Ram Samudrala

Last updated
Ram Samudrala
Samudrala biography.jpg
Born (1972-03-23) March 23, 1972 (age 51)
Alma mater Ohio Wesleyan University, University of Maryland, Stanford University
Known for Protein structure prediction, free music philosophy
Awards
Scientific career
Fields Computational biology
Institutions University at Buffalo, University of Washington
Doctoral advisor John Moult
Other academic advisors Michael Levitt
Website ram.org
compbio.org

Ram Samudrala is a professor of computational biology and bioinformatics at the University at Buffalo, United States. [3] He researches protein folding, structure, function, interaction, design, and evolution. [4]

Contents

Education and career

Samudrala received his undergraduate degrees in Computing Science and Genetics from Ohio Wesleyan University as a Wesleyan Scholar, and completed his Ph.D. in Computational Biology with John Moult at the University of Maryland in 1997 as a Life Technologies Fellow.[ citation needed ]

From 1997-2000, he was a postdoctoral fellow with Michael Levitt at Stanford University. In 2001, Samudrala became the first faculty member to be recruited to the University of Washington 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." [5] He was promoted to associate professor in 2006. In 2014, he became professor and chief of the Division of Bioinformatics at the State University of New York, Buffalo. [6]

Research

Samudrala's research focuses on proteomics and he has regularly taken part in the CASP protein structure prediction challenges since their inception. His work with Moult and Levitt are among the first improvements of blinded protein structure prediction in both comparative and template free modelling categories. [7] [8] [9] With Moult, he was the first to develop and apply probabilistic and graph-theoretic methods to accurately predict interactions for comparative modelling of protein structures. [10] [11] With Levitt, he developed a combined hierarchical approach for de novo structure prediction [12] as well as the Decoys 'R' Us database to evaluate discrimination functions. [13]

At the University of Washington, Samudrala's research group developed a series of algorithms and web server modules to predict protein structure, [14] function, [15] and interactions [16] known as Protinfo. [17] [18] The group then applied these methods to organismal proteomes, creating a framework known as the Bioverse [19] [20] for exploring the relationships among the atomic, molecular, genomic, proteomic, systems, and organismal worlds. The Bioverse framework performs 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. The framework was used to annotate the finished rice genome sequence published in 2005. [21]

Samudrala's group has also applied these methods to drug discovery, resulting in the Computational Analysis of Novel Drug Opportunities (CANDO) platform which ranks therapeutics for all indications by analysis and comparison of structural compound-proteome interaction signatures. [22] [23] [24] [25] 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, [26] [27] dental caries, herpes, lupus, and malaria [28] along with indication-specific collaborators. [29]

Other areas of application include predicting HIV drug resistance/susceptibility; [30] nanobiotechnology, where small multifunctional peptides that bind to inorganic substrates are designed computationally; [31] [32] [33] and interactomics of several organisms, including the Nutritious Rice for the World (NRW) project. [34]

Awards and honours

Samudrala received a Searle Scholar Award which funds exceptional young scientists in 2002 [1] and was named one of the world's top young innovators (TR100) by MIT Technology Review in 2003, [2] [35] [36] In 2005, he received a NSF CAREER Award [37] 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. In 2010, he received the NIH Director's Pioneer Award for the CANDO drug discovery platform. [23] [22] In 2019, Samudrala was presented with a NIH NCATS ASPIRE Design Challenge Award, [38] [6] which was followed by a NIH NCATS ASPIRE Reduction-to-Practice Award grand prize presented in 2022. [39] [40] [41]

Personal life

Samudrala is also a musician who has published and recorded work under the pseudonym TWISTED HELICES. [42] In 1994, he published the Free music Philosophy, [43] which 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. [44] [45] [46] His work in this area was reported as early as 1997 by diverse media outlets including Billboard , [47] and The New York Times . [48]

Related Research Articles

<span class="mw-page-title-main">Bioinformatics</span> Computational analysis of large, complex sets of biological data

Bioinformatics is an interdisciplinary field of science that develops methods and software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, chemistry, physics, computer science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The subsequent process of analyzing and interpreting data is referred to as computational biology.

<span class="mw-page-title-main">Membrane topology</span>

Topology of a transmembrane protein refers to locations of N- and C-termini of membrane-spanning polypeptide chain with respect to the inner or outer sides of the biological membrane occupied by the protein.

In bioinformatics, sequence analysis is the process of subjecting a DNA, RNA or peptide sequence to any of a wide range of analytical methods to understand its features, function, structure, or evolution. Methodologies used include sequence alignment, searches against biological databases, and others.

<span class="mw-page-title-main">Structural bioinformatics</span> Bioinformatics subfield

Structural bioinformatics is the branch of bioinformatics that is related to the analysis and prediction of the three-dimensional structure of biological macromolecules such as proteins, RNA, and DNA. It deals with generalizations about macromolecular 3D structures such as comparisons of overall folds and local motifs, principles of molecular folding, evolution, binding interactions, and structure/function relationships, working both from experimentally solved structures and from computational models. The term structural has the same meaning as in structural biology, and structural bioinformatics can be seen as a part of computational structural biology. The main objective of structural bioinformatics is the creation of new methods of analysing and manipulating biological macromolecular data in order to solve problems in biology and generate new knowledge.

<span class="mw-page-title-main">Interactome</span> Complete set of molecular interactions in a biological cell

In molecular biology, an interactome is the whole set of molecular interactions in a particular cell. The term specifically refers to physical interactions among molecules but can also describe sets of indirect interactions among genes.

<span class="mw-page-title-main">UniProt</span> Database of protein sequences and functional information

(See also: List of proteins in the human body)

Protein subcellular localization prediction involves the prediction of where a protein resides in a cell, its subcellular localization.

Macromolecular docking is the computational modelling of the quaternary structure of complexes formed by two or more interacting biological macromolecules. Protein–protein complexes are the most commonly attempted targets of such modelling, followed by protein–nucleic acid complexes.

In academia, computational immunology is a field of science that encompasses high-throughput genomic and bioinformatics approaches to immunology. The field's main aim is to convert immunological data into computational problems, solve these problems using mathematical and computational approaches and then convert these results into immunologically meaningful interpretations.

<span class="mw-page-title-main">Michael Levitt</span> Nobel laureate, biophysicist, and professor of structural biology (born 1947)

Michael Levitt, is a South African-born biophysicist and a professor of structural biology at Stanford University, a position he has held since 1987. Levitt received the 2013 Nobel Prize in Chemistry, together with Martin Karplus and Arieh Warshel, for "the development of multiscale models for complex chemical systems". In 2018, Levitt was a founding co-editor of the Annual Review of Biomedical Data Science.

In computational biology, de novo protein structure prediction refers to an algorithmic process by which protein tertiary structure is predicted from its amino acid primary sequence. The problem itself has occupied leading scientists for decades while still remaining unsolved. According to Science, the problem remains one of the top 125 outstanding issues in modern science. At present, some of the most successful methods have a reasonable probability of predicting the folds of small, single-domain proteins within 1.5 angstroms over the entire structure.

BIOBASE is an international bioinformatics company headquartered in Wolfenbüttel, Germany. The company focuses on the generation, maintenance, and licensing of databases in the field of molecular biology, and their related software platforms.

<span class="mw-page-title-main">Short linear motif</span>

In molecular biology short linear motifs (SLiMs), linear motifs or minimotifs are short stretches of protein sequence that mediate protein–protein interaction.

Computational Resources for Drug Discovery (CRDD) is one of the important silico modules of Open Source for Drug Discovery (OSDD). The CRDD web portal provides computer resources related to drug discovery on a single platform. It provides computational resources for researchers in computer-aided drug design, a discussion forum, and resources to maintain Wikipedia related to drug discovery, predict inhibitors, and predict the ADME-Tox property of molecules One of the major objectives of CRDD is to promote open source software in the field of chemoinformatics and pharmacoinformatics.

Protein function prediction methods are techniques that bioinformatics researchers use to assign biological or biochemical roles to proteins. These proteins are usually ones that are poorly studied or predicted based on genomic sequence data. These predictions are often driven by data-intensive computational procedures. Information may come from nucleic acid sequence homology, gene expression profiles, protein domain structures, text mining of publications, phylogenetic profiles, phenotypic profiles, and protein-protein interaction. Protein function is a broad term: the roles of proteins range from catalysis of biochemical reactions to transport to signal transduction, and a single protein may play a role in multiple processes or cellular pathways.

<span class="mw-page-title-main">Cyrus Chothia</span> English biochemist (1942–2019)

Cyrus Homi Chothia was an English biochemist who was an emeritus scientist at the Medical Research Council (MRC) Laboratory of Molecular Biology (LMB) at the University of Cambridge and emeritus fellow of Wolfson College, Cambridge.

<span class="mw-page-title-main">Burkhard Rost</span> German computational biology researcher

Burkhard Rost is a scientist leading the Department for Computational Biology & Bioinformatics at the Faculty of Informatics of the Technical University of Munich (TUM). Rost chairs the Study Section Bioinformatics Munich involving the TUM and the Ludwig Maximilian University of Munich (LMU) in Munich. From 2007-2014 Rost was President of the International Society for Computational Biology (ISCB).


Computer Atlas of Surface Topography of Proteins (CASTp) aims to provide comprehensive and detailed quantitative characterization of topographic features of protein, is now updated to version 3.0. Since its release in 2006, the CASTp server has ~ 45 000 visits and fulfills ~ 33 000 calculation requests annually. CASTp has been proven as a confident tool for a wide range of researches, including investigations of signaling receptors, discoveries of cancer therapeutics, understanding of mechanism of drug actions, studies of immune disorder diseases, analysis of protein–nanoparticle interactions, inference of protein functions and development of high-throughput computational tools. This server is maintained by Jie Liang's lab in University of Illinois at Chicago.

Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, including genomics, proteomics, microarrays, systems biology, evolution, and text mining.

References

  1. 1 2 Searle Scholar Award profile for Ram Samudrala
  2. 1 2 MIT Technology Review Profile naming Ram Samudrala one of the world's top young innovators
  3. ↑ Ram Samudrala's personal web site
  4. ↑ Samudrala Computational Biology Group
  5. ↑ Roseth B. Funding forward vision. University Week, March 1 2001. Archived 2011-06-04 at the Wayback Machine
  6. 1 2 Ram Samudrala's curriculum vitae
  7. ↑ 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.
  8. ↑ 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.
  9. ↑ Samudrala R, Xia Y, Huang ES, Levitt M. Ab initio prediction of protein structure using a combined hierarchical approach. Proteins: Structure, Function, and Genetics S3: 194-198, 1999.
  10. ↑ 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.
  11. ↑ Samudrala R, Moult J. A graph-theoretic algorithm for comparative modelling of protein structure. Journal of Molecular Biology 279: 287-302, 1998.
  12. ↑ 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.
  13. ↑ Samudrala R, Levitt M. Decoys 'R' Us: A database of incorrect protein conformations to improve protein structure prediction. Protein Science 9: 1399-1401, 2000.
  14. ↑ Liu T, Horst J, Samudrala R. A novel method for predicting and using distance constraints of high accuracy for refining protein structure prediction. Proteins: Structure, Function, and Bioinformatics 77: 220-234, 2009.
  15. ↑ 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.
  16. ↑ Kittichotirat W, Guerquin M, Bumgarner R, Samudrala R. Protinfo PPC: A web server for atomic level prediction of protein complexes. Nucleic Acids Research 37: W519-W525, 2009.
  17. ↑ Hung L-H, Samudrala R. PROTINFO: Secondary and tertiary protein structure prediction. Nucleic Acids Research 31: 3296-3299, 2003.
  18. ↑ 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.
  19. ↑ McDermott J, Samudrala R. BIOVERSE: Functional, structural, and contextual annotation of proteins and proteomes. Nucleic Acids Research 31: 3736-3737, 2003.
  20. ↑ 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.
  21. ↑ 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. PLoS Biology 3: e38, 2005.
  22. 1 2 Minie M, Chopra G, Sethi G, Horst J, White G, Roy A, Hatti K, Samudrala R. CANDO and the infinite drug discovery frontier. "Drug Discovery Today" 19: 1353-1363, 2014.
  23. 1 2 Computational analysis of novel drug opportunities (CANDO)
  24. ↑ Sethi G, Chopra G, Samudrala R. Multiscale modelling of relationships between protein classes and drug behavior across all diseases using the CANDO platform. Mini Reviews in Medicinal Chemistry, 2015. in press.
  25. ↑ Horst JA, Laurenzi A, Bernard B, Samudrala R. Computational multitarget drug discovery. "Polypharmacology "263-301, 2012.
  26. ↑ Costin JM, Jenwitheesuk E, Lok S-M, Hunsperger E, Conrads KA, Fontaine KA, Rees CR, Rossmann MG, Isern S, Samudrala R, Michael SF. Structural optimization and de novo design of dengue virus entry inhibitory peptides. PLoS Neglected Tropical Diseases 4: e721, 2010.
  27. ↑ Nicholson CO, Costin JM, Rowe DK, Lin L, Jenwitheesuk E, Samudrala R, Isern S, Michael SF. Viral entry inhibitors block dengue antibody-dependent enhancement in vitro. Antiviral Research 89: 71-74, 2011.
  28. ↑ 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.
  29. ↑ Computational analysis of novel drug opportunities (CANDO) indications and collaborations
  30. ↑ Jenwitheesuk E, Wang K, Mittler J, Samudrala R. PIRSpred: A webserver for reliable HIV-1 protein-inhibitor resistance/susceptibility prediction. Trends in Microbiology 13: 150-151, 2005.
  31. ↑ Cementomimetics-constructing a cementum-like biomineralized microlayer via amelogenin-derived peptides. Gungormus M, Oren EE, Horst JA, Fong H, Hnilova M, Somerman MJ, Snead ML, Samudrala R, Tamerler C, Sarikaya M. International Journal of Oral Sciences 2: 69-77, 2012.
  32. ↑ 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.
  33. ↑ Evans JS, Samudrala R, Walsh TR, Oren EE, Tamerler C. Molecular design of inorganic-binding polypeptides. MRS Bulletin 33: 514-518, 2008.
  34. ↑ Nutritious Rice for the World web site
  35. ↑ Kurian V. 10 Indian innovators in MIT list. The Hindu Business Line, October 4 2003.
  36. ↑ 10 of Indian Origin in MIT's Technology Review. Hindustan Times, March 1 2007. Archived 2010-08-23 at the Wayback Machine
  37. ↑ CAREER Award
  38. ↑ NIH HEAL Initiative issues awards to UB researchers to develop non-addictive painkillers, September 30 2019.
  39. ↑ "2020 NCATS ASPIRE Reduction-to-Practice Challenge Winners". NIH . October 28, 2021. Retrieved May 5, 2023.
  40. ↑ "Jacobs School Researchers Capture ASPIRE Challenge". University at Buffalo . February 21, 2023. Retrieved July 21, 2023.
  41. ↑ "Samudrala, Falls Get Funding for Various Research Projects". University at Buffalo . April 4, 2023. Retrieved July 21, 2023.
  42. ↑ TWISTED HELICES
  43. ↑ Free Music Philosophy
  44. ↑ Samudrala R. The future of music, 1997
  45. ↑ Story of a Revolution: Napster & the Music Industry. MusicDish, 2000
  46. ↑ Schulman BM. The song heard 'round the world: The copyright implications of MP3s and the future of digital music. Harvard Journal of Law and Technology 12: 3, 1999. Archived 2012-04-09 at the Wayback Machine
  47. ↑ Reece D. Industry grapples with MP3 dilemma. Billboard, July 18 1998.
  48. ↑ Napoli L. Fans of MP3 forced the issue. The New York Times, December 16 1998.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.