My scientific research

• Samudrala Computational Biology Research Group at the University of Washington, Seattle

The general area of my scientific research is computational biology, genomics, and proteomics. The goal is to elucidate processes responsible for DNA, RNA, protein and interactome structure, function, design, and evolution so as to understand (and reproduce by computing simulations) how an organism's genome specifies the behaviour and characteristics of the organism. The major things I've touched upon thusfar are listed below (for those interested in a more formal presentation, including an ordered list of publications, check out my CV).

I am a principal investigator (Associate Professor) at the University of Washington in Seattle interested in the following topics:

• protein structure
• protein function
• protein design and evolution
• protein-protein and protein-substrate interaction
• genomics and proteomics (structure and function of components within a cell)
• understanding higher level organisation (interaction and expression) of components in a cell (DNA, RNA, proteins, and small molecules) and simulating their behaviour

• algorithmic studies of astronomically large spaces
• bioinformatics/data mining
• massively parallel and distributed computing

Specific areas of ongoing research are listed below. The work described in the publications is generally encapsulated into a variety of webservers/applications/services (links included) and downloadable software.

• Application
• Systems
• Interaction
• Function
• Structure
• Infrastructure

Application

Structural and functional studies of biologically important proteins, systems, and problems. Use the structure and function prediction tools developed by us to help guide experimentalists in manipulating proteins and extracting information about their function and structure in vivo, both at the single molecule as well as at the genomic/systems levels. Some key areas include work on therapeutic (inhibitor) discovery and nanobiotechnology. This work is usually done in collaboration with experimentalists. I list these papers first since they demonstrate a true application of the work we do. In many cases, these are prospective verification (i.e., a prediction is made before the answer is known and verified).

Therapeutics

• Jenwitheesuk E, Horst JA, Rivas K, Van Voorhis WC, Samudrala R. Novel paradigms for drug discovery: Computational multitarget screening. Trends in Pharmacological Sciences 29: 62-71, 2008.
• Samudrala R, Jenwitheesuk E. Identification of potential HIV-1 targets of minocycline. Bioinformatics 23: 2797-2799, 2007.
• Wang K, Mittler J, Samudrala R. Comment on "Evidence for positive epistatis in HIV-1". Science 312: 848b, 2006.
• Jenwitheesuk E, Samudrala R. Heptad-repeat-2 mutations enhance the stability of the enfuvirtide-resistant HIV-1 gp41 hairpin structure. Antiviral Therapy 10: 893-900, 2005.
• Jenwitheesuk E, Samudrala R. Identification of potential multitarget antimalarial drugs. Journal of the American Medical Association 294: 1490-1491, 2005.
• Jenwitheesuk E, Wang K, Mittler J, Samudrala R. PIRSpred: A webserver for reliable HIV-1 protein-inhibitor resistance/susceptibility prediction. Trends in Microbiology 4: 150-151, 2005.
• Jenwitheesuk E, Samudrala R. Virtual screening of HIV-1 protease inhibitors against human cytomegalovirus protease using docking and molecular dynamics. AIDS 19: 529-533, 2005.
• Jenwitheesuk E, Samudrala R. Prediction of HIV-1 protease inhibitor resistance using a protein-inhibitor flexible docking approach. Antiviral Therapy 10: 157-166, 2005.
• Wang J, Samudrala R, Mittler J. HIV-1 genotypic drug resistance interpretation algorithms need to include hypersusceptibility mutations. Journal of Infectious Diseases 190: 2055-2056, 2004.
• Wang J, Samudrala R, Mittler J. Antivirogram or PhenoSense: a comparison of their reproducibility and an analysis of their correlation. Antiviral Therapy 9: 703-712, 2004.
• Jenwitheesuk E, Wang K, Mittler J, Samudrala R. Improved accuracy of HIV-1 genotypic susceptibility interpretation using a consensus approach. AIDS 18: 1858-1859, 2004.
• Wang K, Jenwitheesuk E, Samudrala R, Mittler J. Simple linear model provides highly accurate genotypic predictions of HIV-1 drug resistance. Antiviral Therapy 9: 343-352, 2004.
• Wang K, Samudrala R, Mittler J. Weak agreement between predictions of ``reduced susceptibility'' from Antivirogram and PhenoSense assays. Journal of Clinical Microbiology 42: 2353-2354, 2004.
• Jenwitheesuk E, Samudrala R. Identifying inhibitors of the SARS coronavirus proteinase. Bioorganic & Medicinal Chemistry Letters, 13: 3989-3992, 2003.
• Jenwitheesuk E, Samudrala R. Improved prediction of HIV-1 protease-inhibitor binding energies by molecular dynamics simulations. BMC Structural Biology, 3: 2-11, 2003.

Nanobiotechnology

• Samudrala R, Oren EE, Cheng C, Horst, J, Bernard B, Gungormus M, Hnilova M, Fong H, Tamerler C, Sarikaya M. Knowledge-based design of inorganic binding peptides. Proceedings of the conference on the Foundations of Nanoscience: Self-Assembled Architectures and Devices, 2008.
• Evans JS, Samudrala R, Walsh TR, Oren EE, Tamerler C. Molecular design of inorganic-binding polypeptides. MRS Bulletin 33: 514-518, 2008. (Accompanying introductory article with biographies, pages 504-512.)
• 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.

Other

• Jenkins C, Samudrala R, Geary S, Djordjevic SP. Structural and functional characterisation of an organic hydroperoxide resistance (Ohr) protein from Mycoplasma gallisepticum. Journal of Bacteriology 190: 2206-2208, 2008.
• Chevance FFV, Takahashi N, Karlinsey JE, Gnerer J, Hirano T, Samudrala R, Aizawa S-I, Hughes KT. The mechanism of outer membrane penetration by the eubacterial flagellum and implications for spirochete evolution. Genes and Development 21: 2326-2335, 2007.
• Bockhorst J, Lu F, Janes JH, Keebler J, Gamain B, Awadalla P, Su X, Samudrala R, Jojic N, Smith JD. Structural polymorphism and diversifying selection on the pregnancy malaria vaccine candidate VAR2CSA. Molecular and Biochemical Parasitology 155: 103-112, 2007.
• Berube PM, Samudrala R, Stahl DA. Transcription of amoC is associated with the recovery of Nitrosomonas europaea from ammonia starvation. Journal of Bacteriology 89: 3935-3944, 2007.
• Korotkova N, Le Trong I, Samudrala R, Korotkov K, Van Loy CP, Bui A-L, Moseley SL, Stenkamp RE. Crystal structure and mutational analysis of the DaaE adhesin of Escherichia coli. Journal of Biological Chemistry 281: 22367-22377, 2006.
• Howell DPG, Samudrala R, Smith JD. Disguising itself - insights into Plasmodium falciparum binding and immune evasion from the DBL crystal structure. Molecular and Biochemical Parasitology 148: 1-9, 2006.
• Wang W, Zheng H, Yang S, Yu H, Li J, Jiang H, Su J, Yang L, Zhang J, McDermott J, Samudrala R, Wang J, Yang H, Yu J, Kristiansen K, Wong GK, Wang J. Origin and evolution of new exons in rodents. Genome Research 15: 1258-1264, 2005.
• Liu T, Jenwitheesuk E, Teller D, Samudrala R. Structural insights into the Cellular Retinaldehyde Binding Protein (CRALBP). Proteins: Structure, Function, and Bioinformatics 61: 412-422, 2005.
• Ekwa-Ekok C, Diaza GA, Carlson C, Hasegawad T, Samudrala R, Limf K, Yabug JM, Levya B, Schnapp LM. Genomic organization and sequence variation of the human integrin subunit 8 gene (ITGA8). Matrix Biology 23: 487-496, 2004.
• Wang J, Zhang J, Zheng H, Li J, Liu D, Li H, Samudrala R, Yu J, Wong GK. Mouse transcriptome: Neutral evolution of "non-coding" complementary DNAs. Nature 431, 2004.
• Jenkins C, Samudrala R, Anderson I, Hedlund BP, Petroni G, Michailova N, Pinel N, Overbeek R, Rosati G, Staley JT. Genes for the cytoskeletal protein tubulin in the bacteria genus Prosthecobacter. Proceedings of the National Academy of Sciences 99: 17049-17054, 2002.
• Van Loy CP, Sokurenko EP, Samudrala R, Moseley S. Identification of a DAF binding domain in the Dr adhesin. Molecular Microbiology 45: 439-452, 2002.
• Samudrala R, Xia Y, Levitt M, Cotton NJ, Huang ES, Davis R. Probing structure-function relationships of the DNA polymerase alpha-associated zinc-finger protein using computational approaches. In Altman R, Dunker K, Hunter L, Klein T, Lauderdale K, eds. Proceedings of the Pacific Symposium on Biocomputing 179-189, 2000.

• Protinfo structure and function prediction server

Systems

Application and integration of single molecule structure and function prediction techniques to whole genomes and proteomes in an integrated manner. Combine single molecule and genomic/proteomic data to to explore the relationships among the molecular and organismal (systems) worlds and create a comprehensive picture of the relationship between genotype and phenotype.

• McDermott J, Samudrala R. Bioinformatic characterization of plant networks. Proceedings of the Asia Pacific Conference on Plant Tissue Culture and Agrobiotechnology, 2007.
• Chang AN, McDermott J, Guerquin M, Frazier Z, Samudrala R. Integrator: Interactive graphical search of large protein interactomes over the Web. BMC Bioinformatics 7: 146, 2006.
• McDermott J, Bumgarner RE, Samudrala R. Functional annotation from predicted protein interaction networks. Bioinformatics 3217-3226, 2005.
• McDermott J, Guerquin M, Frazier Z, Chang AN, Samudrala R. BIOVERSE: Enhancements to the framework for structural, functional, and contextual annotations of proteins and proteomes. Nucleic Acids Research 33: W324-W325, 2005.
• Chang AN, McDermott J, Samudrala R. An enhanced java graph applet interface for visualizing interactomes. Bioinformatics 21: 1741-1742, 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.
• McDermott J, Samudrala R. Enhanced functional information from protein networks. Trends in Biotechnology 22: 60-62, 2004.
• McDermott J, Samudrala R. BIOVERSE: Functional, structural, and contextual annotation of proteins and proteomes. Nucleic Acids Research 31: 3736-3737, 2003.
• McDermott J, Samudrala R. The Bioverse: An object-oriented genomic database and webserver written in Python. In Proceedings of the conference on Objects in Bio- & Chem-Informatics, 2002.
• Bioverse framework
• Protinfo structure and function prediction server

Interaction

Methods for predicting interactions between molecules.

• McDermott J, Bumgarner RE, Samudrala R. Functional annotation from predicted protein interaction networks. Bioinformatics 3217-3226, 2005.
• McDermott J, Samudrala R. Enhanced functional information from protein networks. Trends in Biotechnology 22: 60-62, 2004.
• Bioverse framework

Function

Generally applicable methods for predicting protein function from sequence and/or structure.

• Wang K, Horst J, Cheng G, Nickle D, Samudrala R. Protein meta-functional signatures from combining sequence, structure, evolution and amino acid property information. PLoS Computational Biology 4: e1000181, 2008.
• Wang K, Samudrala R. Incorporating background frequency improves entropy-based residue conservation measures. BMC Bioinformatics 7: 385, 2006.
• Wang K, Samudrala R. Automated functional classification of experimental and predicted protein structures. BMC Bioinformatics 7: 278, 2006.
• Cheng G, Qian B, Samudrala R, Baker D. Improvement in protein functional site prediction by distinguishing structural and functional constraints on protein family evolution using computational design. Nucleic Acids Research 33: 5861-5867, 2005.
• McDermott J, Bumgarner RE, Samudrala R. Functional annotation from predicted protein interaction networks. Bioinformatics 3217-3226, 2005.
• Wang K, Samudrala R. FSSA: A novel method for identifying functional signatures from structural alignments. Bioinformatics 21: 2969-2977, 2005.
• McDermott J, Samudrala R. Enhanced functional information from protein networks. Trends in Biotechnology 22: 60-62, 2004.
• Protinfo structure and function prediction server

Structure

Protein structure from combining theory and experiment

Use the structure prediction methods described below with experimental data to produce better results.

• 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.
• Hung L-H, Samudrala R. PROTINFO: Secondary and tertiary protein structure prediction. Nucleic Acids Research 31: 3296-3299, 2003.
• Hong L-H, Samudrala R. Accurate and automated assignment of secondary structure with PsiCSI. Protein Science 12: 12: 288-295, 2003.
• Protinfo structure and function prediction server

De novo protein structure prediction

The basic paradigm is to sample the conformational space exhaustively (using lattice models) or semi-exhaustively (using discrete states for each amino acids with some stochastic search process) such that native-like conformations are observed. These conformations are selected using the all-atom based scoring functions described below. The methods have had good success in the CASP blind prediction experiments.

• Hung L-H, Ngan S-C, Samudrala R. De novo protein structure prediction. In Xu Y, Xu D, Liang J, editors. Computational Methods for Protein Structure Prediction and Modeling 2: 43-64, 2007.
• 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.
• Hung L-H, Samudrala R. PROTINFO: Secondary and tertiary protein structure prediction. Nucleic Acids Research 31: 3296-3299, 2003.
• Samudrala R, Levitt M. A comprehensive analysis of 40 blind protein structure predictions. BMC Structural Biology 2: 3-18, 2002.
• Samudrala R. Lessons from blind protein structure prediction experiments. In Grohima M, Selvaraj S, eds. Recent Research Developments in Protein Folding, Stability, and Design, 123-139, 2002.
• Xia Y, Huang ES, Levitt M, Samudrala R. Ab initio construction of protein tertiary structures using a hierarchical approach. Journal of Molecular Biology, 300: 171-185, 2000.
• Samudrala R, Xia Y, Levitt M. Huang ES. Ab initio prediction of protein structure using a combined hierarchical approach. Proteins: Structure, Function, and Genetics S3: 194-198, 1999.
• Huang ES, Samudrala R, Ponder JW. Ab initio protein structure prediction results using a simple distance geometry method. unpublished.
• Huang ES, Samudrala R, Ponder JW. Ab initio fold prediction of small helical proteins using distance geometry and knowledge-based scoring functions. Journal of Molecular Biology 290:267-281, 1999.
• Samudrala R, Xia Y, Levitt M, Huang ES. A combined approach for ab initio construction of low resolution protein tertiary structures from sequence. In Altman R, Dunker K, Hunter L, Klein T, Lauderdale K, eds. Proceedings of the Pacific Symposium on Biocomputing 505-516, 1999.
• Huang ES, Samudrala R, Ponder JW. Distance geometry generates native-like folds for small helical proteins using the consensus distances of predicted protein structures. Protein Science 7: 1998-2003, 1998.
• Protinfo structure and function prediction server

Comparative modelling of protein structure

This primarily uses a graph-theoretic clique-finding method to handle context-sensitivity issues in protein structures. The methods have had good success in the CASP blind prediction experiments.

• Liu T, Guerquin M, Samudrala R. Improving the accuracy of template-based predictions by mixing and matching between initial models. BMC Structural Biology 8: 24, 2008.
• 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.
• Hung L-H, Samudrala R. PROTINFO: Secondary and tertiary protein structure prediction. Nucleic Acids Research 31: 3296-3299, 2003.
• Samudrala R, Levitt M. A comprehensive analysis of 40 blind protein structure predictions. BMC Structural Biology 2: 3-18, 2002.
• Samudrala R. Lessons from blind protein structure prediction experiments. In Grohima M, Selvaraj S, eds. Recent Research Developments in Protein Folding, Stability, and Design, 123-139, 2002.
• Samudrala R, Moult J. A graph-theoretic algorithm for comparative modelling of protein structure. Journal of Molecular Biology 279:287-302, 1998.
• Samudrala R, Moult J. Handling context-sensitivity in protein structures using graph theory: bona fide prediction Proteins: Structure, Function, and Genetics 29S: 43-49, 1997.
• Samudrala R. A graph-theoretic solution to the context-sensitivity problem in protein structure prediction. Ph.D. thesis , 1997.
• Samudrala R, Pedersen JT, Zhou H, Luo R, Fidelis K, Moult J. Confronting the problem of interconnected structural changes in the comparative modelling of proteins. Proteins: Structure, Function, and Genetics 23: 327-336, 1995.
• Protinfo structure and function prediction server

Scoring/discriminatory functions for protein structure prediction

We primarily use an all-atom distance dependent conditional probability discriminatory function that is surprisingly accurate at selecting correct from incorrect protein conformations. It is used both for ab initio prediction and comparative modelling. We also use a number of other scoring functions as filters, and also develop databases of incorrect conformations ("decoys") to help evaluate scoring functions.

• Ngan S-C, Hung L-H, Liu T, Samudrala R. Scoring functions for de novo protein structure prediction revisited. Methods in Molecular Biology 413: 243-282, 2007.
• Liu T, Samudrala R. The effect of experimental resolution on the performance of knowledge-based discriminatory functions for protein structure selection. Protein Engineering, Design and Selection 19: 431-437, 2006.
• Ngan S-C, Inouye M, Samudrala R. A knowledge-based scoring function based on residue triplets for protein structure prediction. Protein Engineering, Design and Selection 19: 187-193, 2006.
• Wang K, Fain B, Levitt M, Samudrala R. Improved protein structure selection using decoy-dependent discriminatory functions. BMC Structural Biology 4: 8, 2004.
• Samudrala R, Levitt M. Decoys 'R' Us: A database of incorrect protein conformations for evaluating scoring functions. Protein Science, 9: 1399-1401, 2000.
• Huang ES, Samudrala R, Park BH. Scoring functions for ab initio folding. In Walker J, Webster D, eds. Predicting Protein Structure: Methods and Protocols Humana Press, 2000.
• Samudrala R, Moult J. An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction. Journal of Molecular Biology 275: 893-914, 1998.
• Decoys 'R' Us database

Side chain prediction

There are two papers in this area. The first is a work on exactly what it is that primarily determines side chain conformational preferences in proteins. The main thrust here is the use of the discriminatory function to select the most probable side chain rotamers given a large number of possible conformations. The second paper compares different methods for side chain prediction.

• Samudrala R, Huang ES, Koehl P, Levitt M. Side chain construction on non-native main chains using an all-atom discriminatory function. Protein Engineering, 7: 453-457, 2000.
• Samudrala R, Moult J. Determinants of side chain conformational preferences in protein structures. Protein Engineering 11: 991-997, 1998.

Infrastructure

We prefer to make our clusters from cheap components that can be readily discarded, and prefer to completely decentralise our systems.

• Samudrala R. Linux Cluster HOWTO, 2003.
• McDermott J, Samudrala R. The Bioverse: An object-oriented genomic database and webserver written in Python. In Proceedings of the conference on Objects in Bio- & Chem-Informatics, 2002.
• Samudrala R. Installing and using RAID. In Danesh A, Gautam D, eds. Special Edition Using Linux System Administration, Que Publishing, 2000.