Associate Professor of Microbiology
Office Location: 208 Rosen Building
Campus Box: 358070
A fundamental biological challenge is to understand how the linear information in an organism's genome is processed to produce the resulting behavior or phenotype. Genes, made up of DNA, are transcribed into RNA, and translated into proteins which together form the vast majority of functional elements in an organism. Evolutionary processes ensure that these functional elements interact with their environment in a manner that is beneficial to the organism, using a variety of molecules to catalyse reactions, recognise cellular signals, build cellular structures, and to perform a host of other diverse biological functions.
Our research aims to understand these processes by developing computational algorithms to model, annotate, and understand the relationships between the sequences, structures, functions, and interactions of proteins, DNA, proteins and metabolites, at both the molecular and the genomic/systems levels. The goal is to develop a coherent picture of the mechanistic basis (wiring diagram) of molecular and organismal structure, function, networks, and evolution within a fundamental scientific framework.
Our specific aims are to develop novel methods to:
Structure: Predict atomic level three dimensional structures of biologically important molecules (with focus on proteins) given their sequence.
Function: Predict function using the resulting models with the aid of available experimental information.
Interaction: Predict interactions between and among these molecules, including biological substrates and inhibitors.
Systems: Integrate the structure, function, and interaction information with the expression (copy number) of these molecules.
Application: Apply the methodologies developed to study specific biological problems of interest.
Infrastructure: Develop an infrastructure to publish the integrated information so that it is useful for biologists to pose and answer precise scientific questions about systems and organismal biology.
Detailed information on these methods are available as part of our ongoing research and also the PI's CV
We expect that the biological role of every protein sequence can eventually be deduced from its three dimensional structure in the context of its environment in the cell. This information will enable us to probe that organism's cellular pathways with an exquisite degree of sensitivity and also help us understand and treat infectious and inherited disease in an increasingly efficient and rational manner. The development of algorithms and tools to understand organismal genomes will have practical utility for pharmacogenomics and genetic engineering, and will be of use to the general research community to pose and answer ever more precise biological questions.
Understanding organismal biology from a genomic perspective requires expertise in several scientific disciplines, including computing science, mathematics, physics, chemistry, and biology. The problems that need to be solved generally involve exploration of large search spaces and finding objects of interest within those spaces, as well as managing the large amount of data produced and making predictions from analysis of the data. Thus our research has significance in not only answering biological questions, but is also relevant for solving problems of a similar nature in other scientific disciplines.
Long term goals:
Our research involves integrating knowledge from the fields of computing science, mathematics, biology, physics, and chemistry to:
- Achieve better understanding of protein structure, protein function, and molecular evolution.
- Analyse genomes and study interactions of individual genes and their corresponding proteins to understand and model their roles in infectious and inherited disease.
- Use knowledge about the structure of proteins, protein expression, protein-protein and protein-substrate interactions to model complete cellular pathways and systems within an organism of interest.
- Develop therapeutics and molecular machines to improve human health and quality of life.
Major Research Resources Developed:
PROTINFO structure and function prediction webserver: The webserver consists of modules to perform protein structure prediction, integrate limited or noisy experimental data with our structure predictionalgorithms, predict HIV-1 drug resistance/susceptibility, assign proteins to particular functional categories and predict functionally important residues. Currently structure and function predictions for up to 100 molecules/day are performed.
The Bioverse database and webserver: The Bioverse provides a framework for exploring the relationships among the molecular, genomic, proteomic, systems, and organismal worlds. Currently used by several hundred visitors accessing more than 1000 molecules/day.
Jenwitheesuk E, Horst JA, Rivas K, Van Voorhis WC, Samudrala R. New paradigms for drug discovery: Computational multitarget screening. Trends in Pharmacological Sciences 29: 62-71, 2008.
Oren EE, Tamerler C, Sahin D, Hnilova M, Seker UOS, Sarikaya M, Samudrala R. A novel knowledge-based approach for designing inorganic binding peptides. Bioinformatics 23: 2816-2822, 2007.
Hung L-H, Samudrala R. An automated assignment-free Bayesian approach for accurately identifying proton contacts from NOESY data. Journal of Biomolecular NMR 36: 189-198, 2006.
Wang K, Samudrala R. Automated functional classification of experimental and predicted protein structures. BMC Bioinformatics 7: 278, 2006.
McDermott J, Bumgarner RE, Samudrala R. Functional annotation from predicted protein interaction networks. Bioinformatics 21: 3217-3226, 2005.
Hung L-H, Ngan S-C, Liu T, Samudrala R. PROTINFO: New algorithms for enhanced protein structure prediction. Nucleic Acids Research 33: W77-W80, 2005.
Wang K, Samudrala R. FSSA: A novel method for identifying functional signatures from structural alignments. Bioinformatics 21: 2969-2977, 2005.
Jenwitheesuk E, Wang K, Mittler J, Samudrala R. PIRSpred: A web server for reliable HIV-1 protein-inhibitor resistance/susceptibility prediction. Trends in Microbiology 13: 150-151, 2005.
Yu J, Wang J, Lin W, Li S, Li H, Zhou J, ..., McDermott J, Samudrala R, Wang J, Wong GK. The genomes of Oryza sativa: A history of duplications. Public Library of Science Biology 3: e38, 2005.