Generative $β$-hairpin design using a residue-based physicochemical property landscape

Satalkar, Vardhan; Degaga, Gemechis D.; Li, Wei; Pang, Yui Tik; McShan, Andrew C.; Gumbart, James C.; Mitchell, Julie C.; Torres, Matthew P.

doi:10.1016/j.bpj.2024.01.029

Title: Generative $$β$$-hairpin design using a residue-based physicochemical property landscape

Journal Article · 01 February 2024 · Biophysical Journal

DOI: https://doi.org/10.1016/j.bpj.2024.01.029 · OSTI ID:2311293

Satalkar, Vardhan ^[1]; Degaga, Gemechis D. ^[2]; Li, Wei ^[1]; Pang, Yui Tik ^[1]; McShan, Andrew C. ^[1]; Gumbart, James C. ^[1]; Mitchell, Julie C. ^[2];

^[1]

Georgia Institute of Technology, Atlanta, GA (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de novo peptide design are largely based on sequence homology that can be restricted based on evolutionarily derived protein sequences and lack the physicochemical context essential in protein folding. Generative machine learning for de novo peptide design is a promising way to synthesize theoretical data that are based on, but unique from, the observable universe. In this study, we created and tested a custom peptide generative adversarial network intended to design peptide sequences that can fold into the -hairpin secondary structure. This deep neural network model is designed to establish a preliminary foundation of the generative approach based on physicochemical and conformational properties of 20 canonical amino acids, for example, hydrophobicity and residue volume, using extant structure-specific sequence data from the PDB. The beta generative adversarial network model robustly distinguishes secondary structures of hairpin from α helix and intrinsically disordered peptides with an accuracy of up to 96% and generates artificial -hairpin peptide sequences with minimum sequence identities around 31% and 50% when compared against the current NCBI PDB and nonredundant databases, respectively. These results highlight the potential of generative models specifically anchored by physicochemical and conformational property features of amino acids to expand the sequence-to-structure landscape of proteins beyond evolutionary limits.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE; National Science Foundation (NSF); Simons Foundation; National Institutes of Health (NIH); Extreme Science and Engineering Discovery Environment (XSEDE)

Grant/Contract Number:: AC05-00OR22725; 1764406; R01-GM148586; ACI-1548562; 1828187; TG-MCB130173

OSTI ID:: 2311293

Journal Information:: Biophysical Journal, Vol. 123; ISSN 0006-3495

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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STRIDE: a web server for secondary structure assignment from known atomic coordinates of proteins Heinig, M.; Frishman, D. Nucleic Acids Research, Vol. 32, Issue Web Server https://doi.org/10.1093/nar/gkh429	journal	July 2004
Serverless Prediction of Peptide Properties with Recurrent Neural Networks Ansari, Mehrad; White, Andrew D. Journal of Chemical Information and Modeling, Vol. 63, Issue 8 https://doi.org/10.1021/acs.jcim.2c01317	journal	April 2023
Scoring function for automated assessment of protein structure template quality Zhang, Yang; Skolnick, Jeffrey Proteins: Structure, Function, and Bioinformatics, Vol. 57, Issue 4 https://doi.org/10.1002/prot.20264	journal	January 2004
How significant is a protein structure similarity with TM-score = 0.5? Xu, Jinrui; Zhang, Yang Bioinformatics, Vol. 26, Issue 7 https://doi.org/10.1093/bioinformatics/btq066	journal	February 2010
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Binding, folding and insertion of a β-hairpin peptide at a lipid bilayer surface: Influence of electrostatics and lipid tail packing Reid, Keon A.; Davis, Caitlin M.; Dyer, R. Brian Biochimica et Biophysica Acta (BBA) - Biomembranes, Vol. 1860, Issue 3 https://doi.org/10.1016/j.bbamem.2017.12.019	journal	March 2018
Solution Structure of Amyloid β-Peptide (25−35) in Different Media D'Ursi, Anna M.; Armenante, Maria R.; Guerrini, Remo Journal of Medicinal Chemistry, Vol. 47, Issue 17 https://doi.org/10.1021/jm040773o	journal	July 2004
Novel β-Hairpin Antimicrobial Peptides Containing the β-Turn Sequence of -RRRF- Having High Cell Selectivity and Low Incidence of Drug Resistance Li, Beibei; Ouyang, Xu; Ba, Zufang Journal of Medicinal Chemistry, Vol. 65, Issue 7 https://doi.org/10.1021/acs.jmedchem.1c02140	journal	March 2022

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