Orientational potentials extracted from protein structures improve native fold recognition.
about
Statistical potential for assessment and prediction of protein structuresA new generation of statistical potentials for proteins.Delaunay-based nonlocal interactions are sufficient and accurate in protein fold recognition.SSThread: Template-free protein structure prediction by threading pairs of contacting secondary structures followed by assembly of overlapping pairs.A free-rotating and self-avoiding chain model for deriving statistical potentials based on protein structuresOPUS-PSP: an orientation-dependent statistical all-atom potential derived from side-chain packing.A knowledge-based structure-discriminating function that requires only main-chain atom coordinates.Explicit orientation dependence in empirical potentials and its significance to side-chain modelingPredicting local quality of a sequence-structure alignment.Statistical potentials for improved structurally constrained evolutionary models.New statistical potential for quality assessment of protein models and a survey of energy functionsOrientation-dependent backbone-only residue pair scoring functions for fixed backbone protein design.Protein structure modelling and evaluation based on a 4-distance description of side-chain interactionsA simple probabilistic model of multibody interactions in proteins.De novo backbone scaffolds for protein design.Balancing energy and entropy: a minimalist model for the characterization of protein folding landscapesCOFFDROP: A Coarse-Grained Nonbonded Force Field for Proteins Derived from All-Atom Explicit-Solvent Molecular Dynamics Simulations of Amino AcidsStatistical potential for modeling and ranking of protein-ligand interactionsCoarse-grained peptide modeling using a systematic multiscale approachReduced C(beta) statistical potentials can outperform all-atom potentials in decoy identification.OPUS-Ca: a knowledge-based potential function requiring only Calpha positions.The multiscale coarse-graining method. II. Numerical implementation for coarse-grained molecular models.The multiscale coarse-graining method. I. A rigorous bridge between atomistic and coarse-grained models.The ruggedness of protein-protein energy landscape and the cutoff for 1/r(n) potentials.Inferring ideal amino acid interaction forms from statistical protein contact potentials.Constructing multi-resolution Markov State Models (MSMs) to elucidate RNA hairpin folding mechanismsThe dominant role of side-chain backbone interactions in structural realization of amino acid code. ChiRotor: a side-chain prediction algorithm based on side-chain backbone interactionsTunable Coarse Graining for Monte Carlo Simulations of Proteins via Smoothed Energy Tables: Direct and Exchange Simulations.A coarse-grained alpha-carbon protein model with anisotropic hydrogen-bonding.A coarse-grained potential for fold recognition and molecular dynamics simulations of proteins.Energetics of protein-DNA interactions.Local quality assessment in homology models using statistical potentials and support vector machines.The multiscale coarse-graining method. VIII. Multiresolution hierarchical basis functions and basis function selection in the construction of coarse-grained force fields.The multiscale coarse-graining method. X. Improved algorithms for constructing coarse-grained potentials for molecular systems.The multiscale coarse-graining method. III. A test of pairwise additivity of the coarse-grained potential and of new basis functions for the variational calculation.A distance- and orientation-dependent energy function of amino acid key blocks.The multiscale coarse-graining method. IX. A general method for construction of three body coarse-grained force fields.Pairwise energies for polypeptide coarse-grained models derived from atomic force fields.The multiscale coarse-graining method. V. Isothermal-isobaric ensemble.Orientation-dependent potential of mean force for protein folding.
P2860
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P2860
Orientational potentials extracted from protein structures improve native fold recognition.
description
2004 nî lūn-bûn
@nan
2004年の論文
@ja
2004年論文
@yue
2004年論文
@zh-hant
2004年論文
@zh-hk
2004年論文
@zh-mo
2004年論文
@zh-tw
2004年论文
@wuu
2004年论文
@zh
2004年论文
@zh-cn
name
Orientational potentials extra ...... prove native fold recognition.
@ast
Orientational potentials extra ...... prove native fold recognition.
@en
type
label
Orientational potentials extra ...... prove native fold recognition.
@ast
Orientational potentials extra ...... prove native fold recognition.
@en
prefLabel
Orientational potentials extra ...... prove native fold recognition.
@ast
Orientational potentials extra ...... prove native fold recognition.
@en
P2093
P2860
P356
P1433
P1476
Orientational potentials extra ...... prove native fold recognition.
@en
P2093
Devarajan Thirumalai
John E Straub
Nicolae-Viorel Buchete
P2860
P304
P356
10.1110/PS.03488704
P577
2004-04-01T00:00:00Z