Recent advances in de novo protein design: Principles, methods, and applications

被引:144
作者
Pan, Xingjie [1 ,2 ]
Kortemme, Tanja [1 ,2 ,3 ]
机构
[1] Univ Calif San Francisco, Dept Bioengn & Therapeut Sci, San Francisco, CA 94143 USA
[2] Univ Calif San Francisco, UC Berkeley UCSF Grad Program Bioengn, San Francisco, CA 94143 USA
[3] Univ Calif San Francisco, Quantitat Biosci Inst QBI, San Francisco, CA 94143 USA
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
COMPUTATIONAL DESIGN; STRUCTURE PREDICTION; MACROMOLECULAR ENERGY; RATIONAL DESIGN; ACCURATE DESIGN; END ELIMINATION; SIMPLE-MODEL; FORCE-FIELD; AMINO-ACIDS; BINDING;
D O I
10.1016/j.jbc.2021.100558
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
The computational de novo protein design is increasingly applied to address a number of key challenges in biomedicine and biological engineering. Successes in expanding applications are driven by advances in design principles and methods over several decades. Here, we review recent innovations in major aspects of the de novo protein design and include how these advances were informed by principles of protein architecture and interactions derived from the wealth of structures in the Protein Data Bank. We describe developments in de novo generation of designable backbone structures, optimization of sequences, design scoring functions, and the design of the function. The advances not only highlight design goals reachable now but also point to the challenges and opportunities for the future of the field.
引用
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页数:16
相关论文
共 156 条
[1]   Protein Structure Prediction and Design in a Biologically Realistic Implicit Membrane [J].
Alford, Rebecca F. ;
Fleming, Patrick J. ;
Fleming, Karen G. ;
Gray, Jeffrey J. .
BIOPHYSICAL JOURNAL, 2020, 118 (08) :2042-2055
[2]   The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design [J].
Alford, Rebecca F. ;
Leaver-Fay, Andrew ;
Jeliazkov, Jeliazko R. ;
O'Meara, Matthew J. ;
DiMaio, Frank P. ;
Park, Hahnbeom ;
Shapovalov, Maxim V. ;
Renfrew, P. Douglas ;
Mulligan, Vikram K. ;
Kappel, Kalli ;
Labonte, Jason W. ;
Pacella, Michael S. ;
Bonneau, Richard ;
Bradley, Philip ;
Dunbrack, Roland L., Jr. ;
Das, Rhiju ;
Baker, David ;
Kuhlman, Brian ;
Kortemme, Tanja ;
Gray, Jeffrey J. .
JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2017, 13 (06) :3031-3048
[3]   An Integrated Framework Advancing Membrane Protein Modeling and Design [J].
Alford, Rebecca F. ;
Leman, Julia Koehler ;
Weitzner, Brian D. ;
Duran, Amanda M. ;
Tilley, Drew C. ;
Elazar, Assaf ;
Gray, Jeffrey J. .
PLOS COMPUTATIONAL BIOLOGY, 2015, 11 (09)
[4]   An Efficient Algorithm for Multistate Protein Design Based on FASTER [J].
Allen, Benjamin D. ;
Mayo, Stephen L. .
JOURNAL OF COMPUTATIONAL CHEMISTRY, 2010, 31 (05) :904-916
[5]   Unified rational protein engineering with sequence-based deep representation learning [J].
Alley, Ethan C. ;
Khimulya, Grigory ;
Biswas, Surojit ;
AlQuraishi, Mohammed ;
Church, George M. .
NATURE METHODS, 2019, 16 (12) :1315-+
[6]   Computational design of a single amino acid sequence that can switch between two distinct protein folds [J].
Ambroggio, XI ;
Kuhlman, B .
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY, 2006, 128 (04) :1154-1161
[7]  
Anand N., 2019, INT C LEARNING REPRE
[8]   Computational redesign of endonuclease DNA binding and cleavage specificity [J].
Ashworth, Justin ;
Havranek, James J. ;
Duarte, Carlos M. ;
Sussman, Django ;
Monnat, Raymond J., Jr. ;
Stoddard, Barry L. ;
Baker, David .
NATURE, 2006, 441 (7093) :656-659
[9]   What has de novo protein design taught us about protein folding and biophysics? [J].
Baker, David .
PROTEIN SCIENCE, 2019, 28 (04) :678-683
[10]   An enumerative algorithm for de novo design of proteins with diverse pocket structures [J].
Basanta, Benjamin ;
Bick, Matthew J. ;
Bera, Asim K. ;
Norn, Christoffer ;
Chow, Cameron M. ;
Carter, Lauren P. ;
Goreshnik, Inna ;
Dimaio, Frank ;
Baker, David .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2020, 117 (36) :22135-22145