Rishi Mukhopadhyay

Ryan Yandle, Rishi Mukhopadhyay, Homayoun Valafar

The method of Probability Density Profile Analysis has been introduced previously as a tool to find the best match between a set of experimentally generated Residual Dipolar Couplings and a set of known protein structures. While it proved effective on small databases in identifying protein fold families, and for picking the best result from computational protein folding tool ROBETTA, for larger data sets, more data is required. Here, the method of 2-D Probability Density Profile Analysis is presented which incorporates paired RDC data from 2 alignment media for N-H vectors. The method was tested using synthetic RDC data generated with +/-1 Hz error. The results show that the addition of information from a second alignment medium makes 2-D PDPA a much more effective tool that is able to identify a structure from a database of 600 protein fold family representatives.

P. Shealy, R. Mukhopadhyay, S. Smith et al.

Resonance assignment is a critical first step in the investigation of protein structures using NMR spectroscopy. The development of assignment methods that require less experimental data is possible with prior knowledge of the macromolecular structure. Automated methods of performing the task of resonance assignment can significantly reduce the financial cost and time requirement for protein structure determination. Such methods can also be beneficial in validating a protein's solution state structure. Here we present a new approach to the assignment problem. Our approach uses only RDC data to assign backbone resonances. It provides simultaneous order tensor estimation and assignment. Our approach compares independent order tensor estimates to determine when the correct order tensor has been found. We demonstrate the algorithm's viability using simulated data from the protein domain 1A1Z.

Rishi Mukhopadhyay, Paul Shealy, Homayoun Valafar

Despite many advances in computational modeling of protein structures, these methods have not been widely utilized by experimental structural biologists. Two major obstacles are preventing the transition from a purely-experimental to a purely-computational mode of protein structure determination. The first problem is that most computational methods need a large library of computed structures that span a large variety of protein fold families, while structural genomics initiatives have slowed in their ability to provide novel protein folds in recent years. The second problem is an unwillingness to trust computational models that have no experimental backing. In this paper we test a potential solution to these problems that we have called Probability Density Profile Analysis (PDPA) that utilizes unassigned residual dipolar coupling data that are relatively cheap to acquire from NMR experiments.