| scholarly article | Q13442814 |
| P356 | DOI | 10.1002/(SICI)1097-0134(20000201)38:2<134::AID-PROT3>3.0.CO;2-A |
| P698 | PubMed publication ID | 10656261 |
| P50 | author | Michele Vendruscolo | Q28322492 |
| Rafael J. Najmanovich | Q41488364 | ||
| P2093 | author name string | Domany E | |
| P2860 | cites work | Errors in protein structures | Q27860776 |
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| How to derive a protein folding potential? A new approach to an old problem | Q30426274 | ||
| Recovery of protein structure from contact maps | Q30428899 | ||
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| Structure-derived potentials and protein simulations | Q41052933 | ||
| Medium- and long-range interaction parameters between amino acids for predicting three-dimensional structures of proteins | Q41063147 | ||
| Folding proteins with a simple energy function and extensive conformational searching | Q42845170 | ||
| Simulations of protein folding and unfolding | Q44760159 | ||
| Statistical potentials extracted from protein structures: how accurate are they? | Q46096070 | ||
| Factors influencing the ability of knowledge-based potentials to identify native sequence-structure matches | Q47633923 | ||
| Simulations of the folding of a globular protein | Q47890039 | ||
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| "New View" of Protein Folding Reconciled with the Old Through Multiple Unfolding Simulations | Q52528318 | ||
| The statistical mechanics of learning a rule | Q61968813 | ||
| Protein folding: Evaluation of some simple rules for the assembly of helices into tertiary structures with myoglobin as an example | Q66990357 | ||
| Identification of native protein folds amongst a large number of incorrect models. The calculation of low energy conformations from potentials of mean force | Q68363646 | ||
| Using a hydrophobic contact potential to evaluate native and near-native folds generated by molecular dynamics simulations | Q71121760 | ||
| Kinetics and thermodynamics of folding of a de novo designed four-helix bundle protein | Q71768491 | ||
| Protein fold recognition and dynamics in the space of contact maps | Q71961398 | ||
| Molecular dynamics of native protein. I. Computer simulation of trajectories | Q72709884 | ||
| Factors affecting the ability of energy functions to discriminate correct from incorrect folds | Q73221953 | ||
| Interaction potentials for protein folding | Q74363284 | ||
| Molecular mechanisms for cooperative folding of proteins | Q74450596 | ||
| Learning the Unlearnable | Q95500262 | ||
| P433 | issue | 2 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | protein folding | Q847556 |
| P304 | page(s) | 134-148 | |
| P577 | publication date | 2000-02-01 | |
| P1433 | published in | Proteins | Q7251514 |
| P1476 | title | Can a pairwise contact potential stabilize native protein folds against decoys obtained by threading? | |
| P478 | volume | 38 |
| Q30416969 | A Position-Specific Distance-Dependent Statistical Potential for Protein Structure and Functional Study |
| Q37519863 | A contact energy function considering residue hydrophobic environment and its application in protein fold recognition |
| Q44990381 | A distance- and orientation-dependent energy function of amino acid key blocks |
| Q33248388 | A maximum likelihood framework for protein design |
| Q24801602 | A protein folding potential that places the native states of a large number of proteins near a local minimum |
| Q47444421 | A simple procedure to weight empirical potentials in a fitness function so as to optimize its performance in ab initio protein-folding problem |
| Q35667592 | A statistical mechanical method to optimize energy functions for protein folding |
| Q24792248 | Amino acid empirical contact energy definitions for fold recognition in the space of contact maps |
| Q33425252 | Anti-cooperativity and cooperativity in hydrophobic interactions: Three-body free energy landscapes and comparison with implicit-solvent potential functions for proteins |
| Q42708071 | Assessment of the quality of energy functions for protein folding by using a criterion derived with the help of the noisy go model |
| Q30332785 | Can correct protein models be identified? |
| Q59223629 | Computational design of amyloid self-assembling peptides bearing aromatic residues and the cell adhesive motif Arg-Gly-Asp |
| Q30342170 | Database-derived potentials dependent on protein size for in silico folding and design. |
| Q30351513 | Divergence, recombination and retention of functionality during protein evolution. |
| Q75377351 | Effective interactions cannot replace solvent effects in a lattice model of proteins |
| Q35206581 | Energy design for protein-protein interactions |
| Q35019414 | Experimental and computational studies of determinants of membrane-protein folding |
| Q30329266 | Exploratory studies of ab initio protein structure prediction: Multiple copy simulated annealing, AMBER energy functions, and a generalized born/solvent accessibility solvation model |
| Q30363012 | How good are simplified models for protein structure prediction? |
| Q31922968 | How to guarantee optimal stability for most representative structures in the Protein Data Bank. |
| Q43043597 | Identification of correct regions in protein models using structural, alignment, and consensus information |
| Q58033000 | Inference of the solvation energy parameters of amino acids using maximum entropy approach |
| Q36476809 | Lessons from the design of a novel atomic potential for protein folding |
| Q30393355 | Novel Nonlinear Knowledge-Based Mean Force Potentials Based on Machine Learning |
| Q35222696 | On simplified global nonlinear function for fitness landscape: a case study of inverse protein folding |
| Q30389746 | Optimal contact definition for reconstruction of contact maps |
| Q30371877 | Optimized distance-dependent atom-pair-based potential DOOP for protein structure prediction. |
| Q42149736 | PIE-efficient filters and coarse grained potentials for unbound protein-protein docking |
| Q47658298 | Pairwise contact energy statistical potentials can help to find probability of point mutations. |
| Q38513300 | Perceptron learning of pairwise contact energies for proteins incorporating the amino acid environment |
| Q51967777 | Prediction of inter-residue contacts map based on genetic algorithm optimized radial basis function neural network and binary input encoding scheme |
| Q34482239 | Random Coil to Globular Thermal Response of a Protein (H3.1) with Three Knowledge-Based Coarse-Grained Potentials |
| Q34440613 | Random field model reveals structure of the protein recombinational landscape |
| Q30157238 | Reconstruction and stability of secondary structure elements in the context of protein structure prediction |
| Q40196625 | Role of native-state topology in the stabilization of intracellular antibodies |
| Q41957147 | SELECTpro: effective protein model selection using a structure-based energy function resistant to BLUNDERs |
| Q33279691 | Scoring predictive models using a reduced representation of proteins: model and energy definition. |
| Q57971783 | Statistical mechanical refinement of protein structure prediction schemes. II. Mayer cluster expansion approach |
| Q57971785 | Statistical mechanical refinement of protein structure prediction schemes: Cumulant expansion approach |
| Q29615145 | Statistical potential for assessment and prediction of protein structures |
| Q35635713 | Statistical potential for modeling and ranking of protein-ligand interactions |
| Q30329417 | Statistical potentials for fold assessment |
| Q30333234 | TOUCHSTONE II: a new approach to ab initio protein structure prediction |
| Q41909892 | Toward Correct Protein Folding Potentials |
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