TPepPro: a deep learning model for predicting peptide-protein interactions

Bioinformatics, Год журнала: 2024, Номер 41(1)

Опубликована: Ноя. 25, 2024

Abstract Motivation Peptides and their derivatives hold potential as therapeutic agents. The rising interest in developing peptide drugs is evidenced by increasing approval rates the FDA of USA. To identify most peptides, study on peptide-protein interactions (PepPIs) presents a very important approach but poses considerable technical challenges. In experimental aspects, transient nature PepPIs high flexibility peptides contribute to elevated costs inefficiency. Traditional docking molecular dynamics simulation methods require substantial computational resources, predictive accuracy results remain unsatisfactory. Results address this gap, we proposed TPepPro, Transformer-based model for PepPI prediction. We trained TPepPro dataset 19,187 pairs complexes with both sequential structural features. utilizes strategy that combines local protein sequence feature extraction global structure extraction. Moreover, optimizes architecture featuring neural network BN-ReLU arrangement, which notably reduced amount computing resources required According comparison analysis, reached 0.855 achieving an 8.1% improvement compared second-best TAGPPI. achieved AUC 0.922, surpassing TAGPPI 0.844. newly developed certain can be validated according previous evidence, thus indicating efficiency detect would helpful amino acid drug applications. Availability implementation source code available at https://github.com/wanglabhku/TPepPro.

Язык: Английский

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Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions

Frontiers in Oncology, Год журнала: 2023, Номер 13

Опубликована: Фев. 23, 2023

Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen host. Pathogen disrupt homeostasis host cells, with their toxins interfering mechanisms, resulting infections, diseases, disorders, extending from AIDS COVID-19, to cancer. Studies of three-dimensional (3D) structures host-pathogen complexes aim understand how pathogens interact hosts. They also contribute development rational therapeutics, as well preventive measures. However, structural studies are fraught challenges toward these aims. This review describes state-of-the-art protein-protein (PPIs) between standpoint. It discusses computational aspects predicting PPIs, including machine learning (ML) artificial intelligence (AI)-driven, overviews available methods challenges. concludes examples theoretical approaches can result a therapeutic agent potential being used clinics, future directions.

Язык: Английский

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Optimizing Drug Discovery: Molecular Docking with Glow-Worm Swarm Optimization

Blockchain technologies, Год журнала: 2024, Номер unknown, С. 369 - 417

Опубликована: Янв. 1, 2024

Язык: Английский

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Machine Learning Methods in Protein–Protein Docking

Methods in molecular biology, Год журнала: 2024, Номер unknown, С. 107 - 126

Опубликована: Янв. 1, 2024

Язык: Английский

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Assessment of Protein–Protein Docking Models Using Deep Learning

Methods in molecular biology, Год журнала: 2024, Номер unknown, С. 149 - 162

Опубликована: Янв. 1, 2024

Язык: Английский

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