AIMC Topic: Peptides

Clear Filters Showing 451 to 460 of 567 articles

Lead Informed Artificial Intelligence Mining of Antitubercular Host Defense Peptides.

Biomacromolecules
Identifying host defense peptides (HDPs) that are effective against drug-resistant infections is challenging due to their vast sequence space. Artificial intelligence (AI)-guided design can accelerate HDP discovery, but it traditionally requires larg...

Blood-brain barrier crossing biopolymer targeting c-Myc and anti-PD-1 activate primary brain lymphoma immunity: Artificial intelligence analysis.

Journal of controlled release : official journal of the Controlled Release Society
Primary Central Nervous System Lymphoma is an aggressive central nervous system neoplasm with poor response to pharmacological treatment, partially due to insufficient drug delivery across blood-brain barrier. In this study, we developed a novel ther...

ESMpHLA: Evolutionary Scale Model-Based Deep Learning Prediction of HLA Class I Binding Peptides.

HLA
The recognition of endogenous peptides by HLA class I plays a crucial role in CD8+ T cell immune responses and human adaptive cell immune. Thus, the prediction of HLA class I-peptide binding affinities is always the core issue for the research of imm...

A robust ensemble framework for anticancer peptide classification using multi-model voting approach.

Computers in biology and medicine
Anticancer peptides (ACPs) hold great potential for cancer therapeutics, yet accurately identifying them remains a challenging task due to the complexity of peptide sequences and their interactions with biological systems. In this study, we propose a...

Deep learning in GPCR drug discovery: benchmarking the path to accurate peptide binding.

Briefings in bioinformatics
Deep learning (DL) methods have drastically advanced structure-based drug discovery by directly predicting protein structures from sequences. Recently, these methods have become increasingly accurate in predicting complexes formed by multiple protein...

iMFP-LG: Identify Novel Multi-functional Peptides Using Protein Language Models and Graph-based Deep Learning.

Genomics, proteomics & bioinformatics
Functional peptides are short amino acid fragments that have a wide range of beneficial functions for living organisms. The majority of previous studies have focused on mono-functional peptides, but an increasing number of multi-functional peptides h...

Predicting depression severity using machine learning models: Insights from mitochondrial peptides and clinical factors.

PloS one
Depression presents a significant challenge to global mental health, often intertwined with factors including oxidative stress. Although the precise relationship with mitochondrial pathways remains elusive, recent advances in machine learning present...

ACP-DPE: A Dual-Channel Deep Learning Model for Anticancer Peptide Prediction.

IET systems biology
Cancer is a serious and complex disease caused by uncontrolled cell growth and is becoming one of the leading causes of death worldwide. Anticancer peptides (ACPs), as a bioactive peptide with lower toxicity, emerge as a promising means of effectivel...

StackAHTPs: An explainable antihypertensive peptides identifier based on heterogeneous features and stacked learning approach.

IET systems biology
Hypertension, often known as high blood pressure, is a major concern to millions of individuals globally. Recent studies have demonstrated the significant efficacy of naturally derived peptides in reducing blood pressure. Hypertension is one of the r...

iACVP-MR: Accurate Identification of Anti-coronavirus Peptide based on Multiple Features Information and Recurrent Neural Network.

Current medicinal chemistry
BACKGROUND: Over the years, viruses have caused human illness and threatened human health. Therefore, it is pressing to develop anti-coronavirus infection drugs with clear function, low cost, and high safety. Anti-coronavirus peptide (ACVP) is a key ...