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Computational Structural Biology: Successes, Future Directions, and Challenges

Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
Departments of Computer Science, Department of Bioengineering, and School of Systems Biology, George Mason University, Fairfax, VA 22030, USA
Author to whom correspondence should be addressed.
Academic Editors: Filip Jagodzinski and Brian Y. Chen
Molecules 2019, 24(3), 637;
Received: 11 January 2019 / Revised: 5 February 2019 / Accepted: 10 February 2019 / Published: 12 February 2019
PDF [232 KB, uploaded 12 February 2019]


Computational biology has made powerful advances. Among these, trends in human health have been uncovered through heterogeneous ‘big data’ integration, and disease-associated genes were identified and classified. Along a different front, the dynamic organization of chromatin is being elucidated to gain insight into the fundamental question of genome regulation. Powerful conformational sampling methods have also been developed to yield a detailed molecular view of cellular processes. when combining these methods with the advancements in the modeling of supramolecular assemblies, including those at the membrane, we are finally able to get a glimpse into how cells’ actions are regulated. Perhaps most intriguingly, a major thrust is on to decipher the mystery of how the brain is coded. Here, we aim to provide a broad, yet concise, sketch of modern aspects of computational biology, with a special focus on computational structural biology. We attempt to forecast the areas that computational structural biology will embrace in the future and the challenges that it may face. We skirt details, highlight successes, note failures, and map directions. View Full-Text
Keywords: big data; machine intelligence; bioinformatics; biological modeling; free-energy landscape; mutations big data; machine intelligence; bioinformatics; biological modeling; free-energy landscape; mutations
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Nussinov, R.; Tsai, C.-J.; Shehu, A.; Jang, H. Computational Structural Biology: Successes, Future Directions, and Challenges. Molecules 2019, 24, 637.

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