The structure-fitness landscape of pairwise relations in generative sequence models
The structure-fitness landscape of pairwise relations in generative sequence models
复制标题
生成序列模型中成对关系的结构适应度景观
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
S. Ovchinnikov
中科院分区：
文献类型：
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作者：
D. Marshall;Haobo Wang;M. Stiffler;J. Dauparas;Peter K. Koo;S. Ovchinnikov
如果正确地解开纠缠，从特定蛋白质家族的进化相关序列中提取的模式可以告知它们的特征，例如它们的结构和功能。近年来，为了捕获这些模式，生成模型的复杂性有所增加；从站点到成对再到深度和变分。在本研究中，我们通过一套逐渐复杂的模型来评估结构和适应性模式的程度。我们引入了两两显著性，一种评估捕获结构信息程度的新方法。我们还通过使用这些模型来预测突变序列的适应度，然后将这些预测与测量的适应度值相关联，从而量化这些模型所获得的适应度信息。我们观察到，告知结构的模型不一定告知适应度，反之亦然，对比该领域最近的主张。我们的工作强调了适应度分析缺乏一致性，并为理解由给定生成序列模型学习的成对分解关系提供了一种通用方法。
If disentangled properly, patterns distilled from evolutionarily related sequences of a given protein family can inform their traits - such as their structure and function. Recent years have seen an increase in the complexity of generative models towards capturing these patterns; from sitewise to pairwise to deep and variational. In this study we evaluate the degree of structure and fitness patterns learned by a suite of progressively complex models. We introduce pairwise saliency, a novel method for evaluating the degree of captured structural information. We also quantify the fitness information learned by these models by using them to predict the fitness of mutant sequences and then correlate these predictions against their measured fitness values. We observe that models that inform structure do not necessarily inform fitness and vice versa, contrasting recent claims in this field. Our work highlights a dearth of consistency across fitness assays as well as divergently provides a general approach for understanding the pairwise decomposable relations learned by a given generative sequence model.
影响因子：
14.9
作者：
Burley SK;Berman HM;Bhikadiya C;Bi C;Chen L;Di Costanzo L;Christie C;Dalenberg K;Duarte JM;Dutta S;Feng Z;Ghosh S;Goodsell DS;Green RK;Guranovic V;Guzenko D;Hudson BP;Kalro T;Liang Y;Lowe R;Namkoong H;Peisach E;Periskova I;Prlic A;Randle C;Rose A;Rose P;Sala R;Sekharan M;Shao C;Tan L;Tao YP;Valasatava Y;Voigt M;Westbrook J;Woo J;Yang H;Young J;Zhuravleva M;Zardecki C
通讯作者：
Zardecki C