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astronomy

Ram Samudrala

Ram Samudrala is a astronomy topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Ram Samudrala rather than just read about it. In short: Ram Samudrala is a professor of computational biology and bioinformatics at the University at Buffalo, United States. He researches protein folding, structure, function, interaction, design, and evolution.

Ram Samudrala — main illustration
Ram Samudrala — illustration

Key takeaways

  • Ram Samudrala belongs to astronomy; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Ram Samudrala to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Ram Samudrala from memory before moving on to harder problems.

Reference excerpt

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

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. 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." 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.

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. 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. With Levitt, he developed a combined hierarchical approach for de novo structure prediction as well as the Decoys 'R' Us database to evaluate discrimination functions. At the University of Washington, Samudrala's research group developed a series of algorithms and web server modules to predict protein structure, function, and interactions known as Protinfo. The group then applied these methods to 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 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. 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 performing multiscale analytics of 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 areas of application include 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.

Awards and honours Samudrala received a Searle Scholar Award which funds exceptional young scientists in 2002 and was named one of the world's top young innovators (TR100) by MIT Technology Review in 2003, 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. In 2010, he received the NIH Director's Pioneer Award for the CANDO drug discovery platform. In 2019, Samudrala was presented with a NIH NCATS ASPIRE Design Challenge Award, which was followed by a NIH NCATS ASPIRE Reduction-to-Practice Award grand prize presented in 2022.

Personal life 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 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, and The New York Times.

References

External links

Samudrala Computational Biology Group web site Ram Samudrala's personal web site Nutritious Rice for the World web site (press and video sections) Protinfo web server modules: ABCM - PIRSPred - PSICSI - NMR - PPC - MFS Bioverse web server

Illustrations

Ram Samudrala illustration

Worked examples

Example 1 — a first encounter with Ram Samudrala

Start with the simplest possible case. Write down what Ram Samudrala claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In astronomy, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Ram Samudrala before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Ram Samudrala ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Ram Samudrala

In research
Ram Samudrala appears in astronomy research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Ram Samudrala in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Ram Samudrala is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1972 births, 21st-century American biologists, American bioinformaticians, so understanding it makes those chapters shorter.
In everyday life
Look for Ram Samudrala outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Ram Samudrala in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Ram Samudrala means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Ram Samudrala out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Ram Samudrala in simple terms?

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

Why does Ram Samudrala matter?

Because it connects several astronomy ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Ram Samudrala?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Ram Samudrala.

Tags

  • 1972 births
  • 21st-century American biologists
  • American bioinformaticians
  • Living people
  • Ohio Wesleyan University alumni
  • Stanford University alumni
  • University at Buffalo faculty
  • University of Washington faculty

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