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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Protein Design by Sampling an Undirected Graphical Model of Residue Constraints
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Protein Design by Sampling an Undirected Graphical Model of Residue Constraints

机译：通过采样残留约束的无向图形模型进行蛋白质设计

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摘要

This paper develops an approach for designing protein variants by sampling sequences that satisfy residue constraints encoded in an undirected probabilistic graphical model. Due to evolutionary pressures on proteins to maintain structure and function, the sequence record of a protein family contains valuable information regarding position-specific residue conservation and coupling (or covariation) constraints. Representing these constraints with a graphical model provides two key benefits for protein design: a probabilistic semantics enabling evaluation of possible sequences for consistency with the constraints, and an explicit factorization of residue dependence and independence supporting efficient exploration of the constrained sequence space. We leverage these benefits in developing two complementary MCMC algorithms for protein design: constrained shuffling mixes wild-type sequences positionwise and evaluates graphical model likelihood, while component sampling directly generates sequences by sampling clique values and propagating to other cliques. We apply our methods to design WW domains. We demonstrate that likelihood under a model of wild-type WWs is highly predictive of foldedness of new WWs. We then show both theoretical and rapid empirical convergence of our algorithms in generating high-likelihood, diverse new sequences. We further show that these sequences capture the original sequence constraints, yielding a model as predictive of foldedness as the original one.
机译：本文提出了一种通过采样满足无向概率图形模型中编码的残基限制的序列来设计蛋白质变体的方法。由于蛋白质对维持结构和功能的进化压力，蛋白质家族的序列记录包含有关位置特异性残基保守和偶联（或协变）约束的有价值的信息。用图形模型表示这些限制条件为蛋白质设计提供了两个主要好处：概率语义支持对可能的序列进行评估，以确保与限制条件的一致性；以及残基依赖性和独立性的显式分解，支持对受限序列空间的有效探索。我们在开发两个互补的MCMC算法进行蛋白质设计时利用了这些好处：约束改组在位置上混合野生型序列并评估图形模型的可能性，而组件采样通过采样集团值并传播到其他集团来直接生成序列。我们将我们的方法应用于设计WW域。我们证明，在野生型WW模型下的可能性可以高度预测新WW的折叠性。然后，我们展示了我们的算法在产生高可能性，多样的新序列中的理论和快速经验收敛。我们进一步表明，这些序列捕获了原始序列约束，从而产生了一种可预测原始折叠性的模型。

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