Browsing by Author "Eramian, D."

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    A Composite Score for Predicting Errors in Protein Structure Models
    (2006) Eramian, D.; Melo Ledermann, Francisco Javier
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    A composite score for predicting errors in protein structure models
    (2006) Eramian, D.; Shen, M. Y.; Devos D.; Melo Ledermann, Francisco Javier; Sali, A.; Marti Renom, M. A.
    Reliable prediction of model accuracy is an important unsolved problem in protein structure modeling. To address this problem, we studied 24 individual assessment scores, including physics-based energy functions, statistical potentials, and machine learning–based scoring functions. Individual scores were also used to construct ∼85,000 composite scoring functions using support vector machine (SVM) regression. The scores were tested for their abilities to identify the most native-like models from a set of 6000 comparative models of 20 representative protein structures. Each of the 20 targets was modeled using a template of <30% sequence identity, corresponding to challenging comparative modeling cases. The best SVM score outperformed all individual scores by decreasing the average RMSD difference between the model identified as the best of the set and the model with the lowest RMSD (ΔRMSD) from 0.63 Å to 0.45 Å, while having a higher Pearson correlation coefficient to RMSD (r = 0.87) than any other tested score. The most accurate score is based on a combination of the DOPE non-hydrogen atom statistical potential; surface, contact, and combined statistical potentials from MODPIPE; and two PSIPRED/DSSP scores. It was implemented in the SVMod program, which can now be applied to select the final model in various modeling problems, including fold assignment, target–template alignment, and loop modeling.
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    MODBASE: a database of annotated comparative protein structure models and associated resources
    (2006) Pieper, U.; Eswar, N.; Davis, F. P.; Braberg, H.; Madhusudhan, M. S.; Rossi, A.; Marti Renom, M.; Karchin, R.; Webb, B. M.; Eramian, D.; Shen, M. Y.; Kelly, L.; Melo Ledermann, Francisco Javier; Sali, A.
    MODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models for all available protein sequences that can be matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on MODELLER for fold assignment, sequence–structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE is updated regularly to reflect the growth in protein sequence and structure databases, and improvements in the software for calculating the models. MODBASE currently contains 3 094 524 reliable models for domains in 1 094 750 out of 1 817 889 unique protein sequences in the UniProt database (July 5, 2005); only models based on statistically significant alignments and models assessed to have the correct fold despite insignificant alignments are included. MODBASE also allows users to generate comparative models for proteins of interest with the automated modeling server MODWEB (http://salilab.org/modweb). Our other resources integrated with MODBASE include comprehensive databases of multiple protein structure alignments (DBAli, http://salilab.org/dbali), structurally defined ligand binding sites and structurally defined binary domain interfaces (PIBASE, http://salilab.org/pibase) as well as predictions of ligand binding sites, interactions between yeast proteins, and functional consequences of human nsSNPs (LS-SNP, http://salilab.org/LS-SNP).