@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 {Kolinski2001, title = {Generalized comparative modeling (GENECOMP): a combination of sequence comparison, threading, and lattice modeling for protein structure prediction and refinement}, journal = {Proteins}, volume = {44}, number = {2}, year = {2001}, month = {aug}, pages = {133{\textendash}149}, abstract = {An improved generalized comparative modeling method, GENECOMP, for the refinement of threading models is developed and validated on the Fischer database of 68 probe-template pairs, a standard benchmark used to evaluate threading approaches. The basic idea is to perform ab initio folding using a lattice protein model, SICHO, near the template provided by the new threading algorithm PROSPECTOR. PROSPECTOR also provides predicted contacts and secondary structure for the template-aligned regions, and possibly for the unaligned regions by garnering additional information from other top-scoring threaded structures. Since the lowest-energy structure generated by the simulations is not necessarily the best structure, we employed two structure-selection protocols: distance geometry and clustering. In general, clustering is found to generate somewhat better quality structures in 38 of 68 cases. When applied to the Fischer database, the protocol does no harm and in a significant number of cases improves upon the initial threading model, sometimes dramatically. The procedure is readily automated and can be implemented on a genomic scale.}, keywords = {Algorithms, Chemical, Combinatorial Chemistry Techniques, Combinatorial Chemistry Techniques: methods, Computational Biology, Computational Biology: methods, Computer Simulation, Databases, Factual, Models, Molecular, Monte Carlo Method, Protein Folding, Proteins, Proteins: chemistry, Sequence Alignment, Sequence Alignment: methods}, issn = {0887-3585}, url = {http://www.ncbi.nlm.nih.gov/pubmed/11391776}, author = {Andrzej Koli{\'n}ski and Marcos Betancourt and Daisuke Kihara and Piotr Rotkiewicz and Jeffrey Skolnick} } @article {Hu1997, title = {Improved method for prediction of protein backbone U-turn positions and major secondary structural elements between U-turns}, journal = {Proteins}, volume = {29}, number = {4}, year = {1997}, pages = {443{\textendash}460}, abstract = {A new and more accurate method has been developed for predicting the backbone U-turn positions (where the chain reverses global direction) and the dominant secondary structure elements between U-turns in globular proteins. The current approach uses sequence-specific secondary structure propensities and multiple sequence information. The latter plays an important role in the enhanced success of this approach. Application to two sets (total 108) of small to medium-sized, single-domain proteins indicates that approximately 94\% of the U-turn locations are correctly predicted within three residues, as are 88\% of dominant secondary structure elements. These results are significantly better than our previous method (Kolinski et al., Proteins 27:290-308, 1997). The current study strongly suggests that the U-turn locations are primarily determined by local interactions. Furthermore, both global length constraints and local interactions contribute significantly to the determination of the secondary structure types between U-turns. Accurate U-turn predictions are crucial for accurate secondary structure predictions in the current method. Protein structure modeling, tertiary structure predictions, and possibly, fold recognition should benefit from the predicted structural data provided by this new method.}, keywords = {Amino Acid, Amino Acid Sequence, Amino Acids, Amino Acids: chemistry, Data Interpretation, Models, Molecular, Molecular Sequence Data, Protein Structure, Proteins, Proteins: chemistry, Reproducibility of Results, Secondary, Sequence Alignment, Sequence Alignment: methods, Sequence Alignment: statistics \& numerical data, Sequence Homology, Statistical}, issn = {0887-3585}, url = {http://www.ncbi.nlm.nih.gov/pubmed/9408942}, author = {Wei-Ping Hu and Andrzej Koli{\'n}ski and Jeffrey Skolnick} }