FINDSITE-metal: integrating evolutionary information and machine learning for structure-based metal-binding site prediction at the proteome level.
about
Adverse drug reaction prediction using scores produced by large-scale drug-protein target docking on high-performance computing machinesExploring the "dark matter" of a mammalian proteome by protein structure and function modeling.Validation of metal-binding sites in macromolecular structures with the CheckMyMetal web server.Computational approaches for de novo design and redesign of metal-binding sites on proteinsUnleashing the power of meta-threading for evolution/structure-based function inference of proteins.Candida albicans scavenges host zinc via Pra1 during endothelial invasion.Advances in the molecular understanding of biological zinc transport.Enhancing protein-vitamin binding residues prediction by multiple heterogeneous subspace SVMs ensembleStructural basis for misfolding in myocilin-associated glaucoma.Using support vector machines to improve elemental ion identification in macromolecular crystal structuresCpipe: a comprehensive computational platform for sequence and structure-based analyses of Cysteine residues.Are predicted protein structures of any value for binding site prediction and virtual ligand screening?Secondary structure preferences of mn (2+) binding sites in bacterial proteins.Calciomics: integrative studies of Ca2+-binding proteins and their interactomes in biological systems.Zinc signals and immune function.Multiple impacts of zinc on immune function.The Physiological, Biochemical, and Molecular Roles of Zinc Transporters in Zinc Homeostasis and Metabolism.Polypharmacology in Drug Development: A Minireview of Current Technologies.Validation and correction of Zn-CysxHisy complexes.Predicting Ca2+ -binding sites using refined carbon clusters.MetalDetector v2.0: predicting the geometry of metal binding sites from protein sequence.Identification of major zinc-binding proteins from a marine cyanobacterium: insight into metal uptake in oligotrophic environments.eFindSite: improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands.TargetATPsite: a template-free method for ATP-binding sites prediction with residue evolution image sparse representation and classifier ensemble.Trace Elements and Healthcare: A Bioinformatics Perspective.Role of structural water for prediction of cation binding sites in apoproteins.ATPbind: Accurate Protein-ATP Binding Site Prediction by Combining Sequence-Profiling and Structure-Based Comparisons.Design of metalloproteins and novel protein folds using variational autoencoders
P2860
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P2860
FINDSITE-metal: integrating evolutionary information and machine learning for structure-based metal-binding site prediction at the proteome level.
description
2010 nî lūn-bûn
@nan
2010 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի դեկտեմբերին հրատարակված գիտական հոդված
@hy
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
name
FINDSITE-metal: integrating ev ...... diction at the proteome level.
@ast
FINDSITE-metal: integrating ev ...... diction at the proteome level.
@en
FINDSITE-metal: integrating ev ...... diction at the proteome level.
@nl
type
label
FINDSITE-metal: integrating ev ...... diction at the proteome level.
@ast
FINDSITE-metal: integrating ev ...... diction at the proteome level.
@en
FINDSITE-metal: integrating ev ...... diction at the proteome level.
@nl
prefLabel
FINDSITE-metal: integrating ev ...... diction at the proteome level.
@ast
FINDSITE-metal: integrating ev ...... diction at the proteome level.
@en
FINDSITE-metal: integrating ev ...... diction at the proteome level.
@nl
P2860
P356
P1433
P1476
FINDSITE-metal: integrating ev ...... diction at the proteome level.
@en
P2860
P304
P356
10.1002/PROT.22913
P407
P577
2010-12-06T00:00:00Z