AIMC Topic: Amino Acid Sequence

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Language models reveal a complex sequence basis for adaptive convergent evolution of protein functions.

Proceedings of the National Academy of Sciences of the United States of America
Convergent evolution, or convergence, refers to repeated, independent emergences of the same trait in two or more lineages of species during evolution, often indicating functional adaptation to specific environmental factors. Many computational metho...

iBitter-Stack: A multi-representation ensemble learning model for accurate bitter peptide identification.

Journal of molecular biology
The identification of bitter peptides is crucial in various domains, including food science, drug discovery, and biochemical research. These peptides not only contribute to the undesirable taste of hydrolyzed proteins but also play key roles in physi...

MCMFPP: A Multifunctional Peptides Prediction Method Based on Class Feature Enhancement and Classifier Fusion.

Journal of chemical information and modeling
With the increasing discovery of peptide sequences and the growing demand for peptide-targeted drugs, traditional wet-lab experiment methods have become inadequate for peptide function prediction due to their high cost and that they are time consumin...

PhyCysID: Plant Cystatin Protein Prediction by an Artificial Intelligence Approach.

Journal of chemical information and modeling
Phytocystatins are proteinaceous inhibitors found in plants that competitively target various classes of cysteine proteinases, including papain-like enzymes, cathepsins, and legumains. Based on structural characteristics and gene organization, phytoc...

From AI-Driven Sequence Generation to Molecular Simulation: A Comprehensive Framework for Antimicrobial Peptide Discovery.

Journal of chemical information and modeling
Antimicrobial Peptides (AMPs) are a promising strategy to address bacterial resistance, yet only a limited number have advanced to clinical trials. Recent advances in deep learning provide new opportunities for AMP design. Here, we propose an integra...

Design, characterization, and application of novel antimicrobial peptides against Bacillus cereus.

International journal of food microbiology
Foodborne pathogens such as Bacillus cereus threaten food safety, necessitating novel antimicrobial solutions. Antimicrobial peptides (AMPs) offer broad-spectrum activity and potential applications in food preservation. In this study, we designed a l...

Ai-driven de novo design of customizable membrane permeable cyclic peptides.

Journal of computer-aided molecular design
Cyclic peptides, prized for their remarkable bioactivity and stability, hold great promise across various fields. Yet, designing membrane-penetrating bioactive cyclic peptides via traditional methods is complex and resource-intensive. To address this...

Bag-of-words is competitive with sum-of-embeddings language-inspired representations on protein inference.

PloS one
Inferring protein function is a fundamental and long-standing problem in biology. Laboratory experiments in this field are often expensive, and therefore large-scale computational protein inference from readily available amino acid sequences is neede...

PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural context.

Nature communications
Accurate prediction of the functional impact of missense variants is important for disease gene discovery, clinical genetic diagnostics, therapeutic strategies, and protein engineering. Previous efforts have focused on predicting a binary pathogenici...

Multimodal deep learning for allergenic proteins prediction.

BMC biology
BACKGROUND: Accurate prediction of allergens is essential for identifying the sources of allergic reactions and preventing future exposure to harmful triggers; however, the limited performance of current prediction tools hinders their practical appli...