Data-driven computational protein design
Name
Frappier_Keating_COSB_accepted.pdf
Description
Accepted version
Size
474.04 KB
Format
Adobe PDF
Checksum (MD5)
56a3bf6772651523164854956bef6599
Author(s)
Frappier, Vincent
Keating, Amy E.
Date Issued
August 2021
Journal
Current Opinion in Structural Biology
Publisher
Elsevier BV
Citation
Frappier, Vincent and Amy E. Keating. "Data-driven computational protein design." Current Opinion in Structural Biology 69 (August 2021): 63-69. © 2021 Elsevier Ltd
Version
Author's final manuscript
Abstract
Computational protein design can generate proteins not found in nature that adopt desired structures and perform novel functions. Although proteins could, in theory, be designed with ab initio methods, practical success has come from using large amounts of data that describe the sequences, structures, and functions of existing proteins and their variants. We present recent creative uses of multiple-sequence alignments, protein structures, and high-throughput functional assays in computational protein design. Approaches range from enhancing structure-based design with experimental data to building regression models to training deep neural nets that generate novel sequences. Looking ahead, deep learning will be increasingly important for maximizing the value of data for protein design.
Subjects
Molecular Biology
Structural Biology
MIT Department
Massachusetts Institute of Technology. Department of Biology
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.sbi.2021.03.009
