AIMC Topic: Peptides

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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...
Anti-Infective Agents Peptides Computational Biology Neural Networks, Computer Software
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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...
Humans Machine Learning Hormones Analysis of Variance Animals Databases, Protein Peptides Amino Acids Support Vector Machine Carrier Proteins Computational Biology Computer Simulation Reproducibility of Results Software Algorithms
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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...
Peptides Machine Learning 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
Machine Learning Peptides Computational Biology Drug Discovery Proteins
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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...
Peptides Drug Discovery Inflammation Antimicrobial Cationic Peptides Antineoplastic Agents Anti-Infective Agents 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...
Peptides Drug Design Humans Machine Learning Models, Molecular Protein Binding Proteins
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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...
Dipeptides Peptides Molecular Dynamics Simulation Neural Networks, Computer
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Deep learning improves antimicrobial peptide recognition.

Bioinformatics (Oxford, England) Aug 15, 2018
MOTIVATION: Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by wet-laborat...
Anti-Infective Agents Deep Learning Computational Biology Peptides Sequence Analysis, Protein
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New tools for MHC research from machine learning and predictive algorithms to the tumour immunopeptidome.

Immunology Jul 1, 2018
At a time when immunology seeks to progress ever more rapidly from characterization of a microbial or tumour antigen to the immune correlates that may define protective T-cell immunity, there is a need for robust tools to enable accurate predictions ...
Major Histocompatibility Complex Research T-Lymphocytes Humans Machine Learning Neoplasms Peptides Epitopes, T-Lymphocyte Cancer Vaccines Immunotherapy, Adoptive
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Structure-based prediction of protein- peptide binding regions using Random Forest.

Bioinformatics (Oxford, England) Feb 1, 2018
MOTIVATION: Protein-peptide interactions are one of the most important biological interactions and play crucial role in many diseases including cancer. Therefore, knowledge of these interactions provides invaluable insights into all cellular processe...
Humans Machine Learning Computational Biology Peptides Protein Domains Proteins Protein Binding Sequence Analysis, Protein Protein Tyrosine Phosphatase, Non-Receptor Type 4
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