Gniewek, P.

Jamroz, M.

Kmiecik, S.

Kolinski, A.

Poland}

Zwolinska, A.

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John von Neumann Institute for ComputingDesigning an Automatic Pipeline for ProteinStructure PredictionS. Kmiecik, M. Jamroz, A. Zwolinska,P. Gniewek, A. Kolinskipublished inFrom Computational Biophysics to Systems Biology (CBSB08),Proceedings of the NIC Workshop 2008,Ulrich H. E. Hansmann, Jan H. Meinke, Sandipan Mohanty,Walter Nadler, Olav Zimmermann (Editors),John von Neumann Institute for Computing, Ju¨lich,NIC Series, Vol. 40, ISBN 978-3-9810843-6-8, pp. 105-108, 2008.c© 2008 by John von Neumann Institute for ComputingPermission to make digital or hard copies of portions of this work forpersonal or classroom use is granted provided that the copies are notmade or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. To copy otherwiserequires prior specific permission by the publisher mentioned above.http://www.fz-juelich.de/nic-series/volume40Designing an Automatic Pipeline forProtein Structure PredictionSebastian Kmiecik1, Michal Jamroz1, Anna Zwolinska1,Pawel Gniewek1, and Andrzej Kolinski21 Selvita Sp. z o.o.,Ostatnia 1c, 31-444 Cracow, PolandE-mail: sebastian.kmiecik@selvita.com2 Laboratory of Theory of Biopolymers,Faculty of Chemistry, University of Warsaw,02-093 Warsaw, PolandBuilding accurate 3D structural models of proteins and protein assemblies is a challengingtask. Our modeling technology is based on the CABS model, extensively tested, state-of-the-art approach to protein structure prediction. The modeling process is divided into two stages:CABS fold assembly followed by the model refinement/selection procedure, using an all-atomrepresentation and a more exact interaction scheme enabling high resolution structure predic-tion. Fold assembly can be done in a framework of a standard comparative modeling pro-cedure, where spatial restraints are derived from alternative sequence alignments with a tem-plate/templates. Preferentially in more difficult modeling cases, a new approach to comparativemodeling can be used, which does not require the prior alignment. Selvita’s goal is to providean integrated tool-kit for automated protein structure predictions. However, like blind predic-tion experiments show, due to high complexity of prediction tasks, fully automated approachoften doesn’t guarantee the highest possible performance. Therefore, human intervention ismade possible at every stage of modeling.1 IntroductionThanks to international effort in the genome sequencing projects, enormous library of pro-tein sequences is now available. Despite extensive efforts in structural genomics, the num-ber of experimentally determined protein structures, typically by costly X-ray crystallogra-phy or NMR spectroscopy procedures, is lagging far behind the number of known proteinsequences. Since proteins are involved in practically all functions performed by a cell,knowledge of protein structures is necessary for understanding and controlling molecularmechanisms of life. Current assumptions are, that for a large fraction of proteins whosestructures will not be determined experimentally, computational methods can provide valu-able information1.2 Multiscale Approach to Structure Prediction: ComparativeModeling and Fold RecognitionDuring computational protein structure determination the following main challenges canbe identified: 1) High accuracy structure prediction, at the resolution comparable to exper-imental methods, to enable predicted models utilization in a number of protein structure-based approaches (e.g. drug design, protein design, molecular docking, molecular replace-ment), which is now possible in Comparative Modeling (CM) cases2, 2) Structure pre-diction of proteins or protein fragments for which sequence search methods failed to find105unambiguous homologs with known structure (Fold Recognition (FR) and New Fold (NF)prediction)To meet criteria of both challenges, precise interaction scheme, sensitive to smallatomic rearrangement, should be somehow combined with high efficiency in exploringproteins conformational space. That can be achieved by combining all-atom and reducedmodeling: the multiscale modeling. Properly designed reduced models make possible veryeffective search of the protein’s conformational space3 and all-atom modeling enable exactscoring and refinement of the models. Our modeling technology is based on a such hier-archical approach2. Reduced-space search of the conformational space by the CABS3 isfollowed by a reliable transition into the all-atom resolution and by subsequent fine-tuningand assessment of the final models. Such multiscale approach enable high-resolution pro-tein structure predictions, predictions of protein interactions4, computer-aided drug designand even study of protein dynamics5.CABS computational technology has been rigorously tested during CASP6 (CriticalAssessment of Techniques for Protein Structure Prediction) world-wide experiment by theKolinski-Bujnicki group, which ranked second best among over 200 groups participating,and ranked first when the consistency of the prediction was used as a criterion (the numberof CASP targets placed in the top 20 of the best predictions)6.The design of CABS model enable easy implementation of spatial restraints. Such re-straints can be derived by a large number of bioinformatics tools from appropriate knownstructures or from experimental sources e.g. from sparse NMR data. Therefore, essentiallythe same approach is possible at various levels of protein modeling difficulty from CM, toFR and NF cases. For the sake of flexibility two basic modeling pathways were designedand one alternative to make the prediction more effective. The entire prediction pipelinecould by briefly outlined as follows (see the flowchart in the Figure 1): 1) Pre-processing:Template identification, secondary structure prediction, target- template alignments, in-put for more sophisticated user defined FR multiple alignments, 2a) Fast modeling track(easy CM cases) including fast scoring of alternative alignments and generation of spatialrestraints, 2b) Rigorous modeling track (hard CM and FR cases) including 3D threadingand generation of spatial restraints, 2c) Alternative modeling track by TRACER (hard CMand FR cases) - without prior alignments7, 3) CABS modeling, 4) Post-processing: trajec-tory clustering, selection of clusters representatives, rebuilding from reduced to all-atomrepresentation and finally all-atom models refinement and ranking.Additionally in the most difficult cases (NF) ab initio modeling based only on targetsequence can be performed (the accuracy of the resulting models is sometimes sufficientfor structure-based protein function identification).3 Automatic or Human Driven?As blind structure prediction experiments demonstrated, human expert experience and in-tuition becomes a key point to the best possible performance, especially in difficult CM andFR1. Also in high resolution structure prediction, when a fraction of an Angstrom of thefinal model resolution matters, human intervention may by helpful by manual insertions ofa template structure fragments into the final model. However, our goal is to develop fullyautomated structure prediction protocol which enable structure prediction on a genomicscale. Considering difficult modeling cases, the modeling approach without prior align-106Figure 1. The protein structure prediction flowchart - see the text.ments7, included in our pipeline, seems to be an extremely promising step towards fullyautomated modeling (errors in alignments seem to be the main source of failures in proteinstructure prediction1).References1. O. Schueler-Furman, C. Wang, P. Bradley, K. Misura, D. Baker, Progress in modelingof protein structures and interactions, Science 310, 638-42, 2005.2. S. Kmiecik, D. Gront, A. Kolinski, Towards high-resolution protein structure predic-tion. Fast refinement of reduced models with all-atom force field., BMC StructuralBiology , 7:43, 2007.3. A. Kolinski, Protein modeling and structure prediction with a reduced representation,Acta Biochim. Pol. 51, 349-371, 2004.4. M. Kurcinski, A. Kolinski, Hierarchical modeling of protein interactions, J MolModel 13, 691-8, 2007.5. DA Debe, JF Danzer, WA Goddard, A. Poleksic, STRUCTFAST: Protein sequenceremote homology detection and alignment using novel dynamic programming andprofile-profile scoring, Proteins 64, 960-967, 2006.6. S. Kmiecik, A. Kolinski, Characterization of protein-folding pathways by reduced-space modeling., Proc Natl Acad Sci USA 104, 12330-5, 2007.7. A. Kolinski, D. Gront, Comparative modeling without implicit sequence alignments.,Bioinformatics 23, 2522-27, 2007.107

Designing an Automatic Pipeline for Protein Structure Prediction

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Designing an Automatic Pipeline for Protein Structure Prediction

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