AIMC Topic: Peptides

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iATP: A Sequence Based Method for Identifying Anti-tubercular Peptides.

Medicinal chemistry (Shariqah (United Arab Emirates)) Jan 1, 2020
BACKGROUND: Tuberculosis is one of the biggest threats to human health. Recent studies have demonstrated that anti-tubercular peptides are promising candidates for the discovery of new anti-tubercular drugs. Since experimental methods are still labor...
Humans Support Vector Machine Databases, Protein Antitubercular Agents Peptides Computational Biology
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ACME: pan-specific peptide-MHC class I binding prediction through attention-based deep neural networks.

Bioinformatics (Oxford, England) Dec 1, 2019
MOTIVATION: Prediction of peptide binding to the major histocompatibility complex (MHC) plays a vital role in the development of therapeutic vaccines for the treatment of cancer. Algorithms with improved correlations between predicted and actual bind...
Binding Sites Histocompatibility Antigens Class I Attention Algorithms Peptides Neural Networks, Computer Protein Binding Computational Biology
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Deep learning adds an extra dimension to peptide fragmentation.

Nature methods Jun 1, 2019
Machine Learning Peptides Tandem Mass Spectrometry Deep Learning Proteome
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Identifying antimicrobial peptides using word embedding with deep recurrent neural networks.

Bioinformatics (Oxford, England) Jun 1, 2019
MOTIVATION: Antibiotic resistance constitutes a major public health crisis, and finding new sources of antimicrobial drugs is crucial to solving it. Bacteriocins, which are bacterially produced antimicrobial peptide products, are candidates for broad...
Neural Networks, Computer Software Computational Biology Anti-Infective Agents Peptides
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Identification of hormone binding proteins based on machine learning methods.

Mathematical biosciences and engineering : MBE Mar 22, 2019
The soluble carrier hormone binding protein (HBP) plays an important role in the growth of human and other animals. HBP can also selectively and non-covalently interact with hormone. Therefore, accurate identification of HBP is an important prerequis...
Hormones Analysis of Variance Support Vector Machine Carrier Proteins Databases, Protein Amino Acids Peptides Computational Biology Humans Reproducibility of Results Machine Learning Animals Algorithms Computer Simulation Software
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Identification of Anti-cancer Peptides Based on Multi-classifier System.

Combinatorial chemistry & high throughput screening Jan 1, 2019
AIMS AND OBJECTIVE: Cancer is one of the deadliest diseases, taking the lives of millions every year. Traditional methods of treating cancer are expensive and toxic to normal cells. Fortunately, anti-cancer peptides (ACPs) can eliminate this side eff...
Machine Learning Peptides Antineoplastic Agents
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The Application of Machine Learning Techniques in Protein Drugs and Drug Targets Recognition.

Current drug metabolism Jan 1, 2019
Computational Biology Proteins Peptides Machine Learning Drug Discovery
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Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery.

Current topics in medicinal chemistry Jan 1, 2019
Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the pe...
Antimicrobial Cationic Peptides Antineoplastic Agents Inflammation Peptides Anti-Infective Agents Drug Discovery Neoplasms Humans Machine Learning
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Machine Learning in Quantitative Protein-peptide Affinity Prediction: Implications for Therapeutic Peptide Design.

Current drug metabolism Jan 1, 2019
BACKGROUND: Protein-peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recen...
Humans Drug Design Proteins Machine Learning Protein Binding Models, Molecular Peptides
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Collective variable discovery and enhanced sampling using autoencoders: Innovations in network architecture and error function design.

The Journal of chemical physics Aug 21, 2018
Auto-associative neural networks ("autoencoders") present a powerful nonlinear dimensionality reduction technique to mine data-driven collective variables from molecular simulation trajectories. This technique furnishes explicit and differentiable ex...
Molecular Dynamics Simulation Neural Networks, Computer Dipeptides Peptides
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