SKEMPI: a Structural Kinetic and Energetic database of Mutant Protein Interactions and its use in empirical models.
about
Understanding the fabric of protein crystals: computational classification of biological interfaces and crystal contactsStructural and physico-chemical effects of disease and non-disease nsSNPs on proteinsCharacterizing changes in the rate of protein-protein dissociation upon interface mutation using hotspot energy and organizationCombining structural modeling with ensemble machine learning to accurately predict protein fold stability and binding affinity effects upon mutationPredicting Binding Free Energy Change Caused by Point Mutations with Knowledge-Modified MM/PBSA MethodmCSM-lig: quantifying the effects of mutations on protein-small molecule affinity in genetic disease and emergence of drug resistanceA Web Resource for Standardized Benchmark Datasets, Metrics, and Rosetta Protocols for Macromolecular Modeling and DesignDetermination of binding affinity upon mutation for type I dockerin-cohesin complexes from Clostridium thermocellum and Clostridium cellulolyticum using deep sequencingStructure-based inhibition of protein-protein interactions.Platinum: a database of experimentally measured effects of mutations on structurally defined protein-ligand complexes.Computational approaches to study the effects of small genomic variations.Data-driven models for protein interaction and design.Inferring the microscopic surface energy of protein-protein interfaces from mutation data.Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning.Predicting Protein-protein Association Rates using Coarse-grained Simulation and Machine Learning.The scoring of poses in protein-protein docking: current capabilities and future directionsIntegrating water exclusion theory into β contacts to predict binding free energy changes and binding hot spotsPredicting the Impact of Missense Mutations on Protein-Protein Binding Affinity.On the energy components governing molecular recognition in the framework of continuum approachesPredicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles.Co-Occurring Atomic Contacts for the Characterization of Protein Binding Hot SpotsA model for non-obligate oligomer formation in protein aggregration.BindProfX: Assessing Mutation-Induced Binding Affinity Change by Protein Interface Profiles with Pseudo-Counts.Insights from engineering the Affibody-Fc interaction with a computational-experimental method.PROXiMATE: A database of mutant protein-protein complex thermodynamics and kinetics.SAAMBE: Webserver to Predict the Charge of Binding Free Energy Caused by Amino Acids Mutations.Elucidating common structural features of human pathogenic variations using large-scale atomic-resolution protein networksMutaBind estimates and interprets the effects of sequence variants on protein-protein interactions.Allosteric Dynamic Control of Binding.Integration of structural dynamics and molecular evolution via protein interaction networks: a new era in genomic medicine.A systematic analysis of scoring functions in rigid-body protein docking: The delicate balance between the predictive rate improvement and the risk of overtraining.Computational Approaches for Predicting Binding Partners, Interface Residues, and Binding Affinity of Protein-Protein Complexes.Exploring the interplay between experimental methods and the performance of predictors of binding affinity change upon mutations in protein complexes.AB-Bind: Antibody binding mutational database for computational affinity predictions.On human disease-causing amino acid variants: statistical study of sequence and structural patterns.A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces.Prediction of Protein Hotspots from Whole Protein Sequences by a Random Projection Ensemble System.SpotOn: High Accuracy Identification of Protein-Protein Interface Hot-Spots.mCSM: predicting the effects of mutations in proteins using graph-based signatures.An integrated computational approach can classify VHL missense mutations according to risk of clear cell renal carcinoma.
P2860
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P2860
SKEMPI: a Structural Kinetic and Energetic database of Mutant Protein Interactions and its use in empirical models.
description
2012 nî lūn-bûn
@nan
2012年の論文
@ja
2012年論文
@yue
2012年論文
@zh-hant
2012年論文
@zh-hk
2012年論文
@zh-mo
2012年論文
@zh-tw
2012年论文
@wuu
2012年论文
@zh
2012年论文
@zh-cn
name
SKEMPI: a Structural Kinetic a ...... d its use in empirical models.
@en
SKEMPI: a Structural Kinetic a ...... d its use in empirical models.
@nl
type
label
SKEMPI: a Structural Kinetic a ...... d its use in empirical models.
@en
SKEMPI: a Structural Kinetic a ...... d its use in empirical models.
@nl
prefLabel
SKEMPI: a Structural Kinetic a ...... d its use in empirical models.
@en
SKEMPI: a Structural Kinetic a ...... d its use in empirical models.
@nl
P2860
P356
P1433
P1476
SKEMPI: a Structural Kinetic a ...... nd its use in empirical models
@en
P2093
Iain H Moal
Juan Fernández-Recio
P2860
P304
P356
10.1093/BIOINFORMATICS/BTS489
P407
P577
2012-08-01T00:00:00Z