8TXS | pdb_00008txs

Solution NMR structure of designed peptide BH26 (RGVTVPHNGESKDYSV)

PDB DOI: https://doi.org/10.2210/pdb8TXS/pdb
BMRB: 31101

Classification: DE NOVO PROTEIN
Organism(s): synthetic construct
Mutation(s): No

Deposited: 2023-08-24 Released: 2023-09-06
Deposition Author(s): McShan, A.C., Torres, M.P.
Funding Organization(s): National Institutes of Health/National Institute of General Medical Sciences (NIH/NIGMS)

Experimental Data Snapshot

Method: SOLUTION NMR
Conformers Calculated: 200
Conformers Submitted: 10
Selection Criteria: structures with the lowest energy

wwPDB Validation 3D Report Full Report

This is version 1.3 of the entry. See complete history.

Literature

Generative beta-hairpin design using a residue-based physicochemical property landscape.

Satalkar, V., Degaga, G.D., Li, W., Pang, Y.T., McShan, A.C., Gumbart, J.C., Mitchell, J.C., Torres, M.P.

(2024) Biophys J 123: 2790-2806

PubMed: 38297834 Search on PubMed
DOI: https://doi.org/10.1016/j.bpj.2024.01.029
Primary Citation Related Structures:
8T61, 8T62, 8T63, 8TXS

PubMed Abstract:
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 β-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 β hairpin from α helix and intrinsically disordered peptides with an accuracy of up to 96% and generates artificial β-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.