%0 Journal Article %J Journal of c\Computational Chemistry %D 2011 %T CABS-NMR–De novo tool for rapid global fold determination from chemical shifts, residual dipolar couplings and sparse methyl-methyl NOEs %A Dorota Latek %A Andrzej Koliński %K Algorithms %K Animals %K Cattle %K Magnetic Resonance Spectroscopy %K Magnetic Resonance Spectroscopy: methods %K Models %K Molecular %K Monte Carlo Method %K Protein Conformation %K Protein Folding %K Proteins %K Proteins: chemistry %K S100 Proteins %K S100 Proteins: chemistry %X Recent development of nuclear magnetic resonance (NMR) techniques provided new types of structural restraints that can be successfully used in fast and low-cost global protein fold determination. Here, we present CABS-NMR, an efficient protein modeling tool, which takes advantage of such structural restraints. The restraints are converted from original NMR data to fit the coarse grained protein representation of the C-Alpha-Beta-Side-group (CABS) algorithm. CABS is a Monte Carlo search algorithm that uses a knowledge-based force field. Its versatile structure enables a variety of protein-modeling protocols, including purely de novo folding, folding guided by restraints derived from template structures or, structure assembly based on experimental data. In particular, CABS-NMR uses the distance and angular restraints set derived from various NMR experiments. This new modeling technique was successfully tested in structure determination of 10 globular proteins of size up to 216 residues, for which sparse NMR data were available. Additional detailed analysis was performed for a S100A1 protein. Namely, we successfully predicted Nuclear Overhauser Effect signals on the basis of low-energy structures obtained from chemical shifts by CABS-NMR. It has been observed that utility of chemical shifts and other types of experimental data (i.e. residual dipolar couplings and methyl-methyl Nuclear Overhauser Effect signals) in the presented modeling pipeline depends mainly on size of a protein and complexity of its topology. In this work, we have provided tools for either post-experiment processing of various kinds of NMR data or fast and low-cost structural analysis in the still challenging field of new fold predictions. %B Journal of c\Computational Chemistry %V 32 %P 536–44 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/20806263 %R 10.1002/jcc.21640 %0 Journal Article %J BMC Structural Biology %D 2008 %T Contact prediction in protein modeling: scoring, folding and refinement of coarse-grained models %A Dorota Latek %A Andrzej Koliński %K Algorithms %K Caspase 6 %K Caspase 6: chemistry %K Caspase 6: genetics %K Computer Simulation %K Databases %K Models %K Molecular %K Protein %K Protein Folding %K Proteins %K Proteins: chemistry %K Proteins: genetics %X

Several different methods for contact prediction succeeded within the Sixth Critical Assessment of Techniques for Protein Structure Prediction (CASP6). The most relevant were non-local contact predictions for targets from the most difficult categories: fold recognition-analogy and new fold. Such contacts could provide valuable structural information in case a template structure cannot be found in the PDB.

%B BMC Structural Biology %V 8 %P 36 %8 jan %G eng %U http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2527566&tool=pmcentrez&rendertype=abstract %R 10.1186/1472-6807-8-36 %0 Journal Article %J Journal of Computational Chemistry %D 2007 %T Protein structure prediction: combining de novo modeling with sparse experimental data %A Dorota Latek %A Dariusz Ekonomiuk %A Andrzej Koliński %K Algorithms %K Computer Simulation %K Magnetic Resonance Spectroscopy %K Models %K Molecular %K Protein Folding %K Protein Structure %K Proteins %K Proteins: chemistry %K Secondary %K Software %X Routine structure prediction of new folds is still a challenging task for computational biology. The challenge is not only in the proper determination of overall fold but also in building models of acceptable resolution, useful for modeling the drug interactions and protein-protein complexes. In this work we propose and test a comprehensive approach to protein structure modeling supported by sparse, and relatively easy to obtain, experimental data. We focus on chemical shift-based restraints from NMR, although other sparse restraints could be easily included. In particular, we demonstrate that combining the typical NMR software with artificial intelligence-based prediction of secondary structure enhances significantly the accuracy of the restraints for molecular modeling. The computational procedure is based on the reduced representation approach implemented in the CABS modeling software, which proved to be a versatile tool for protein structure prediction during the CASP (CASP stands for critical assessment of techniques for protein structure prediction) experiments (see http://predictioncenter/CASP6/org). The method is successfully tested on a small set of representative globular proteins of different size and topology, including the two CASP6 targets, for which the required NMR data already exist. The method is implemented in a semi-automated pipeline applicable to a large scale structural annotation of genomic data. Here, we limit the computations to relatively small set. This enabled, without a loss of generality, a detailed discussion of various factors determining accuracy of the proposed approach to the protein structure prediction. %B Journal of Computational Chemistry %V 28 %P 1668–76 %8 jul %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/17342709 %R 10.1002/jcc.20657