Fast flexible modeling of RNA structure using internal coordinates.

Modeling the structure and dynamics of large macromolecules remains a critical challenge. Molecular dynamics (MD) simulations are expensive because they model every atom independently, and are difficult to combine with experimentally derived knowledge. Assembly of molecules using fragments from libraries relies on the database of known structures and thus may not work for novel motifs. Coarse-grained modeling methods have yielded good results on large molecules but can suffer from difficulties in creating more detailed full atomic realizations. There is therefore a need for molecular modeling algorithms that remain chemically accurate and economical for large molecules, do not rely on fragment libraries, and can incorporate experimental information. RNABuilder works in the internal coordinate space of dihedral angles and thus has time requirements proportional to the number of moving parts rather than the number of atoms. It provides accurate physics-based response to applied forces, but also allows user-specified forces for incorporating experimental information. A particular strength of RNABuilder is that all Leontis-Westhof basepairs can be specified as primitives by the user to be satisfied during model construction. We apply RNABuilder to predict the structure of an RNA molecule with 160 bases from its secondary structure, as well as experimental information. Our model matches the known structure to 10.2 Angstroms RMSD and has low computational expense.

Efficient algorithms to explore conformation spaces of flexible protein loops.

Several applications in biology - e.g., incorporation of protein flexibility in ligand docking algorithms, interpretation of fuzzy X-ray crystallographic data, and homology modeling - require computing the internal parameters of a flexible fragment (usually, a loop) of a protein in order to connect its termini to the rest of the protein without causing any steric clash. One must often sample many such conformations in order to explore and adequately represent the conformational range of the studied loop. While sampling must be fast, it is made difficult by the fact that two conflicting constraints - kinematic closure and clash avoidance - must be satisfied concurrently. This paper describes two efficient and complementary sampling algorithms to explore the space of closed clash-free conformations of a flexible protein loop. The "seed sampling" algorithm samples broadly from this space, while the "deformation sampling" algorithm uses seed conformations as starting points to explore the conformation space around them at a finer grain. Computational results are presented for various loops ranging from 5 to 25 residues. More specific results also show that the combination of the sampling algorithms with a functional site prediction software (FEATURE) makes it possible to compute and recognize calcium-binding loop conformations. The sampling algorithms are implemented in a toolkit (LoopTK), which is available at https://simtk.org/home/looptk.

Interview: Russ Altman speaks to Shreeya Nanda, Commissioning Editor.

Russ Biagio Altman is a professor of bioengineering, genetics, and medicine (and of computer science by courtesy) and chairman of the Bioengineering Department at Stanford University, CA, USA. His primary research interests are in the application of computing technology to basic molecular biological problems of relevance to medicine. He is currently developing techniques for collaborative scientific computation over the internet, including novel user interfaces to biological data, particularly for pharmacogenomics. Other work focuses on the analysis of functional microenvironments within macromolecules and the application of algorithms for determining the structure, dynamics and function of biological macromolecules. Dr Altman holds an MD from Stanford Medical School, a PhD in medical information sciences from Stanford, and an AB from Harvard College, MA, USA. He has been the recipient of the US Presidential Early Career Award for Scientists and Engineers and a National Science Foundation CAREER Award. He is a fellow of the American College of Physicians and the American College of Medical Informatics. He is a past-president and founding board member of the International Society for Computational Biology and an organizer of the annual Pacific Symposium on Biocomputing. He leads one of seven NIH-supported National Centers for Biomedical Computation, focusing on physics-based simulation of biological structures. He won the Stanford Medical School graduate teaching award in 2000.