AIMC Topic: Peptides

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HLAncPred: a method for predicting promiscuous non-classical HLA binding sites.

Briefings in bioinformatics Sep 20, 2022
Human leukocyte antigens (HLA) regulate various innate and adaptive immune responses and play a crucial immunomodulatory role. Recent studies revealed that non-classical HLA-(HLA-E & HLA-G) based immunotherapies have many advantages over traditional ...
COVID-19 Peptides Artificial Intelligence Binding Sites HLA-G Antigens Spike Glycoprotein, Coronavirus Protein Binding Humans
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DeepSCP: utilizing deep learning to boost single-cell proteome coverage.

Briefings in bioinformatics Jul 18, 2022
Multiplexed single-cell proteomes (SCPs) quantification by mass spectrometry greatly improves the SCP coverage. However, it still suffers from a low number of protein identifications and there is much room to boost proteins identification by computat...
Proteome Tandem Mass Spectrometry Deep Learning Proteomics Peptides
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Predicting protein-peptide binding residues via interpretable deep learning.

Bioinformatics (Oxford, England) Jun 27, 2022
SUMMARY: Identifying the protein-peptide binding residues is fundamentally important to understand the mechanisms of protein functions and explore drug discovery. Although several computational methods have been developed, most of them highly rely on...
Amino Acid Sequence Deep Learning Proteins Protein Binding Peptides
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InterPepScore: a deep learning score for improving the FlexPepDock refinement protocol.

Bioinformatics (Oxford, England) Jun 13, 2022
MOTIVATION: Interactions between peptide fragments and protein receptors are vital to cell function yet difficult to experimentally determine in structural details of. As such, many computational methods have been developed to aid in peptide-protein ...
Computer Simulation Peptides Protein Conformation Monte Carlo Method Proteins Deep Learning
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A neural network for large-scale clustering of peptide mass spectra.

Nature methods Jun 1, 2022
Cluster Analysis Mass Spectrometry Neural Networks, Computer Peptides Algorithms
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Structured Sparse Regularized TSK Fuzzy System for predicting therapeutic peptides.

Briefings in bioinformatics May 13, 2022
Therapeutic peptides act on the skeletal system, digestive system and blood system, have antibacterial properties and help relieve inflammation. In order to reduce the resource consumption of wet experiments for the identification of therapeutic pept...
Peptides Fuzzy Logic Machine Learning Algorithms
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Do deep learning models make a difference in the identification of antimicrobial peptides?

Briefings in bioinformatics May 13, 2022
In the last few decades, antimicrobial peptides (AMPs) have been explored as an alternative to classical antibiotics, which in turn motivated the development of machine learning models to predict antimicrobial activities in peptides. The first genera...
Machine Learning Antimicrobial Peptides Peptides Deep Learning Amino Acid Sequence
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ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning.

Bioinformatics (Oxford, England) Mar 4, 2022
MOTIVATION: Recently, peptides have emerged as a promising class of pharmaceuticals for various diseases treatment poised between traditional small molecule drugs and therapeutic proteins. However, one of the key bottlenecks preventing them from ther...
Humans Peptides Algorithms Software Proteins Machine Learning
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SPEQ: quality assessment of peptide tandem mass spectra with deep learning.

Bioinformatics (Oxford, England) Mar 4, 2022
MOTIVATION: In proteomics, database search programs are routinely used for peptide identification from tandem mass spectrometry data. However, many low-quality spectra cannot be interpreted by any programs. Meanwhile, certain high-quality spectra may...
Tandem Mass Spectrometry Peptides Deep Learning Algorithms Software
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Accelerating bioactive peptide discovery via mutual information-based meta-learning.

Briefings in bioinformatics Jan 17, 2022
Recently, machine learning methods have been developed to identify various peptide bio-activities. However, due to the lack of experimentally validated peptides, machine learning methods cannot provide a sufficiently trained model, easily resulting i...
Software Machine Learning Peptides
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