Ram Samudrala, PhD
Office: 208, Rosen
Adjunct Associate Professor, Oral Health Sciences
Associate Professor, Microbiology
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 catalyze reactions, recognize cellular signals, build cellular structures, and to perform a host of other diverse biological functions.
Our research aims to understand these processes performing sophisticated analyses on genomic sequence data to predict and understand relationships between the sequence, structure, and function of DNA, RNA, proteins and metabolites, at both the molecular and the genomic/systems levels. Our goal is to develop a coherent picture of molecular and organismal structure, function, networks, and evolution within a fundamental scientific framework.
Our specific aims are to develop novel methods to:
- Predict atomic-level three-dimensional structures of biologically important molecules (with focus on proteins) given their sequence.
- Predict function using the resulting models with the aid of available experimental information.
- Predict interactions between and among these molecules.
- Integrate the structure, function, and interaction information with the expression (copy number) of these molecules.
- Develop confidence assessments to evaluate the quality of any given prediction.
- Publish the integrated information so that it is useful for biologists to pose and answer precise scientific questions about systems and organismal biology.
CONJ 548 – Modeling Proteins and Proteomes
- Jenwitheesuk E., Horst J.A., Rivas K., Van Vorhis W.C., Samudrala R. New paradigms for drug discovery; computational multitarget screening. Trends in Pharmacological Sciences 29:62-71, 2008.
- Oren E.E., Tamerler C., Sahin D., Hnilova M., Seker U.O.S., Sarikaya M., Samudrala R. A novel knowledge-based approach for designing inorganic binding peptides. Bioinformatics 23:2797-2799, 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.
- Jenwitheesuk E., Samudrala R. Heptad-repeat-2 mutations enhance the stability of the enfuvirtide-resistant HIV-1gp41 hairpin structure. Antiviral Therapy 10:893-900, 2005.
- Jenwitheesuk E, Samudrala R. Identification of potential multi-target antimalarial drugs. Journal of the American Medical Association 294: 1490-1491, 2005.