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Open data-driven infrastructure for building biomolecular force fields for predictive biophysics and drug design

开放数据驱动的基础设施，用于构建用于预测生物物理学和药物设计的生物分子力场

基本信息

批准号：
10580156
负责人：
Michael R Shirts
金额：
$ 60.16万
依托单位：
UNIVERSITY OF COLORADO
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-01 至 2025-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10580156
关键词：
Acceleration Address Area Automobile Driving Bayesian Analysis Binding Biological Biology Biophysical Process Biophysics Charge Chemicals Chemistry Complex Computer Simulation Computer software DNA Data Data Set Databases Development Disease Drug Design Electrostatics Ensure Error Sources Formulation Generations Goals Heart Individual Infrastructure Investigation Learning Life Measurement Methods Modeling Modernization Modification Molecular Nucleic Acids Occupations Perception Performance Pharmaceutical Preparations Process Property Proteins RNA Readability Reproducibility Research Roentgen Rays Scheme Science Scientist Specific qualifier value Structure System Techniques Technology Temperature Therapeutic Thermodynamics Training Uncertainty Validation Work behavior influence biophysical model chemical synthesis cheminformatics data infrastructure design drug discovery experience experimental study improved interest machine learning method macromolecule mechanical force models and simulation molecular mechanics multidisciplinary new technology next generation novel therapeutics nucleic acid-based therapeutics open data open source physical model physical property quantum rational design simulation simulation software small molecule software infrastructure sound tool unnatural amino acids

项目摘要

PROJECT SUMMARY/ABSTRACT The study of biomolecular interactions and design of new therapeutics requires accurate physical models of the atomistic interactions between small molecules and biological macromolecules. Over the least few decades, molecular mechanics force ﬁelds have demonstrated the potential that physical models hold for quantitative biophysical modeling and predictive molecular design. However, a signiﬁcant technology gap exists in our ability to build force ﬁelds that achieve high accuracy, can be systematically improved in a statistically robust manner, be extended to new areas of chemistry, can model post-translational and covalent modiﬁcations, are able to quantify systematic errors in predictions, and can be broadly applied across a high-performance software packages. In this project, we aim to bridge this technology gap to enable new generations of accurate quantitative biomolec- ular modeling and (bio)molecular design for chemical biology and drug discovery. In Aim 1, we will produce a modern, open infrastructure to enable practitioners to rapidly and conveniently construct and employ accurate and statistically robust physical force ﬁelds via automated machine learning methods. In Aim 2, we will construct open, machine-readable experimental and quantum chemical datasets that will accelerate next-generation force ﬁeld development. In Aim 3, we will develop statistically robust Bayesian inference techniques to enable the auto- mated construction of type assignment schemes that avoid overﬁtting and selection of physical functional forms statistically justﬁed by the data. This approach will also provide an estimate of the systematic error in predicted properties arising from uncertainty in parameters or functional form choices—generally the dominant source of error—to be quantiﬁed with little added expense. In Aim 4, we will integrate and apply this infrastructure to produce open, transferable, self-consistent force ﬁelds that achieve high accuracy and broad coverage for modeling small molecule interactions with biomolecules (including unnatural amino or nucleic acids and covalent modiﬁcations by organic molecules), with the ultimate goal of covering all major biomolecules. This research is signiﬁcant in that the technology developed in this project has the potential to radically transform the study of biomolecular phenomena by providing highly accurate force ﬁelds with exceptionally broad chemical coverage via fully consistent parameterization of organic (bio)molecules. In addition, we will produce new tools to automate force ﬁeld creation and tailoring to speciﬁc problem domains, quantify the systematic error in predictions, and identify new data for improving force ﬁeld accuracy. This will greatly improve our ability to study diverse biophysical processes at the molecular level, and to rationally design new small-molecule, protein, and nucleic acid therapeutics. This approach will bring statistical rigor to the ﬁeld of force ﬁeld construction and application by providing a means to make data-driven decisions, while enhancing reproducibility by enabling it to become a rigorous and reproducible science using a fully open infrastructure and datasets.

项目总结/文摘