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

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

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

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.

The Robetta server

As the number of gene sequences in databases, public and private, increase dramatically, so do the number of genes of unknown function. Of the protein sequences currently available approximately

ABSTRACT We present a novel method, HMMSTR-CM, for protein contact map predictions. Contact potentials were calculated by using HMMSTR, a hidden Markov model for local sequence structure correlations. Targets were aligned against protein templates using a Bayesian method, and contact maps were generated by using these alignments. Contact potentials then were used to evaluate these templates. An ab initio method based on the target contact potentials using a rule-based strategy to model the protein-folding pathway was developed. Fold recognition and ab initio methods were combined to produce accurate, protein-like contact maps. Pathways sometimes led to an unambiguous prediction of topology, even without using templates. The results on CASP5 targets are discussed. Also included is a brief update on the quality of fully automated ab initio predictions using the I-sites server. Proteins 2003;53:497–502.

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

ABSTRACT A major limitation of current comparative modeling methods is the accuracy with which regions that are structurally divergent from homologues of known structure can be modeled. Because structural differences between homologous proteins are responsible for variations in protein function and specificity, the ability to model these differences has important functional consequences. Although existing methods can provide reasonably accurate models of short loop regions, modeling longer structurally divergent regions is an unsolved problem. Here we describe a method based on the de novo structure prediction algorithm, Rosetta, for predicting conformations of structurally divergent regions in comparative models.

Thisstudyexplorestheuseofmultiplesequencealignment (MSA)informationand globalmeasuresofhydrophobiccoreformationfor improvingtheRosettaabinitioproteinstructure predictionmethod.Themosteffectiveuseofthe MSAinformationisachievedbycarryingoutindependentfoldingsimulationsforasubsetofthe homologoussequencesintheMSAandthenidentifyingthefreeenergyminimacommontoallfolded sequencesviasimultaneousclusteringoftheindependentfoldingruns. Globalmeasuresofhydrophobiccoreformation, usingellipsoidalratherthan sphericalrepresentationsofthehydrophobiccore, arefoundtobeusefulinremovingnon-nativeconformationsbeforeclusteranalysis. ThroughthiscombinationofMSAinformationandglobalmeasuresof proteincoreformation,wesignificantlyincrease theperformanceofRosettaonachallengingtestset. Proteins2001;43:1--11.2001Wiley-Liss,Inc.