AIMC Topic: Peptides
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THPep: A machine learning-based approach for predicting tumor homing peptides.
Computational biology and chemistry
May 24, 2019
In the present era, a major drawback of current anti-cancer drugs is the lack of satisfactory specificity towards tumor cells. Despite the presence of several therapies against cancer, tumor homing peptides are gaining importance as therapeutic agent...
mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides.
International journal of molecular sciences
Apr 22, 2019
Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from given peptide sequences remains as an open problem in the field of immunoinformatics. Recently, machine learning ...
Determining gradient conditions for peptide purification in RPLC with machine-learning-based retention time predictions.
Journal of chromatography. A
Mar 29, 2019
A strategy for determining a suitable solvent gradient in silico in preparative peptide separations is presented. The strategy utilizes a machine-learning-based method, called ELUDE, for peptide retention time predictions based on the amino acid sequ...
Predicting protein-peptide interaction sites using distant protein complexes as structural templates.
Scientific reports
Mar 12, 2019
Protein-peptide interactions play an important role in major cellular processes, and are associated with several human diseases. To understand and potentially regulate these cellular function and diseases it is important to know the molecular details...
AntiVPP 1.0: A portable tool for prediction of antiviral peptides.
Computers in biology and medicine
Feb 19, 2019
Viruses are worldwide pathogens with a high impact on the human population. Despite the constant efforts to fight viral infections, there is a need to discover and design new drug candidates. Antiviral peptides are molecules with confirmed activity a...
Toward Building Protein Force Fields by Residue-Based Systematic Molecular Fragmentation and Neural Network.
Journal of chemical theory and computation
Jan 7, 2019
Accurate force fields are crucial for molecular dynamics investigation of complex biological systems. Building accurate protein force fields from quantum mechanical (QM) calculations is challenging due to the complexity of proteins and high computati...
Predicting peptide presentation by major histocompatibility complex class I: an improved machine learning approach to the immunopeptidome.
BMC bioinformatics
Jan 5, 2019
BACKGROUND: To further our understanding of immunopeptidomics, improved tools are needed to identify peptides presented by major histocompatibility complex class I (MHC-I). Many existing tools are limited by their reliance upon chemical affinity data...
Peptide Extract from Exhibits Broad-Spectrum Antibacterial Activity.
BioMed research international
Dec 23, 2018
Increasing reports of infectious diseases worldwide have become a global concern in recent times. Depleted antibiotic pipelines, rapid and complex cases of antimicrobial resistance, and emergence and re-emergence of infectious disease have necessitat...
Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry.
Nature methods
Dec 20, 2018
We present DeepNovo-DIA, a de novo peptide-sequencing method for data-independent acquisition (DIA) mass spectrometry data. We use neural networks to capture precursor and fragment ions across m/z, retention-time, and intensity dimensions. They are t...
Photoelectrochemical biosensor for protein kinase A detection based on carbon microspheres, peptide functionalized Au-ZIF-8 and TiO/g-CN.
Talanta
Dec 12, 2018
In this work, a novel and sensitive photoelectrochemical (PEC) strategy was designed for protein kinase A (PKA) detection, comprising carbon microsphere (CMS) modified ITO electrode, TiO as the phosphate group recognition material and graphite-carbon...
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