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In-silico prediction of protein-peptide interactions.

蛋白质-肽相互作用的计算机预测。

基本信息

批准号：
10653086
负责人：
MICHEL F. SANNER
金额：
$ 40.73万
依托单位：
SCRIPPS RESEARCH INSTITUTE, THE
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-04-15 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10653086
关键词：
Amino Acids Area Artificial Intelligence Automobile Driving Award Benchmarking Binding Binding Proteins Biological Biological Availability Biological Process Biomedical Research Cell physiology Cells Chemicals Collaborations Communities Complex Computational Biology Computer software Cyclic Peptides Data Set Development Disease Docking Documentation Drug Design Educational workshop Feedback Free Energy Funding Goals Half-Life Influenza Insulin Libraries Licensing Ligands Machine Learning Malignant Neoplasms Marketing Mathematics Mediating Medicine Metabolic Diseases Methods Modeling Modernization Mutate Pathway interactions Peptides Performance Peripheral Permeability Pharmaceutical Preparations Play Process Production Property Proteins Renaissance Research Role Scientist Set protein Signal Pathway Software Engineering Software Framework Software Tools Software Validation Specificity Structure Techniques Testing Therapeutic Thrombosis Toxic effect Training Update Validation Work combinatorial complex data computerized tools computing resources design flexibility globular protein graphical user interface improved in silico insight interest interoperability novel open source peptide drug predictive tools programs protein protein interaction receptor screening small molecule success symposium therapeutic target tool translational applications translational model translational study usability virtual screening

项目摘要

IN-SILICO PREDICTION OF PROTEIN-PEPTIDE INTERACTIONS Automated docking methods are used extensively for gaining a mechanistic understanding of the molecular interactions underpinning cellular processes. While these tools work well for small molecules they perform poorly for peptides and cannot handle Intrinsically Disordered Proteins (IDPs) which play very important roles in these processes. The goal of this project is the development of an efficient and practical peptide docking software, useful for designing therapeutic peptides and gaining insight into IDPs binding ordered proteins. The proposed software supports biomedical applications ranging from investigating chemical pathways to designing and optimizing therapeutic molecules for diseases such as cancer and metabolic disorders. Under the previous award we developed and released a new method for docking fully-flexible peptides with up to 20 standard amino acids: AutoDock CrankPep (ADCP). We showed that it outperforms current state-of-the-art docking methods. For the next award, we propose to: 1) further develop ADCP to support docking IPDs with up to 70 amino acids and improve support for therapeutic peptides containing modified amino acids and complex macrocycles; 2) develop peptide-specific scoring functions to increase docking success rates and methods for predicting the free energy of binding of peptides. This will be done by exploiting the latest advances in statistical potentials for docking, as well as applying machine-learning techniques; 3) test and validate the software on our datasets, community benchmarks, and through our collaborations with outstanding biologists working on biomedical applications spanning from designing drugs for thrombosis and influenza, to modeling IDPs interacting with globular proteins; and 4) document the software and release it under an open source license on a regular basis along with datasets we compile and update on regularly. The proposed research will occur in the context of collaborations with experimental biologists working on highly relevant biomedical projects and providing experimental feedback and validation. In addition, this project will benefit from various collaborations with experts in the fields of computational biology, applied mathematics and artificial intelligence. This docking software tool will be developed by applying best practices in software engineering and be implemented as a modular, extensible, component-based software framework for peptide docking. This docking engine will be part of the widely used AutoDock software suite. The ability to model complexes formed by proteins and fully-flexible peptides or IDPs is in high demand and will greatly extend the range of peptide-based therapeutic approaches for which automated docking can be successfully applied. It will also support gaining insights into interactions of IDPs with proteins. As such, it will impact the research of many medicinal chemists and biologist and extend the use of computational tools to a wider community of scientists, thereby supporting the advancement of biomedical research.

蛋白质-肽相互作用的计算机预测

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Improving Docking Power for Short Peptides Using Random Forest.

DOI：
10.1021/acs.jcim.1c00573
发表时间：
2021-06-28
期刊：
Journal of chemical information and modeling
影响因子：
5.6
作者：
Sanner MF;Dieguez L;Forli S;Lis E
通讯作者：
Lis E

Computational protein-ligand docking and virtual drug screening with the AutoDock suite.

DOI：
10.1038/nprot.2016.051
发表时间：
2016-05
期刊：
Nature protocols
影响因子：
14.8
作者：
Forli S;Huey R;Pique ME;Sanner MF;Goodsell DS;Olson AJ
通讯作者：
Olson AJ

Activated protein C light chain provides an extended binding surface for its anticoagulant cofactor, protein S.

活化的蛋白 C 轻链为其抗凝辅助因子蛋白 S 提供了扩展的结合表面。

DOI：
10.1182/bloodadvances.2017007005
发表时间：
2017
期刊：
Blood advances
影响因子：
7.5
作者：
Fernández,JoséA;Xu,Xiao;Sinha,RanjeetK;Mosnier,LaurentO;Sanner,MichelF;Griffin,JohnH
通讯作者：
Griffin,JohnH

AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility.