Ram Samudrala is a professor of computational biology and bioinformatics at the University at Buffalo, and formerly at the University of Washington in Seattle, United States. He researches protein and proteome folding, structure, function, interaction, design, and evolution spanning atomic to organismal levels of description. He has published more than 120 manuscripts in a variety of journals including Science, Nature, PLoS Biology, Drug Discovery Today, the Proceedings of the National Academy of Sciences, and the Journal of the American Medical Association.
Samudrala is also a musician who has published and recorded work under the pseudonym TWISTED HELICES. In 1994, he published the Free music Philosophy, which accurately predicted how the ease of copying and transmitting digital information by the Internet would lead to unprecedented violations of copyright laws and new models of distribution for music and other digital media. His work in this area was reported as early as 1997 by diverse media outlets including Billboard, Forbes , Levi's Original Music Magazine, The Free Radical, Wired and The New York Times .
Prior to joining the faculty at the University of Washington, Samudrala was a post-doctoral fellow with Michael Levitt at Stanford University from 1997–2000, with a fellowship from the Program in Mathematics and Molecular Biology (funded by the NSF and the Burroughs Wellcome Fund). He received his undergraduate degrees in Computing Science and Genetics from Ohio Wesleyan University (1990–1993) as a Wesleyan Scholar, and completed his Ph.D. in Computational Structural Biology with John Moult at the Center for Advanced Research in Biotechnology in Rockville, MD (1993–1997) as a Life Technologies Fellow. In 2001, Samudrala became the first faculty member to be recruited, as an Assistant Professor, under the Advanced Technology Initiative in Infectious Diseases created by the Washington State Legislature "as a bridge between cutting-edge research and education, and new economic activity." He was promoted to Associate Professor with tenure in 2006. In 2014, he became Professor and Chief of the Division of Bioinformatics at the State University of New York, Buffalo.
Samudrala received a Searle Scholar Award which funds exceptional young scientists in 2002, was named one of the world's top young innovators (TR100) by MIT Technology Review in 2003, and was selected to present the University of Washington New Investigator Science in Medicine Lecture in 2004. In 2005, he received a NSF CAREER Award which recognizes "outstanding scientists and engineers who show exceptional potential for leadership at the frontiers of knowledge". In 2008, he received the Alberta Heritage Foundation for Medical Research Visiting Scientist Award and was awarded honorary diplomas from the cities of Casma and Yautan, Peru, for his work on vaccine discovery. He was a NIH Director's Pioneer Award finalist in 2006 (25/465 applicants were selected as finalists) for a novel idea to determine the structures of all proteins in a solution that he then presented at the seventh community wide assessment of protein structure prediction methods (CASP7). In 2010, he again became a finalist and went on to receive the Pioneer Award for his Computational Analysis of Novel Drug Opportunities (CANDO) drug discovery platform to screen every known drug against every known target structure in a shotgun manner to discover new repurposeable therapeutics, particularly for underserved diseases. That same year, he received the Best Undergraduate Research Mentor Award from the University of Washington. In 2019, Samudrala was named a University at Buffalo Exceptional Scholar  and also awarded a NIH NCATS ASPIRE Design Challenge Award.
Samudrala's research has focused on understanding how the genome of an organism specifies its behaviour and characteristics, and applying that information to improve health and quality of life. His vision is to produce a computing model of life focused on atomic level detail, organisation, and arrangements of all the components involved, which he calls the "structeome". The structeome, which is the actual structural organization of components at the atomic level, by its very nature includes single molecules such as DNA, RNA, proteins, and metabolites, as well larger groupings such genomes, proteome, interactomes, connectomes, and so on. Since the vision is of a large collection of atoms with subgroupings of atoms that work together in a complex dynamic manner, a protein would be a collection of atoms, many of which are covalently bonded, that interact together to perform a specific biological function. Samudrala's work has thus focussed on proteins, which is the fundamental unit of biological function within the structeome. Atoms in a structeome interact with the environment which may include other structeomes (or components thereof) thereby causing a strange loop or a tangled hierarchy of interactions. Thus a structeome would include not only all atoms and their interactions within that structeome, but also all interactions to other structeomes. Samudrala has extended this theoretical framework to explain how evolution works by recursion of existing information and has used it to solve research problems with practical applications in medicine, such as therapeutic discovery based on docking with dynamics, multitargeting, and drug repurposing in a shotgun manner, as well as in nanobiotechnology, such as engineering tooth tissue by designing novel peptides that bind to various inorganic substrates.
Specifically, on a more grounded level, he has consistently taken part in, spoken at, and published in the proceedings of blind protein structure prediction experiments, known as CASP since its inception. His work with John Moult at CASP1 in 1994 and CASP2 in 1996 and with Michael Levitt at CASP3 in 1998 are among the very first improvements of blinded protein structure prediction in both comparative and template free modelling categories. With John Moult, as part of his PhD thesis, he was the first to develop and apply an all heavy atom knowledge-based conditional probability discriminatory function  as well as graph-theoretic methods  to accurately predict interactomic interactions for comparative modelling of protein structures. With Michael Levitt, as part of his postdoctoral fellowship, he developed a combined hierarchical approach for de novo structure prediction  as well as the Decoys 'R' Us database to evaluate discrimination functions.
After he joined the faculty of the University of Washington, Samudrala's Computational Biology Research Group developed a series of algorithms and web server modules to predict protein structure, function, and interactions known as Protinfo.
Samudrala's group then applied these methods to entire organismal proteomes, creating a framework known as the Bioverse for exploring the relationships among the atomic, molecular, genomic, proteomic, systems, and organismal worlds. The Bioverse framework performs sophisticated analyses and predictions based on genomic sequence data to annotate and understand the interaction of protein sequence, structure, and function, both at the single molecule as well as at the systems levels. A set of first pass predictions is available for more than 50 organismal proteomes and the framework was used to annotate the finished rice genome sequence published in 2005. He is currently working on integrating a vast amount of protein interaction data (to other proteins, DNA, RNA, and smaller ligands) and modelling them at the atomic level. The end goal of the Bioverse project is to understand and simulate life at an atomic level. A subset of this atomic level interactome, consisting of a large set human ingestible small molecules and a set of structures representing the protein universe, has already been constructed as part of the drug discovery platform described below.
Finally, Samudrala's group has pioneered the successful applications of these basic science methods to drug discovery, including the Computational Analysis of Novel Drug Opportunities (CANDO) platform funded by a 2010 NIH Director's Pioneer Award that ranks therapeutics for all indications by analysis and comparison of structural compound-proteome interaction signatures. A combination of novel docking methods and/or its use in the CANDO platform has led to prospectively validated predictions of putative drugs against dengue, dental caries, herpes, lupus, and malaria along with indication-specific collaborators.
Other successful areas of application include medicine, predicting HIV drug resistance/susceptibility; nanobiotechnology, where small multifunctional peptides that bind to inorganic substrates are designed computationally; and interactomics of several organisms, including the Nutritious Rice for the World (NRW) project where protein structure prediction methods are applied to all tractable proteins encoded by the rice genome on the IBM World Community Grid as well as the 1KP project to predict protein structures. functions, and interactions of 1000 plant proteomes. The NRW project harnessed the power of individual PCs via the Grid to perform its computations to help design better rice strains with higher yield and range of bioavailable nutrients, and was covered by more than 200 media outlets worldwide including The New York Times , BusinessWeek, NSF, The Times of India, and Fortune.