@proceedings {Steczkiewicz2011, title = {Human telomerase model shows the role of the TEN domain in advancing the double helix for the next polymerization step}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {108}, number = {23}, year = {2011}, month = {jun}, pages = {9443{\textendash}8}, abstract = {Telomerases constitute a group of specialized ribonucleoprotein enzymes that remediate chromosomal shrinkage resulting from the "end-replication" problem. Defects in telomere length regulation are associated with several diseases as well as with aging and cancer. Despite significant progress in understanding the roles of telomerase, the complete structure of the human telomerase enzyme bound to telomeric DNA remains elusive, with the detailed molecular mechanism of telomere elongation still unknown. By application of computational methods for distant homology detection, comparative modeling, and molecular docking, guided by available experimental data, we have generated a three-dimensional structural model of a partial telomerase elongation complex composed of three essential protein domains bound to a single-stranded telomeric DNA sequence in the form of a heteroduplex with the template region of the human RNA subunit, TER. This model provides a structural mechanism for the processivity of telomerase and offers new insights into elongation. We conclude that the RNADNA heteroduplex is constrained by the telomerase TEN domain through repeated extension cycles and that the TEN domain controls the process by moving the template ahead one base at a time by translation and rotation of the double helix. The RNA region directly following the template can bind complementarily to the newly synthesized telomeric DNA, while the template itself is reused in the telomerase active site during the next reaction cycle. This first structural model of the human telomerase enzyme provides many details of the molecular mechanism of telomerase and immediately provides an important target for rational drug design.}, keywords = {Amino Acid, Amino Acid Sequence, Binding Sites, Binding Sites: genetics, Catalytic Domain, Computer Simulation, DNA, DNA: chemistry, DNA: genetics, DNA: metabolism, Humans, Kinetics, Models, Molecular, Molecular Sequence Data, Nucleic Acid Conformation, Nucleic Acid Heteroduplexes, Nucleic Acid Heteroduplexes: chemistry, Nucleic Acid Heteroduplexes: genetics, Nucleic Acid Heteroduplexes: metabolism, Polymerization, Protein Binding, Protein Structure, RNA, RNA: chemistry, RNA: genetics, RNA: metabolism, Secondary, Sequence Homology, Telomerase, Telomerase: chemistry, Telomerase: genetics, Telomerase: metabolism, Telomere, Telomere: chemistry, Telomere: genetics, Telomere: metabolism, Tertiary}, issn = {1091-6490}, doi = {10.1073/pnas.1015399108}, url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3111281\&tool=pmcentrez\&rendertype=abstract}, author = {Kamil Steczkiewicz and Michael T. Zimmermann and Mateusz Kurcinski and Benjamin A. Lewis and Drena Dobbs and Andrzej Kloczkowski and Robert L. Jernigan and Andrzej Koli{\'n}ski and Krzysztof Ginalski} } @article {Gniewek2011a, title = {Multibody coarse-grained potentials for native structure recognition and quality assessment of protein models}, journal = {Proteins}, volume = {79}, number = {6}, year = {2011}, month = {jun}, pages = {1923{\textendash}9}, abstract = {Multibody potentials have been of much interest recently because they take into account three dimensional interactions related to residue packing and capture the cooperativity of these interactions in protein structures. Our goal was to combine long range multibody potentials and short range potentials to improve recognition of native structure among misfolded decoys. We optimized the weights for four-body nonsequential, four-body sequential, and short range potentials to obtain optimal model ranking results for threading and have compared these data against results obtained with other potentials (26 different coarse-grained potentials from the Potentials {\textquoteright}R{\textquoteright}Us web server have been used). Our optimized multibody potentials outperform all other contact potentials in the recognition of the native structure among decoys, both for models from homology template-based modeling and from template-free modeling in CASP8 decoy sets. We have compared the results obtained for this optimized coarse-grained potentials, where each residue is represented by a single point, with results obtained by using the DFIRE potential, which takes into account atomic level information of proteins. We found that for all proteins larger than 80 amino acids our optimized coarse-grained potentials yield results comparable to those obtained with the atomic DFIRE potential.}, keywords = {Amino Acids, Amino Acids: chemistry, Computational Biology, Computational Biology: methods, Models, Molecular, Protein Conformation, Proteins, Proteins: chemistry}, issn = {1097-0134}, doi = {10.1002/prot.23015}, url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3093657\&tool=pmcentrez\&rendertype=abstract}, author = {Pawel Gniewek and Sumudu P. Leelananda and Andrzej Koli{\'n}ski and Robert L. Jernigan and Andrzej Kloczkowski} } @article {Kloczkowski2009, title = {Distance matrix-based approach to protein structure prediction}, journal = {Journal of Structural and Functional Genomics}, volume = {10}, number = {1}, year = {2009}, month = {mar}, pages = {67{\textendash}81}, abstract = {

Much structural information is encoded in the internal distances; a distance matrix-based approach can be used to predict protein structure and dynamics, and for structural refinement. Our approach is based on the square distance matrix D = [r(ij)(2)] containing all square distances between residues in proteins. This distance matrix contains more information than the contact matrix C, that has elements of either 0 or 1 depending on whether the distance r (ij) is greater or less than a cutoff value r (cutoff). We have performed spectral decomposition of the distance matrices D = sigma lambda(k)V(k)V(kT), in terms of eigenvalues lambda kappa and the corresponding eigenvectors v kappa and found that it contains at most five nonzero terms. A dominant eigenvector is proportional to r (2){\textendash}the square distance of points from the center of mass, with the next three being the principal components of the system of points. By predicting r (2) from the sequence we can approximate a distance matrix of a protein with an expected RMSD value of about 7.3 A, and by combining it with the prediction of the first principal component we can improve this approximation to 4.0 A. We can also explain the role of hydrophobic interactions for the protein structure, because r is highly correlated with the hydrophobic profile of the sequence. Moreover, r is highly correlated with several sequence profiles which are useful in protein structure prediction, such as contact number, the residue-wise contact order (RWCO) or mean square fluctuations (i.e. crystallographic temperature factors). We have also shown that the next three components are related to spatial directionality of the secondary structure elements, and they may be also predicted from the sequence, improving overall structure prediction. We have also shown that the large number of available HIV-1 protease structures provides a remarkable sampling of conformations, which can be viewed as direct structural information about the dynamics. After structure matching, we apply principal component analysis (PCA) to obtain the important apparent motions for both bound and unbound structures. There are significant similarities between the first few key motions and the first few low-frequency normal modes calculated from a static representative structure with an elastic network model (ENM) that is based on the contact matrix C (related to D), strongly suggesting that the variations among the observed structures and the corresponding conformational changes are facilitated by the low-frequency, global motions intrinsic to the structure. Similarities are also found when the approach is applied to an NMR ensemble, as well as to atomic molecular dynamics (MD) trajectories. Thus, a sufficiently large number of experimental structures can directly provide important information about protein dynamics, but ENM can also provide a similar sampling of conformations. Finally, we use distance constraints from databases of known protein structures for structure refinement. We use the distributions of distances of various types in known protein structures to obtain the most probable ranges or the mean-force potentials for the distances. We then impose these constraints on structures to be refined or include the mean-force potentials directly in the energy minimization so that more plausible structural models can be built. This approach has been successfully used by us in 2006 in the CASPR structure refinement (http://predictioncenter.org/caspR).

}, keywords = {Binding Sites, Computer Simulation, Databases, Models, Molecular, Principal Component Analysis, Protein, Protein Conformation, Proteins, Proteins: chemistry}, issn = {1570-0267}, doi = {10.1007/s10969-009-9062-2}, url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3018873\&tool=pmcentrez\&rendertype=abstract}, author = {Andrzej Kloczkowski and Robert L. Jernigan and Zhijun Wu and Guang Song and Lei Yang and Andrzej Koli{\'n}ski and Piotr Pokarowski} } @article {Sen2008, title = {Predicting the complex structure and functional motions of the outer membrane transporter and signal transducer FecA}, journal = {Biophysical journal}, volume = {94}, number = {7}, year = {2008}, month = {apr}, pages = {2482{\textendash}91}, publisher = {Elsevier}, abstract = {Escherichia coli requires an efficient transport and signaling system to successfully sequester iron from its environment. FecA, a TonB-dependent protein, serves a critical role in this process: first, it binds and transports iron in the form of ferric citrate, and second, it initiates a signaling cascade that results in the transcription of several iron transporter genes in interaction with inner membrane proteins. The structure of the plug and barrel domains and the periplasmic N-terminal domain (NTD) are separately available. However, the linker connecting the plug and barrel and the NTD domains is highly mobile, which may prevent the determination of the FecA structure as a whole assembly. Here, we reduce the conformation space of this linker into most probable structural models using the modeling tool CABS, then apply normal-mode analysis to investigate the motions of the whole structure of FecA by using elastic network models. We relate the FecA domain motions to the outer-inner membrane communication, which initiates transcription. We observe that the global motions of FecA assign flexibility to the TonB box and the NTD, and control the exposure of the TonB box for binding to the TonB inner membrane protein, suggesting how these motions relate to FecA function. Our simulations suggest the presence of a communication between the loops on both ends of the protein, a signaling mechanism by which a signal could be transmitted by conformational transitions in response to the binding of ferric citrate.}, keywords = {Cell Membrane, Cell Membrane: chemistry, Cell Surface, Cell Surface: chemistry, Cell Surface: ultrastructure, Chemical, Computer Simulation, Escherichia coli Proteins, Escherichia coli Proteins: chemistry, Escherichia coli Proteins: ultrastructure, Models, Molecular, Motion, Protein Conformation, Receptors}, isbn = {5152944294}, issn = {1542-0086}, doi = {10.1529/biophysj.107.116046}, url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2267147\&tool=pmcentrez\&rendertype=abstract}, author = {Taner Z. Sen and Margaret Kloster and Robert L. Jernigan and Andrzej Koli{\'n}ski and Janusz M. Bujnicki and Andrzej Kloczkowski} } @article {Pokarowski2005, title = {Inferring ideal amino acid interaction forms from statistical protein contact potentials}, journal = {Proteins}, volume = {59}, number = {1}, year = {2005}, month = {apr}, pages = {49{\textendash}57}, abstract = {We have analyzed 29 different published matrices of protein pairwise contact potentials (CPs) between amino acids derived from different sets of proteins, either crystallographic structures taken from the Protein Data Bank (PDB) or computer-generated decoys. Each of the CPs is similar to 1 of the 2 matrices derived in the work of Miyazawa and Jernigan (Proteins 1999;34:49-68). The CP matrices of the first class can be approximated with a correlation of order 0.9 by the formula e(ij) = h(i) + h(j), 1