AIMC Topic: Peptides

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Machine learning reveals a non-canonical mode of peptide binding to MHC class II molecules.

Immunology Jun 19, 2017
MHC class II molecules play a fundamental role in the cellular immune system: they load short peptide fragments derived from extracellular proteins and present them on the cell surface. It is currently thought that the peptide binds lying more or les...
Databases, Protein Antibodies, Monoclonal Structure-Activity Relationship Humans Peptides Neural Networks, Computer Protein Binding Machine Learning Computational Biology Binding Sites Protein Interaction Domains and Motifs Histocompatibility Antigens Class II Mutation Epitopes Ligands
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FFLUX: Transferability of polarizable machine-learned electrostatics in peptide chains.

Journal of computational chemistry Mar 10, 2017
The fully polarizable, multipolar, and atomistic force field protein FFLUX is being built from machine learning (i.e., kriging) models, each of which predicts an atomic property. Each atom of a given protein geometry needs to be assigned such a krigi...
Models, Theoretical Proteins Models, Molecular Machine Learning Peptides Thermodynamics Physical Phenomena Amino Acids Protein Conformation Static Electricity
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Machine learning classifier for identification of damaging missense mutations exclusive to human mitochondrial DNA-encoded polypeptides.

BMC bioinformatics Mar 7, 2017
BACKGROUND: Several methods have been developed to predict the pathogenicity of missense mutations but none has been specifically designed for classification of variants in mtDNA-encoded polypeptides. Moreover, there is not available curated dataset ...
Humans Mitochondria DNA Mutational Analysis Mutation, Missense Peptides Machine Learning Computational Biology Sensitivity and Specificity DNA, Mitochondrial
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Unveiling antimicrobial peptide-generating human proteases using PROTEASIX.

Journal of proteomics Feb 27, 2017
UNLABELLED: Extracting information from peptidomics data is a major current challenge, as endogenous peptides can result from the activity of multiple enzymes. Proteolytic enzymes can display overlapping or complementary specificity. The activity spe...
Peptide Hydrolases Protein Precursors Substrate Specificity Humans Peptides Computational Biology Proteome Antimicrobial Cationic Peptides Knowledge Bases
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HemoPred: a web server for predicting the hemolytic activity of peptides.

Future medicinal chemistry Feb 17, 2017
AIM: Toxicity arising from hemolytic activity of peptides hinders its further progress as drug candidates.
Hemolytic Agents Software Computer Simulation Peptides Databases, Protein Amino Acid Sequence Hemolysis Humans Machine Learning
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MUMAL2: Improving sensitivity in shotgun proteomics using cost sensitive artificial neural networks and a threshold selector algorithm.

BMC bioinformatics Dec 15, 2016
BACKGROUND: This work presents a machine learning strategy to increase sensitivity in tandem mass spectrometry (MS/MS) data analysis for peptide/protein identification. MS/MS yields thousands of spectra in a single run which are then interpreted by s...
Proteome Algorithms Neural Networks, Computer Databases, Protein Peptides Probability Software Tandem Mass Spectrometry Proteomics
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Mapping membrane activity in undiscovered peptide sequence space using machine learning.

Proceedings of the National Academy of Sciences of the United States of America Nov 14, 2016
There are some ∼1,100 known antimicrobial peptides (AMPs), which permeabilize microbial membranes but have diverse sequences. Here, we develop a support vector machine (SVM)-based classifier to investigate ⍺-helical AMPs and the interrelated nature o...
Machine Learning Anti-Bacterial Agents Anti-Infective Agents Peptides Microbial Sensitivity Tests Amino Acid Sequence Cell Membrane X-Ray Diffraction Models, Theoretical Support Vector Machine Antimicrobial Cationic Peptides
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ASAP: a machine learning framework for local protein properties.

Database : the journal of biological databases and curation Oct 2, 2016
Determining residue-level protein properties, such as sites of post-translational modifications (PTMs), is vital to understanding protein function. Experimental methods are costly and time-consuming, while traditional rule-based computational methods...
Internet Molecular Sequence Annotation Sequence Analysis, Protein Machine Learning Peptides
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Dynamic Bayesian Network for Accurate Detection of Peptides from Tandem Mass Spectra.

Journal of proteome research Jul 22, 2016
A central problem in mass spectrometry analysis involves identifying, for each observed tandem mass spectrum, the corresponding generating peptide. We present a dynamic Bayesian network (DBN) toolkit that addresses this problem by using a machine lea...
Tandem Mass Spectrometry Proteomics Databases, Protein Bayes Theorem Software Peptides Machine Learning
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Pan-Specific Prediction of Peptide-MHC Class I Complex Stability, a Correlate of T Cell Immunogenicity.

Journal of immunology (Baltimore, Md. : 1950) Jul 8, 2016
Binding of peptides to MHC class I (MHC-I) molecules is the most selective event in the processing and presentation of Ags to CTL, and insights into the mechanisms that govern peptide-MHC-I binding should facilitate our understanding of CTL biology. ...
Humans Neural Networks, Computer Epitopes, T-Lymphocyte Protein Stability Peptides Antigen Presentation Lymphocyte Activation Histocompatibility Antigens Class I
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