@article {Kolinski2007, title = {Comparative modeling without implicit sequence alignments}, journal = {Bioinformatics (Oxford, England)}, volume = {23}, number = {19}, year = {2007}, pages = {2522{\textendash}7}, abstract = {

MOTIVATION: The number of known protein sequences is about thousand times larger than the number of experimentally solved 3D structures. For more than half of the protein sequences a close or distant structural analog could be identified. The key starting point in a classical comparative modeling is to generate the best possible sequence alignment with a template or templates. With decreasing sequence similarity, the number of errors in the alignments increases and these errors are the main causes of the decreasing accuracy of the molecular models generated. Here we propose a new approach to comparative modeling, which does not require the implicit alignment - the model building phase explores geometric, evolutionary and physical properties of a template (or templates). RESULTS: The proposed method requires prior identification of a template, although the initial sequence alignment is ignored. The model is built using a very efficient reduced representation search engine CABS to find the best possible superposition of the query protein onto the template represented as a 3D multi-featured scaffold. The criteria used include: sequence similarity, predicted secondary structure consistency, local geometric features and hydrophobicity profile. For more difficult cases, the new method qualitatively outperforms existing schemes of comparative modeling. The algorithm unifies de novo modeling, 3D threading and sequence-based methods. The main idea is general and could be easily combined with other efficient modeling tools as Rosetta, UNRES and others.

}, keywords = {Algorithms, Amino Acid Sequence, Chemical, Computer Simulation, Models, Molecular, Molecular Sequence Data, Protein, Protein Conformation, Protein: methods, Proteins, Proteins: chemistry, Proteins: ultrastructure, Sequence Alignment, Sequence Alignment: methods, Sequence Analysis}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btm380}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17660201}, author = {Andrzej Koli{\'n}ski and Dominik Gront} } @article {Gront2006, title = {BioShell{\textendash}a package of tools for structural biology computations}, journal = {Bioinformatics (Oxford, England)}, volume = {22}, number = {5}, year = {2006}, month = {mar}, pages = {621{\textendash}622}, abstract = {

SUMMARY: BioShell is a suite of programs performing common tasks accompanying protein structure modeling. BioShell design is based on UNIX shell flexibility and should be used as its extension. Using BioShell various molecular modeling procedures can be integrated in a single pipeline. AVAILABILITY: BioShell package can be downloaded from its website http://biocomp.chem.uw.edu.pl/BioShell and these pages provide many examples and a detailed documentation for the newest version.

}, keywords = {Chemical, Computational Biology, Computational Biology: methods, Computer Simulation, Databases, Models, Protein, Protein: methods, Proteins, Proteins: analysis, Proteins: chemistry, Proteins: classification, Sequence Alignment, Sequence Alignment: methods, Sequence Analysis, Software}, issn = {1367-4803}, doi = {10.1093/bioinformatics/btk037}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16407320}, author = {Dominik Gront and Andrzej Koli{\'n}ski} } @article {Gront2005, title = {HCPM{\textendash}program for hierarchical clustering of protein models}, journal = {Bioinformatics}, volume = {21}, number = {14}, year = {2005}, pages = {3179{\textendash}80}, abstract = {HCPM is a tool for clustering protein structures from comparative modeling, ab initio structure prediction, etc. A hierarchical clustering algorithm is designed and tested, and a heuristic is provided for an optimal cluster selection. The method has been successfully tested during the CASP6 experiment.}, keywords = {Algorithms, Chemical, Cluster Analysis, Computer Simulation, Internet, Models, Molecular, Protein, Protein: methods, Proteins, Proteins: analysis, Proteins: chemistry, Sequence Alignment, Sequence Alignment: methods, Sequence Analysis, Software, User-Computer Interface}, issn = {1367-4803}, doi = {10.1093/bioinformatics/bti450}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15840705}, author = {Dominik Gront and Andrzej Koli{\'n}ski} } @article {Gront2005a, title = {A new approach to prediction of short-range conformational propensities in proteins}, journal = {Bioinformatics (Oxford, England)}, volume = {21}, number = {7}, year = {2005}, pages = {981{\textendash}987}, abstract = {

MOTIVATION: Knowledge-based potentials are valuable tools for protein structure modeling and evaluation of the quality of the structure prediction obtained by a variety of methods. Potentials of such type could be significantly enhanced by a proper exploitation of the evolutionary information encoded in related protein sequences. The new potentials could be valuable components of threading algorithms, ab-initio protein structure prediction, comparative modeling and structure modeling based on fragmentary experimental data. RESULTS: A new potential for scoring local protein geometry is designed and evaluated. The approach is based on the similarity of short protein fragments measured by an alignment of their sequence profiles. Sequence specificity of the resulting energy function has been compared with the specificity of simpler potentials using gapless threading and the ability to predict specific geometry of protein fragments. Significant improvement in threading sensitivity and in the ability to generate sequence-specific protein-like conformations has been achieved.

}, keywords = {Algorithms, Amino Acid, Artificial Intelligence, Chemical, Computer Simulation, Databases, Gas Chromatography-Mass Spectrometry, Gas Chromatography-Mass Spectrometry: methods, Models, Protein, Protein Conformation, Protein: methods, Proteins, Proteins: analysis, Proteins: chemistry, Sequence Alignment, Sequence Alignment: methods, Sequence Analysis, Sequence Homology, Structure-Activity Relationship}, issn = {1367-4803}, doi = {10.1093/bioinformatics/bti080}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15509604}, author = {Dominik Gront and Andrzej Koli{\'n}ski} } @article {Zhang2003, title = {TOUCHSTONE II: a new approach to ab initio protein structure prediction}, journal = {Biophysical Journal}, volume = {85}, number = {2}, year = {2003}, pages = {1145{\textendash}64}, abstract = {We have developed a new combined approach for ab initio protein structure prediction. The protein conformation is described as a lattice chain connecting C(alpha) atoms, with attached C(beta) atoms and side-chain centers of mass. The model force field includes various short-range and long-range knowledge-based potentials derived from a statistical analysis of the regularities of protein structures. The combination of these energy terms is optimized through the maximization of correlation for 30 x 60,000 decoys between the root mean square deviation (RMSD) to native and energies, as well as the energy gap between native and the decoy ensemble. To accelerate the conformational search, a newly developed parallel hyperbolic sampling algorithm with a composite movement set is used in the Monte Carlo simulation processes. We exploit this strategy to successfully fold 41/100 small proteins (36 approximately 120 residues) with predicted structures having a RMSD from native below 6.5 A in the top five cluster centroids. To fold larger-size proteins as well as to improve the folding yield of small proteins, we incorporate into the basic force field side-chain contact predictions from our threading program PROSPECTOR where homologous proteins were excluded from the data base. With these threading-based restraints, the program can fold 83/125 test proteins (36 approximately 174 residues) with structures having a RMSD to native below 6.5 A in the top five cluster centroids. This shows the significant improvement of folding by using predicted tertiary restraints, especially when the accuracy of side-chain contact prediction is \>20\%. For native fold selection, we introduce quantities dependent on the cluster density and the combination of energy and free energy, which show a higher discriminative power to select the native structure than the previously used cluster energy or cluster size, and which can be used in native structure identification in blind simulations. These procedures are readily automated and are being implemented on a genomic scale.}, keywords = {Algorithms, Amino Acid Sequence, Computer Simulation, Crystallography, Crystallography: methods, Energy Transfer, Models, Molecular, Molecular Sequence Data, Protein, Protein Conformation, Protein Folding, Protein Structure, Protein: methods, Proteins, Proteins: chemistry, Secondary, Sequence Analysis, Software, Static Electricity, Statistical}, issn = {0006-3495}, doi = {10.1016/S0006-3495(03)74551-2}, url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1303233\&tool=pmcentrez\&rendertype=abstract}, author = {Yang Zhang and Andrzej Koli{\'n}ski and Jeffrey Skolnick} }