Department of Chemistry, Southern Methodist University, Dallas, TX 75205, USA.
Department of Computer Science, Southern Methodist University, Dallas, TX 75205, USA.
Int J Mol Sci. 2021 Jan 30;22(3):1392. doi: 10.3390/ijms22031392.
Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently shown excellent performance in PLI prediction. However, the performance is highly dependent on protein and ligand features utilized for the DNN model. Moreover, in current models, the deciphering of how protein features determine the underlying principles that govern PLI is not trivial. In this work, we developed a DNN framework named SSnet that utilizes secondary structure information of proteins extracted as the curvature and torsion of the protein backbone to predict PLI. We demonstrate the performance of SSnet by comparing against a variety of currently popular machine and non-Machine Learning (ML) models using various metrics. We visualize the intermediate layers of SSnet to show a potential latent space for proteins, in particular to extract structural elements in a protein that the model finds influential for ligand binding, which is one of the key features of SSnet. We observed in our study that SSnet learns information about locations in a protein where a ligand can bind, including binding sites, allosteric sites and cryptic sites, regardless of the conformation used. We further observed that SSnet is not biased to any specific molecular interaction and extracts the protein fold information critical for PLI prediction. Our work forms an important gateway to the general exploration of secondary structure-based Deep Learning (DL), which is not just confined to protein-ligand interactions, and as such will have a large impact on protein research, while being readily accessible for de novo drug designers as a standalone package.
计算蛋白质配体相互作用（PLI）的预测是现代药物发现管道中的重要步骤，因为它可以降低筛选新型治疗剂所需的成本、时间和资源。深度神经网络（DNN）最近在 PLI 预测中表现出了优异的性能。然而，性能高度依赖于用于 DNN 模型的蛋白质和配体特征。此外，在当前的模型中，解析蛋白质特征如何确定控制 PLI 的基本原理并非易事。在这项工作中，我们开发了一种名为 SSnet 的 DNN 框架，该框架利用从蛋白质骨架的曲率和扭转中提取的蛋白质二级结构信息来预测 PLI。我们通过使用各种指标与各种当前流行的机器和非机器学习（ML）模型进行比较，展示了 SSnet 的性能。我们可视化 SSnet 的中间层，以显示蛋白质的潜在潜在空间，特别是提取模型认为对配体结合有影响的蛋白质中的结构元素，这是 SSnet 的关键特征之一。我们在研究中观察到，SSnet 学习了有关配体可以结合的蛋白质位置的信息，包括结合位点、变构位点和隐蔽位点，而不管使用的构象如何。我们进一步观察到，SSnet 不受任何特定分子相互作用的影响，并提取对 PLI 预测至关重要的蛋白质折叠信息。我们的工作为基于二级结构的深度学习（DL）的一般探索奠定了重要基础，不仅限于蛋白质 - 配体相互作用，因此将对蛋白质研究产生重大影响，同时作为一个独立的软件包，也易于新的药物设计师使用。
