AI Medical Compendium Topic:
Databases, Protein

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Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12.

Proteins
We develop two complementary pipelines, "Zhang-Server" and "QUARK", based on I-TASSER and QUARK pipelines for template-based modeling (TBM) and free modeling (FM), and test them in the CASP12 experiment. The combination of I-TASSER and QUARK successf...

Graphical Interpretation and Analysis of Proteins and their Ontologies (GiaPronto): A One-Click Graph Visualization Software for Proteomics Data Sets.

Molecular & cellular proteomics : MCP
Here we present a free interactive web tool to process and visualize proteomics data sets with a single click. GiaPronto can process all proteomics quantification methods, label-free, SILAC and isobaric labeling, and analyze post-translational modif...

Specific and intrinsic sequence patterns extracted by deep learning from intra-protein binding and non-binding peptide fragments.

Scientific reports
The key finding in the DNA double helix model is the specific pairing or binding between nucleotides A-T and C-G, and the pairing rules are the molecule basis of genetic code. Unfortunately, no such rules have been discovered for proteins. Here we sh...

Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning.

Proteins
In this study, we report the evaluation of the residue-residue contacts predicted by our three different methods in the CASP12 experiment, focusing on studying the impact of multiple sequence alignment, residue coevolution, and machine learning on co...

Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC.

Journal of theoretical biology
Predicting protein subcellular location with support vector machine has been a popular research area recently because of the dramatic explosion of bioinformation. Though substantial achievements have been obtained, few researchers considered the prob...

ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network.

Molecules (Basel, Switzerland)
With the development of next generation sequencing techniques, it is fast and cheap to determine protein sequences but relatively slow and expensive to extract useful information from protein sequences because of limitations of traditional biological...

PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology.

PloS one
It has been acknowledged that source databases recording experimentally supported human protein-protein interactions (PPIs) exhibit limited overlap. Thus, the reconstruction of a comprehensive PPI network requires appropriate integration of multiple ...

Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2.

Biochemical and biophysical research communications
Here we report the development of a machine-learning model to predict binding affinity based on the crystallographic structures of protein-ligand complexes. We used an ensemble of crystallographic structures (resolution better than 1.5 Å resolution) ...

HashGO: hashing gene ontology for protein function prediction.

Computational biology and chemistry
Gene ontology (GO) is a standardized and controlled vocabulary of terms that describe the molecular functions, biological roles and cellular locations of proteins. GO terms and GO hierarchy are regularly updated as the accumulated biological knowledg...

Revealing protein functions based on relationships of interacting proteins and GO terms.

Journal of biomedical semantics
BACKGROUND: In recent years, numerous computational methods predicted protein function based on the protein-protein interaction (PPI) network. These methods supposed that two proteins share the same function if they interact with each other. However,...