Leveraging machine learning models for peptide–protein interaction prediction

Yin, Song; Mi, Xuenan; Shukla, Diwakar

doi:10.1039/d3cb00208j

Cited by 7 publications

(1 citation statement)

References 138 publications

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“…The MD simulations clearly show the potential of in silico methods in therapeutic applications for cardiac tissue, both in understanding the underlying mechanisms in detail and in developing manipulable and state-of-the-art approaches. Although these state-of-the-art methods provide comprehensive analyzes for a deep understanding of peptide protein interactions, they also require a high-performance computing environment and time, especially due to the flexible nature of peptides (Yin et al 2024 ). Therefore, current perspectives suggest that conventional computational models and simulations can be improved through machine learning methods.…”

Section: In Silico Methods For Peptide Designmentioning

confidence: 99%

Therapeutic peptides for coronary artery diseases: in silico methods and current perspectives

Aslan,

Ari Yuka

2024

Amino Acids

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Many drug formulations containing small active molecules are used for the treatment of coronary artery disease, which affects a significant part of the world’s population. However, the inadequate profile of these molecules in terms of therapeutic efficacy has led to the therapeutic use of protein and peptide-based biomolecules with superior properties, such as target-specific affinity and low immunogenicity, in critical diseases. Protein‒protein interactions, as a consequence of advances in molecular techniques with strategies involving the combined use of in silico methods, have enabled the design of therapeutic peptides to reach an advanced dimension. In particular, with the advantages provided by protein/peptide structural modeling, molecular docking for the study of their interactions, molecular dynamics simulations for their interactions under physiological conditions and machine learning techniques that can work in combination with all these, significant progress has been made in approaches to developing therapeutic peptides that can modulate the development and progression of coronary artery diseases. In this scope, this review discusses in silico methods for the development of peptide therapeutics for the treatment of coronary artery disease and strategies for identifying the molecular mechanisms that can be modulated by these designs and provides a comprehensive perspective for future studies.

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Section: In Silico Methods For Peptide Designmentioning

confidence: 99%

Therapeutic peptides for coronary artery diseases: in silico methods and current perspectives

Aslan,

Ari Yuka

2024

Amino Acids

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Artificial intelligence in food bioactive peptides screening: Recent advances and future prospects

Chang,

Wang,

et al. 2025

Trends in Food Science & Technology

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A quantitative intracellular peptide binding assay reveals recognition determinants and context dependence of short linear motifs

Subbanna,

Winters,

Örd

et al. 2024

Preprint

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Transient protein-protein interactions play key roles in controlling dynamic cellular responses. Many examples involve globular protein domains that bind to peptide sequences known as Short Linear Motifs (SLiMs), which are enriched in intrinsically disordered regions of proteins. Here we describe a novel functional assay for measuring SLiM binding, called Systematic Intracellular Motif Binding Analysis (SIMBA). In this method, binding of a foreign globular domain to its cognate SLiM peptide allows yeast cells to proliferate by blocking a growth arrest signal. A high-throughput application of the SIMBA method involving competitive growth and deep sequencing provides rapid quantification of the relative binding strength for thousands of SLiM sequence variants, and a comprehensive interrogation of SLiM sequence features that control their recognition and potency. We show that multiple distinct classes of SLiM-binding domains can be analyzed by this method, and that the relative binding strength of peptides in vivo correlates with their biochemical affinities measured in vitro. Deep mutational scanning provides high-resolution definitions of motif recognition determinants and reveals how sequence variations at non-core positions can modulate binding strength. Furthermore, mutational scanning of multiple parent peptides that bind human tankyrase ARC or YAP WW domains identifies distinct binding modes and uncovers context effects in which the preferred residues at one position depend on residues elsewhere. The findings establish SIMBA as a fast and incisive approach for interrogating SLiM recognition via massively parallel quantification of protein-peptide binding strength in vivo.

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Leveraging machine learning models for peptide–protein interaction prediction

Abstract: A timeline showcasing the progress of machine learning and deep learning methods for peptide–protein interaction predictions.

Cited by 7 publications

References 138 publications

Therapeutic peptides for coronary artery diseases: in silico methods and current perspectives

Therapeutic peptides for coronary artery diseases: in silico methods and current perspectives

Artificial intelligence in food bioactive peptides screening: Recent advances and future prospects

A quantitative intracellular peptide binding assay reveals recognition determinants and context dependence of short linear motifs

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