A novel method for predicting and using distance constraints of high accuracy for refining protein structure prediction

Rv0100, a proposed acyl carrier protein in Mycobacterium tuberculosis: expression, purification and crystallization

Acyl carrier proteins (ACPs) are important components in fatty-acid biosynthesis in prokaryotes. Rv0100 is predicted to be an essential ACP in Mycobacterium tuberculosis, the pathogen that is the causative agent of tuberculosis, and therefore has the potential to be a novel antituberculosis drug target. Here, the successful cloning and purification of Rv0100 using Mycobacterium smegmatis as a host is reported. Crystals of the purified protein were obtained that diffracted to a resolution of 1.9 Å. Overall, this work lays the foundation for the future pursuit of drug discovery and development against this potentially novel drug target.

Exploring Polypharmacology in Drug Discovery and Repurposing Using the CANDO Platform

BACKGROUND

Traditional drug discovery approaches focus on a limited set of target molecules for treatment against specific indications/diseases. However, drug absorption, dispersion, metabolism, and excretion (ADME) involve interactions with multiple protein systems. Drugs approved for particular indication(s) may be repurposed as novel therapeutics for others. The severely declining rate of discovery and increasing costs of new drugs illustrate the limitations of the traditional reductionist paradigm in drug discovery.

METHODS

We developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform based on a hypothesis that drugs function by interacting with multiple protein targets to create a molecular interaction signature that can be exploited for therapeutic repurposing and discovery. We compiled a library of compounds that are human ingestible with minimal side effects, followed by an 'all-compounds' vs 'all-proteins' fragment-based multitarget docking with dynamics screen to construct compound-proteome interaction matrices that were then analyzed to determine similarity of drug behavior. The proteomic signature similarity of drugs is then ranked to make putative drug predictions for all indications in a shotgun manner.

RESULTS

We have previously applied this platform with success in both retrospective benchmarking and prospective validation, and to understand the effect of druggable protein classes on repurposing accuracy. Here we use the CANDO platform to analyze and determine the contribution of multitargeting (polypharmacology) to drug repurposing benchmarking accuracy. Taken together with the previous work, our results indicate that a large number of protein structures with diverse fold space and a specific polypharmacological interactome is necessary for accurate drug predictions using our proteomic and evolutionary drug discovery and repurposing platform.

CONCLUSION

These results have implications for future drug development and repurposing in the context of polypharmacology.

A fragment based method for modeling of protein segments into cryo-EM density maps

BACKGROUND

Single-particle analysis of electron cryo-microscopy (cryo-EM) is a key technology for elucidation of macromolecular structures. Recent technical advances in hardware and software developments significantly enhanced the resolution of cryo-EM density maps and broadened the applicability and the circle of users. To facilitate modeling of macromolecules into cryo-EM density maps, fast and easy to use methods for modeling are now demanded.

RESULTS

Here we investigated and benchmarked the suitability of a classical and well established fragment-based approach for modeling of segments into cryo-EM density maps (termed FragFit). FragFit uses a hierarchical strategy to select fragments from a pre-calculated set of billions of fragments derived from structures deposited in the Protein Data Bank, based on sequence similarly, fit of stem atoms and fit to a cryo-EM density map. The user only has to specify the sequence of the segment and the number of the N- and C-terminal stem-residues in the protein. Using a representative data set of protein structures, we show that protein segments can be accurately modeled into cryo-EM density maps of different resolution by FragFit. Prediction quality depends on segment length, the type of secondary structure of the segment and local quality of the map.

CONCLUSION

Fast and automated calculation of FragFit renders it applicable for implementation of interactive web-applications e.g. to model missing segments, flexible protein parts or hinge-regions into cryo-EM density maps.

Diversity of protein structures and difficulties in fold recognition: the curious case of protein G

We examine the ability of current state-of-the-art methods in protein structure prediction to discriminate topologically distant folds encoded by highly similar (>90% sequence identity) designed proteins in blind protein structure prediction experiments. We detail the corresponding prognosis for the protein fold recognition field and highlight the features of the methodologies that successfully deciphered this folding riddle.