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- W4366083959 abstract "Abstract Motivation A protein can be represented in several forms, including its 1D sequence, 3D atom coordinates, and molecular surface. A protein surface contains rich structural and chemical features directly related to the protein’s function such as its ability to interact with other molecules. While many methods have been developed for comparing the similarity of proteins using the sequence and structural representations, computational methods based on molecular surface representation are limited. Results Here, we describe “Surface ID,” a geometric deep learning system for high-throughput surface comparison based on geometric and chemical features. Surface ID offers a novel grouping and alignment algorithm useful for clustering proteins by function, visualization, and in silico screening of potential binding partners to a target molecule. Our method demonstrates top performance in surface similarity assessment, indicating great potential for protein functional annotation, a major need in protein engineering and therapeutic design. Availability and implementation Source code for the Surface ID model, trained weights, and inference script are available at https://github.com/Sanofi-Public/LMR-SurfaceID." @default.
- W4366083959 created "2023-04-19" @default.
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- W4366083959 date "2023-04-01" @default.
- W4366083959 modified "2023-10-14" @default.
- W4366083959 title "Surface ID: a geometry-aware system for protein molecular surface comparison" @default.
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- W4366083959 doi "https://doi.org/10.1093/bioinformatics/btad196" @default.
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