[FAH] Folding@home 公开发表论文列表

碧城仙

来源：Folding@home 官方网站 - http://folding.stanford.edu/home/papers/
主题：Folding@home 公开发表论文列表

截止到 2013 年 7 月 31 日，公开发表的与 Folding@home 项目相关的论文，列表共 109 篇。
拟翻译 Folding@home 公开发表论文列表，暂仅翻译官方列表及概要，不深入挖掘原文。
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= = = = = 任务认领情况 = = = = =
引言
        —— 详见 2# 翻译 —— [已翻译待校对]

目录（按发表时间倒序排列）

109. To milliseconds and beyond: challenges in the simulation of protein folding.
        —— 详见 3# 翻译 —— [已翻译待校对]
108. OpenMM 4: A Reusable, Extensible, Hardware Independent Library for High Performance Molecular Simulation.
        —— 详见 6# 翻译 —— [已翻译待校对]
107. MSMExplorer: Visualizing Markov State Models for Biomolecule Folding Simulations.
        —— 详见 9# 翻译 —— [已翻译待校对]
106. Building Markov state models with solvent dynamics
        —— 详见 15# 翻译 —— [已翻译待校对]
105. Effects of familial mutations on the monomer structure of Aβ₄₂
        —— 详见 16# 翻译 —— [已翻译待校对]
104. Mechanistic and structural insight into the functional dichotomy between IL-2 and IL-15.
        —— 详见 17# 翻译 —— [已翻译待校对]
103. Marked difference in saxitoxin and tetrodotoxin affinity for the human nociceptive voltage-gated sodium channel (Nav1.7)
        —— 详见 19# 翻译
102. Simple few-state models reveal hidden complexity in protein folding.
101. Slow unfolded-state structuring in Acyl-CoA binding protein folding revealed by simulation and experiment.
100. Exploiting a natural conformational switch to engineer an interleukin-2 ‘superkine’
99. Investigating how peptide length and a pathogenic mutation modify the structural ensemble of amyloid beta monomer
98. Slow Unfolded-State Structuring in Acyl-CoA Binding Protein Folding Revealed by Simulation and Experiment
97. A Simple Model Predicts Experimental Folding Rates and a Hub-Like Topology
96. Design of β-Amyloid Aggregation Inhibitors from a Predicted Structural Motif
95. Rationally designed turn promoting mutation in the amyloid β-peptide sequence to stabilize oligomers in solution.
94. Copernicus: A new paradigm for parallel adaptive molecular dynamics.
93. Quantitative comparison of villin headpiece subdomain simulations and triplet–triplet energy transfer experiments.
92. A smoothly decoupled particle interface (SDPI): new methods for coupling explicit and implicit solvent.
91. Taming the complexity of protein folding.
90. Atomistic folding simulations of the five helix bundle protein λ6-85.
89. Non-Bulk-Like Solvent Behavior in the Ribosome Exit Tunnel
88. Speeding development and execution speed with Just In Time GPU code development approaches.
87. Large-scale Chemical Informatics on the GPU.
86. Hard Data on Soft Errors: A Large-Scale Assessment of Real-World Error Rates in GPGPU.
85. Everything you wanted to know about Markov State Models but were afraid to ask.
84. Protein folded states are kinetic hubs.
83. Enhanced Modeling via Network Theory: Adaptive Sampling of Markov State Models.
82. OpenMM: A Hardware Abstraction Layer for Molecular Simulations.
81. Current Status of the AMOEBA Polarizable Force Field.
80. Unfolded state dynamics and structure of protein L characterized by simulation and experiment.
79. Network models for molecular kinetics and their initial applications to human health.
78. Water Ordering at Membrane Interfaces Controls Fusion Dynamics
77. Equilibrium conformational dynamics in an RNA tetraloop from massively parallel molecular dynamics
76. Evaluating Molecular Mechanical Potentials for Helical Peptides and Proteins.
75. A simple theory of protein folding kinetics.
74. Protein folded states are kinetic hubs.
73. Atomic-Resolution Simulations Predict a Transition State for Vesicle Fusion Defined by Contact of a Few Lipid Tails.
72. Molecular Simulation of ab Initio Protein Folding for a Millisecond Folder NTL9(1−39)
71. Bayesian comparison of Markov models of molecular dynamics with detailed balance constraint
70. Combining Molecular Dynamics with Bayesian Analysis To Predict and Evaluate Ligand-Binding Mutations in Influenza Hemagglutinin.
69. The Roles of Entropy and Kinetics in Structure Prediction.
68. Using generalized ensemble simulations and Markov state models to identify conformational states.
67. Folding@home: lessons from eight years of distributed computing.
66. Probing the nanosecond dynamics of a designed three-stranded beta-sheet with massively parallel molecular dynamics simulation.
65. A multiscale approach to sampling nascent peptide chains in the ribosomal exit tunnel.
64. The Fip35 WW Domain Folds with Structural and Mechanistic Heterogeneity in Molecular Dynamics Simulations.
63. Accelerating Molecular Dynamic Simulation on Graphics Processing Units.
62. The predicted structure of the headpiece of the Huntingtin protein and its implications on Huntingtin aggregation
61. Combining Mutual Information with Structural Analysis to Screen for Functionally Important Residues in Influenza Hemagglutinin.
60. Accelerating Molecular Dynamic Simulation on the Cell processor and PlayStation 3
59. Side-chain recognition and gating in the ribosome exit tunnel
58. Simulating oligomerization at experimental concentrations and long timescales: A Markov state model approach
57. Structural Insight into RNA Hairpin Folding Intermediates
56. Convergence of folding free energy landscapes via application of enhanced sampling methods in a distributed computing environment.
55. N-Body simulation on GPUs
        —— 详见 4# 翻译 —— [已翻译待校对]
54. Calculation of the distribution of eigenvalues and eigenvectors in Markovian state models for molecular dynamics
        —— 详见 5# 翻译 —— [已翻译待校对]
53. Heterogeneity Even at the Speed Limit of Folding: Large-scale Molecular Dynamics Study of a Fast-folding Variant of the Villin Headpiece
        —— 详见 14# 翻译 —— [已翻译待校对]
52. Control of Membrane Fusion Mechanism by Lipid Composition: Predictions from Ensemble Molecular Dynamics.
51. Persistent voids: a new structural metric for membrane fusion.
50. Protein folding under confinement: a role for solvent.
49. Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics.
48. Storage@home: Petascale Distributed Storage
47. Predicting structure and dynamics of loosely-ordered protein complexes: influenza hemagglutinin fusion peptide.
46. A Bayesian Update Method for Adaptive Weighted Sampling.
45. Local structure formation in simulations of two small proteins.
44. Kinetic Definition of Protein Folding Transition State Ensembles and Reaction Coordinates.
43. Parallelized Over Parts Computation of Absolute Binding Free Energy with Docking and Molecular Dynamics.
42. Folding Simulations of the Villin Headpiece in All-Atom Detail.
41. Ensemble molecular dynamics yields submillisecond kinetics and intermediates of membrane fusion
40. Electric Fields at the Active Site of an Enzyme: Direct Comparison of Experiment with Theory.
39. Kinetic Computational Alanine Scanning: Application to p53 Oligomerization
38. Validation of Markov state models using Shannon’s entropy.
37. On the role of chemical detail in simulating protein folding kinetics.
36. Nanotube confinement denatures protein helices.
35. The solvation interface is a determining factor in peptide conformational preferences.
34. Can conformational change be described by only a few normal modes?
33. How large is alpha-helix in solution? Studies of the radii of gyration of helical peptides by SAXS and MD.
32. Error Analysis in Markovian State Models for protein folding.
31. Direct calculation of the binding free energies of FKBP ligands using the Fujitsu BioServer massively parallel computer.
30. A New Set of Molecular Mechanics Parameters for Hydroxyproline and Its Use in Molecular Dynamics Simulations of Collagen-Like Peptides.
29. Comparison of efficiency and bias of free energies computed by exponential averaging, the Bennett acceptance ratio, and thermodynamic integration.
28. Solvation free energies of amino acid side chain analogs for common molecular mechanics water models.
27. Foldamer dynamics expressed via Markov state models. I. Explicit solvent molecular-dynamics simulations in acetonitrile, chloroform, methanol, and water.
26. Foldamer dynamics expressed via Markov state models. II. State space decomposition.
25. Unusual compactness of a polyproline type II structure.
24. How well can simulation predict protein folding kinetics and thermodynamics?
23. Empirical Force-Field Assessment: The Interplay Between Backbone Torsions and Noncovalent Term Scaling.
22. Exploring the Helix-Coil Transition via All-atom Equilibrium Ensemble Simulations.
21. Does Water Play a Structural Role in the Folding of Small Nucleic Acids?
20. Dimerization of the p53 Oligomerization Domain: Identification of a Folding Nucleus by Molecular Dynamics Simulations.
19. Using path sampling to build better Markovian state models: Predicting the folding rate and mechanism of a tryptophan zipper beta hairpin.
18. Simulations of the role of water in the protein-folding mechanism.
17. Trp zipper folding kinetics by molecular dynamics and temperature-jump spectroscopy.
16. Does Native State Topology Determine the RNA Folding Mechanism?
15. Structural correspondence between the alpha-helix and the random-flight chain resolves how unfolded proteins can have native-like properties.
14. Equilibrium Free Energies from Nonequilibrium Measurements Using Maximum-Likelihood Methods.
13. Extremely precise free energy calculations of amino acid side chain analogs: Comparison of common molecular mechanics force fields for proteins.
12. Solvent Viscosity Dependence of the Folding Rate of a Small Protein: Distributed Computing Study.
11. Insights Into Nucleic Acid Conformational Dynamics from Massively Parallel Stochastic Simulations.
10. Multiplexed-Replica Exchange Molecular Dynamics Method for Protein Folding Simulation.
        —— 已认领
9. The Trp Cage: Folding Kinetics and Unfolded State Topology via Molecular Dynamics Simulations.
        —— 已认领
8. Absolute comparison of simulated and experimental protein-folding dynamics.
        —— 已认领
7. Native-like Mean Structure in the Unfolded Ensemble of Small Proteins.
        —— 已认领
6. Simulation of Folding of a Small Alpha-helical Protein in Atomistic Detail using Worldwidedistributed Computing.
        —— 已认领
5. Folding@home and Genome@Home: Using distributed computing to tackle previously intractable problems in computational biology.
        —— 已认领
4. Atomistic protein folding simulations on the submillisecond timescale using worldwide distributed computing.
        —— 详见 23# 翻译 —— [已翻译待校对]
3. b-Hairpin Folding Simulations in Atomistic Detail Using an Implicit Solvent Model.
        —— 详见 22# 翻译 —— [已翻译待校对]
2. Mathematical Foundations of ensemble dynamics.
        —— 详见 21# 翻译 —— [已翻译待校对]
1. Screen savers of the world, Unite!
        —— 详见 20# 翻译 —— [已翻译待校对]

碧城仙

认领“引言”部分。

INTRODUCTION
引言

Here are our peer-reviewed results from Folding@home. For summaries of these methods and papers, as well as the scientific background behind Folding@home, please see the Folding@home article on Wikipedia.
这是我们从 Folding@home 项目中研究总结，并经过同行评议后公开发表的成果。对于这些方法和论文，以及 Folding@home 项目背后的科学背景，详见维基百科的 Folding@home 条目。

For all of the papers from the Pande Lab (not just those from Folding@home), please see our group papers page. Note that it can take quite a while to go from a result to a published peer review article (often as much as a year). These papers represent our progress to date that’s publicly available, with lots more on the way.
对于 Pande Lab 发表的论文（不仅限于 Folding@home 项目相关的），详见我们的 团队论文页面。需要说明的是，我们可能需要相当长的一段时间，从我们获知一个研究成果到经过同行评议后公开发表，通常需要多达一年的时间。这里列出了我们迄今为止所有公开发表的论文，还有一些论文尚在同行评议过程中。

The distribution rules for published papers vary by the publication in which the paper appears. Due to these rules, a public web-source of each paper may not be immediately available. If full version is not linked below or available elsewhere on the Internet (Google Scholar can be helpful for this), most, if not all of these publications are freely available at a local municipal or collegial library. Note these articles are written for fellow scientists, so the contents are fairly technical.
发表在纸质出版物上的论文，基于版权有不同的分发规则。受限于这些规则，每篇论文的公共网页源可能无法完整查阅。如果完整版没有被链接到下面的列表中，或者无法在网络上直接查阅（Google 学术搜索可能对此有帮助），大多数情况下，这些论文都可以通过当地的政府或者学院图书馆数据库免费查阅。请注意，这些文章都是为科研工作者们写的，所以内容相当地技术。

碧城仙

认领第 109 篇论文。

109. To milliseconds and beyond: challenges in the simulation of protein folding.

达到毫秒级甚至超越毫秒级：蛋白质折叠过程在分子动力学研究和模拟中面临的挑战

Lane TJ, Shukla D, Beauchamp KA, Pande VS.

Current Opinion in Structural Biology (Feb 2013)

Lane TJ, Shukla D, Beauchamp KA, Pande VS 发表于《现代结构生物学评论（Current Opinion in Structural Biology）》2013年2月刊

The folding times accessible by simulation have increased exponentially over the past decade. Shown are all protein folding simulations conducted using unbiased, all-atom MD in empirical force-fields reported in the literature. Some folding times for the same protein differ, due to various mutations. FAH results are in blue, results from Shaw’s Anton supercomputer are in red.

在过去的十年里，通过分子动力学模拟技术，对蛋白质折叠过程的观测时间分辨率取得了成倍的提高。图中所显示的，是目前已发表在文献报告中的，采用在实验室领域内已得到公认的全原子水平的分子动力学模拟技术，对蛋白质折叠过程进行模拟所取得的研究成果。对于相同的蛋白质，折叠过程会出现各种不同的突变。图中横坐标显示研究成果取得的年份，纵坐标显示研究成果对应的蛋白质折叠过程观测时间分辨率，纵坐标单位是微秒（μs）。图中通过 Folding@home 项目取得的研究成果用蓝色圆点标记，通过 Shaw’s Anton 超算（美国 David Shaw’s DESRES 研究所的分子动力学模拟专用超级计算机 Anton）取得的研究成果用红色圆点标记。

SUMMARY.
概要

This a review of protein folding achievement from Folding@home and other researchers. Our findings demonstrate that Folding@home is capable of simulating large, complex, and slow-folding proteins, beyond the capabilities of other systems, including the specialized hardware in the supercomputer from David Shaw’s DESRES group.

这是 Folding@home 项目及其他研究人员在研究观测蛋白质的折叠过程所取得的成果。我们的研究成果表面，Folding@home 能够实现对大型、复杂、缓慢折叠的蛋白质进行模拟，已经超越了其他系统，甚至包括美国 David Shaw’s DESRES 研究所专用超级计算机的能力。

ABSTRACT.
摘要

Quantitatively accurate all-atom molecular dynamics (MD) simulations of protein folding have long been considered a holy grail of computational biology. Due to the large system sizes and long timescales involved, such a pursuit was for many years computationally intractable. Further, sufficiently accurate forcefields needed to be developed in order to realistically model folding. This decade, however, saw the first reports of folding simulations describing kinetics on the order of milliseconds, placing many proteins firmly within reach of these methods. Progress in sampling and forcefield accuracy, however, presents a new challenge: how to turn huge MD datasets into scientific understanding. Here, we review recent progress in MD simulation techniques and show how the vast datasets generated by such techniques present new challenges for analysis. We critically discuss the state of the art, including reaction coordinate and Markov state model (MSM) methods, and provide a perspective for the future.

定量准确的对蛋白质折叠过程进行全原子水平的分子动力学模拟（Molecular Dynamics simulations，简称 MD simulation），长期以来都被称为是计算生物学的“圣杯”。由于蛋白质折叠过程与大系统尺度、长时间尺度密切相关，所以对“圣杯”的追求多年来一直难以实现。此外，还需要足够精确的全原子水平的力场，才能模拟真实的蛋白质折叠过程。这十年来，终于，我们首次看到了在毫秒级时间尺度上实现对蛋白质折叠从头开始到形成稳定结构的分子动力学模拟的报道。虽然在采样效率、力场精度上已经取得了进步，但是又提出了新的挑战：如何将庞大的分子动力学模拟数据库（MD datasets）融入科学诠释。本文中，我们回顾了分子动力学模拟（ MD simulation）技术的发展进程，并展示了如何通过技术将庞大的数据库应用于科研分析的这一新的挑战。我们批判性地讨论了当前技术的状态，包括应用反应坐标（Reaction Coordinate）、马尔可夫模型（Markov state model，简称 MSM）预测蛋白质结构的方法，并为未来预测蛋白质结构的方法提供了一个全新的视角。

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备注：已翻译完毕，较原文内容略有些文字说明增补。

xx318088

第55篇 占坑
55. N-Body simulation on GPUs

Erich Elsen, Mike Houston, V. Vishal, Eric Darve, Pat Hanrahan, and Vijay Pande. Proceedings of the 2006 ACM/IEEE conference on Supercomputing (2006).

SUMMARY.
This paper details our first efforts with GPU’s for molecular dynamics. This work lead to the GPU1 FAH core. We have other papers in the works describing the successor to the GPU1 core as well as the PS3 core.

ABSTRACT.
Commercial graphics processors (GPUs) have high compute capacity at very low cost, which makes them attractive for general purpose scientific computing. In this poster we show how graphics processors can be used for N-body simulations to obtain large improvements in performance over current generation CPUs. We have developed a highly optimized algorithm for performing the O(N^2) force calculations that constitute the major part of stellar and molecular dynamics simulations. In the calculations, we achieve sustained performance of nearly 100 GFlops on an ATI X1900XTX. The performance on GPUs 25x an Intel Pentium 4, and 2x specialized hardware such as GRAPE-6A, but at a fraction of the cost. Furthermore, the wide availability of GPUs has significant implications for cluster computing and distributed computing efforts like Folding@home.

You can find more information at the DOI for ACM or download the preprint PDF.

在 GPUs 上进行 N-body 模拟

Erich Elsen, Mike Houston, V. Vishal, Eric Darve, Pat Hanrahan, and Vijay Pande 发表于 2006 年 ACM/IEEE 的超级计算大会。

概要
这篇论文详细的描述了我们首次尝试将 GPU 应用于分子动力学的研究中去的努力。这次的研究诞生了 FAH 的 GPU1 核心。我们还发表了其他的论文来描述下一代 GPU1 核心，也就是用于 PS3 上的那个。

摘要
商用图形处理器（GPUs）计算能力强悍且价格低廉，这对通用科学计算来说很有吸引力。在这张图上我们可以看到将 GPU 计算用于  N-body 模拟相对于的目前的 CPU 计算获得了多大的提升。我们已经写了一个专门为 O(N^2) 优化过的算法，O(N^2) 主要用于计算研究宇宙星体的主要组成部分和模拟研究分子动力学。在实际运行中，我们在一块 ATI X1900XTX 上获得了近 100 GFlops 的持久算力。GPU 的性能表现 25 倍于 Intel Pentium 4，2 倍于像 GRAPE-6A 这样的专业级计算卡，但是价钱却要低很多。此外，GPU 计算的广泛应用对于集群计算和像 Folding@home 这样的分布式计算有显著意义。
你可以在 DOI for ACM 了解到更多信息，或下载 preprint PDF 文档 。

xx318088

第54篇
占坑


54. Calculation of the distribution of eigenvalues and eigenvectors in Markovian state models for molecular dynamics
Nina Singhal Heinrichs and Vijay S. Pande. Journal of Chemical Physics (2007)

SUMMARY. This paper lays out how one can revamp FAH calculations to make them considerably more efficient, perhaps by as much as 1000x reduction in the needed computer time. The basic idea is that we use FAH to build a model of the problem in question (a so-called Markovian state model or MSM) and then use the MSM to predict experimental quantities. When using an MSM to make predictions, the question is usually have we done enough computation to make a sufficiently good (precise) prediction. By calculating the uncertainty (precision) on the fly, we can now send FAH clients to the parts of the problem which are uncertainty limiting. We show that this approach can be considerably more efficiently (1000x) than just running with even sampling. This approach is being incorporated into the FAH server code. One exciting ramification of this work is that while MSM’s were originally formulated as a means to use a large distributed cluster (like Folding@home with 300,000 processors) to try to reproduce what a single, hypothetical machine which is 300,000x faster (which doesn’t exist) could do. However, even if that 300,000x faster machine did exist, we show that our approach would be more efficient than a single, long trajectory, suggesting that MSM-based methods should be useful for a very broad set of computer hardware, not just distributed computing platforms.


计算分子动力学中马尔可夫模型（Markovian state models）的特征值和特征向量的分布状态

Nina Singhal Heinrichs 和 Vijay S. Pande.发表于 《化学物理（Journal of Chemical Physics）》 (2007)

概要。本文展示了如何改进 FAH 的计算效率使之能够缩短上千倍的时间。基本思路是使用 FAH 建立一个问题模型（称为 Markovian state mode，马尔可夫模型，简称 MSM）然后使用 MSM 来预测实验量。使用 MSM 作预测时，这个问题通常是我们已经做了足够多的计算来确保能作出很好（精确）的预测。通过计算 fly 上的不确定性（精度），我们现在可以向 FAH 客户端发送这些限制性不确定因素的部分。这种方法可以在运行超采样率时提升上千倍的效率。这种方法正写入到 FAH 的服务器代码中。这项工作另一个令人激动的发现是 MSM 原先是用做大型分布式集群的（就像拥有 300000 个处理器的 Folding@home）来尝试模拟假设单一一台快 300000 倍的机器（当然现实是不存在的）能做什么。然而，即使真的有快 300000 倍的机器存在，我们也能证明我们的方法会更有效，这也揭示了基于 MSM 的方法在电脑硬件方面拥有广阔的前景，而并不只限于分布式计算。

碧城仙

认领第 108 篇论文。

108. OpenMM 4: A Reusable, Extensible, Hardware Independent Library for High Performance Molecular Simulation.

OpenMM 4：一套可复用的、可扩展的、不依赖于硬件系统的、高性能分子动力学模拟程序库

Eastman P, Friedrichs MS, Chodera JD, Radmer RJ, Bruns CM, Ku JP, Beauchamp KA, Lane TJ, Wang LP, Shukla D, Tye T, Houston M, Stich T, Klein C, Shirts MR, Pande VS.
Journal of Chemical Theory and Computation (Jan 2013)

Eastman P, Friedrichs MS, Chodera JD, Radmer RJ, Bruns CM, Ku JP, Beauchamp KA, Lane TJ, Wang LP, Shukla D, Tye T, Houston M, Stich T, Klein C, Shirts MR, Pande VS 发表于《化学理论与计算（Journal of Chemical Theory and Computation）》2013年1月刊

SUMMARY.
概要

This paper discusses OpenMM 4, powerful and adaptable library for molecular dynamics, is one of the key components behind our current GPU FahCores. A more recent release, OpenMM 5, powers the upcoming FahCore 17 (Zeta). We are looking forward to OpenMM 5.1, which should be a big release from the user’s perspective and offers a lot of exciting scientific features.

本文讨论 OpenMM 4，是一个很给力的适应很强的分子动力学模拟程序库，是我们目前 GPU FahCores 程序背后最关键的部件之一。最新的一个版本，OpenMM 5，即将使用在 FahCore 17 (Zeta) 中。我们期盼着 OpenMM 5.1，从使用者的角度来看，它将是一个强大的版本，能提供许多令人兴奋的科学功能。

ABSTRACT.
摘要

OpenMM is a software toolkit for performing molecular simulations on a range of high performance computing architectures. It is based on a layered architecture: the lower layers function as a reusable library that can be invoked by any application, while the upper layers form a complete environment for running molecular simulations. The library API hides all hardware-specific dependencies and optimizations from the users and developers of simulation programs: they can be run without modification on any hardware on which the API has been implemented. The current implementations of OpenMM include support for graphics processing units using the OpenCL and CUDA frameworks. In addition, OpenMM was designed to be extensible, so new hardware architectures can be accommodated and new functionality (e.g., energy terms and integrators) can be easily added.

OpenMM 是一款在各种高性能计算架构上进行分子动力学模拟的软件工具包。OpenMM 基于一个分层次的结构：下层的功能，可以作为被任何应用程序调用的一个可重复使用的库，而上层则形成一个可供运行分子动力学模拟的完整环境。该库 API 隐藏了所有的硬件特定的依赖和优化，对于使用者和开发人员的模拟程序而言：他们可以不需要为不同的硬件架构编写不同版本的程序代码，而直接在任何硬件上运行该 API。OpenMM 目前已实现对 OpenCL 和 CUDA 架构图形处理器的支持。此外，OpenMM 还提供了一个通用的接口，具有良好的可扩展性，因此新的硬件架构可以融入 OpenMM，新的功能体系也可以较容易地增加 OpenMM。

Stella

你们先认领，到寒假的时候没人要的文章我通通领回家

碧城仙

Stella 发表于 2013-12-16 08:27
你们先认领，到寒假的时候没人要的文章我通通领回家

我在这里十年，第一次见着这么爽快的人

碧城仙

认领第 107 篇论文。

107. MSMExplorer: Visualizing Markov State Models for Biomolecule Folding Simulations.
Cronkite-Ratcliff B, Pande V.
Bioinformatics. (Jan 2013)

MSMExplorer：可视化的用于生物分子折叠模拟的马尔科夫模型（Markov State Models）
Cronkite-Ratcliff B, Pande V. 发表于《生物信息学（Bioinformatics）》2013 年 1 月刊

ABSTRACT.
摘要

Markov state models (MSMs) for the study of biomolecule folding simulations have emerged as a powerful tool for computational study of folding dynamics. MSMExplorer is a visualization application purpose-built to visualize these MSMs with an aim to increase the efficacy and reach of MSM science.

马尔科夫模型（Markov State Models，简称 MSMs）已经成为生物分子折叠研究领域内用于折叠过程动力学计算研究的一款强有力的工具。MSMExplorer 是一个可视化的应用程序，专门用来建立 MSMs 的可视化界面，旨在提高 MSM 科学研究的效率。

MSMExplorer is available for download from https://simtk.org/home/msmexplorer. The source code is made available under the GNU Lesser General Public License at https://github.com/SimTk/msmexplorer.

MSMExplorer 可以从 https://simtk.org/home/msmexplorer 网站下载。MSMExplorer 源代码基于 GNU 宽通用公共许可证（GNU Lesser General Public License）可以从 https://github.com/SimTk/msmexplorer 网站下载。

碧城仙

Stella 发表于 2013-12-16 08:27
你们先认领，到寒假的时候没人要的文章我通通领回家

尽管翻译版块不欢迎灌水，但是在翻译过程中，搜索到了一个与 FAH 论文翻译相关的博客，这里链接一下，“PS3 与Folding@Home 与游戏与科研 2011.01.22” http://blog.sina.com.cn/s/blog_48e1c35b01011juo.html ，说明在中文世界里关心 FAH 研究成果的人至少还是有的。

关于本帖翻译的意义，尽管我本人也不是很看好，@Stella 在 FAH 版块的帖子里也表达了，但是我们仍然选择坚持我们十年前开始翻译分布式计算相关网站、中文化相关报道信息的最初的想法。这个帖子开了之后，通过一些关键字在 Google 上搜索，这个帖子已经排在首页了，总会有人某一天搜索就发现了这里的，继而了解到 Folding@home 项目，继而了解到分布式计算这一方法，或多或少对科学的发展起到些推动，那便是我们翻译的意义。

Stella

碧城仙 发表于 2013-12-16 12:29
尽管翻译版块不欢迎灌水，但是在翻译过程中，搜索到了一个与 FAH 论文翻译相关的博客，这里链接一下，“PS ...

我知道你们的想法，哪怕翻译文章对于推广项目来说效率不高，但总归也是有一点效率的，更何况现在的条件下我们也没有什么太多比较有效的方法
我只是在考虑重心应该放在何处   这个问题

碧城仙

Stella 发表于 2013-12-16 12:54
我知道你们的想法，哪怕翻译文章对于推广项目来说效率不高，但总归也是有一点效率的，更何况现在的条件下 ...

哈哈，分布式计算推广和做科普差不多，就像是使用鸡尾酒疗法治疗艾滋病，大家都不知道重点，就像打散弹枪，期望很多子弹一下子打出去，总会有一颗子弹说不定就起到了什么作用。
科学松鼠会也发过几篇分布式计算的文章，他们也曾翻译过这类分布式的报道，例如这里“Your computer needs you” http://songshuhui.net/archives/4405 ，翻译“你的电脑需要你” http://songshuhui.net/archives/17292 翻译的质量相当好啊，可是也不见得马上就看到什么效果，他们还翻译或者自行组织编写了一大堆科普文章，他们有微博、微信、豆瓣、知乎各种科普渠道，他们还有很多专业的写手，有很多专业的科研工作者粉丝，可是也不见得让我们身边的什么人突然就有了什么科学素养，可是千百年来多得是这样的事情这样的人在坚持。
哈哈，你知道我们的想法就好。

Stella

碧城仙 发表于 2013-12-16 13:11
哈哈，分布式计算推广和做科普差不多，就像是使用鸡尾酒疗法治疗艾滋病，大家都不知道重点，就像打散弹枪 ...

艾滋病这种妖孽也只能用霰弹枪了。。之前说痊愈的两位患者现在又复发了，表明现有的理论还是没有覆盖到病毒隐藏的每一个角落。。

xx318088

第53篇
占坑


53. Heterogeneity Even at the Speed Limit of Folding: Large-scale Molecular Dynamics Study of a Fast-folding Variant of the Villin Headpiece
D. Ensign, P. M. Kasson, and V. S. Pande. Journal of Molecular Biology (2007)

限速折叠下的异质性：对绒毛蛋白头质快速折叠变异的大分子动力学模拟研究
D. Ensign, P. M. Kasson, 和 V. S. Pande发表于《分子生物学》（2007）

SUMMARY: This paper describes the first set of results generated using the SMP clients. The main advantage of using SMP for these sorts of calculations is that the amount of computation that one client can do is several times larger than the traditional clients. This means that our simulations can get many times longer that before; in fact, this has allowed us to generate several hundred folding trajectories of the fastest-folding protein known, the HP35-NleNle variant of the villin headpiece subdomain. In this paper, because our simulation time scales compare well to the 700-nanosecond experimental folding time of this protein, AND we’ve generated enough trajectories to get good statistics, we can shed some light on the experimental results. To summarize the result, the first helix of the protein was thought to be highly structured in the unfolded state of the protein; we’ve suggested that structure in this part of the molecule is not enough to lead to fast folding, and that longer time scales than the 700-ns mark may be present in this system.

摘要：本文叙述了使用SMP客户端所生成的第一部分成果。使用 SMP 来进行这类计算的主要优势在于一个客户端所进行的计算量可以数倍于传统的客户端。这意味着我们可以得到比以前长得多的模拟时间，实际上这已经让我们得到了几百条已知的最快的蛋白质折叠的轨迹——HP35-NleNle型绒毛蛋白头质子域。在本文中，我们模拟次数和规模相较于700纳秒的蛋白质折叠实验表现的不错而且我们也得到了足够多的折叠轨迹来做出令人满意的统计结果，我们从实验结果中得到了一些启发。把这些结果总结起来说就是该蛋白质在未折叠状态下第一螺旋线看来是高度结构化的。我们已经得出这样的结论：在目前的系统状态下这部分结构的分子不足以引发快速的折叠并且比700纳秒目标要花更长时间测量。


Check out the movie: it shows some simulation we did for this work, although watching one trajectory is emphatically NOT statistically significant! Some more visualizations of villin from our earlier work can be found on this page.

这段视频演示了我们为这项工作所做的一些模拟，虽然只观察一个轨迹在统计上没什么意义。在这里也能找到一些我们早期为绒毛蛋白可视化所做的工作

We have also made the raw data available to researchers on a SimTk.org page. This site includes the raw data, as well as scripts to automate the process and a VMD plugin to allow for browsing of the data. Please contact simbiosfeedback@stanford.edu if you need help with doing this.

我们也为研究员提供了可用的原始数据在SimTk.org网站上。这个网站提供原始的数据和自动脚本以及用来浏览数据用的VMD插件。如果有疑问请发邮件至simbiosfeedback@stanford.edu询问。

ABSTRACT: We have performed molecular dynamics simulations on a set of nine unfolded conformations of the fastest-folding protein yet discovered, a variant of the villin headpiece subdomain (HP-35 NleNle). The simulations were generated using a new distributed computing method, yielding hundreds of trajectories each on a time scale comparable to the experimental folding time, despite the large (10,000 atom) size of the simulation system. This strategy eliminates the need to assume a two-state kinetic model or to build a Markov state model. The relaxation to the folded state at 300 K from the unfolded configurations (generated by simulation at 373 K) was monitored by a method intended to reflect the experimental observable (quenching of tryptophan by histidine). We also monitored the relaxation to the native state by directly comparing structural snapshots with the native state. The rate of relaxation to the native state and the number of resolvable kinetic time scales both depend upon starting structure. Moreover, starting structures with folding rates most similar to experiment show some native-like structure in the N-terminal helix (helix 1) and the phenylalanine residues constituting the hydrophobic core, suggesting that these elements may exist in the experimentally relevant unfolded state. Our large-scale simulation data reveal kinetic complexity not resolved in the experimental data. Based on these findings, we propose additional experiments to further probe the kinetics of villin folding.

概论：我们已经在一个已知的九未折叠构造的快速折叠蛋白质——变异绒毛蛋白头质子域 (HP-35 NleNle)上实施了一次分子动力学模拟。这次模拟产生了一种新的分布式计算的方法。通过同一时间产生出数百道轨迹来与实验中的折叠相对比，尽管这是个大型的模拟体系（10000个原子）。这种方式不需要假设一个双轨制的动力模型来建立一个马尔科夫模型。把折叠状态放宽到300K，从未折叠状态（模拟于373K下）可以作为一种持续监视实验状态的方法（色氨酸由组氨酸淬火）。

碧城仙

认领第 106 篇论文。

106. Building Markov state models with solvent dynamics
Gu C, Chang HW, Maibaum L, Pande VS, Carlsson GE, Guibas LJ.
BMC Bioinformatics, (Jan 2013) doi: 10.1186/1471-2105-14-S2-S8

借助溶剂动力学（Solvent Dynamics）建立马尔科夫模型（Markov state models）

Gu C, Chang HW, Maibaum L, Pande VS, Carlsson GE, Guibas LJ 发表于《BMC Bioinformatics》2013 年 1 月刊，索引号 10.1186/1471-2105-14-S2-S8

SUMMARY.
概要

In this paper we have initiated an study to build Markov state models for molecular dynamical systems with solvent degrees of freedom. The methods we described should also be broadly applicable to a wide range of biomolecular simulation analyses.

在本篇论文中，我们已经开展了一项研究，已借助溶剂自由度（Solvent degrees of freedom）来建立马尔科夫模型（Markov state models）用于分子动力学系统研究。我们所描述的方法，也广泛地适用于其他领域的生物分子模拟分析。

ABSTRACT.
摘要

Markov state models have been widely used to study conformational changes of biological macromolecules. These models are built from short timescale simulations and then propagated to extract long timescale dynamics. However, the solvent information in molecular simulations are often ignored in current methods, because of the large number of solvent molecules in a system and the indistinguishability of solvent molecules upon their exchange.

马尔科夫模型（Markov state models）已经被广泛应用于研究生物大分子的构象变化。这些模型从短时间尺度开始模拟建模，从而衍生成长时间尺度的分子动力学模拟。但是，目前已有的方法，分子模拟中的溶剂信息常常被忽视掉，因为大量的溶剂分子在一个系统中发生交换是不可分辨的。

We present a solvent signature that compactly summarizes the solvent distribution in the high-dimensional data, and then define a distance metric between different configurations using this signature. We next incorporate the solvent information into the construction of Markov state models and present a fast geometric clustering algorithm which combines both the solute-based and solvent-based distances.

我们提出了一种溶剂签名（solvent signature），可以高维数据（high-dimensional data）形式对溶剂分布情况精确地描述，然后确定使用不同签名的不同参数的溶剂之间的距离尺度。接下来，我们把溶剂信息融入马尔科夫模型的建立中，并提出一种快速几何聚类算法（fast geometric clustering algorithm），该算法融合了“以溶质为基础（solute-based）”和“以溶剂为基础（solvent-based）”来分类距离的算法。