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Computational Structure-Based Protein Design

基于计算结构的蛋白质设计

基本信息

批准号：
9915930
负责人：
Bruce R. Donald
金额：
$ 35.4万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-04-15 至 2022-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9915930
关键词：
Acceleration Address Affinity Algorithm Design Algorithmic Software Algorithms Amino Acid Sequence Antibodies Area Basic Science Binding Biochemical Biological Candida glabrata Capsid Proteins Cell Proliferation Cells Clinical Clinical Trials Clinical Trials Design Combinatorial Optimization Community-Acquired Infections Computer software Crystallization Cystic Fibrosis Cystic Fibrosis Transmembrane Conductance Regulator Designer Drugs Development Disease Resistance Drug resistance Escherichia coli Foundations Future Genes Glycoproteins Grant Guanosine Triphosphate Phosphohydrolases HIV HIV Antibodies HIV envelope protein HIV resistance HIV therapy HIV-1 Human Influenza Investigation KRAS2 gene Laboratories Lead Ligands Malaria Malignant Neoplasms Mathematics Measures Medical Research Membrane Membrane Proteins Methodology Methods Modeling Molecular Molecular Conformation Mutate Mutation Pancreatic Ductal Adenocarcinoma Passive Immunization Peptides Pharmaceutical Preparations Pharmacology Process Protein Engineering Proteins Proto-Oncogenes Resistance Side Signal Transduction Site Specificity Speed Structure Symptoms System Testing Therapeutic Thermodynamics Time Tuberculosis Vancomycin resistant enterococcus Vertebral column antibiotic design base cystic fibrosis patients design env Gene Products enzyme activity experimental study flexibility immunogenic improved in vivo inhibitor/antagonist methicillin resistant Staphylococcus aureus model design molecular mechanics molecular modeling mutant neutralizing antibody new therapeutic target novel novel therapeutics open source pathogen prospective protein protein interaction protein structure protein transport resistance mutation response software development structured data success unnatural amino acids

项目摘要

Project Summary. Computational structure-based protein design is a transformative field with exciting prospects for advancing both basic science and translational medical research. My laboratory has developed new protein design algorithms and used them to predict MRSA resistance to new antibiotics; design a broadly neutralizing antibody VRC07-523LS against HIV with unprecedented breadth and potency that is now in clinical trials; design protein-peptide interactions to treat cystic fibrosis; perform antigenicity-guided structural design of HIV gp140 envelope protein (Env) trimer constructs to delineate mechanism and fix conformation; and design a new antigenic membrane-bound membrane proximal external region (MPER) trimer for examining immunogenic responses to the HIV viral coat protein gp41. Central to protein design methodology is the need to optimize the amino acid sequence, placement of side chains, and backbone conformations in protein structures. By developing advanced search and scoring algorithms for combinatorial optimization of protein and ligand structure and sequence, we showed that desired structure, affinity, and activity can be designed by (a) modeling improved molecular flexibility and (b) exploiting ensembles of structures for accurate predictions. Our suite of algorithms has mathematical guarantees on the solution quality (up to the accuracy of the input model, which includes the initial structures, molecular flexibility to be modeled, and an empirical molecular mechanics energy function). Specifically, our algorithms guarantee to compute the global minimum energy conformation (GMEC), a gap-free list of sequences and structures in order of predicted energy, and a provably-good approximation to the binding affinity by bounding partition functions over molecular ensembles. We propose to build on our foundation of protein design algorithms, called OSPREY, and apply them in areas of biochemical and pharmacological importance. We will (1) predict future resistance mutations in protein targets of novel drugs; (2) design inhibitors of protein:protein interactions to target today’s “undruggable” proteins; and (3) use OSPREY to redesign and improve broadly neutralizing HIV antibodies. Improvements to our protein design algorithms will be implemented to improve accuracy and scope, and we will advance the state of the art in protein design by making algorithmic and modeling improvements to accomplish the Aims (1- 3) above, including: the modeling of more protein/ligand flexibility and improved energy functions during large- scale design; new combinatorial optimization and energy-fitting methods to accelerate the design search; and design of affinity and specificity using novel multi-state design algorithms that model thermodynamic molecular ensembles. We will test our design predictions prospectively, by making novel predicted mutant proteins and performing biochemical, biological, and structural studies. We will also validate our algorithms retrospectively, using existing structures and data. All software will be released open-source.

项目总结。基于计算结构的蛋白质设计是一个令人兴奋的变革领域