Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America.
Department of Chemistry, Vanderbilt University, Nashville, Tennessee, United States of America.
PLoS Comput Biol. 2022 Apr 20;18(4):e1010038. doi: 10.1371/journal.pcbi.1010038. eCollection 2022 Apr.
Recent advances in experimental and computational protein structure determination have provided access to high-quality structures for most human proteins and mutants thereof. However, linking changes in structure in protein mutants to functional impact remains an active area of method development. If successful, such methods can ultimately assist physicians in taking appropriate treatment decisions. This work presents three artificial neural network (ANN)-based predictive models that classify four key functional parameters of KCNQ1 variants as normal or dysfunctional using PSSM-based evolutionary and/or biophysical descriptors. Recent advances in predicting protein structure and variant properties with artificial intelligence (AI) rely heavily on the availability of evolutionary features and thus fail to directly assess the biophysical underpinnings of a change in structure and/or function. The central goal of this work was to develop an ANN model based on structure and physiochemical properties of KCNQ1 potassium channels that performs comparably or better than algorithms using only on PSSM-based evolutionary features. These biophysical features highlight the structure-function relationships that govern protein stability, function, and regulation. The input sensitivity algorithm incorporates the roles of hydrophobicity, polarizability, and functional densities on key functional parameters of the KCNQ1 channel. Inclusion of the biophysical features outperforms exclusive use of PSSM-based evolutionary features in predicting activation voltage dependence and deactivation time. As AI is increasingly applied to problems in biology, biophysical understanding will be critical with respect to 'explainable AI', i.e., understanding the relation of sequence, structure, and function of proteins. Our model is available at www.kcnq1predict.org.
最近在实验和计算蛋白质结构测定方面的进展为大多数人类蛋白质及其突变体提供了高质量的结构。然而，将蛋白质突变体结构的变化与功能影响联系起来仍然是方法开发的一个活跃领域。如果成功，这些方法最终可以帮助医生做出适当的治疗决策。本工作提出了三个基于人工神经网络（ANN）的预测模型，这些模型使用基于 PSSM 的进化和/或物理化学描述符，将 KCNQ1 变体的四个关键功能参数分类为正常或功能失调。最近，利用人工智能（AI）预测蛋白质结构和变体特性的进展在很大程度上依赖于进化特征的可用性，因此无法直接评估结构和/或功能变化的物理化学基础。本工作的中心目标是开发一个基于 KCNQ1 钾通道结构和物理化学特性的 ANN 模型，该模型的性能与仅使用基于 PSSM 的进化特征的算法相当或更好。这些物理化学特征突出了控制蛋白质稳定性、功能和调节的结构-功能关系。输入灵敏度算法包含疏水性、极化率和关键功能参数的功能密度在 KCNQ1 通道中的作用。包含物理化学特征在预测激活电压依赖性和失活时间方面优于仅使用基于 PSSM 的进化特征。随着 AI 在生物学问题中的应用越来越广泛，物理化学理解将对于“可解释 AI”至关重要，即理解蛋白质的序列、结构和功能之间的关系。我们的模型可在 www.kcnq1predict.org 获得。
