ProT-Diff: A Modularized and Efficient Strategy for De Novo Generation of Antimicrobial Peptide Sequences by Integrating Protein Language and Diffusion Models

CPL-Diff: A Diffusion Model for De Novo Design of Functional Peptide Sequences with Fixed Length

Peptides are recognized as next-generation therapeutic drugs due to their unique properties and are essential for treating human diseases. In recent years, a number of deep generation models for generating peptides have been proposed and have shown great potential. However, these models cannot well control the length of the generated sequence, while the sequence length has a very important impact on the physical and chemical properties and therapeutic effects of peptides. Here, a diffusion model is introduced, capable of controlling the length of generated functional peptide sequences, named CPL-Diff. CPL-Diff can control the length of generated polypeptide sequences using only attention masking. Additionally, CPL-Diff can generate single-functional polypeptide sequences based on given conditional information. Experiments demonstrate that the peptides generated by CPL-Diff exhibit lower perplexity and similarity compared to those produced by the current state-of-the-art models, and further exhibit relevant physicochemical properties similar to real sequences. The interpretability analysis is also performed on CPL-Diff to understand how it controls the length of generated sequences and the decision-making process involved in generating polypeptide sequences, with the aim of providing important theoretical guidance for polypeptide design. The code for CPL-Diff is available at https://github.com/luozhenjie1997/CPL-Diff.

Deep Learning Combined with Quantitative Structure‒Activity Relationship Accelerates De Novo Design of Antifungal Peptides

Novel antifungal drugs that evade resistance are urgently needed for Candida infections. Antifungal peptides (AFPs) are potential candidates due to their specific mechanism of action, which makes them less prone to developing drug resistance. An AFP de novo design method, Deep Learning-Quantitative Structure‒Activity Relationship Empirical Screening (DL-QSARES), is developed by integrating deep learning and quantitative structure‒activity relationship  empirical screening. After generating candidate AFPs (c_AFPs) through the recombination of dominant amino acids and dipeptide compositions, natural language processing models are utilized and quantitative structure‒activity relationship (QSAR) approaches based on physicochemical properties to screen for promising c_AFPs. Forty-nine promising c_AFPs are screened, and their minimum inhibitory concentrations (MICs) against C. albicans are determined to be 3.9-125 µg mL-1, of which four leading c_AFPs (AFP-8, -10, -11, and -13) has MICs of <10 µg mL-1 against the four tested pathogenic fungi, and AFP-13 has excellent therapeutic efficacy in the animal model.

Deep Learning for Antimicrobial Peptides: Computational Models and Databases

Antimicrobial peptides are a promising strategy to combat antimicrobial resistance. However, the experimental discovery of antimicrobial peptides is both time-consuming and laborious. In recent years, the development of computational technologies (especially deep learning) has provided new opportunities for antimicrobial peptide prediction. Various computational models have been proposed to predict antimicrobial peptide. In this review, we focus on deep learning models for antimicrobial peptide prediction. We first collected and summarized available data resources for antimicrobial peptides. Subsequently, we summarized existing deep learning models for antimicrobial peptides and discussed their limitations and challenges. This study aims to help computational biologists design better deep learning models for antimicrobial peptide prediction.

Leveraging large language models for peptide antibiotic design

Large language models (LLMs) have significantly impacted various domains of our society, including recent applications in complex fields such as biology and chemistry. These models, built on sophisticated neural network architectures and trained on extensive datasets, are powerful tools for designing, optimizing, and generating molecules. This review explores the role of LLMs in discovering and designing antibiotics, focusing on peptide molecules. We highlight advancements in drug design and outline the challenges of applying LLMs in these areas.