Modeling of Biomolecular Systems Interactions, Dynamics, and Allostery: Bridging Experiments and Computations - September 10-14, 2014, Istanbul, Turkey

Modeling of Biomolecular Systems Interactions, Dynamics, and Allostery Poster Session I

41-POS Board 41 The ProteinModelPortal - How Good is My Prediction? –First Results from The Continuous Automated Model EvaluatiOn (CAMEO) Juergen Haas 1,2 , Alessandro Barbato 1,2 , Steven Roth 1,2 , Tobias Schmidt 1,2 , Khaled Mostaguir 1,2 , Stefan Bienert 1,2 , Andrew M. Waterhouse 1,2 , Tiziano Gallo-Cassarino 1,2 , Valentina Romano 1,2 , Lorenza Bordoli 1,2 , Torsten Schwede 1,2 . 1 Swiss Institute of Bioinformatics, Basel, Basel, Switzerland, 2 Biozentrum, Universitaet Basel, Basel, Switzerland. Protein structure modeling is widely used in the life science community to build models for proteins, where no experimental structures are available. The ProteinModelPortal (PMP, http://www.proteinmodelportal.org) contains more than 21 million models from various modeling servers for more than 5.1 million distinct UniProt sequences and is regularly updated. Depending on the difficulty of the target protein the various modeling approaches differ in performance and we established the Continuous Automated Model EvaluatiOn (CAMEO, http://www.cameo3d.org) platform assessing the performance of servers predicting protein structures in its 3D category. Based on the weekly PDB pre-release, protein sequences (targets) are submitted to participating servers. After the release of the 3D coordinates four days later, the returned predictions are then compared to the corresponding PDB structures (references). Within 121 weeks 49837 protein structure predictions by 34 servers for 2129 targets were assessed in the 3D category. Besides 3D predictions, CAMEO assesses ligand binding residues predictions in proteins (16 servers registered). Since the quality of models ultimately determine their utility, a new category "Quality Estimation of protein structures models" is being introduced to CAMEO. So far two servers and 4 stand-alone versions of the most popular quality estimation methods along with a custom naïve predictor based on CAMEO 3D data are being evaluated. Continuous assessment of prediction servers allows to retrospectively analyze the performance of a given server - with implications for PMP, as the quality of the predictions may vary significantly among different servers depending on the specific target protein and the chosen approach. The blind predictions obtained by CAMEO are directly suited for publication and the straightforward modular extension of CAMEO allows new categories and scores to be added on demand of the respective communities.

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