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392
An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction
- J. Mol. Biol
, 1998
"... Any algorithm that attempts to predict protein structure requires a discriminatory function that can distinguish between correct and incorrect conformations. These discriminatory functions can be ..."
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Cited by 163 (23 self)
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Any algorithm that attempts to predict protein structure requires a discriminatory function that can distinguish between correct and incorrect conformations. These discriminatory functions can be
Protein structure prediction and analysis using the Robetta server
- Nucleic Acids Res
, 2004
"... The Robetta server ..."
3D-Jury: A simple approach to improve protein structure predictions
- Bioinformatics
"... Motivation: Consensus structure prediction methods (meta-predictors) have higher accuracy than individual structure prediction algorithms (their components). The goal for the development of the 3D-Jury system is to create a simple but powerful procedure for generating meta-predictions using variable ..."
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Cited by 137 (18 self)
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Motivation: Consensus structure prediction methods (meta-predictors) have higher accuracy than individual structure prediction algorithms (their components). The goal for the development of the 3D-Jury system is to create a simple but powerful procedure for generating meta-predictions using variable sets of models obtained from diverse sources. The resulting protocol should help to improve the quality of structural annotations of novel proteins. Results: The 3D-Jury system generates meta-predictions from sets of models created using variable methods. It is not necessary to know prior characteristics of the methods. The system is able to utilize immediately new components (additional prediction providers). The accuracy of the system is comparable with other well-tuned prediction servers. The algorithm resembles methods of selecting models generated using ab initio folding simulations. It is simple and offers a portable solution to improve the accuracy of other protein structure prediction protocols. Availability: The 3D-Jury system is available via the Structure Prediction Meta Server
Scratch: a protein structure and structural feature prediction server
- Nucleic Acids Res
, 2005
"... server ..."
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ROSETTA3: An Object-Oriented Software Suite for the Simulation and Design of Macromolecules
"... 2.1. Preserving existing functionality 548 ..."
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Improved recognition of native-like protein structures using a combination of sequence-dependent and sequence-independent features of proteins
- Proteins
, 1999
"... ABSTRACT We describe the development of a scoring function based on the decomposition P(structure0sequence) � P(sequence0structure) *P(structure), which outperforms previous scoring functions in correctly identifying native-like protein structures in large ensembles of compact decoys. The first ter ..."
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Cited by 87 (21 self)
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ABSTRACT We describe the development of a scoring function based on the decomposition P(structure0sequence) � P(sequence0structure) *P(structure), which outperforms previous scoring functions in correctly identifying native-like protein structures in large ensembles of compact decoys. The first term captures sequence-dependent features of protein structures, such as the burial of hydrophobic residues in the core, the second term, universal sequence-independent features, such as the assembly of �-strands into �-sheets. The efficacies of a wide variety of sequence-dependent and sequence-independent features of protein structures for recognizing native-like structures were systematically evaluated using ensembles ofD30,000 compact conformations with fixed secondary structure for each of 17 small protein domains. The best results were obtained using a core scoring function with P(sequence0structure) parameterized similarly to our previous work (Simons et al., J Mol Biol 1997;268:209–225] and P(structure) focused on secondary structure packing preferences; while several additional features had some discriminatory power on their own, they did not provide any additional discriminatory power when combined with the core scoring function. Our results, on both the training set and the independent decoy set of Park and Levitt (J Mol Biol 1996;258:367–392), suggest that this scoring function should contribute to the prediction of tertiary structure from knowledge of sequence and secondary structure. Proteins 1999;34:82–95. � 1999 Wiley-Liss, Inc. Key words: protein folding; structure prediction; knowledge-based scoring functions; fold recognition
A distance-dependent atomic knowledge-based potential for improved protein structure selection, Proteins 44:223–232
, 2001
"... ABSTRACT A heavy atom distance-dependent knowledge-based pairwise potential has been devel-oped. This statistical potential is first evaluated and optimized with the native structure z-scores from gapless threading. The potential is then used to recognize the native and near-native structures from b ..."
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Cited by 83 (1 self)
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ABSTRACT A heavy atom distance-dependent knowledge-based pairwise potential has been devel-oped. This statistical potential is first evaluated and optimized with the native structure z-scores from gapless threading. The potential is then used to recognize the native and near-native structures from both published decoy test sets, as well as decoys obtained from our group’s protein structure prediction program. In the gapless threading test, there is an average z-score improvement of 4 units in the optimized atomic potential over the residue-based quasichemical potential. Examination of the z-scores for individual pairwise distance shells indi-cates that the specificity for the native protein structure is greatest at pairwise distances of 3.5–6.5 Å, i.e., in the first solvation shell. On applying the current atomic potential to test sets obtained from the web, composed of native protein and decoy structures, the current generation of the potential performs better than residue-based potentials as well as the other published atomic potentials in the task of selecting native and near-native structures. This newly developed potential is also applied to structures of varying quality generated by our group’s protein structure prediction program. The current atomic potential tends to pick lower RMSD structures than do residue-based contact potentials. In particular, this atomic pairwise interaction poten-tial has better selectivity especially for near-native structures. As such, it can be used to select near-native folds generated by structure prediction algo-rithms as well as for protein structure refinement.
Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks
- Proteins
, 2000
"... ABSTRACT By using an unsupervised cluster analyzer, we have identified a local structural alphabet composed of 16 folding patterns of five consecutive C � (“protein blocks”). The dependence that exists between successive blocks is explicitly taken into account. A Bayesian approach based on the relat ..."
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Cited by 77 (16 self)
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ABSTRACT By using an unsupervised cluster analyzer, we have identified a local structural alphabet composed of 16 folding patterns of five consecutive C � (“protein blocks”). The dependence that exists between successive blocks is explicitly taken into account. A Bayesian approach based on the relation protein block-amino acid propensity is used for prediction and leads to a success rate close to 35%. Sharing sequence windows associated with certain blocks into “sequence families ” improves the prediction accuracy by 6%. This prediction accuracy exceeds 75 % when keeping the first four predicted protein blocks at each site of the protein. In addition, two different strategies are proposed: the first one defines the number of protein blocks in each site needed for respecting a user-fixed prediction accuracy, and alternatively, the second one defines the different protein sites to be predicted with a user-fixed number of blocks and a chosen accuracy. This last strategy applied to the ubiquitin conjugating enzyme (�/ � protein) shows that 91 % of the sites may be predicted with a prediction accuracy larger than 77 % considering only three blocks per site. The prediction strategies proposed improve our knowledge about sequence-structure dependence and should be very useful in ab initio protein modelling. Proteins 2000;41:271–287. © 2000 Wiley-Liss, Inc. Key words: protein backbone structure; unsupervised classifier; structure-sequence relationships; structure prediction; protein block; Bayesian approach; prediction strategies
Can correct protein models be identified
- Protein Sci
, 2003
"... The ability to separate correct models of protein structures from less correct models is of the greatest importance for protein structure prediction methods. Several studies have examined the ability of different types of energy function to detect the native, or native-like, protein structure from a ..."
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Cited by 77 (4 self)
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The ability to separate correct models of protein structures from less correct models is of the greatest importance for protein structure prediction methods. Several studies have examined the ability of different types of energy function to detect the native, or native-like, protein structure from a large set of decoys. In contrast to earlier studies, we examine here the ability to detect models that only show limited structural similarity to the native structure. These correct models are defined by the existence of a fragment that shows significant similarity between this model and the native structure. It has been shown that the existence of such fragments is useful for comparing the performance between different fold recognition methods and that this performance correlates well with performance in fold recognition. We have developed ProQ, a neuralnetwork-based method to predict the quality of a protein model that extracts structural features, such as frequency of atom–atom contacts, and predicts the quality of a model, as measured either by LGscore or MaxSub. We show that ProQ performs at least as well as other measures when identifying the native structure and is better at the detection of correct models. This performance is maintained over several different test sets. ProQ can also be combined with the Pcons fold recognition predictor (Pmodeller) to increase its performance, with the main advantage being the elimination of a few high-scoring incorrect models. Pmodeller was successful in CASP5 and results from the latest LiveBench, LiveBench-6, indicating that Pmodeller has a higher specificity than Pcons alone.
Prospects for ab initio protein structural genomics
- J Mol Biol
"... We present the results of a large-scale testing of the ROSETTA method for ab initio protein structure prediction. Models were generated for two independently generated lists of small proteins (up to 150 amino acid residues), and the results were evaluated using traditional rmsd based measures and a ..."
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Cited by 72 (11 self)
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We present the results of a large-scale testing of the ROSETTA method for ab initio protein structure prediction. Models were generated for two independently generated lists of small proteins (up to 150 amino acid residues), and the results were evaluated using traditional rmsd based measures and a novel measure based on the structure-based comparison of the models to the structures in the PDB using DALI. For 111 of 136 all a and a/b proteins 50 to 150 residues in length, the method produced at least one model within 7 AÊ rmsd of the native structure in 1000 attempts. For 60 of these proteins, the closest structure match in the PDB to at least one of the ten most frequently generated conformations was found to be structurally related (four standard deviations above background) to the native protein. These results suggest that ab initio structure prediction approaches may soon be useful for generating low resolution models and identifying distantly related proteins with similar structures and perhaps functions for these classes of proteins on the genome scale.
