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Development of a pipeline for parallel elucidation of protein structures

开发并行阐明蛋白质结构的管道

基本信息

批准号：
10231713
负责人：
Gabriel C Lander
金额：
$ 22.19万
依托单位：
SCRIPPS RESEARCH INSTITUTE, THE
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10231713
关键词：
Active Sites Amino Acid Sequence Biochemical Biochemical Process Biological Biological Assay Biophysics Biotechnology Classification Clinic Complex Computer Models Cryoelectron Microscopy Crystallography Data Data Analyses Data Set Development Diagnostic Disease Drug Design Engineering Enzymes Feedback Foundations Genetic Variation Heart Human Genome Image Knowledge Lead Libraries Location Medical Methodology Methods Modeling Modification Molecular Conformation Molecular Weight Mutation Names Nature Performance Preparation Production Property Protein Biochemistry Protein Engineering Proteins Proteomics Research Design Research Personnel Resolution Sampling Shapes Statistical Methods Structural Models Structural Protein Structure Structure-Activity Relationship System Techniques Technology Testing Therapeutic Training Variant Work X-Ray Crystallography base classification algorithm de novo mutation deep learning design detection limit experimental study genome sequencing human disease image processing improved in silico insight knowledge base machine learning algorithm particle prevent programs protein complex protein folding protein function protein structure protein structure prediction rapid technique screening success therapeutic development tool variant of unknown significance

项目摘要

Advances in biophysical technologies have accelerated our ability to probe the mechanisms of even the most complex cellular systems, and such studies have enabled researchers to design modifications to known protein structures and design completely new proteins. This “protein design” technology has given rise to an ability to manipulate protein structures as a means of improving on or introducing new medical diagnostics and therapeutics. The bases of these studies rely on computational modeling of protein candidates, although the accuracy of protein structure prediction, protein de novo design, and single-mutation effects prediction remain below the threshold for many use cases, such as structure-guided drug design and rational enzyme engineering. Thus, success of a protein engineering effort relies on high-resolution structure determination, which involves laborious screening and optimization in order to obtain stable proteins or active enzyme variants. However, our ability to observe protein structure using common structure determination strategies (X-ray crystallography, NMR, and cryo-electron microscopy (cryo-EM)) lags far behind our ability to design and produce new sequences, creating a knowledge gap that prevents biochemists from accessing the range of protein functions seen in nature. While current technologies enable rapid synthesis of hundreds of proteins with varied sequences, there do not exist technologies for rapid structural characterization of these generated proteins. The ability to obtain high- resolution structural information for hundreds of sequences in parallel would provide invaluable insights in protein engineering methods. Importantly, rapid structure determination would enable structural characterization of genetic variation in the human genome underlying disease by enabling the structural and mechanistic interpretation of rare and de novo disease-related variants. Cryo-EM enables numerous high-resolution structures to be determined from a small amount of sample without requiring homogeneity, an aspect of this method that we plan to exploit for parallel elucidation of protein structures. We will establish the feasibility of this technique for rapidly investigate the structures of engineered protein libraries, where the molecular weight range is near or below the lower detection limit of cryo-EM. We will also probe the limits of our ability to identify the location and structural impact of tested mutations at limited structural locations, such as active sites. We will explore the feasibility of our parallel structure determination approach in two aims: Aim 1 will identify the limit of current single-particle analysis methods to discriminate between structurally similar protein complexes. Aim 2 will implement machine learning algorithms to push the current limits of classification using a combination of synthetic and real data. These exploratory studies will pave the way to rapid structure determination of multiple protein complexes from a single cryo-EM experiment, providing the ability to rapidly obtain high-resolution structures for many engineered proteins, thereby enabling unprecedented design and testing feedback cycles to help treat human disease.

生物物理技术的进步已经加速了我们探索甚至是大多数生物的机制的能力