Year: 2013
Type: review
Abstract: We outline a set of strategies to infer protein function from structure. The overall approach depends on extensive use of homology modeling, the exploitation of a wide range of global and local geometric relationships between protein structures and the use of machine learning techniques. The combination of modeling with broad searches of protein structure space defines a "structural BLAST" approach to infer function with high genomic coverage. Applications are described to the prediction of protein-protein and protein-ligand interactions. In the context of protein-protein interactions, our structure-based prediction algorithm, PrePPI, has comparable accuracy to high-throughput experiments. An essential feature of PrePPI involves the use of Bayesian methods to combine structure-derived information with non-structural evidence (e.g. co-expression) to assign a likelihood for each predicted interaction. This, combined with a structural BLAST approach significantly expands the range of applications of protein structure in the annotation of protein function, including systems level biological applications where it has previously played little role.
Source: Protein Science
DOI: 10.1002/pro.2225
Institutions: Howard Hughes Medical Institute, Columbia University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: bronze
Awards: National Center: Multiscale Analysis of Genomic and Cellular Networks (MAGNet), Computer Studies of Protein Structure and Function, G5533905641, COMPUTER STUDIES OF PROTEIN STRUCTURE AND FUNCTION, G5728810838, Structural Genomics of Eukaryotic Domain Families, G6674624523, Improving Protein Structure Analysis and Function Annotation by Using Ligand Information, G9000019087, G9000022848, G9000281410
OpenAlex ID: W2126124383