Recognizing protein binding sites using statistical descriptions of their 3D environments.
about
The FEATURE framework for protein function annotation: modeling new functions, improving performance, and extending to novel applicationsThe 2006 Automated Function Prediction MeetingPrediction of water and metal binding sites and their affinities by using the Fold-X force field.Bioinformatics in support of molecular medicineCharacterizing the microenvironment surrounding phosphorylated protein sites.Robust recognition of zinc binding sites in proteins.Identification of recurring protein structure microenvironments and discovery of novel functional sites around CYS residuesIntegration of Diverse Research Methods to Analyze and Engineer Ca-Binding Proteins: From Prediction to ProductionDefining and searching for structural motifs using DeepView/Swiss-PdbViewer.Improving structure-based function prediction using molecular dynamics.Prediction of functional sites based on the fuzzy oil drop modelThe SeqFEATURE library of 3D functional site models: comparison to existing methods and applications to protein function annotation.Prediction of calcium-binding sites by combining loop-modeling with machine learning.Fast and automated functional classification with MED-SuMo: an application on purine-binding proteins.WebFEATURE: An interactive web tool for identifying and visualizing functional sites on macromolecular structures.Local functional descriptors for surface comparison based binding prediction.High precision prediction of functional sites in protein structures.Identification of cation-binding sites on actin that drive polymerization and modulate bending stiffnessHigh Resolution Prediction of Calcium-Binding Sites in 3D Protein Structures Using FEATURE.Combining molecular dynamics and machine learning to improve protein function recognitionEfficient algorithms to explore conformation spaces of flexible protein loops.Nucleotide Dependent Switching in Rho GTPase: Conformational Heterogeneity and Competing Molecular Interactions.INTEGRATING COMPUTATIONAL PROTEIN FUNCTION PREDICTION INTO DRUG DISCOVERY INITIATIVES.Microenvironment analysis and identification of magnesium binding sites in RNATowards predicting Ca2+-binding sites with different coordination numbers in proteins with atomic resolution.Predicting Ca2+ -binding sites using refined carbon clusters.Catalytic residues in hydrolases: analysis of methods designed for ligand-binding site prediction.Analysis and prediction of calcium-binding pockets from apo-protein structures exhibiting calcium-induced localized conformational changes.Clustering protein environments for function prediction: finding PROSITE motifs in 3DGraphlet kernels for prediction of functional residues in protein structures.Predicting small ligand binding sites in proteins using backbone structure.
P2860
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P2860
Recognizing protein binding sites using statistical descriptions of their 3D environments.
description
1998 nî lūn-bûn
@nan
1998年の論文
@ja
1998年論文
@yue
1998年論文
@zh-hant
1998年論文
@zh-hk
1998年論文
@zh-mo
1998年論文
@zh-tw
1998年论文
@wuu
1998年论文
@zh
1998年论文
@zh-cn
name
Recognizing protein binding si ...... ions of their 3D environments.
@en
type
label
Recognizing protein binding si ...... ions of their 3D environments.
@en
prefLabel
Recognizing protein binding si ...... ions of their 3D environments.
@en
P1476
Recognizing protein binding si ...... ions of their 3D environments.
@en
P304
P577
1998-01-01T00:00:00Z