E & R
Site Map

16th, Nov. 2008 updated

 Frontiers of Computational Biology on Protein Structures

Date: 2nd, Dec., 2008 (13:30 - 16:50)
Place: Meeting Room, 2F, Science Building #3, Ochanomizu University 2-1-1, Otsuka, Bunkyo, Tokyo 112-8610, Japan
Sponsor: Training Program in Bioinformatics for Women Graduate Students and Center of Informational Biology at Ochanomizu University

Proteins, not genes, play acting roles in the cells. The mechanistic and evolutionary studies of the proteins help understand how the function of each protein contributes to the behavior of the cell. In this seminar, we invited four researchers in computational biology on protein structures and introduce the frontline of the field. We welcome students and researchers in molecular biophysics, molecular biology and related fields.



Prediction of Protein-RNA, Protein-Protein and Protein-Ligand Interfaces Based on Protein Sequences and 3D-Structures
Kei Yura (Center for Informational Biology, Ochanomizu Univ.)
A vast amount of DNA sequence data, protein three-dimensional (3D) structure data, and RNA expression data have been produced by the efforts of genome sequencing, structural genomics, and omics projects. We are now at the stage where comprehensive views of cell activity and molecular mechanisms of life can be deduced. But in reality, we are inundated with massive amounts of data and are still in the process of finding ways to fully utilize the data. Here, I would like to present our observations on the growth of protein 3D structure data and our effort to deduce the functions from the 3D structures. We found that the 3D structure of quite a high proportion of proteins derived from genome sequences can be now predicted and methods to predict the functions from 3D structures are in high demand. We have developed a method to predict RNA, protein and ligand interfaces on protein surfaces based on those 3D structures and DNA sequences with relatively high accuracy. The method and its application are presented.

Protein Conformational Dynamics in Crystal Lattice
Osamu Miyashita (Univ. of Arizona)
Decades of investigation have confirmed the importance of protein?@dynamics to functions, and X-ray crystallography has been the most?@dominant source of information. It provides information on structure and dynamics of biological molecules, which are routinely used to discuss the structure/function relationship of proteins. However, such an interpretation is not straightforward. Proteins are flexible and take different conformations in solution, and there are many examples where different conformations of a protein were solved by X-ray crystallography. X-ray structures represent one snapshot of the conformational ensemble, which is selected by crystal packing. There is no standard approach to reconstruction of the ensemble in solution from X-ray structures. In this talk, I will present our recent studies to enhance our knowledge on protein structure and dynamics in crystal lattice to refine our interpretation of X-ray data to discuss protein structure/function relationship.

Evolution: A Bridge between Integrative Systems Biology and Protein Design
Olivier Lichtarge (Baylor College of Medicine)
Protein-protein interactions are the elementary links from which molecular pathways and cellular networks are built. A complete description of the functional surfaces that determine protein binding still eludes us, however. The Evolutionary Trace (ET) approach to this problem is to integrate information from protein sequences, evolutionary trees, and structures to rank the relative evolutionary importance of protein sequence amino acids. Retrospective analyses as well as bona fide experiments show that the top-ranked and thus most evolutionarily important amino acids have important properties. They cluster spatially; match functional sites in the structure; control activity and specificity; and thus reveal elementary units of function and of interaction. The systematic computational discovery of these most evolutionarily important amino acid residues enables experimentalists to rationally and efficiently target mutagenesis in order to re-design activity, for example to create separation of function mutations or to rewire protein binding. In practice, this permits to identify some of the molecular determinants of function in any protein family that is sufficiently large. This is shown by examples drawn from DNA maintenance and stability pathways, transcriptional regulation pathways, and signaling pathways?|including initial experiments that identify and reprogram separately the key residues that mediate ligand binding and those that mediate efferent specificity in G protein-coupled receptors. The scalability and generality of ET suggest that widespread functional site characterization and engineering are within reach, opening a path for proteome-wide annotation of protein function and for the rational molecular manipulation of the cellular pathways they control.

Unveiling Conformational Changes of Biological Molecules Using Multiscale Modeling and Multiresolution Experiments
Florence Tama (Univ. of Arizona)
Multipronged approaches have recently gained interest for tackling structural problems related to large biological complexes. Structural dynamical information is often obtained by low-resolution experimental techniques, such as Cryo Electron Microscopy (cryo-EM), Small Angle X-ray Scattering (SAXS) and Fluorescence Resonance Energy Transfer (FRET). Each of these techniques offers different advantages and meet with different pitfalls, artifacts and limitations. Therefore a more accurate description could be obtained if all pieces of experimental data were taken together to annotate conformational states. To achieve this goal we will present our current developments of multi-resolution/multi-scale computational tools to interpret conformational changes of biological molecules based on cryo-EM, SAXS or distance constraints. Normal Mode Analysis or Molecular Dynamics simulations are used to deform, in a physical manner, X-ray structures to fit low-resolution data. Using simulated data, we will show that such approaches are successful to predict structures in the range of 2~3 A resolution.