acs.jctc.7b00182
Hao Wu; Feliks Nüske; Fabian Paul; Stefan Klus; Péter Koltai; Frank Noé
2017-04-21 (print)
The Journal of Chemical Physics (J. Chem. Phys.). 146, 15, 154104. doi:10.1063/1.4979344 arxiv:1610.06773
Provides a better way of "symetrizing" tICA correlation matrix. In tICA, you assume that the dynamics are reversible. When we're learning from finite data, this reversibility isn't respected. Historically, you take your correlation matrix, add its transpose, and divide by two. This is an especially poor approximation if you have many short trajectories. This paper is analogous to the MLE method for symetrizing MSM counts matrices.
Show References| Num | Entry | Why |
|---|---|---|
| 46 | 2015-uncertainty-estimation | Reversibility makes analysis easier |
| 50 | 2013-noe-variational | |
| 51 | 2013-noe-tica | |
| 52 | 2014-nuske-variational |
msm-theory
Avanti Shrikumar; Peyton Greenside; Anshul Kundaje
2017-04-10 (online)
Decompose ouput predictions
machine-learning deep-learning
Lee-Ping Wang; Keri A. McKiernan; Joseph Gomes; Kyle A. Beauchamp; Teresa Head-Gordon; Julia E. Rice; William C. Swope; Todd J. Martínez; Vijay S. Pande
2017-04-06 (online) – 2017-04-27 (print)
The Journal of Physical Chemistry B (J. Phys. Chem. B). 121, 16, 4023-4039. doi:10.1021/acs.jpcb.7b02320
forcefield
Matthew P. Harrigan; Keri A. McKiernan; Veerabahu Shanmugasundaram; Rajiah Aldrin Denny; Vijay S. Pande
2017-04-04 (online) – 2017-12-01 (print)
Scientific Reports (Sci. Rep.). 7, 1, doi:10.1038/s41598-017-00256-y
Weiwei Wang; Roderick MacKinnon
2017-04-01 (print)
Cell (Cell). 169, 3, 422-430.e10. doi:10.1016/j.cell.2017.03.048
kv
Marcus Weber; Konstantin Fackeldey; Christof Schütte
2017-03-28 (print)
The Journal of Chemical Physics (J. Chem. Phys.). 146, 12, 124133. doi:10.1063/1.4978501
Collection of m "base points" they make a gaussian RBF of distances to base points. They normalize it to unity. This is the softmax function, but they don't call it that.
They add base points adaptively and use PCCA+ to lump.
Note that they have stopped calling this "meshless" or "mesh-free", probably because the regular MSM is also meshless. Now the abstract says "This kind of meshless discretization..."
Rui-Ning Sun; Haipeng Gong
2017-02-09 (online) – 2017-03-02 (print)
The Journal of Physical Chemistry Letters (J. Phys. Chem. Lett.). 8, 5, 901-908. doi:10.1021/acs.jpclett.7b00023
They simulated only one domain. They took the NavAb vsd and connected it via molecular dynamics to a homology model to a sea squirt vsd. They used a polarizable force-field, which is more expensive than normal (fixed charge) forcefields but that might be important for something so intertwined with moving electrical charges!
nav
Robert T. McGibbon; Brooke E. Husic; Vijay S. Pande
2017-01-28 (print)
The Journal of Chemical Physics (J. Chem. Phys.). 146, 4, 044109. doi:10.1063/1.4974306 arxiv:1602.08776
The authors argue for a definition of the reaction coordinate as the projection on the dominant eigenfunciton of the propogator. Notably, they say that path-based coordinates are no good, because progress is only defined along the path. They argue that the coordinate shouldn't depend on start and end points. They say the projection should be maximally predictive. This means finding the slowest modes. They note 2006-nadler-diffusion-maps (ref. 61) and 2011-rohrdanz-diffusion-maps (ref. 62) have used this definition.
They go on to show tICA finds this reaction coordinate. To make tICA more interpretable, they develop an algorithm for introducing a sparsity pattern. It's a pseudo-l0 regularization (made smooth so the optimization works).
They also use a unique dihedral featurization: instead of taking the sine and cosine to get around periodicity concerns; they project the values on a bunch of evenly spaced von-mises (periodic gaussians) distributions around the unit circle. Each dihedral is expanded into several numbers. It's like a smooth histogramming. This probably won't work as the number of dihedrals gets large (too many features).
Show References| Num | Entry | Why |
|---|---|---|
| 61 | 2006-nadler-diffusion-maps | |
| 62 | 2011-rohrdanz-diffusion-maps |
msm-theory tica features
Matthew P. Harrigan; Mohammad M. Sultan; Carlos X. Hernández; Brooke E. Husic; Peter Eastman; Christian R. Schwantes; Kyle A. Beauchamp; Robert T. McGibbon; Vijay S. Pande
2017-01-01 (print)
Biophysical Journal (Biophys. J.). 112, 1, 10-15. doi:10.1016/j.bpj.2016.10.042
software
Brooke E. Husic; Robert T. McGibbon; Mohammad M. Sultan; Vijay S. Pande
2016-11-21 (print)
The Journal of Chemical Physics (J. Chem. Phys.). 145, 19, 194103. doi:10.1063/1.4967809
The authors perform GMRQ cross validation on the twelve 2011-larsen-folding folding trajectories to give guidelines for MSM construction.
They present a flowchart for MSM construction that shows the three paths towards clustering: from an rmsd distance metric, from features, or from tICA learned on features.
They introduce GMRQ cross validation in the tradition of 2015-mcgibbon-gmrq (ref. 44).
They present results but stress that you have to do your own cross validataion to be sure. Some conclusions include: 1. tICA and PCA are better than direct clustering of features 2. when using tica, you can use kcenters, kmeans, or minibatch kmeans to the same effect
On one protein (2p6j) they look at all different features and show that they vary a lot. It's unfortunate that this was only done on one protein.
Show References| Num | Entry | Why |
|---|---|---|
| 41 | 2013-noe-variational | Variational principle |
| 42 | 2014-nuske-variational | Variational principle |
| 5 | 2008-anton | Generated the trajectories. |
| 18 | 2011-larsen-folding | Re-analyzed these simulations. "Diversity of proteins analyzed" |
| 44 | 2015-mcgibbon-gmrq | Cross-validation |
msm-theory
Frank Noé; Ralf Banisch; Cecilia Clementi
2016-11-08 (print)
Journal of Chemical Theory and Computation (J. Chem. Theory Comput.). 12, 11, 5620-5630. doi:10.1021/acs.jctc.6b00762
Scale tIC coordinates by a function of the timescale. See also 2015-kinetic-mapping.
msm-theory
Alex Graves; Greg Wayne; Malcolm Reynolds; Tim Harley; Ivo Danihelka; Agnieszka Grabska-Barwińska; Sergio Gómez Colmenarejo; Edward Grefenstette; Tiago Ramalho; John Agapiou; Adrià Puigdomènech Badia; Karl Moritz Hermann; Yori Zwols; Georg Ostrovski; Adam Cain; Helen King; Christopher Summerfield; Phil Blunsom; Koray Kavukcuoglu; Demis Hassabis
2016-10-12 (online)
Nature (Nature). 538, 7626, 471-476. doi:10.1038/nature20101
Augment deep networks with an external memory (RAM) matrix.
Bart says: "TL;DR: This work follows a line of research that teaches deep-nets to learn algorithmic tasks (addition, sorting, multiplication, key-value look-up). This paper goes a bit further and teaches their network to do shortest-path finding in graphs and demonstrates on maps of the London underground. Cool demo with nice results, but the hype-machine has blown it out of proportion (check out the FT article for a breathless take claiming thinking computers are one step closer...)"
machine-learning deep-learning
Rafael Gómez-Bombarelli; David Duvenaud; José Miguel Hernández-Lobato; Jorge Aguilera-Iparraguirre; Timothy D. Hirzel; Ryan P. Adams; Alán Aspuru-Guzik
2016-10-07 (online)
The authors train an auto-encoder to provide a vector representation for small molecules. Small molecules are graphs with varying sizes, so they're hard to feed into neural nets (which require fixed-length bitvectors). By fusing together an encoder and decoder (and making the "middle" representation sufficiently small), they learn a vector representation.
The authors lean heavily on arxiv:1511.06349 (ref. 25) to autoencode SMILES strings.
They use a variational autoencoder (noisy) to avoid "dead zones" in latent space.
They optomize OLED properties as an example.
Show References| Num | Entry | Why |
|---|---|---|
| 25 | arxiv:1511.06349 |
machine-learning cheminformatics misc
Kathryn M. Hart; Chris M. W. Ho; Supratik Dutta; Michael L. Gross; Gregory R. Bowman
2016-10-06 (online)
Nature Communications (Nat. Commun.). 7, 12965. doi:10.1038/ncomms12965
Labmate summarizes:
They generated ensembles using MD, then docked to those ensembles, then re-weighted the docking scores based on the MSM. This gave a huge improvement in the predictive power of docking to predict affinity/potency. It turned an inverse relationship (when docking using xtal structures) into a highly correlated trend.
They confirmed their hypothesis about the protein flexibility by using a mass spec. method.
They identified a loop movement important in the anti-antibacterial activity of the enzyme that was different from one previously proposed/suspected.
They proposed mutants that would stabilize their proposed loop, and tested them experimentally.
The power of using the MSM to re-weight other analyses is also very encouraging to see yet again. Also note that they did all this with what looks like a pretty low amount of aggregate sampling (few microseconds per mutant).
misc
Eyob A. Sete; William J. Zeng; Chad T. Rigetti
2016-10-01 (print)
2016 IEEE International Conference on Rebooting Computing (ICRC) (2016 IEEE International Conference on Rebooting Computing (ICRC)). doi:10.1109/ICRC.2016.7738703
Quantum simulation algorithsm (ref. 1) (ref. 2) (ref. 3).
Quantum machine learning (ref. 4)
Quantum error correction benchmarks (ref. 5) (ref. 6) (ref. 7).
Variational quantum eigensolvers (ref. 8) (ref. 9) (ref. 10).
Correlated material simulations (ref. 11).
Approximate optimization (ref. 12).
For the problems of catalysts (ref. 13) and high temperature superconductivity (ref. 9) show promise.
Cryo operation and superconducting materials means no sissipation preserving quantum coherance.
Transmon qubits have large coherence time (ref. 14). Fluxonium qubits have wide frequency tunability and strong nonlinearity (ref. 15). This means fluxonium are better for two-qubit gates.
Quantum limited amplifiers (ref. 16) (ref. 17) (ref. 18): Josephson parametric amplifier, Josephson bifurcation amplifier, and Josephson parametric converter. Non-linear resonators.
Can do rotations Rx and Ry on any qubit. Can do SWAP between any transmon and fluxonium. Can do CPhase between any fluxonium and half the transmons. All gates can be made with these primitives (ref. 19).
Introduce "TQF" estimate of width * depth of quantum circuit you can run. (ref. 1) runs electronic structure for very small molecules.
Transmon can be "data" for surface code error correction (ref. 24) (ref. 25) and fluxonium as ancillas for parity measurement.
Vedran Dunjko; Jacob M. Taylor; Hans J. Briegel
2016-09-20 (online)
Physical Review Letters (Phys. Rev. Lett.). 117, 13, doi:10.1103/PhysRevLett.117.130501
Stefano Martiniani; K. Julian Schrenk; Jacob D. Stevenson; David J. Wales; Daan Frenkel
2016-09-15 (online)
Physical Review E (Phys. Rev. E). 94, 3, doi:10.1103/PhysRevE.94.031301
Use multistate benett acceptance (MBAR) to find volumes in high dimensions.
misc
Robert T. McGibbon; Carlos X. Hernández; Matthew P. Harrigan; Steven Kearnes; Mohammad M. Sultan; Stanislaw Jastrzebski; Brooke E. Husic; Vijay S. Pande
2016-09-07 (print)
The Journal of Open Source Software (The Journal of Open Source Software). 1, 5, doi:10.21105/joss.00034
Nicholas Guttenberg; Martin Biehl; Ryota Kanai
2016-09-01 (online)
Somehow uses deep networks to extract slow modes from dynamical signals.
machine-learning deep-learning
Huong T. Kratochvil; Joshua K. Carr; Kimberly Matulef; Alvin W. Annen; Hui Li; Michał Maj; Jared Ostmeyer; Arnaldo L. Serrano; H. Raghuraman; Sean D. Moran; J. L. Skinner; Eduardo Perozo; Benoît Roux; Francis I. Valiyaveetil; Martin T. Zanni
2016-09-01 (online) – 2016-09-02 (print)
Science (Science). 353, 6303, 1040-1044. doi:10.1126/science.aag1447
Jianping Wu; Zhen Yan; Zhangqiang Li; Xingyang Qian; Shan Lu; Mengqiu Dong; Qiang Zhou; Nieng Yan
2016-08-31 (online)
Nature (Nature). 537, 7619, 191-196. doi:10.1038/nature19321
Cav structure. What's the diference between 2015-cav-structure
cav
Robert S. Smith; Michael J. Curtis; William J. Zeng
2016-08-11 (online)
Yongqiang Zhang; Yuzhe Du; Dingxin Jiang; Caitlyn Behnke; Yoshiko Nomura; Boris S. Zhorov; Ke Dong
2016-08-03 (online) – 2016-09-16 (print)
Journal of Biological Chemistry (J. Biol. Chem.). 291, 38, 20113-20124. doi:10.1074/jbc.M116.742056
Homology model to open cockroach channel and docking study
nav
P. J. J. O’Malley; R. Babbush; I. D. Kivlichan; J. Romero; J. R. McClean; R. Barends; J. Kelly; P. Roushan; A. Tranter; N. Ding; B. Campbell; Y. Chen; Z. Chen; B. Chiaro; A. Dunsworth; A. G. Fowler; E. Jeffrey; E. Lucero; A. Megrant; J. Y. Mutus; M. Neeley; C. Neill; C. Quintana; D. Sank; A. Vainsencher; J. Wenner; T. C. White; P. V. Coveney; P. J. Love; H. Neven; A. Aspuru-Guzik; J. M. Martinis
2016-07-18 (online)
Physical Review X (Physical Review X). 6, 3, doi:10.1103/PhysRevX.6.031007
Solves molecular hydrogen with variational quantum eigensolver (which is hybrid quantum - classical) and compares to trotterization and quantum phase estimation. The VQE is better.
Daniel L. Parton; Patrick B. Grinaway; Sonya M. Hanson; Kyle A. Beauchamp; John D. Chodera
2016-06-23 (online)
PLOS Computational Biology (PLoS Comput. Biol.). 12, 6, e1004728. doi:10.1371/journal.pcbi.1004728
Automatic generation of homology models of protein families
Conor McClenaghan; Marcus Schewe; Prafulla Aryal; Elisabeth P. Carpenter; Thomas Baukrowitz; Stephen J. Tucker
2016-05-30 (online) – 2016-06-01 (print)
The Journal of General Physiology (J. Gen. Physiol.). 147, 6, 497-505. doi:10.1085/jgp.201611601
Martín Abadi; Paul Barham; Jianmin Chen; Zhifeng Chen; Andy Davis; Jeffrey Dean; Matthieu Devin; Sanjay Ghemawat; Geoffrey Irving; Michael Isard; Manjunath Kudlur; Josh Levenberg; Rajat Monga; Sherry Moore; Derek G. Murray; Benoit Steiner; Paul Tucker; Vijay Vasudevan; Pete Warden; Martin Wicke; Yuan Yu; Xiaoqiang Zheng
2016-05-27 (online)
Josef Melcr; Daniel Bonhenry; Štěpán Timr; Pavel Jungwirth
2016-05-10 (print)
Journal of Chemical Theory and Computation (J. Chem. Theory Comput.). 12, 5, 2418-2425. doi:10.1021/acs.jctc.5b01202
Compare external electric field with ion imbalance.
S. Doerr; M. J. Harvey; Frank Noé; G. De Fabritiis
2016-04-12 (print)
Journal of Chemical Theory and Computation (J. Chem. Theory Comput.). 12, 4, 1845-1852. doi:10.1021/acs.jctc.6b00049
This can make MSMs in addition to being a one-stop shop for running MD.
It's available under an academic license.
software python
Christian R. Schwantes; Diwakar Shukla; Vijay S. Pande
2016-04-01 (print)
Biophysical Journal (Biophys. J.). 110, 8, 1716-1719. doi:10.1016/j.bpj.2016.03.026
They find an intermediate in 2011-larsen-folding NuG2 trajectories that is a register shift that was missed before tICA+MSM.
Robert McGibbon
2016-03-01 (print)
Chapter 1 is a bespoke introduction to MD and MSMs
Chapter 2 is adapted from 2013-mcgibbon-kdml (ref. 37).
Chapter 3 is adapted from 2014-mcgibbon-hmm (ref. 92).
Chapter 4 is adapted from 2015-ratematrix (ref. 120).
Chapter 5 is adapted from 2014-mcgibbon-bic (ref. 162).
Chapter 6 is adapted from 2015-mcgibbon-gmrq (ref. 214).
Chapter 7 is adapted from 2016-sparsetica.
Chapter 8 is adapted from 2015-mdtraj.
Show References| Num | Entry | Why |
|---|---|---|
| 37 | 2013-mcgibbon-kdml | |
| 92 | 2014-mcgibbon-hmm | |
| 120 | 2015-ratematrix | |
| 162 | 2014-mcgibbon-bic | |
| 214 | 2015-mcgibbon-gmrq |
Ren-Gong Zhuo; Peng Peng; Xiao-Yan Liu; Hai-Tao Yan; Jiang-Ping Xu; Jian-Quan Zheng; Xiao-Li Wei; Xiao-Yun Ma
2016-02-16 (online) – 2016-08-01 (print)
Scientific Reports (Sci. Rep.). 6, 1, doi:10.1038/srep21248
Robert McGibbon
2016-02-12 (online)
Marcus Schewe; Ehsan Nematian-Ardestani; Han Sun; Marianne Musinszki; Sönke Cordeiro; Giovanna Bucci; Bert L. de Groot; Stephen J. Tucker; Markus Rapedius; Thomas Baukrowitz
2016-02-01 (print)
Cell (Cell). 164, 5, 937-949. doi:10.1016/j.cell.2016.02.002
Rebecca D. Tarvin; Juan C. Santos; Lauren A. O'Connell; Harold H. Zakon; David C. Cannatella
2016-01-18 (online) – 2016-04-01 (print)
Molecular Biology and Evolution (Mol. Biol. Evol.). 33, 4, 1068-1081. doi:10.1093/molbev/msv350
Evolutionary analysis and docking study on NaV 1.4 in frogs immune to toxins
nav
Jumin Lee; Xi Cheng; Jason M. Swails; Min Sun Yeom; Peter K. Eastman; Justin A. Lemkul; Shuai Wei; Joshua Buckner; Jong Cheol Jeong; Yifei Qi; Sunhwan Jo; Vijay S. Pande; David A. Case; Charles L. Brooks; Alexander D. MacKerell; Jeffery B. Klauda; Wonpil Im
2016-01-12 (print)
Journal of Chemical Theory and Computation (J. Chem. Theory Comput.). 12, 1, 405-413. doi:10.1021/acs.jctc.5b00935
software
J. Wu; Z. Yan; Z. Li; C. Yan; S. Lu; M. Dong; N. Yan
2015-12-17 (online) – 2015-12-18 (print)
Science (Science). 350, 6267, aad2395-aad2395. doi:10.1126/science.aad2395
also a cav structure.
cav
Samuel R. Bowman; Luke Vilnis; Oriol Vinyals; Andrew M. Dai; Rafal Jozefowicz; Samy Bengio
2015-11-19 (online)
Advances in autoencoding text, used by 2016-aspuru-mol-feat.
misc
Martin K. Scherer; Benjamin Trendelkamp-Schroer; Fabian Paul; Guillermo Pérez-Hernández; Moritz Hoffmann; Nuria Plattner; Christoph Wehmeyer; Jan-Hendrik Prinz; Frank Noé
2015-11-10 (print)
Journal of Chemical Theory and Computation (J. Chem. Theory Comput.). 11, 11, 5525-5542. doi:10.1021/acs.jctc.5b00743
software python
Benjamin Trendelkamp-Schroer; Hao Wu; Fabian Paul; Frank Noé
2015-11-07 (print)
The Journal of Chemical Physics (J. Chem. Phys.). 143, 17, 174101. doi:10.1063/1.4934536
Reversible estimates for MSMs
Robert T. McGibbon; Kyle A. Beauchamp; Matthew P. Harrigan; Christoph Klein; Jason M. Swails; Carlos X. Hernández; Christian R. Schwantes; Lee-Ping Wang; Thomas J. Lane; Vijay S. Pande
2015-10-01 (print)
Biophysical Journal (Biophys. J.). 109, 8, 1528-1532. doi:10.1016/j.bpj.2015.08.015
software python
F. Vitalini; F. Noé; B. G. Keller
2015-09-08 (print)
Journal of Chemical Theory and Computation (J. Chem. Theory Comput.). 11, 9, 3992-4004. doi:10.1021/acs.jctc.5b00498
Authors simulate individual (capped) amino acids for 1us / each and construct (mini-)MSMs on each one. They use the outerproduct of these mini-MSMs to serve as a basis set for peptides. MiniMSMs are on a grid in phi-psi angles. Since each miniMSM has approx 3 modes, the full basis would be 3^(N), which is way too big! They call the second and third modes "excited states" and use a basis set that contains a singly-exited residue. E.g. 11111 + [ [21111, 121111, 112111, 111211, ...] ].
Alanine preceded by a proline is taken as a special case.
msm-theory
Mark James Abraham; Teemu Murtola; Roland Schulz; Szilárd Páll; Jeremy C. Smith; Berk Hess; Erik Lindahl
2015-09-01 (print)
SoftwareX (SoftwareX). 1-2, 19-25. doi:10.1016/j.softx.2015.06.001
software
Benoît Valiron; Neil J. Ross; Peter Selinger; D. Scott Alexander; Jonathan M. Smith
2015-07-23 (print)
Communications of the ACM (Commun. ACM). 58, 8, 52-61. doi:10.1145/2699415
Quantum programming language implemented inside Haskell. Invisions quantum co-processor.
Robert T. McGibbon; Vijay S. Pande
2015-07-21 (print)
The Journal of Chemical Physics (J. Chem. Phys.). 143, 3, 034109. doi:10.1063/1.4926516
msm-theory
Ch. Schütte; M. Sarich
2015-06-22 (online) – 2015-09-01 (print)
The European Physical Journal Special Topics (The European Physical Journal Special Topics). 224, 12, 2445-2462. doi:10.1140/epjst/e2015-02421-0
Transfer operator 1999-schutte-msm (ref. 1).
Coarse grain MSM states 2000-pcca (ref. 2) 2005-pcca (ref. 3).
Meshless MSMs 2006-meshless-msm-thesis (ref. 24) 2011-meshless-msm (ref. 32) 2011-meshless-msm (ref. 33). Wikipedia says these are also called "meshfree" methods.
Show References| Num | Entry | Why |
|---|---|---|
| 1 | 1999-schutte-msm | |
| 2 | 2000-pcca | |
| 3 | 2005-pcca | |
| 24 | 2006-meshless-msm-thesis | |
| 32 | 2011-meshless-msm | |
| 33 | 2011-meshless-msm |
Frank Noe; Cecilia Clementi
2015-06-20 (online)
Scale tIC coorindates by the eigenvalue. See also 2016-commute-maps.
Christian Schwantes
2015-05-01 (print)
Section 1.2 is adapted from 2015-schwantes-ktica (ref. 27) and 2014-mcgibbon-bic (ref. 28).
Chapter 2 is adapted from 2014-mcgibbon-bic (ref. 28).
Chapter 3 is adapted from 2013-schwantes-tica (ref. 73).
Chapter 4 is adapted from 2016-schwantes-nug2 (ref. 122).
Chapter 5 is adapted from 2015-schwantes-ktica (ref. 27).
Chapter 6 is supposed to have been submitted for publication.
Show References| Num | Entry | Why |
|---|---|---|
| 27 | 2015-schwantes-ktica | |
| 28 | 2014-mcgibbon-bic | |
| 28 | 2014-mcgibbon-bic | |
| 73 | 2013-schwantes-tica | |
| 122 | 2016-schwantes-nug2 | |
| 27 | 2015-schwantes-ktica |
Robert T. McGibbon; Vijay S. Pande
2015-03-28 (print)
The Journal of Chemical Physics (J. Chem. Phys.). 142, 12, 124105. doi:10.1063/1.4916292
msm-theory variational
Y. Y. Dong; A. C. W. Pike; A. Mackenzie; C. McClenaghan; P. Aryal; L. Dong; A. Quigley; M. Grieben; S. Goubin; S. Mukhopadhyay; G. F. Ruda; M. V. Clausen; L. Cao; P. E. Brennan; N. A. Burgess-Brown; M. S. P. Sansom; S. J. Tucker; E. P. Carpenter
2015-03-12 (online) – 2015-03-13 (print)
Science (Science). 347, 6227, 1256-1259. doi:10.1126/science.1261512
Structures of up and down trek2.
Cites 2010-k2p-review (ref. 1) for background.
4XDJ (down state), 4BW5 (up state), 4XDL (Br-fluoxetine complex, down), 4XDK (norfluoxetine complex, down)
Show References| Num | Entry | Why |
|---|---|---|
| 1 | 2010-k2p-review |
kchan structures
Matthew P. Harrigan; Diwakar Shukla; Vijay S. Pande
2015-03-10 (print)
Journal of Chemical Theory and Computation (J. Chem. Theory Comput.). 11, 3, 1094-1101. doi:10.1021/ct5010017
Solvent-shells featurization for including solvent in MSM construction.
Hao Wu; Frank Noé
2015-02-28 (print)
The Journal of Chemical Physics (J. Chem. Phys.). 142, 8, 084104. doi:10.1063/1.4913214
Variational method 2013-noe-variational (ref. 26) 2014-nuske-variational (ref. 27).
MSM is variational with step functions 2013-noe-variational (ref. 26).
"Markov transition models (MTMs)", specifically Gaussian mixtures (GMTM).
Show References| Num | Entry | Why |
|---|---|---|
| 26 | 2013-noe-variational | |
| 27 | 2014-nuske-variational | |
| 26 | 2013-noe-variational |
msm-theory
Diwakar Shukla; Carlos X. Hernández; Jeffrey K. Weber; Vijay S. Pande
2015-02-17 (print)
Accounts of Chemical Research (Acc. Chem. Res.). 48, 2, 414-422. doi:10.1021/ar5002999
review
Christian R. Schwantes; Vijay S. Pande
2015-02-10 (print)
Journal of Chemical Theory and Computation (J. Chem. Theory Comput.). 11, 2, 600-608. doi:10.1021/ct5007357
They introduce kernel tICA as an extension to tICA. This is useful to get non-linear solutions to the tICA equation. They claim you can estimate eigenprocesses without building an MSM.
They briefly introduce the transfer operator. They introduce the variational principle of conformation dynamics per 2011-prinz (ref. 25). They introduce tICA as maximizing the autocorrelation. They say that solutions to tICA are the same as solutions to the variational problem per 2013-noe-tica (ref. 28). Linearity makes them crude solutions.
They explain that a natural approach to introduce non-linearity is to expand the original representation into a higher dimensional space and do tICA there. They say this is impractical. The expanded space probably has to be huge. You can perform analysis in the big representation without explicitly representing it by using the "kernel trick". They reproduce an example of the kernel trick from 1998-scholkopf-kernel-pca (ref. 39).
They re-write the tICA problem only in terms of inner products so you can apply the kernel trick. They introduce normalization. They choose a gaussian kernel. They simulate a four-well potential, muller potential, alanine dipeptide, and fip35ww. They need to do MLE cross validation over parameters (kernel width and regularization strength).
This uses so much RAM! Huge matrices to solve (that scale with the amount of data!!)
Show References| Num | Entry | Why |
|---|---|---|
| 21 | 2014-msm-perspective | Data needs analysis |
| 25 | 2011-prinz | Details of transfer operator approach. |
| 33 | 2001-schutte-variational | Details of transfer operator approach. |
| 34 | 2013-noe-variational | "It was shown that a variational principle can be derived for the eignvalues of the transfer operator." The autocorelation of a function is less than the autocorrelation of the first dynamical eigenfunction of the transfer operator. This is used to argue that you don't have to estimate the operator itself. Just estimate its eigenfunctions |
| 35 | 2014-nuske-variational | "Successfully constructed estimates of the top eigenfunctions in the span of a prespecified library of basis functions." Contrast with this work, which "does not require a predefined basis set" |
| 22 | 2013-schwantes-tica | Citing tICA |
| 28 | 2013-noe-tica | solutions to tica provide estimates of the slowest eigenfunctions of the transfer operator. |
| 36 | doi:10.1103/PhysRevLett.72.3634 | Citing tICA |
| 37 | doi:10.1162/neco.2006.18.10.2495 | Citing tICA |
| 39 | 1998-scholkopf-kernel-pca | Used to introduce ther kernel trick. |
msm-theory
Markus Rapedius; Matthias R. Schmidt; Chetan Sharma; Phillip J. Stansfeld; Mark S.P. Sansom; Thomas Baukrowitz; Stephen J. Tucker
2014-10-31 (online) – 2012-11-18 (print)
Channels (Channels). 6, 6, 473-478. doi:10.4161/chan.22153
Jacques Noël; Guillaume Sandoz; Florian Lesage
2014-10-27 (online) – 2011-09-01 (print)
Channels (Channels). 5, 5, 402-409. doi:10.4161/chan.5.5.16469
G. Hummer
2014-10-16 (online) – 2014-10-17 (print)
Science (Science). 346, 6207, 303-303. doi:10.1126/science.1260555
D. A. Kopfer; C. Song; T. Gruene; G. M. Sheldrick; U. Zachariae; B. L. de Groot
2014-10-16 (online) – 2014-10-17 (print)
Science (Science). 346, 6207, 352-355. doi:10.1126/science.1254840
Introduces a new way of simulating a membrane potential: They stack two membranes on top of one another, creating an "inside" between the two. This doesn't hurt simulation throughput, because you get twice as much protein motion data in the same amount of simulation time (ignore extra factor of log n in system size). This seems to be a refinement on their earlier work in 2011-kutzner-double-membrane (ref. 25).
They hide the startling fact that every time an ion moves through the channel, they have to instantaneously move it back inside. Benoit has argued that this instantaneous jump, which can be a 100 mV difference is rediculous.
The main point of this paper is that ions translocate through the four sites of potassium channel without any waters between them. This "hard knock" mechanism is in contrast to a "soft knock" mechanism where the ion-ion interactions are softened by interviening waters.
They re-refine the xray data to show it is consistent with the hard-knock mechanism.
Show References| Num | Entry | Why |
|---|---|---|
| 25 | 2011-kutzner-double-membrane |
kchan md-applications
C. R. Schwantes; R. T. McGibbon; V. S. Pande
2014-09-07 (print)
The Journal of Chemical Physics (J. Chem. Phys.). 141, 9, 090901. doi:10.1063/1.4895044
Very good perspective on the importance of analysis (particularly MSM analysis) for understanding large, modern MD datasets. Money quote: "we believe that quantitative analysis has increasingly become a limiting factor in the application of MD"
msm-theory perspective
Prafulla Aryal; Firdaus Abd-Wahab; Giovanna Bucci; Mark S. P. Sansom; Stephen J. Tucker
2014-07-08 (online)
Nature Communications (Nat. Commun.). 5, doi:10.1038/ncomms5377
Robert T. McGibbon; Christian R. Schwantes; Vijay S. Pande
2014-06-19 (print)
The Journal of Physical Chemistry B (J. Phys. Chem. B). 118, 24, 6475-6481. doi:10.1021/jp411822r
This is before 2015-mcgibbon-gmrq GRMQ cross-validation. They explicitly find the volume of voronoi cells (in low number of tIC space) to find a likelihood. They use AIC/BIC to find the number of states to use. Finding volumes is tough and you still can't compare across protocols (so you can basically only scan number of states or clustering method), but! this was the first paper to seriously suggest using a smaller number of states to avoid overfitting.
msm cross-validation
Timothy C. Moore; Christopher R. Iacovella; Clare McCabe
2014-06-14 (print)
The Journal of Chemical Physics (J. Chem. Phys.). 140, 22, 224104. doi:10.1063/1.4880555
Coarse-graining?
Yann Dauphin; Razvan Pascanu; Caglar Gulcehre; Kyunghyun Cho; Surya Ganguli; Yoshua Bengio
2014-06-10 (online)
Labmate summarizes:
This one is a really cool paper. One of those "we've all been doing it wrong" papers that could have a big impact. Their main conclusions are
1. When optimizing functions in high dimensional spaces, saddle points are a much bigger problem than local minima. There are far more of them, and the few local minima that do exist mostly have values only slightly worse than the global minimum.
2. Standard optimization methods deal really badly with saddle points (and hence work really badly in high dimensional spaces). First order methods like gradient descent start taking tiny steps, so they take a really long time to escape. Quasi-Newton methods are even worse. They just converge to the saddle point and never escape.
3. They describe a new approach that doesn't have these problems and goes right through saddle points without slowing down.
They do all this in the context of neural networks, but it likely applies just as well to other high dimensional optimization problems. Proteins, for example. When you use an algorithm like L-BFGS for energy minimization, it's probably converging to a saddle point, not a local minimum. It could be really interesting to try their method. Could we fold a protein to the native state just by a straightforward energy minimization?
Force field optimization is another case whether this approach could be really useful.
They also show that at a saddle point, there's a strong monotonic relationship between the error and the fraction of negative eigenvalues of the Hessian. Potentially that could be used as a way to measure how far you are from the global minimum. For example, when optimizing force field parameters, it would tell you whether your parameters are close to optimal, or whether there's still a lot of room to improve them further.
misc machine-learning deep-learning
Matthew R. Perkett; Michael F. Hagan
2014-06-07 (print)
The Journal of Chemical Physics (J. Chem. Phys.). 140, 21, 214101. doi:10.1063/1.4878494
Cited by 2015-wetmsm as successful MSM application for self-assembly
Lee-Ping Wang; Todd J. Martinez; Vijay S. Pande
2014-06-05 (print)
The Journal of Physical Chemistry Letters (J. Phys. Chem. Lett.). 5, 11, 1885-1891. doi:10.1021/jz500737m
forcefield
Robert McGibbon
2014-05-01 (online)
Élise Faure; Christine Thompson; Rikard Blunck
2014-04-17 (online) – 2014-06-06 (print)
Journal of Biological Chemistry (J. Biol. Chem.). 289, 23, 16452-16461. doi:10.1074/jbc.M113.537134
Cited by 2015-wetmsm where lipids are important for modulation of ion channel function
Feliks Nüske; Bettina G. Keller; Guillermo Pérez-Hernández; Antonia S. J. S. Mey; Frank Noé
2014-04-08 (print)
Journal of Chemical Theory and Computation (J. Chem. Theory Comput.). 10, 4, 1739-1752. doi:10.1021/ct4009156
This paper is largely redundant with 2013-noe-variational (ref. 65). They cite it as such: "Following the recently introduced variational principle for metastable stochastic processes,(65) we propose a variational approach to molecular kinetics."
They perform their variational approach on 2- and 10-alanine in addition to 1D potentials.
This comes after tICA and cites 2013-schwantes-tica (ref. 57) and 2013-noe-tica (ref. 58) in the intro, but does nothing further with it. In particular, they don't note that tICA is just another choice of basis set.
They cite their error paper 2010-msm-error (ref. 55).
Show References| Num | Entry | Why |
|---|---|---|
| 65 | 2013-noe-variational | |
| 57 | 2013-schwantes-tica | |
| 58 | 2013-noe-tica | |
| 55 | 2010-msm-error |
msm-theory variational
John D Chodera; Frank Noé
2014-04-01 (print)
Current Opinion in Structural Biology (Curr. Opin. Struct. Biol.). 25, 135-144. doi:10.1016/j.sbi.2014.04.002
Overview of MSMs, stressing eigensystem and variational approach. Includes further reading suggestions.
msm-theory perspective
Diwakar Shukla; Yilin Meng; Benoît Roux; Vijay S. Pande
2014-03-03 (online)
Nature Communications (Nat. Commun.). 5, doi:10.1038/ncomms4397
MSM analysis of c-Src kinase. The MSMBuilder paper uses the dataset from this paper as an example.
msm-applications
Carlos R. Baiz; Yu-Shan Lin; Chunte Sam Peng; Kyle A. Beauchamp; Vincent A. Voelz; Vijay S. Pande; Andrei Tokmakoff
2014-03-01 (print)
Biophysical Journal (Biophys. J.). 106, 6, 1359-1370. doi:10.1016/j.bpj.2014.02.008
Cited by 2015-wetmsm as successful MSM application for folding
Jan-Hendrik Prinz; John D. Chodera; Frank Noé
2014-02-21 (online)
Physical Review X (Physical Review X). 4, 1, doi:10.1103/PhysRevX.4.011020
T. Sun; F.-H. Lin; R. L. Campbell; J. S. Allingham; P. L. Davies
2014-02-13 (online) – 2014-02-14 (print)
Science (Science). 343, 6172, 795-798. doi:10.1126/science.1247407
Cited by 2015-wetmsm where solvent is important
Callum J. Dickson; Benjamin D. Madej; Åge A. Skjevik; Robin M. Betz; Knut Teigen; Ian R. Gould; Ross C. Walker
2014-02-11 (print)
Journal of Chemical Theory and Computation (J. Chem. Theory Comput.). 10, 2, 865-879. doi:10.1021/ct4010307
forcefield
Greg Wilson; D. A. Aruliah; C. Titus Brown; Neil P. Chue Hong; Matt Davis; Richard T. Guy; Steven H. D. Haddock; Kathryn D. Huff; Ian M. Mitchell; Mark D. Plumbley; Ben Waugh; Ethan P. White; Paul Wilson
2014-01-07 (online)
PLoS Biology (PLoS Biol.). 12, 1, e1001745. doi:10.1371/journal.pbio.1001745
Georgios Gousios; Martin Pinzger; Arie van Deursen
2014-01-01 (print)
Proceedings of the 36th International Conference on Software Engineering - ICSE 2014 (Proceedings of the 36th International Conference on Software Engineering - ICSE 2014). doi:10.1145/2568225.2568260
Pull-request based development model
Kai J. Kohlhoff; Diwakar Shukla; Morgan Lawrenz; Gregory R. Bowman; David E. Konerding; Dan Belov; Russ B. Altman; Vijay S. Pande
2013-12-15 (online)
Nature Chemistry (Nature Chem.). 6, 1, 15-21. doi:10.1038/nchem.1821
They used Google's Exacycle to do these simulations. You can cite this for more examples of distributed computing. It's ostensibly about GPCRs.
distributed-computing
Frank Noé; Hao Wu; Jan-Hendrik Prinz; Nuria Plattner
2013-11-14 (print)
The Journal of Chemical Physics (J. Chem. Phys.). 139, 18, 184114. doi:10.1063/1.4828816
Wenwei Zheng; Mary A. Rohrdanz; Cecilia Clementi
2013-10-24 (print)
The Journal of Physical Chemistry B (J. Phys. Chem. B). 117, 42, 12769-12776. doi:10.1021/jp401911h
Use diffusion maps to run umberlla sampling
Kyle Beauchamp
2013-09-01 (print)
Lee-Ping Wang; Teresa Head-Gordon; Jay W. Ponder; Pengyu Ren; John D. Chodera; Peter K. Eastman; Todd J. Martinez; Vijay S. Pande
2013-08-29 (print)
The Journal of Physical Chemistry B (J. Phys. Chem. B). 117, 34, 9956-9972. doi:10.1021/jp403802c
Cited in 2015-wetmsm intro as example of better water models. Optimizes tip3p and tip4p parameters.
Robert T. McGibbon; Vijay S. Pande
2013-07-09 (print)
Journal of Chemical Theory and Computation (J. Chem. Theory Comput.). 9, 7, 2900-2906. doi:10.1021/ct400132h
Learn scaling of coordinates to better approximate kinetics? Redundant with tICA.
Guillermo Pérez-Hernández; Fabian Paul; Toni Giorgino; Gianni De Fabritiis; Frank Noé
2013-07-07 (print)
The Journal of Chemical Physics (J. Chem. Phys.). 139, 1, 015102. doi:10.1063/1.4811489
The Noe group introduces tica concomitantly with 2013-schwantes-tica. They use the variational approach from 2013-noe-variational to derive the tICA equation. They cite a 2001 book about independent component analysis.
msm-theory tica
Christian R. Schwantes; Vijay S. Pande
2013-04-09 (print)
Journal of Chemical Theory and Computation (J. Chem. Theory Comput.). 9, 4, 2000-2009. doi:10.1021/ct300878a
The Pande group introduces tica concomitantly with 2013-noe-tica. This paper uses PCA as inspiration and cites signal processing literature.
msm-theory tica
M. H. Devoret; R. J. Schoelkopf
2013-03-07 (online) – 2013-03-08 (print)
Science (Science). 339, 6124, 1169-1174. doi:10.1126/science.1231930
Thomas J Lane; Diwakar Shukla; Kyle A Beauchamp; Vijay S Pande
2013-02-01 (print)
Current Opinion in Structural Biology (Curr. Opin. Struct. Biol.). 23, 1, 58-65. doi:10.1016/j.sbi.2012.11.002
The state of folding simulations as it was in 2013. Has a nice plot of folding time by year by lab. Discusses the state of MSMs for analysis. Maybe cite this if you're doing folding or want to talk about how timescales are getting longer (and analysis is getting harder). The references include "recommended readings", which is nice.
msm-theory perspective
Peter Eastman; Mark S. Friedrichs; John D. Chodera; Randall J. Radmer; Christopher M. Bruns; Joy P. Ku; Kyle A. Beauchamp; Thomas J. Lane; Lee-Ping Wang; Diwakar Shukla; Tony Tye; Mike Houston; Timo Stich; Christoph Klein; Michael R. Shirts; Vijay S. Pande
2013-01-08 (print)
Journal of Chemical Theory and Computation (J. Chem. Theory Comput.). 9, 1, 461-469. doi:10.1021/ct300857j
software
Chen Gu; Huang-Wei Chang; Lutz Maibaum; Vijay S Pande; Gunnar E Carlsson; Leonidas J Guibas
2013-01-01 (print)
BMC Bioinformatics (BMC Bioinf.). 14, Suppl 2, S8. doi:10.1186/1471-2105-14-S2-S8
2015-wetmsm method based off of this.
Frank Noé; Feliks Nüske
2013-01-01 (print)
Multiscale Modeling & Simulation (Multiscale Model. Simul.). 11, 2, 635-655. doi:10.1137/110858616
I think the point of this versus 2014-nuske-variational is to be "protein agnostic". They allude to proteins, but say this is more general. Their example is a double-well potential.
They introduce the propogator formalism and stipulate that dynamics can be seperated into "fast" and "slow" components. In contrast to a quantum mechanics Hamiltonian, we don't know the propogator here. You have to infer it from data.
They claim the error bound derived in 2010-msm-error (ref. 34) is not constructive, whereas this method *is* constructive.
Math section heavily cites 2010-msm-error (ref. 34).
They adapt the Rayleigh variational principle from quantum mechanics, and cite 1989-szabo-ostlund-qm (ref. 43). They show that the autocorrelation of the true first dynamical eigenfunction is its eigenvalue, and an estimate of the first dynamical eigenfunction necessarily has a smaller eigenvalue. This sets the variational bound. In terms of names that don't seem to be used now that we're in the future: the Ritz method is for when you have no overlap integrals (e.g. MSMs) and the Roothan-Hall method is for when you do (tICA).
They put it to the test on a double well potential. They use indicator basis functions to make an MSM; hermite basis functions so they still have no overlap integrals, but smooth functions; and gaussian basis functions (with overlap integrals). This must have come before tICA because there is no mention made of it, even though it would fit in nicely.
Show References| Num | Entry | Why |
|---|---|---|
| 34 | 2010-msm-error | |
| 34 | 2010-msm-error | |
| 43 | 1989-szabo-ostlund-qm |
msm-theory variational
S. K. Sadiq; F. Noe; G. De Fabritiis
2012-11-26 (online) – 2012-12-11 (print)
Proceedings of the National Academy of Sciences (Proc. Natl. Acad. Sci. U.S.A.). 109, 50, 20449-20454. doi:10.1073/pnas.1210983109
Cited by 2015-wetmsm as successful MSM application for kinase activation
Konstantin Fackeldey; Alexander Bujotzek; Marcus Weber
2012-09-27 (online) – 2013-01-01 (print)
Lecture Notes in Computational Science and Engineering (Lect. Notes Comput. Sci. Eng.). 141-154. doi:10.1007/978-3-642-32979-1_9
Soften the hard clustering 2006-meshless-msm-thesis (ref. 37).
Cite Shepard's approach 1968-shepard-method (ref. 30) like 2006-meshless-msm-thesis does to introduce the softmax function as basis functions with softness parameter alpha. Note that this is not Shepard's method.
They frame everything in the context of lumping and PCCA+ and use ZIBgridfree to simulate trialanine faster than unbiased (100ns vs 10ns).
Show References| Num | Entry | Why |
|---|---|---|
| 37 | 2006-meshless-msm-thesis | |
| 30 | 1968-shepard-method |
Ting Zhou; Amedeo Caflisch
2012-08-14 (print)
Journal of Chemical Theory and Computation (J. Chem. Theory Comput.). 8, 8, 2930-2937. doi:10.1021/ct3003145
A unique featurization that encodes each atom by the first ~3 moments of its distribution of 1/distance to every other atom. Cite this if you use this featurization.
features
Vincent A. Voelz; Marcus Jäger; Shuhuai Yao; Yujie Chen; Li Zhu; Steven A. Waldauer; Gregory R. Bowman; Mark Friedrichs; Olgica Bakajin; Lisa J. Lapidus; Shimon Weiss; Vijay S. Pande
2012-08-01 (print)
Journal of the American Chemical Society (JACS). 134, 30, 12565-12577. doi:10.1021/ja302528z
Cited by 2015-wetmsm as successful MSM application for folding
Martin Senne; Benjamin Trendelkamp-Schroer; Antonia S.J.S. Mey; Christof Schütte; Frank Noé
2012-07-10 (print)
Journal of Chemical Theory and Computation (J. Chem. Theory Comput.). 8, 7, 2223-2238. doi:10.1021/ct300274u
The previous, java version of EMMA. Look at 2015-pyemma instead.
software
Andreas W. Götz; Mark J. Williamson; Dong Xu; Duncan Poole; Scott Le Grand; Ross C. Walker
2012-05-08 (print)
Journal of Chemical Theory and Computation (J. Chem. Theory Comput.). 8, 5, 1542-1555. doi:10.1021/ct200909j
Kyle A. Beauchamp; Yu-Shan Lin; Rhiju Das; Vijay S. Pande
2012-04-10 (print)
Journal of Chemical Theory and Computation (J. Chem. Theory Comput.). 8, 4, 1409-1414. doi:10.1021/ct2007814
forcefield
James Gumbart; Fatemeh Khalili-Araghi; Marcos Sotomayor; Benoît Roux
2012-02-01 (print)
Biochimica et Biophysica Acta (BBA) - Biomembranes (Biochimica et Biophysica Acta (BBA) - Biomembranes). 1818, 2, 294-302. doi:10.1016/j.bbamem.2011.09.030
Constant electric field
Tianbao Yang; Yu-Feng Li; Mehrdad Mahdavi; Rong Jin; Zhi-Hua Zhou
2012-01-01 (print)
Advances in Neural Information Processing Systems (Advances in Neural Information Processing Systems). 476-484.
Natasa Djurdjevac; Marco Sarich; Christof Schütte
2012-01-01 (print)
Multiscale Modeling & Simulation (Multiscale Model. Simul.). 10, 1, 61-81. doi:10.1137/100798910
V. Yarov-Yarovoy; P. G. DeCaen; R. E. Westenbroek; C.-Y. Pan; T. Scheuer; D. Baker; W. A. Catterall
2011-12-12 (online) – 2012-01-10 (print)
Proceedings of the National Academy of Sciences (Proc. Natl. Acad. Sci. U.S.A.). 109, 2, E93-E102. doi:10.1073/pnas.1118434109
Homology modelling to resting, intermediate and activated states of voltage sensing domains. Disulfide locking experiments. Cites [doi:10.1073/pnas.0806486105] and [doi:10.1073/pnas.0912307106] and [doi:10.1073/pnas.1116449108]
nav
Thomas J. Lane; Gregory R. Bowman; Kyle Beauchamp; Vincent A. Voelz; Vijay S. Pande
2011-11-16 (print)
Journal of the American Chemical Society (JACS). 133, 45, 18413-18419. doi:10.1021/ja207470h
Cited by 2015-wetmsm as successful MSM application for folding
K. Lindorff-Larsen; S. Piana; R. O. Dror; D. E. Shaw
2011-10-27 (online) – 2011-10-28 (print)
Science (Science). 334, 6055, 517-520. doi:10.1126/science.1208351
The authors simulated folding trajectories for 12 small proteins. The simulations were between 100 us and 1 ms. This paper was a considerable advance for the field, and more or less closed the book on molecular dynamics for folding.
simulations
Kyle A. Beauchamp; Gregory R. Bowman; Thomas J. Lane; Lutz Maibaum; Imran S. Haque; Vijay S. Pande
2011-10-11 (print)
Journal of Chemical Theory and Computation (J. Chem. Theory Comput.). 7, 10, 3412-3419. doi:10.1021/ct200463m
software
Paula L Piechotta; Markus Rapedius; Phillip J Stansfeld; Murali K Bollepalli; Gunter Erhlich; Isabelle Andres-Enguix; Hariolf Fritzenschaft; Niels Decher; Mark S P Sansom; Stephen J Tucker; Thomas Baukrowitz
2011-08-05 (online) – 2011-08-31 (print)
The EMBO Journal (EMBO J.). 30, 17, 3607-3619. doi:10.1038/emboj.2011.268
Carsten Kutzner; Helmut Grubmüller; Bert L. de Groot; Ulrich Zachariae
2011-08-01 (print)
Biophysical Journal (Biophys. J.). 101, 4, 809-817. doi:doi:10.1016/j.bpj.2011.06.010
They introduce the double-membrane scheme for measuring ion conductance. They do it on a big ol' beta barrel.
Sviatoslav N Bagriantsev; Rémi Peyronnet; Kimberly A Clark; Eric Honoré; Daniel L Minor
2011-07-15 (online) – 2011-08-31 (print)
The EMBO Journal (EMBO J.). 30, 17, 3594-3606. doi:10.1038/emboj.2011.230
Konstantin Fackeldey; Susanna Röblitz; Olga Scharkoi; Marcus Weber
2011-06-22 (online)
They note MSM is a meshfree method with characteristic basis functions.
They define a "hard decomposition" in the obvious way. They define a "soft decomposition" also as a partitioning of unity, but allowing overlap.
They still do PCCA+ and it's unclear what shape function they're using for softness. As an example, they lump 504 soft states into 5 macrostates of alanine dipeptide, sometimes spelled alanin dipeptid
I. Buch; T. Giorgino; G. De Fabritiis
2011-06-06 (online) – 2011-06-21 (print)
Proceedings of the National Academy of Sciences (Proc. Natl. Acad. Sci. U.S.A.). 108, 25, 10184-10189. doi:10.1073/pnas.1103547108
Cited by 2015-wetmsm as successful MSM application for protein-ligand binding
Cited by 2015-wetmsm where solvent is treated by grid of voxels.
Jan-Hendrik Prinz; Hao Wu; Marco Sarich; Bettina Keller; Martin Senne; Martin Held; John D. Chodera; Christof Schütte; Frank Noé
2011-05-07 (print)
The Journal of Chemical Physics (J. Chem. Phys.). 134, 17, 174105. doi:10.1063/1.3565032
Fantastic in-depth intro to MSMs. Figure 1 in this paper is necessary for understanding eigenvectors. This defines and relates the propogator and transfer operator. This shows how we compute timescales from eigenvectors. This discusess state decomposition error and shows that many states are needed in transition regions.
quote: it is clear that a “sufficiently fine” partitioning will be able to resolve “sufficient” detail 2010-msm-error.
Cites 2004-nina-msm for use of the term "MSM".
msm-theory review
Naveen Michaud-Agrawal; Elizabeth J. Denning; Thomas B. Woolf; Oliver Beckstein
2011-04-15 (online) – 2011-07-30 (print)
Journal of Computational Chemistry (J. Comput. Chem.). 32, 10, 2319-2327. doi:10.1002/jcc.21787
Mary A. Rohrdanz; Wenwei Zheng; Mauro Maggioni; Cecilia Clementi
2011-03-28 (print)
The Journal of Chemical Physics (J. Chem. Phys.). 134, 12, 124116. doi:10.1063/1.3569857
other-md-analysis
Yusuke Naritomi; Sotaro Fuchigami
2011-02-14 (print)
The Journal of Chemical Physics (J. Chem. Phys.). 134, 6, 065101. doi:10.1063/1.3554380
Probably the first application of tICA to MD.
msm-theory tica
Susan S. Taylor; Alexandr P. Kornev
2011-02-01 (print)
Trends in Biochemical Sciences (Trends Biochem. Sci.). 36, 2, 65-77. doi:10.1016/j.tibs.2010.09.006
Review of protein kinases. The MSMBuilder paper uses a kinase MD dataset as an example.
review
F. Pedregosa; G. Varoquaux; A. Gramfort; V. Michel; B. Thirion; O. Grisel; M. Blondel; P. Prettenhofer; R. Weiss; V. Dubourg; J. Vanderplas; A. Passos; D. Cournapeau; M. Brucher; M. Perrot; E. Duchesnay
2011-01-01 (print)
Journal of Machine Learning Research (J. Mach. Learn. Res.). 12, 2825-2830.
software python
Francesco Rao; Sean Garrett-Roe; Peter Hamm
2010-12-02 (print)
The Journal of Physical Chemistry B (J. Phys. Chem. B). 114, 47, 15598-15604. doi:10.1021/jp1060792
prior work for water features. Contrast with 2015-wetmsm.
Vijay S. Pande
2010-11-05 (online)
Physical Review Letters (Phys. Rev. Lett.). 105, 19, doi:10.1103/PhysRevLett.105.198101
Non-native interactions and misfolding
D. E. Shaw; P. Maragakis; K. Lindorff-Larsen; S. Piana; R. O. Dror; M. P. Eastwood; J. A. Bank; J. M. Jumper; J. K. Salmon; Y. Shan; W. Wriggers
2010-10-14 (online) – 2010-10-15 (print)
Science (Science). 330, 6002, 341-346. doi:10.1126/science.1187409
Simulation of fip35 ww domain: 2x 100 us. Note this was at 400K so unfolding could be observed.
Simulation of bpti: 1ms. Note this was done with tip4p for reasons.
simulations
Peter L. Freddolino; Christopher B. Harrison; Yanxin Liu; Klaus Schulten
2010-10-01 (online) – 2010-10-01 (print)
Nature Physics (Nat. Phys.). 6, 10, 751-758. doi:10.1038/nphys1713
Cited by 2014-msm-perspective as highlighting analysis as a problem.
Vijay S. Pande; Kyle Beauchamp; Gregory R. Bowman
2010-09-01 (print)
Methods (Methods). 52, 1, 99-105. doi:10.1016/j.ymeth.2010.06.002
Review of MSMs intended for "non-experts". Obviously a little dated by now.
msm-theory review
Luis G. Cuello; Vishwanath Jogini; D. Marien Cortes; Eduardo Perozo
2010-07-08 (print)
Nature (Nature). 466, 7303, 203-208. doi:10.1038/nature09153
Oliver B. Clarke; Alessandro T. Caputo; Adam P. Hill; Jamie I. Vandenberg; Brian J. Smith; Jacqueline M. Gulbis
2010-06-01 (print)
Cell (Cell). 141, 6, 1018-1029. doi:10.1016/j.cell.2010.05.003
P. Enyedi; G. Czirjak
2010-04-14 (online) – 2010-04-01 (print)
Physiological Reviews (Physiol. Rev.). 90, 2, 559-605. doi:10.1152/physrev.00029.2009
Nice review of K2P two-pore potassium channels. They talk about the wide variety of regulatory stimuli
kchan
I. Buch; M. J. Harvey; T. Giorgino; D. P. Anderson; G. De Fabritiis
2010-03-22 (print)
Journal of Chemical Information and Modeling (J. Chem. Inf. Model.). 50, 3, 397-403. doi:10.1021/ci900455r
GPUGRID intro paper. Cite this alongside FAH. They (probably) did GPU distributed computing before FAH.
distributed-computing
M. O. Jensen; D. W. Borhani; K. Lindorff-Larsen; P. Maragakis; V. Jogini; M. P. Eastwood; R. O. Dror; D. E. Shaw
2010-03-15 (online) – 2010-03-30 (print)
Proceedings of the National Academy of Sciences (Proc. Natl. Acad. Sci. U.S.A.). 107, 13, 5833-5838. doi:10.1073/pnas.0911691107
Jay W. Ponder; Chuanjie Wu; Pengyu Ren; Vijay S. Pande; John D. Chodera; Michael J. Schnieders; Imran Haque; David L. Mobley; Daniel S. Lambrecht; Robert A. DiStasio; Martin Head-Gordon; Gary N. I. Clark; Margaret E. Johnson; Teresa Head-Gordon
2010-03-04 (print)
The Journal of Physical Chemistry B (J. Phys. Chem. B). 114, 8, 2549-2564. doi:10.1021/jp910674d
forcefield
Pär Bjelkmar; Per Larsson; Michel A. Cuendet; Berk Hess; Erik Lindahl
2010-02-09 (print)
Journal of Chemical Theory and Computation (J. Chem. Theory Comput.). 6, 2, 459-466. doi:10.1021/ct900549r
forcefield
D. Sculley
2010-01-01 (print)
Proceedings of the 19th international conference on World wide web - WWW '10 (Proceedings of the 19th international conference on World wide web - WWW '10). doi:10.1145/1772690.1772862
Clustering algorithm from sklearn admired for its speed.
algorithm
Marco Sarich; Frank Noé; Christof Schütte
2010-01-01 (print)
Multiscale Modeling & Simulation (Multiscale Model. Simul.). 8, 4, 1154-1177. doi:10.1137/090764049
msm-theory
2009-12-01 (print)
Nature Nanotechnology (Nat. Nanotechnol.). 4, 12, 781-781. doi:10.1038/nnano.2009.356
Editorial about 1960-plenty-of-room-at-the-bottom.
F. Noe; C. Schutte; E. Vanden-Eijnden; L. Reich; T. R. Weikl
2009-11-03 (online) – 2009-11-10 (print)
Proceedings of the National Academy of Sciences (Proc. Natl. Acad. Sci. U.S.A.). 106, 45, 19011-19016. doi:10.1073/pnas.0905466106
Gregory R. Bowman; Xuhui Huang; Vijay S. Pande
2009-10-01 (print)
Methods (Methods). 49, 2, 197-201. doi:10.1016/j.ymeth.2009.04.013
This introduced the first release of MSMBuilder. You probably shouldn't cite this unless you have a good reason to.
software python
Alexander Berezhkovskii; Gerhard Hummer; Attila Szabo
2009-05-28 (print)
The Journal of Chemical Physics (J. Chem. Phys.). 130, 20, 205102. doi:10.1063/1.3139063
Mark S. Friedrichs; Peter Eastman; Vishal Vaidyanathan; Mike Houston; Scott Legrand; Adam L. Beberg; Daniel L. Ensign; Christopher M. Bruns; Vijay S. Pande
2009-04-30 (print)
Journal of Computational Chemistry (J. Comput. Chem.). 30, 6, 864-872. doi:10.1002/jcc.21209
Probably the second instance of using GPUs for molecular dynamics. This became OpenMM.
md-sampling
John L Klepeis; Kresten Lindorff-Larsen; Ron O Dror; David E Shaw
2009-04-01 (print)
Current Opinion in Structural Biology (Curr. Opin. Struct. Biol.). 19, 2, 120-127. doi:10.1016/j.sbi.2009.03.004
D. Thomas; S.A.N. Goldstein
2009-01-01 (print)
Encyclopedia of Neuroscience (Encyclopedia of Neuroscience). 1207-1220. doi:10.1016/b978-008045046-9.01636-3
Philipp Metzner; Christof Schütte; Eric Vanden-Eijnden
2009-01-01 (print)
Multiscale Modeling & Simulation (Multiscale Model. Simul.). 7, 3, 1192-1219. doi:10.1137/070699500
Transition path theory (TPT).
msm-theory msm-postprocessing
David E. Shaw; Kevin J. Bowers; Edmond Chow; Michael P. Eastwood; Douglas J. Ierardi; John L. Klepeis; Jeffrey S. Kuskin; Richard H. Larson; Kresten Lindorff-Larsen; Paul Maragakis; Mark A. Moraes; Ron O. Dror; Stefano Piana; Yibing Shan; Brian Towles; John K. Salmon; J. P. Grossman; Kenneth M. Mackenzie; Joseph A. Bank; Cliff Young; Martin M. Deneroff; Brannon Batson
2009-01-01 (print)
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis - SC '09 (Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis - SC '09). doi:10.1145/1654059.1654126
Pu Liu; Dimitris K. Agrafiotis; Douglas L. Theobald
2009-01-01 (print)
Journal of Computational Chemistry (J. Comput. Chem.). n/a-n/a. doi:10.1002/jcc.21439
This one computes the optimal rotation in addition to (specifically: after) just computing the minimal RMSD value. It uses 2005-theobald-rmsd (ref. 14) for finding the optimal RMSD (ie leading eigenvalue of key matrix).
Show References| Num | Entry | Why |
|---|---|---|
| 14 | 2005-theobald-rmsd |
Peter Eastman; Vijay S. Pande
2009-01-01 (print)
Journal of Computational Chemistry (J. Comput. Chem.). NA-NA. doi:10.1002/jcc.21413
Optimizing below-cutoff nonbonded calculations on the GPU by tricky memory and parallelization management. This was for OpenMM. This is not PME.
Show References| Num | Entry | Why |
|---|
md-sampling md-algorithm
Benoît Roux
2008-11-01 (print)
Biophysical Journal (Biophys. J.). 95, 9, 4205-4216. doi:10.1529/biophysj.108.136499
Constant electric field
Mary Griffin Krone; Lan Hua; Patricia Soto; Ruhong Zhou; B. J. Berne; Joan-Emma Shea
2008-08-01 (print)
Journal of the American Chemical Society (JACS). 130, 33, 11066-11072. doi:10.1021/ja8017303
Cited by 2015-wetmsm where solvent is important for aggregation
David E. Shaw; Jack C. Chao; Michael P. Eastwood; Joseph Gagliardo; J. P. Grossman; C. Richard Ho; Douglas J. Lerardi; István Kolossváry; John L. Klepeis; Timothy Layman; Christine McLeavey; Martin M. Deneroff; Mark A. Moraes; Rolf Mueller; Edward C. Priest; Yibing Shan; Jochen Spengler; Michael Theobald; Brian Towles; Stanley C. Wang; Ron O. Dror; Jeffrey S. Kuskin; Richard H. Larson; John K. Salmon; Cliff Young; Brannon Batson; Kevin J. Bowers
2008-07-01 (print)
Communications of the ACM (Commun. ACM). 51, 7, 91. doi:10.1145/1364782.1364802
The seminal Anton paper. Cite this when talking about single, long trajectories or special-purpose hardware.
md-sampling
Ken A. Dill; S. Banu Ozkan; M. Scott Shell; Thomas R. Weikl
2008-06-01 (print)
Annual Review of Biophysics (Annu. Rev. Biophys.). 37, 1, 289-316. doi:10.1146/annurev.biophys.37.092707.153558
Joshua A. Anderson; Chris D. Lorenz; A. Travesset
2008-05-01 (print)
Journal of Computational Physics (J. Comput. Phys.). 227, 10, 5342-5359. doi:10.1016/j.jcp.2008.01.047
They claim to be the first GPU accelerated MD engine too! Probably led to HOOMD, although they don't call it that in the paper.
md-sampling
Gianni De Fabritiis; Sebastien Geroult; Peter V. Coveney; Gabriel Waksman
2008-04-02 (online)
Proteins: Structure, Function, and Bioinformatics (Proteins Struct. Funct. Bioinf.). 72, 4, 1290-1297. doi:10.1002/prot.22027
Cited by 2015-wetmsm where solvent is treated by grid of voxels.
Beat Vögeli; Jinfa Ying; Alexander Grishaev; Ad Bax
2007-08-01 (print)
Journal of the American Chemical Society (JACS). 129, 30, 9377-9385. doi:10.1021/ja070324o
Tom Young; Robert Abel; Byungchan Kim; Bruce J. Berne; Richard A. Friesner
2007-01-04 (online) – 2007-01-16 (print)
Proceedings of the National Academy of Sciences (Proc. Natl. Acad. Sci. U.S.A.). 104, 3, 808-813. doi:10.1073/pnas.0610202104
Cited by 2015-wetmsm where water is important for protein-ligand binding
Fernando Perez; Brian E. Granger
2007-01-01 (print)
Computing in Science & Engineering (Comput. Sci. Eng.). 9, 3, 21-29. doi:10.1109/MCSE.2007.53
software python
John E. Stone; James C. Phillips; Peter L. Freddolino; David J. Hardy; Leonardo G. Trabuco; Klaus Schulten
2007-01-01 (online) – 2007-12-01 (print)
Journal of Computational Chemistry (J. Comput. Chem.). 28, 16, 2618-2640. doi:10.1002/jcc.20829
(Probably) the first GPU accelerated MD paper. This is for NAMD.
md-sampling
Kevin Bowers; Edmond Chow; Huafeng Xu; Ron Dror; Michael Eastwood; Brent Gregersen; John Klepeis; Istvan Kolossvary; Mark Moraes; Federico Sacerdoti; John Salmon; Yibing Shan; David Shaw
2006-11-01 (print)
ACM/IEEE SC 2006 Conference (SC'06) (ACM/IEEE SC 2006 Conference (SC'06)). doi:10.1109/SC.2006.54
Tobias Blaschke; Pietro Berkes; Laurenz Wiskott
2006-10-01 (print)
Neural Computation (Neural Comput.). 18, 10, 2495-2508. doi:10.1162/neco.2006.18.10.2495
Guanghong Wei; Joan-Emma Shea
2006-09-01 (print)
Biophysical Journal (Biophys. J.). 91, 5, 1638-1647. doi:10.1529/biophysj.105.079186
Cited by 2015-wetmsm where solvent is important for aggregation
Boaz Nadler; Stéphane Lafon; Ronald R. Coifman; Ioannis G. Kevrekidis
2006-07-01 (print)
Applied and Computational Harmonic Analysis (Appl. Comput. Harmon. Anal.). 21, 1, 113-127. doi:10.1016/j.acha.2005.07.004
other-md-analysis
Frank Noé; Dieter Krachtus; Jeremy C. Smith; Stefan Fischer
2006-05-01 (print)
Journal of Chemical Theory and Computation (J. Chem. Theory Comput.). 2, 3, 840-857. doi:10.1021/ct050162r
conformational-change
Eric J. Sorin; Vijay S. Pande
2006-05-01 (print)
Journal of the American Chemical Society (JACS). 128, 19, 6316-6317. doi:10.1021/ja060917j
Cited by 2015-wetmsm where water is important for protein stability
Guha Jayachandran; V. Vishal; Vijay S. Pande
2006-04-28 (print)
The Journal of Chemical Physics (J. Chem. Phys.). 124, 16, 164902. doi:10.1063/1.2186317
Cited by 2015-wetmsm as successful MSM application for folding
Marcus Weber
2006-02-01 (print)
Mainly concerned with lumping (PCCA) and setting up an iterative sampling scheme, released as ZIBgridfree.
Partition of unity using Shepard's method 1968-shepard-method (ref. 117). Definition 4.8 says these need to be positive (greater than zero) which rules out traditional MSMs. Why?
Highlights importants of softness parameter of the shape function, which they call alpha. They say Shepard's method with gaussian RBFs can be seen as a generalized Voronoi Tessellation.
Show References| Num | Entry | Why |
|---|---|---|
| 117 | 1968-shepard-method |
J. L. F. Abascal; C. Vega
2005-12-15 (print)
The Journal of Chemical Physics (J. Chem. Phys.). 123, 23, 234505. doi:10.1063/1.2121687
forcefield
Douglas L. Theobald
2005-06-23 (online) – 2005-07-01 (print)
Acta Crystallographica Section A Foundations of Crystallography (Acta Crystallogr., Sect. A: Found. Crystallogr.). 61, 4, 478-480. doi:10.1107/S0108767305015266
Instead of doing matrix diagonalization or inversion, use netwon-raphson root-finding on a characteristic polynomial.
Mainly builds off of 1987-horn-rmsd (ref. 5).
Show References| Num | Entry | Why |
|---|---|---|
| 5 | 1987-horn-rmsd |
S. Fischer; B. Windshugel; D. Horak; K. C. Holmes; J. C. Smith
2005-04-29 (online) – 2005-05-10 (print)
Proceedings of the National Academy of Sciences (Proc. Natl. Acad. Sci. U.S.A.). 102, 19, 6873-6878. doi:10.1073/pnas.0408784102
conformational-change
Peter Deuflhard; Marcus Weber
2005-03-01 (print)
Linear Algebra and its Applications (Linear Algebra Appl.). 398, 161-184. doi:10.1016/j.laa.2004.10.026
PCCA group states based on an MSM transition matrix. Specifically, it uses the eigenspectrum to do the lumping. Cite this in the methods section of your paper if you use PCCA or PCCA+.
msm-theory msm-postprocessing
James C. Phillips; Rosemary Braun; Wei Wang; James Gumbart; Emad Tajkhorshid; Elizabeth Villa; Christophe Chipot; Robert D. Skeel; Laxmikant Kalé; Klaus Schulten
2005-01-01 (online) – 2005-12-01 (print)
Journal of Computational Chemistry (J. Comput. Chem.). 26, 16, 1781-1802. doi:10.1002/jcc.20289
R. Zhou
2004-09-10 (print)
Science (Science). 305, 5690, 1605-1609. doi:10.1126/science.1101176
Cited by 2015-wetmsm because model system for hydrophobic collapse
Alexander D. Mackerell; Michael Feig; Charles L. Brooks
2004-08-01 (print)
Journal of Computational Chemistry (J. Comput. Chem.). 25, 11, 1400-1415. doi:10.1002/jcc.20065
forcefield
William C. Swope; Jed W. Pitera; Frank Suits
2004-05-01 (print)
The Journal of Physical Chemistry B (J. Phys. Chem. B). 108, 21, 6571-6581. doi:10.1021/jp037421y
The first MSM paper. Gets pretty much everything right. Except they're convinced that you need to do state exploration via NVT or NPT and then calculate transitions by launching bespoke NVE simulations. Obviously, we just run big NPT runs and use that for both state space exploration and counting transitions.
msm-theory
Sanghyun Park; Klaus Schulten
2004-04-01 (print)
The Journal of Chemical Physics (J. Chem. Phys.). 120, 13, 5946-5961. doi:10.1063/1.1651473
Evangelos A. Coutsias; Chaok Seok; Ken A. Dill
2004-01-01 (online) – 2004-11-30 (print)
Journal of Computational Chemistry (J. Comput. Chem.). 25, 15, 1849-1857. doi:10.1002/jcc.20110
Similar to 2005-theobald-rmsd, builds off of 1987-horn-rmsd (ref. 5). Proves identity with normal 3x3 methods.
Derives the derivative of RMSD wrt coordinates, although "it is well known"
Show References| Num | Entry | Why |
|---|---|---|
| 5 | 1987-horn-rmsd |
Nina Singhal; Christopher D. Snow; Vijay S. Pande
2004-01-01 (print)
The Journal of Chemical Physics (J. Chem. Phys.). 121, 1, 415. doi:10.1063/1.1738647
Junmei Wang; Romain M. Wolf; James W. Caldwell; Peter A. Kollman; David A. Case
2004-01-01 (online) – 2004-07-15 (print)
Journal of Computational Chemistry (J. Comput. Chem.). 25, 9, 1157-1174. doi:10.1002/jcc.20035
forcefield
Guillaume Lamoureux; Benoı̂t Roux
2003-08-08 (print)
The Journal of Chemical Physics (J. Chem. Phys.). 119, 6, 3025-3039. doi:10.1063/1.1589749
forcefield
Eric Dennis; Alexei Kitaev; Andrew Landahl; John Preskill
2002-09-01 (print)
Journal of Mathematical Physics (J. Math. Phys.). 43, 9, 4452-4505. doi:10.1063/1.1499754
Called the seminal work in surface code error correction by 2017-fault-tolerant-computation, this long article seems to evaluate the details of the surface code which were introduced in 1997-kitaev-error-correction (ref. 4) and 1997-anyons (ref. 5).
Show References| Num | Entry | Why |
|---|---|---|
| 4 | 1997-kitaev-error-correction | |
| 5 | 1997-anyons |
David Chandler
2002-05-30 (print)
Nature (Nature). 417, 6888, 491-491. doi:10.1038/417491a
Cited by 2015-wetmsm where water is important for hydrophobic collapse
Christopher K. I. Williams; Matthias Seeger
2001-01-01 (print)
Advances in Neural Information Processing Systems (Advances in Neural Information Processing Systems). 13, 682-688.
Ch. Schütte; W. Huisinga; P. Deuflhard
2001-01-01 (print)
Ergodic Theory, Analysis, and Efficient Simulation of Dynamical Systems (Ergodic Theory, Analysis, and Efficient Simulation of Dynamical Systems). 191-223. doi:10.1007/978-3-642-56589-2_9
Full treatment of transfer operator / propagator and build an MSM for a small RNA chain.
msm-theory
M. Shirts
2000-12-08 (print)
Science (Science). 290, 5498, 1903-1904. doi:10.1126/science.290.5498.1903
The seminal Folding at Home paper. Cite this whenever you talk about distributed computing or Folding at Home.
SETI@Home and distributed.net came before this.
distributed-computing
P. Deuflhard; W. Huisinga; A. Fischer; Ch. Schütte
2000-08-01 (print)
Linear Algebra and its Applications (Linear Algebra Appl.). 315, 1-3, 39-59. doi:10.1016/S0024-3795(00)00095-1
Florian Lesage; Cécile Terrenoire; Georges Romey; Michel Lazdunski
2000-07-03 (online) – 2000-09-15 (print)
Journal of Biological Chemistry (J. Biol. Chem.). 275, 37, 28398-28405. doi:10.1074/jbc.m002822200
Simon Bernèche; Benoît Roux
2000-06-01 (print)
Biophysical Journal (Biophys. J.). 78, 6, 2900-2917. doi:10.1016/S0006-3495(00)76831-7
They run 4ns of MD on KcsA potassium channel.
Ch Schütte; A Fischer; W Huisinga; P Deuflhard
1999-05-01 (print)
Journal of Computational Physics (J. Comput. Phys.). 151, 1, 146-168. doi:10.1006/jcph.1999.6231
Maybe the first time conformations were discretized and a Markov operator was made.
Bernhard Schölkopf; Alexander Smola; Klaus-Robert Müller
1998-07-01 (print)
Neural Computation (Neural Comput.). 10, 5, 1299-1319. doi:10.1162/089976698300017467
A. Yu. Kitaev
1997-07-09 (online)
Daniel Gottesman
1997-05-28 (online)
A. Yu. Kitaev
1997-01-01 (print)
Quantum Communication, Computing, and Measurement (Quantum Communication, Computing, and Measurement). 181-188. doi:10.1007/978-1-4615-5923-8_19
William Humphrey; Andrew Dalke; Klaus Schulten
1996-02-01 (print)
Journal of Molecular Graphics (J. Mol. Graph.). 14, 1, 33-38. doi:10.1016/0263-7855(96)00018-5
The only game in town for making movies.
software
Toshiya Senda; Kazuyuki Sugiyama; Hiroki Narita; Takeshi Yamamoto; Kazuhide Kimbara; Masao Fukuda; Mitsuo Sato; Keiji Yano; Yukio Mitsui
1996-02-01 (print)
Journal of Molecular Biology (J. Mol. Biol.). 255, 5, 735-752. doi:10.1006/jmbi.1996.0060
Dmitrij Frishman; Patrick Argos
1995-12-01 (print)
Proteins: Structure, Function, and Genetics (Proteins: Structure, Function, and Genetics). 23, 4, 566-579. doi:10.1002/prot.340230412
VMD wants you to cite this for secondary structure prediction
software
Maria M. Flocco; Sherry L. Mowbray
1995-10-01 (print)
Protein Science (Protein Sci.). 4, 10, 2118-2122. doi:10.1002/pro.5560041017
Alpha carbon featurization
features
Steve Plimpton
1995-03-01 (print)
Journal of Computational Physics (J. Comput. Phys.). 117, 1, 1-19. doi:10.1006/jcph.1995.1039
L. Molgedey; H. G. Schuster
1994-06-06 (online)
Physical Review Letters (Phys. Rev. Lett.). 72, 23, 3634-3637. doi:10.1103/PhysRevLett.72.3634
Shankar Kumar; John M. Rosenberg; Djamal Bouzida; Robert H. Swendsen; Peter A. Kollman
1992-10-01 (print)
Journal of Computational Chemistry (J. Comput. Chem.). 13, 8, 1011-1021. doi:10.1002/jcc.540130812
Wham reweighting algorithm, perhaps used after umbrella sampling.
algorithm md-algorithm
H Frauenfelder; S. Sligar; P. Wolynes
1991-12-13 (print)
Science (Science). 254, 5038, 1598-1603. doi:10.1126/science.1749933
Cited by 2011-prinz to say that there are many metastable states and many timescales.
Toshinori Hoshi; William N. Zagotta; Richard W. Aldrich
1991-10-01 (print)
Neuron (Neuron). 7, 4, 547-556. doi:10.1016/0896-6273(91)90367-9
S. K. Kearsley
1989-02-01 (print)
Acta Crystallographica Section A Foundations of Crystallography (Acta Crystallogr., Sect. A: Found. Crystallogr.). 45, 2, 208-210. doi:10.1107/S0108767388010128
2005-theobald-rmsd cites for quaternion RMSD
Attila Szabo; Neil S. Ostlund
1989-01-01 (print)
Cited by 2013-noe-variational for Rayleigh variational method.
qm
M. P. Allen; D. J. Tildesley
1989-01-01 (print)
R. Diamond
1988-03-01 (print)
Acta Crystallographica Section A Foundations of Crystallography (Acta Crystallogr., Sect. A: Found. Crystallogr.). 44, 2, 211-216. doi:10.1107/S0108767387010535
2005-theobald-rmsd cites for quaternion RMSD
Jay W. Ponder; Frederic M. Richards
1987-10-01 (print)
Journal of Computational Chemistry (J. Comput. Chem.). 8, 7, 1016-1024. doi:10.1002/jcc.540080710
Berthold K. P. Horn
1987-04-01 (online) – 1987-04-01 (print)
Journal of the Optical Society of America A (J. Opt. Soc. Amer. A). 4, 4, 629. doi:10.1364/JOSAA.4.000629
2005-theobald-rmsd cites for quaternion RMSD
Michel H. Devoret; John M. Martinis; John Clarke
1985-10-28 (online)
Physical Review Letters (Phys. Rev. Lett.). 55, 18, 1908-1911. doi:10.1103/PhysRevLett.55.1908
E.N. Baker; R.E. Hubbard
1984-01-01 (print)
Progress in Biophysics and Molecular Biology (Prog. Biophys. Mol. Biol.). 44, 2, 97-179. doi:10.1016/0079-6107(84)90007-5
Hydrogen bond determination
Bernard R. Brooks; Robert E. Bruccoleri; Barry D. Olafson; David J. States; S. Swaminathan; Martin Karplus
1983-01-01 (print)
Journal of Computational Chemistry (J. Comput. Chem.). 4, 2, 187-217. doi:10.1002/jcc.540040211
W. Kabsch
1976-09-01 (print)
Acta Crystallographica Section A (Acta Crystallogr. A). 32, 5, 922-923. doi:10.1107/S0567739476001873
2005-theobald-rmsd says this suffers from rotoinversions because it uses 3x3 rotations instead of 4x4 quaternions.
A. Shrake; J.A. Rupley
1973-09-01 (print)
Journal of Molecular Biology (J. Mol. Biol.). 79, 2, 351-371. doi:10.1016/0022-2836(73)90011-9
Solvation
James A Maier; Carmenza Martinez; Koushik Kasavajhala; Lauren Wickstrom; Kevin E Hauser; Carlos Simmerling
Journal of chemical theory and computation (J. Chem. Theory Comput.). 11, 3696-3713.
Robert McGibbon; Bharath Ramsundar; Mohammad Sultan; Gert Kiss; Vijay Pande
32, 2, 1197-1205.
Use hidden markov models instead of discrete state MSMs.
Eric F Pettersen; Thomas D Goddard; Conrad C Huang; Gregory S Couch; Daniel M Greenblatt; Elaine C Meng; Thomas E Ferrin
Journal of computational chemistry (J. Comput. Chem.). 25, 1605-1612.
N G Van-Kampen
Sergei Izrailev; Sergey Stepaniants; Barry Isralewitz; Dorina Kosztin; Hui Lu; Ferenc Molnar; Willy Wriggers; Klaus Schulten
39-65.
2015-wetmsm cited this for methods: steered MD
Susan S Taylor; Alexandr P Kornev
Trends Biochem. Sci. (Trends Biochem. Sci.). 36, 65-77.
Cited in 2015-wetmsm intro.
T. Darden; D. York; L. Pedersen
J. Chem. Phys. (J. Chem. Phys.). 98, 10089.
2015-wetmsm cited this for methods: pme
R. Zwanzig; A. Szabo; B. Bagchi
Proceedings of the National Academy of Sciences (Proc. Natl. Acad. Sci. U.S.A.). 89, 1, 20-22.
Conformational space is huge, but proteins can fold very fast.
Barry J Grant; Alemayehu A Gorfe; J Andrew McCammon
Curr. Opin. Struc. Bio. (Curr. Opin. Struc. Bio.). 20, 142-147.
Cited in 2015-wetmsm intro.
Matthew P Harrigan; Vijay S Pande
bioRxiv (bioRxiv).
B. Hess; H. Bekker; H.J.C. Berendsen; J.G.E.M. Fraaije
J. Comp. Chem. (J. Comp. Chem.). 18, 1463-1472.
2015-wetmsm cited this for methods: lincs
Kresten Lindorff-Larsen; Stefano Piana; Kim Palmo; Paul Maragakis; John L Klepeis; Ron O Dror; David E Shaw
Proteins: Struct., Funct., and Bioinf. (Proteins: Struct., Funct., and Bioinf.). 78, 1950-1958.
2015-wetmsm cited this for methods: amber99sb-ildn
B. Hess; C. Kutzner; D. van der Spoel; E. Lindahl
J. Chem. Theory Comput. (J. Chem. Theory Comput.). 4, 435-447.
2015-wetmsm cited this for methods: gromacs
Katherine Henzler-Wildman; Dorothee Kern
Nature (Nature). 450, 964-972.
Cited in 2015-wetmsm intro.
Lisa J Lapidus; Srabasti Acharya; Christian R Schwantes; Ling Wu; Diwakar Shukla; Michael King; Stephen J DeCamp; Vijay S Pande
Biophys. J. (Biophys. J.). 107, 947-955.
Cited by 2015-wetmsm where they discard water.
W.L. Jorgensen; J. Chandrasekhar; J.D. Madura; R.W. Impey; M.L. Klein
J. Chem. Phys. (J. Chem. Phys.). 79, 926.
2015-wetmsm cited this for methods: tip3p.
G. R. Bowman; V. S. Pande; F. Noé
797,
DA Case; V Babin; Josh Berryman; RM Betz; Q Cai; DS Cerutti; TE Cheatham Iii; TA Darden; RE Duke; H Gohlke
Imran S Haque; Kyle A Beauchamp; Vijay S Pande
bioRxiv (bioRxiv).
C Levinthal
1968-01-01 (print)
(J. Chim. Phys. Physico-Chim. Biol.). 65, 44-45.
Taken from Nature's protein folding focus
Among the most widely cited-yet least read-papers in the field, partly owing to the difficulties in getting hold of them, Cyrus Levinthal used a simple model to show that a typical polypeptide chain cannot fold through an unbiased search of all conformational space on a reasonable timescale. This is commonly referred to as the "Levinthal's paradox", and led to the concept that proteins fold along discrete pathways. The first paper presents this idea and is usually cited, but the model is actually presented in the second one. Although the model was later shown to be overly simplistic, the work had a crucial role in directing the search and characterization of intermediate states.
Donald Shepard
1968-01-01 (print)
Proceedings of the 1968 23rd ACM national conference on - (Proceedings of the 1968 23rd ACM national conference on -). doi:10.1145/800186.810616
Method for interpolation confusingly cited by 2006-meshless-msm-thesis. I guess he introduces weightedsum(inverse distances) / sum(inverse distances). And instead of inverse distances, you can choose whatever function you want.
Richard Feynman
1960-02-01 (print)
Engineering and Science (Engineering and Science). 23, 5, 22-36.
A. L. Hodgkin; R. D. Keynes
1955-04-28 (online) – 1955-04-28 (print)
The Journal of Physiology (J. Physiol. (Lond.)). 128, 1, 61-88. doi:10.1113/jphysiol.1955.sp005291