DESCRIPTION: This track covers the emerging fields of evolutionary bioinformatics and computational structural genomics, and the interface between the two.
SUMMARY:In order to establish an integrated study of the function of proteins, it is necessary to combine sequence, structure, and functional analysis in the context of their evolutionary history and their role in the organismal genome. This track will encompass all aspects of structural genomics. The track will also cover use of evolutionary analysis to infer and explain the structure, function, and the evolutionary dynamics of protein and genome sequences.
DETAILS:The genomic data available to computational biologists represents the product of the complex processes of evolution. In particular, the forces of mutation, duplication, and selection have acted to sculpt modern protein sequence and structure in the context of changing functional requirements. Just as crystallographers are able to determine protein structures through an analysis of X-ray diffraction patterns, scientists are learning to read the evolutionary history of proteins in order to infer and explain both structures and functions. This pursuit depends on the development of new computational approaches in order to make optimal use of genomic data, and requires interaction with experiment for comparison and verification of computational results.
With the realization that genomes provide a new vantage on protein structure studies, there has been intense interest in understanding structural biology in a genomic context. Each complete genome codes for a full set of functions necessary for a whole organism. This set of proteins can also be considered as a collection of protein folds sufficient for the required cellular activities such as metabolism, replication, and communication. Structural genomics aims to provide structures and theoretical model for all proteins encoded in completed genomes. These large undertakings will vastly increase our knowledge of structural biology and are poised to give us insight into the functions of many broadly conserved yet presently uncharacterized genes. Computational work is guiding the selection of targets for experimental characterization, and the methods of selection are under active development.
Large scale and high quality protein structure prediction and modeling is critical for interpretation and verification of biochemical experiments or computational predictions about protein function. Consideration of frequencies of different folds in genomes provides an unbiased view of protein structure and evolution. Moreover, as our knowledge of protein structures becomes more complete, so to should our ability to understand and predict protein structure: there is a growing realization that proper evolutionary analysis is an essential component of this quest. We expect that the structural genomics data will help to obtain more precise estimates of how protein topology evolves over time, and how this evolution interacts with sequence evolution. It has been known for a long time that in general protein structures change much more slowly than protein sequences over evolutionary time, but also that different proteins evolve much more slowly in topology and/or sequence than others, and that these overall rates of evolution can change with time. Further analysis of the changing relationship between sequence and structure evolution is expected to improve our abilities to predict when major shifts in structural topology have occurred, and to predict changes in functional specificity over evolutionary time.
The evolutionary processes which have shaped modern protein sequences can be visualized as a meandering diffusion over an adaptive landscape of extremely complex nature: different sites have different structural and functional contexts, there is almost certainly some degree of interaction between sites, and the adaptive landscape itself probably changes over time as major features of structure and function evolve. The degrees to which these factors are important are being addressed theoretically, computationally and experimentally by analyzing the patterns of evolution embodied in extant sequences, developing new analytical tools to predict the forces or substitution probabilities involved, and using evolutionary inference as a hypothesis-generator for both in vitro and in silico mutagenesis studies, including experimental paleobiology. The patterns of variation and conservation throughout a homologous sequence set provide signals indicating the underlying shared structure. Analyzing the presence of co-evolving sites in proteins is beginning to make important contributions to the solution as analytical methods improve, as are more refined estimates of the tendencies of different secondary structures and hydrophobic environments to have different substitution rates.
Specifically, we are interested in the following subjects: evolution of structure and function, the protein adaptive landscape, inferring substitution probabilities and patterns, using evolutionary information for the prediction of structure and function, co-evolution of sites and sub-units, experimental paleobiology and other forms of hypothesis-generation for in vitro and in silico mutagenesis and directed evolution studies. We are also interested in large-scale determination of protein structures, structure prediction on complete genomes, selection of targets for experimental characterization, interpretation of solved structures, and inter-genomic comparisons. Of particular interest will be research at the interface between computation and experiment.
This session welcomes papers which aim to address these and related computational aspects of protein evolution in their genomic context. There is also the opportunity to present posters as part of this track, as well as related tutorials.
DATES:CONTACT:More conference information can be found at http://psb.stanford.edu/
Please format papers according to instructions at ftp://ftp-smi.stanford.edu/pub/altman/psb/
Submit papers to altman@smi.stanford.edu (details belows)
For more information about the PROTEIN EVOLUTION AND STRUCTURAL GENOMICS track, contact one or more of the following:
SUBMISSION DETAILS:All papers must be submitted to altman@smi.stanford.edu in electronic format. The file formats we accept are: postscript (*.ps), adobe acrobat (*.pdf) and Microsoft Word documents (*.doc). Attached files should be named with the last name of the first author (e.g. altman.ps, altman.pdf, or altman.doc). Hardcopy submissions or unprocessed TEX or LATEX files will be rejected without review.
Each paper must be accompanied by a cover letter. The cover letter must state the following:
Submitted papers are limited to twelve (12) pages in our publication format. Please format your paper according to instructions found at ftp://ftp-smi.stanford.edu/pub/altman/psb/. If figures can not be easily resized and placed precisely in the text, then it should be clear that with appropriate modifications, the total manuscript length would be within the page limit.
Color pictures can be printed at the expense of the authors. The fee is $500 per page of color pictures, payable at the time of camera ready submission.