AI Medical Compendium Topic:
Databases, Protein

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Subcellular localization prediction of apoptosis proteins based on evolutionary information and support vector machine.

Artificial intelligence in medicine
OBJECTIVES: In this paper, a high-quality sequence encoding scheme is proposed for predicting subcellular location of apoptosis proteins.

Investigating Correlation between Protein Sequence Similarity and Semantic Similarity Using Gene Ontology Annotations.

IEEE/ACM transactions on computational biology and bioinformatics
Sequence similarity is a commonly used measure to compare proteins. With the increasing use of ontologies, semantic (function) similarity is getting importance. The correlation between these measures has been applied in the evaluation of new semantic...

S-SulfPred: A sensitive predictor to capture S-sulfenylation sites based on a resampling one-sided selection undersampling-synthetic minority oversampling technique.

Journal of theoretical biology
Protein S-sulfenylation is a reversible post-translational modification involving covalent attachment of hydroxide to the thiol group of cysteine residues, which is involved in various biological processes including cell signaling, response to stress...

Assessment of Semantic Similarity between Proteins Using Information Content and Topological Properties of the Gene Ontology Graph.

IEEE/ACM transactions on computational biology and bioinformatics
The semantic similarity between two interacting proteins can be estimated by combining the similarity scores of the GO terms associated with the proteins. Greater number of similar GO annotations between two proteins indicates greater interaction aff...

Identify and analysis crotonylation sites in histone by using support vector machines.

Artificial intelligence in medicine
OBJECTIVE: Lysine crotonylation (Kcr) is a newly discovered histone posttranslational modification, which is specifically enriched at active gene promoters and potential enhancers in mammalian cell genomes. Although lysine crotonylation sites can be ...

Improved prediction of protein-protein interactions using novel negative samples, features, and an ensemble classifier.

Artificial intelligence in medicine
Computational methods are employed in bioinformatics to predict protein-protein interactions (PPIs). PPIs and protein-protein non-interactions (PPNIs) display different levels of development, and the number of PPIs is considerably greater than that o...

A Hybrid Knowledge-Based and Empirical Scoring Function for Protein-Ligand Interaction: SMoG2016.

Journal of chemical information and modeling
We present the third generation of our scoring function for the prediction of protein-ligand binding free energy. This function is now a hybrid between a knowledge-based potential and an empirical function. We constructed a diversified set of ∼1000 c...

Systematic analysis of non-structural protein features for the prediction of PTM function potential by artificial neural networks.

PloS one
Post-translational modifications (PTMs) provide an extensible framework for regulation of protein behavior beyond the diversity represented within the genome alone. While the rate of identification of PTMs has rapidly increased in recent years, our k...

HemoPred: a web server for predicting the hemolytic activity of peptides.

Future medicinal chemistry
AIM: Toxicity arising from hemolytic activity of peptides hinders its further progress as drug candidates.

Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC.

Scientific reports
Antimicrobial peptides (AMPs) are important components of the innate immune system that have been found to be effective against disease causing pathogens. Identification of AMPs through wet-lab experiment is expensive. Therefore, development of effic...