Improving the Accuracy of Implicit Solvents with a Physics-Guided Neural Network

利用物理引导神经网络提高隐式溶剂的准确性

基本信息

批准号：
10669809
负责人：
Negin Forouzesh
金额：
$ 18.25万
依托单位：
CALIFORNIA STATE UNIVERSITY LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10669809
关键词：
Acceleration Adherence Affinity Benchmarking Binding Binding Proteins Biological Biological Process Biological Sciences Cells Characteristics Chemicals Communicable Diseases Communities Complex Computer Assisted Computer Models Computer Simulation Computing Methodologies Coupling DNA biosynthesis Data Data Set Databases Drug Design Ensure Free Energy GTP-Binding Proteins Gene Expression Goals Health Human body Hybrids Interdisciplinary Study Ligands Machine Learning Measures Medicine Methods Modeling Modernization Modification Neural Network Simulation Outcomes Research Performance Physics Play Preparation Property Protein Conformation Proteins Protocols documentation Research Running Sampling Side Signal Transduction Signal Transduction Pathway Solvents Source Source Code Structure Study models System Techniques Testing Training Water Weight cost data cleaning data modeling data-driven model design drug candidate drug discovery drug-like compound experimental study flexibility foot graph neural network improved laboratory experiment loss of function molecular recognition neglect neural network novel novel therapeutics physical model prevent programs protein folding protonation prototype response simulation small molecule stem structural biology usability virtual screening web services

项目摘要

Project Summary/Abstract Drug discovery is one of the most challenging tasks in biological sciences; it takes about 10-15 years and $2 billion on average to discover a new drug. The main goal in drug discovery is identifying drug-like com- pounds (ligands) capable of modulating speciﬁc biological targets (proteins). One key feature of protein-ligand interactions is the binding free energy change, G, that occurs between the protein and the ligand upon the ligand's attachment. This physiochemical feature heavily dictates how strongly a protein and ligand interact and is particularly useful to understand for drug design. While wet-lab experiments accurately estimate G, they are signiﬁcantly slow, costly, and laborious. On the other hand, computational simulations enable signiﬁcantly faster estimation of G and shed light on the binding mechanism of various structures that could have been complicated to be examined otherwise. The implicit solvent framework, which treats solvent as a continuum with the dielectric and non-polar properties of water, offer much more efﬁcient estimation of G compared to other computational methodologies, such as alchemical free energy methods. Despite noticeable progress in implicit solvent modeling, serious concerns about its accuracy remain that stem from the underlying physi- cal approximations. This research will employ modern machine learning techniques to bridge the accuracy gap between a physics-based implicit solvent model and experimental references in terms of G calculations. In particular, experimental data will be integrated into a generalized Born (GB) implicit solvent model so that with adherence to the physical model, new structural features could improve the accuracy. In addition to the model accuracy, it is essential to retain interpretability (that accounts for the model simplicity) and transferability (that assures consistent performance on different datasets). To this end, a novel multi-objective loss function will be introduced that takes “accuracy”, “interpretability”, and “transferability” into consideration. Standard protein-ligand databases, benchmarks, and datasets will be used for designing the proposed hybrid model, including host-guest systems, SAMPL challenge benchmarks, PDBbind, and BindingDB. While some of these sources contain clean data, many require further post-processing to prepare for running the GB model. Careful data preparation will be performed by following standard protocols and via popular web services. The modular characteristics of the proposed physics-data model will allow for testing various ﬂavors of implicit solvent (physics-based model) and modiﬁcations to the proposed Graph Convolutional Network (data-driven model). This ﬂexibility of the hybrid model facilitates new interdisciplinary research between the classical physics-based and the modern data-driven ends. The ﬁnal source code and parameterized datasets will be available freely to the public. They could be incorporated into the high-throughput virtual screening of candidate drugs in the early stages of drug discovery. The outcome of this research will beneﬁt the biomolecular modeling community by providing an approach to build novel, accurate, and efﬁcient computational models for studying protein-ligand interactions.

项目摘要/摘要药物发现是生物科学中最具挑战性的任务之一。 20亿美元以发现一种新药。磅（配体）能够调节特定生物靶标（蛋白质）。相互作用是粘合能变化g，它发生在蛋白质和配体之间配体的依恋。对于药物设计特别了解。另一方面，计算模拟显着慢，昂贵且费力。更快地估计G和Shed阐明可能是各种结构的结合机制更复杂的是隐式溶剂框架，将溶剂视为连续使用水的介电性和非极性特性，与G的相比，G相比提供了更多的有效估计用于其他计算方法，例如炼金术自由能法。为了暗示溶剂建模，对其准确性的严重关注仍然是基本的物理学 Cal近似值。基于物理学的隐式溶剂模型和G计算的实验参考特别的，实验数据将集成到广义诞生（GB）隐式溶剂模型中，以便与遵守物理模型，除了模型外，新的结构特征还可以提高精度。准确性，必须保留解释性（解释模型简单性）和可转让性（That）至关重要在此目的引入的，将“准确性”，“可解释性”和“可转移性”介绍为标准蛋白质数据库，基准和数据集将用于设计支撑混合模型，涉及主机 - 吉斯特系统，采样基准，PDBBIND和BINDINGDB。数据，许多需要进一步的后处理才能准备运行GB模型。通过以下标准协议和流行的Web服务执行。支撑物理数据模型将允许测试隐式溶剂的各种型号对支撑图卷积网络（数据驱动模型）的修改。模型促进了基于经典物理与现代数据驱动的新的跨学科研究结束。最终的源代码和参数化数据集将向公众提供在药物drig发现的早期阶段中，纳入了高通用筛选药物。这项研究的结果是通过提供一种建造方法来填补生物分子建模社区用于研究蛋白质 - 配体相互作用的新型，准确和有效的计算机模型。