Mi3-GPU: MCMC-based inverse Ising inference on GPUs for protein covariation analysis
Mi3-GPU: MCMC-based inverse Ising inference on GPUs for protein covariation analysis
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DOI：
10.1016/j.cpc.2020.107312
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发表时间：
2021-01-06
影响因子：
6.3
通讯作者：
Levy, Ronald M.
中科院分区：
文献类型：
--
作者：
Haldane, Allan;Levy, Ronald M.
逆Ising推理是一种根据观察到的位点共变来推断Potts/Ising模型耦合参数的方法，它在蛋白质物理中检测蛋白质家族残基之间的相互作用方面得到了重要的应用。我们介绍了MI3-GPU(“mee-Three”，用于MCMC逆向伊辛推理)软件，用于在GPU上通过并行马尔可夫链蒙特卡罗采样来求解解析近似较少的蛋白质序列数据集的伊辛逆问题。我们还提供了分析和准备蛋白质家族多序列比对(MSA)的工具，以解决有限采样问题，这是反向伊辛推理中误差或偏差的主要来源。我们的方法是“生成性的”，因为推断的模型可以用来生成合成的MSA，其突变统计(边际)可以被验证为匹配数据集的MSA统计，直到有限抽样的效果施加的限制。我们的GPU实现使得能够构建模型，该模型以更接近的方法所不可能的精度再现观测到的MSA的协变模式。我们的方法的主要组成部分是一个经过GPU优化的算法，可以极大地加速MCMC采样，并结合了一种使用Zwanzig重新加权技术的多步拟牛顿参数更新方案。程序摘要程序标题：MI3-GPU程序文件DOI：http://dx.doi.org/10.17632/ftbcfy2p35.1Licensing条款：GPLv3编程语言：PYTHON 3，OPENCL，CNTERATION Of Problem：MI3-GPU解决了逆Ising问题在蛋白质协变分析中的应用。解决方法：Mi3-GPU在GPU上用拟牛顿优化的MarkovChain蒙特卡罗方法求解Ising逆问题。以前，人们用解析近似的方法来解决这个问题，包括“消息传递”、“磁化率传播”、“平均场”方法、伪椭圆形近似和簇扩展。该软件利用GPU来加速MCMC采样，并利用直方图重新加权技术来加速参数优化。(C)2020爱思唯尔B.V.保留所有权利。
Inverse Ising inference is a method for inferring the coupling parameters of a Potts/Ising model based on observed site-covariation, which has found important applications in protein physics for detecting interactions between residues in protein families. We introduce Mi3-GPU ("mee-three", for MCMC Inverse Ising Inference) software for solving the inverse Ising problem for protein-sequence datasets with few analytic approximations, by parallel Markov-Chain Monte Carlo sampling on GPUs. We also provide tools for analysis and preparation of protein-family Multiple Sequence Alignments (MSAs) to account for finite-sampling issues, which are a major source of error or bias in inverse Ising inference. Our method is "generative"in the sense that the inferred model can be used to generate synthetic MSAs whose mutational statistics (marginals) can be verified to match the dataset MSA statistics up to the limits imposed by the effects of finite sampling. Our GPU implementation enables the construction of models which reproduce the covariation patterns of the observed MSA with a precision that is not possible with more approximate methods. The main components of our method are a GPU-optimized algorithm to greatly accelerate MCMC sampling, combined with a multi-step Quasi-Newton parameter update scheme using a "Zwanzig reweighting"technique. We demonstrate the ability of this software to produce generative models on typical protein family datasets for sequence lengths L similar to 300 with 21 residue types with tens of millions of inferred parameters in short running times.Program summaryProgram Title: Mi3-GPUProgram Files doi: http://dx.doi.org/10.17632/ftbcfy2p35.1Licensing provisions: GPLv3Programming languages: Python3, OpenCL, CNature of problem: Mi3-GPU solves the inverse Ising problem for application in protein covariation analysis. The goal is to infer "coupling'' parameters between positions in a Multiple Sequence Alignment of a protein family, with many applications including protein-contact prediction and fitness prediction.Solution method: Mi3-GPU solves the inverse Ising problem with few approximations using MarkovChain Monte Carlo methods with Quasi-Newton optimization on GPUs. This problem previously has been approached by more approximate methods using analytic approximations including "message Passing'', "Susceptibility Propagation", "mean-field"methods, pseudolikelihood approximations, and cluster expansion. The software leverages GPU to accelerate MCMC sampling and a histogram reweighting technique to accelerate parameter optimization. (C) 2020 Elsevier B.V. All rights reserved.