%0 Journal Article %1 ISI:A1993KH38600005 %A KARPEN, ME %A TOBIAS, DJ %A BROOKS, CL %C 1155 16TH ST, NW, WASHINGTON, DC 20036 %D 1993 %I AMER CHEMICAL SOC %J BIOCHEMISTRY %K clustering %N 2 %P 412-420 %T STATISTICAL CLUSTERING-TECHNIQUES FOR THE ANALYSIS OF LONG MOLECULAR-DYNAMICS TRAJECTORIES - ANALYSIS OF 2.2-NS TRAJECTORIES OF YPGDV %V 32 %X The microscopic interactions and mechanisms leading to nascent protein folding events are generally unknown. While such short time-scale events are difficult to study experimentally, molecular dynamics simulations of peptides can provide a useful model for studying events related to protein folding initiation. Recently, two extremely long molecular dynamics simulations (2.2 ns each) were carried out on the pentapeptide Tyr-Pro-Gly-Asp-Val Tobias, D.J., Mertz, J.E., & Brooks, C.L., III (1991) Biochemistry 30, 6054-6058 that forms stable reverse turns in solution. Tobias et al. examined folding events in this large system (approximately 30 000 conformations) using traditional methods of trajectory analysis. The shear magnitude of this problem prompted us to develop an automated approach, based on self-organizing neural nets, to extract the key features of the molecular dynamics trajectory. The neural net is used to perform conformational clustering, which reduces the complexity of a system while minimizing the loss of information. The conformations were grouped together using distances in dihedral angle space as a measure of conformational similarity. The resulting clusters represent `'conformational states'', and transitions between these states were examined to identify mechanisms of conformational change. Many conformational changes involved the rotation of only a single dihedral angle, but concerted angle changes were also found. Most of the conformational information in the 30 000 samples from the full trajectories was retained in the relatively few resultant clusters, providing a powerful tool for analysis of an expanding base of large molecular simulations.

@article{ISI:A1993KH38600005, abstract = {{The microscopic interactions and mechanisms leading to nascent protein folding events are generally unknown. While such short time-scale events are difficult to study experimentally, molecular dynamics simulations of peptides can provide a useful model for studying events related to protein folding initiation. Recently, two extremely long molecular dynamics simulations (2.2 ns each) were carried out on the pentapeptide Tyr-Pro-Gly-Asp-Val {[}Tobias, D.J., Mertz, J.E., \& Brooks, C.L., III (1991) Biochemistry 30, 6054-6058] that forms stable reverse turns in solution. Tobias et al. examined folding events in this large system (approximately 30 000 conformations) using traditional methods of trajectory analysis. The shear magnitude of this problem prompted us to develop an automated approach, based on self-organizing neural nets, to extract the key features of the molecular dynamics trajectory. The neural net is used to perform conformational clustering, which reduces the complexity of a system while minimizing the loss of information. The conformations were grouped together using distances in dihedral angle space as a measure of conformational similarity. The resulting clusters represent `'conformational states'', and transitions between these states were examined to identify mechanisms of conformational change. Many conformational changes involved the rotation of only a single dihedral angle, but concerted angle changes were also found. Most of the conformational information in the 30 000 samples from the full trajectories was retained in the relatively few resultant clusters, providing a powerful tool for analysis of an expanding base of large molecular simulations.}}, added-at = {2009-01-06T08:47:50.000+0100}, address = {{1155 16TH ST, NW, WASHINGTON, DC 20036}}, affiliation = {{CARNEGIE MELLON UNIV,DEPT CHEM,PITTSBURGH,PA 15213.}}, author = {KARPEN, ME and TOBIAS, DJ and BROOKS, CL}, biburl = {https://www.bibsonomy.org/bibtex/279254d45ccfbd41dfe7b70f3af3e8f9d/huiyangsfsu}, cited-references = {{1987, STAT LIBRARY FORTRAN. ADZHUBEI AA, 1987, J BIOMOL STRUCT DYN, V5, P689. BROOKS BR, 1983, J COMPUT CHEM, V4, P187. BROOKS CL, 1989, J MOL BIOL, V208, P159. CARPENTER GA, 1987, APPL OPTICS, V26, P4919. CZERMINSKI R, 1989, P NATL ACAD SCI USA, V86, P6963. DICAPUA FM, 1990, J AM CHEM SOC, V112, P6768. DOBSON CM, 1991, CURR OPIN STRUC BIOL, V1, P22. DUDA RO, 1973, PATTERN CLASSIFICATI. DYSON HJ, 1988, J MOL BIOL, V201, P161. EVERITT B, 1980, CLUSTER ANAL. HERMANS J, 1990, CRYSTALLOGRAPHIC MOD, P95. JORGENSEN WL, 1983, J CHEM PHYS, V79, P926. KARPEN ME, 1991, THESIS CASE W RESERV. KARPEN ME, 1992, IN PRESS PROTEIN SCI. KIM PS, 1990, ANNU REV BIOCHEM, V59, P631. LAZARIDIS T, 1991, J CHEM PHYS, V95, P7615. LEVITT M, 1983, J MOL BIOL, V168, P621. MCKELVEY DR, 1991, J PROTEIN CHEM, V10, P265. PAO YH, 1989, ADAPTIVE PATTERN REC. POLINSKY A, 1992, BIOPOLYMERS, V32, P399. RICHARDSON JS, 1978, P NATL ACAD SCI USA, V75, P2574. RICHARDSON JS, 1989, PREDICTION PROTEIN S, P1. ROOMAN MJ, 1990, J MOL BIOL, V213, P327. SCHELLMAN C, 1980, PROTEIN FOLDING, P53. SNEDDON SF, 1989, J MOL BIOL, V209, P817. SOMAN KV, 1991, BIOPOLYMERS, V31, P1351. SUNDARALINGAM M, 1989, SCIENCE, V244, P1333. TAKANE Y, 1977, PSYCHOMETRIKA, V42, P7. TIRADORIVES J, 1991, BIOCHEMISTRY-US, V30, P3864. TOBIAS DJ, 1991, BIOCHEMISTRY-US, V30, P6054. TOBIAS DJ, 1991, BIOCHEMISTRY-US, V30, P6059. UNGER R, 1989, PROTEINS, V5, P355. ZIMMERMAN SS, 1977, MACROMOLECULES, V10, P1.}}, doc-delivery-number = {{KH386}}, interhash = {4f2a05a1be8282faa274e500ea1d47f8}, intrahash = {79254d45ccfbd41dfe7b70f3af3e8f9d}, issn = {{0006-2960}}, journal = {{BIOCHEMISTRY}}, journal-iso = {{Biochemistry}}, keywords = {clustering}, keywords-plus = {{ALPHA-HELIX; WATER; PROTEIN; THERMODYNAMICS; PENTAPEPTIDE; PEPTIDE; CHARMM; MOTION; ANALOG}}, language = {{English}}, month = {{JAN 19}}, note = {Statistical clustering techniques for the analysis of long molecular dynamics trajectories: analysis of 2.2-ns trajectories of YPGDV Mary E. Karpen, Douglas J. Tobias, Charles L. Brooks pp 412–420 Publication Date: January 1993 () DOI: 10.1021/bi00053a005}, number = {{2}}, number-of-cited-references = {{34}}, pages = {{412-420}}, publisher = {{AMER CHEMICAL SOC}}, subject-category = {{Biochemistry \& Molecular Biology}}, times-cited = {{98}}, timestamp = {2009-01-06T08:47:50.000+0100}, title = {{STATISTICAL CLUSTERING-TECHNIQUES FOR THE ANALYSIS OF LONG MOLECULAR-DYNAMICS TRAJECTORIES - ANALYSIS OF 2.2-NS TRAJECTORIES OF YPGDV}}, type = {{Article}}, unique-id = {{ISI:A1993KH38600005}}, volume = {{32}}, year = {{1993}} }