De novo protein fold design through sequence-independent fragment assembly simulations.

Protein Folding Proteins / chemistry Protein Structure, Secondary Protein Conformation Monte Carlo Method
de novo protein design novel fold of protein replica-exchange Monte Carlo simulation structural design structural motif

Journal

Proceedings of the National Academy of Sciences of the United States of America
ISSN: 1091-6490
Titre abrégé: Proc Natl Acad Sci U S A
Pays: United States
ID NLM: 7505876

Informations de publication

Date de publication:
24 Jan 2023
Historique:
entrez: 19 1 2023
pubmed: 20 1 2023
medline: 24 1 2023
Statut: ppublish

Résumé

De novo protein design generally consists of two steps, including structure and sequence design. Many protein design studies have focused on sequence design with scaffolds adapted from native structures in the PDB, which renders novel areas of protein structure and function space unexplored. We developed FoldDesign to create novel protein folds from specific secondary structure (SS) assignments through sequence-independent replica-exchange Monte Carlo (REMC) simulations. The method was tested on 354 non-redundant topologies, where FoldDesign consistently created stable structural folds, while recapitulating on average 87.7% of the SS elements. Meanwhile, the FoldDesign scaffolds had well-formed structures with buried residues and solvent-exposed areas closely matching their native counterparts. Despite the high fidelity to the input SS restraints and local structural characteristics of native proteins, a large portion of the designed scaffolds possessed global folds completely different from natural proteins in the PDB, highlighting the ability of FoldDesign to explore novel areas of protein fold space. Detailed data analyses revealed that the major contributions to the successful structure design lay in the optimal energy force field, which contains a balanced set of SS packing terms, and REMC simulations, which were coupled with multiple auxiliary movements to efficiently search the conformational space. Additionally, the ability to recognize and assemble uncommon super-SS geometries, rather than the unique arrangement of common SS motifs, was the key to generating novel folds. These results demonstrate a strong potential to explore both structural and functional spaces through computational design simulations that natural proteins have not reached through evolution.

Identifiants

DOI: 10.1073/pnas.2208275120 PMID: 36656852 PMC: PMC9942881
pubmed: 36656852
doi: 10.1073/pnas.2208275120
pmc: PMC9942881
doi:

Substances chimiques

Proteins 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e2208275120

Subventions

Organisme : NCI NIH HHS
ID : U24 CA210967
Pays : United States
Organisme : NIH HHS
ID : S10 OD026825
Pays : United States
Organisme : NIAID NIH HHS
ID : R01 AI134678
Pays : United States
Organisme : NIGMS NIH HHS
ID : R35 GM136422
Pays : United States
Organisme : NIEHS NIH HHS
ID : P30 ES017885
Pays : United States

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Auteurs

Robin Pearce (R)

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109.
ORCID: 0000-0001-6402-734X

Xiaoqiang Huang (X)

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109.
ORCID: 0000-0002-1005-848X

Gilbert S Omenn (GS)

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109.
Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109.
Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109.
School of Public Health, University of Michigan, Ann Arbor, MI 48109.
ORCID: 0000-0002-8976-6074

Yang Zhang (Y)

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109.
Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109.
Department of Computer Science, School of Computing, National University of Singapore 117417, Singapore.
Cancer Science Institute of Singapore, National University of Singapore 117599, Singapore.
ORCID: 0000-0002-2739-1916

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