In quest of an empirical potential for protein structure prediction.
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Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and DynamicsAb initio protein structure assembly using continuous structure fragments and optimized knowledge-based force fieldOctarellin VI: using rosetta to design a putative artificial (β/α)8 proteinFormulation of probabilistic models of protein structure in atomic detail using the reference ratio method.A comparative study of the reported performance of ab initio protein structure prediction algorithms.OPUS-PSP: an orientation-dependent statistical all-atom potential derived from side-chain packing.DL-PRO: A Novel Deep Learning Method for Protein Model Quality Assessment.Optimized distance-dependent atom-pair-based potential DOOP for protein structure prediction.Explicit orientation dependence in empirical potentials and its significance to side-chain modelingAnalyses on hydrophobicity and attractiveness of all-atom distance-dependent potentials.Protein structure prediction using residue- and fragment-environment potentials in CASP11.Quantitative prediction of protein-protein binding affinity with a potential of mean force considering volume correction.Effective protein conformational sampling based on predicted torsion angles.Information-theoretic analysis of the reference state in contact potentials used for protein structure prediction.Sub-AQUA: real-value quality assessment of protein structure models.Fine grained sampling of residue characteristics using molecular dynamics simulation.TASSER_WT: a protein structure prediction algorithm with accurate predicted contact restraints for difficult protein targets.Trends in template/fragment-free protein structure predictionNCACO-score: an effective main-chain dependent scoring function for structure modelingStatistical mechanics-based method to extract atomic distance-dependent potentials from protein structures.GOAP: a generalized orientation-dependent, all-atom statistical potential for protein structure prediction.Designing specific protein-protein interactions using computation, experimental library screening, or integrated methodsA position-specific distance-dependent statistical potential for protein structure and functional studyWhat is the best reference state for designing statistical atomic potentials in protein structure prediction?Extracting knowledge from protein structure geometryAn Anisotropic Coarse-Grained Model for Proteins Based On Gay-Berne and Electric Multipole Potentials.Structural refinement of membrane proteins by restrained molecular dynamics and solvent accessibility data.Scoring predictive models using a reduced representation of proteins: model and energy definition.A novel side-chain orientation dependent potential derived from random-walk reference state for protein fold selection and structure prediction.Computational and experimental approaches to reveal the effects of single nucleotide polymorphisms with respect to disease diagnosticsRecovering physical potentials from a model protein databankA structural-based strategy for recognition of transcription factor binding sites.ROTAS: a rotamer-dependent, atomic statistical potential for assessment and prediction of protein structuresBayesian weighting of statistical potentials in NMR structure calculationOn the importance of the distance measures used to train and test knowledge-based potentials for proteinsFunctional polyesters enable selective siRNA delivery to lung cancer over matched normal cellsMolecular determinants of cadherin ideal bond formation: Conformation-dependent unbinding on a multidimensional landscape.Sequence statistics of tertiary structural motifs reflect protein stabilityOPUS-Ca: a knowledge-based potential function requiring only Calpha positions.Prediction of Protein Loop Conformations using the AGBNP Implicit Solvent Model and Torsion Angle Sampling
P2860
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P2860
In quest of an empirical potential for protein structure prediction.
description
2006 nî lūn-bûn
@nan
2006 թուականի Մարտին հրատարակուած գիտական յօդուած
@hyw
2006 թվականի մարտին հրատարակված գիտական հոդված
@hy
2006年の論文
@ja
2006年論文
@yue
2006年論文
@zh-hant
2006年論文
@zh-hk
2006年論文
@zh-mo
2006年論文
@zh-tw
2006年论文
@wuu
name
In quest of an empirical potential for protein structure prediction.
@ast
In quest of an empirical potential for protein structure prediction.
@en
type
label
In quest of an empirical potential for protein structure prediction.
@ast
In quest of an empirical potential for protein structure prediction.
@en
prefLabel
In quest of an empirical potential for protein structure prediction.
@ast
In quest of an empirical potential for protein structure prediction.
@en
P1476
In quest of an empirical potential for protein structure prediction.
@en
P304
P356
10.1016/J.SBI.2006.02.004
P577
2006-03-09T00:00:00Z