TL;DR
A new concept termed DeepPepQSAR that integrates deep learning into traditional PepQSAR is proposed to quantitatively model, predict, and interpret the BAP universe in an all-in-one manner for artificial intelligence (AI)-driven big-data BAP discovery.
This review systematically examines the key methodological innovations, including peptide representation learning, multi-modal fusion strategies, multi-label learning paradigms, and emerging predictive frameworks empowered by deep neural architectures and ProtLM-based embeddings, and summarizes the practical applicatio...
Experimental results demonstrate that PPsAMP significantly outperforms state-of-the-art models for identifying sAMPs, and has identified 14,839 candidate sAMPs from environmental metagenomes, most of which have not been previously reported.
The proposed two-phase generative–evolutionary framework provides a generalizable approach for balancing functional optimization and distributional realism and can be applied to peptide discovery and data augmentation in imbalanced biological datasets thereby generating high confidence peptides for wet lab validation.
Results showed that the pipeline with PepPI-DRN, molecular docking and molecular dynamics simulation enabled efficient and reliable identification of lead peptides from the venom gland transcriptome of Orientothele washanensis, providing a robust and effective strategy for the discovery and development of natural pepti...
The proposed UniPept is a novel model for peptide property prediction that integrates atomic-level and residue-level features to generate robust peptide representations and outperforms competing approaches in interaction and binding affinity prediction tasks.
Transformer-based protein language models are relatively stable, interpretable and scalable paradigms for AMP discovery and activity prediction, and they can help establish a practical computational pipeline that selects promising peptide candidates for experimental validation at a lower cost compared with traditional...
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