13 research outputs found

    Introducing the new bacterial branch of the RNase A superfamily

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    Bovine pancreatic ribonuclease (RNase A) is the founding member of the RNase A superfamily. Members of this superfamily of ribonucleases have high sequence diversity, but possess a similar structural fold, together with a conserved His-Lys-His catalytic triad and structural disulfide bonds. Until recently, RNase A proteins had exclusively been identified in eukaryotes within vertebrae. Here, we discuss the discovery by Batot et al. of a bacterial RNase A superfamily member, CdiA-CTYkris: a toxin that belongs to an inter-bacterial competition system from Yersinia kristensenii. CdiA-CTYkris exhibits the same structural fold as conventional RNase A family members and displays in vitro and in vivo ribonuclease activity. However, CdiA-CTYkris shares little to no sequence similarity with RNase A, and lacks the conserved disulfide bonds and catalytic triad of RNase A. Interestingly, the CdiA-CTYkris active site more closely resembles the active site composition of various eukaryotic endonucleases. Despite lacking sequence similarity to eukaryotic RNase A family members, CdiA-CTYkris does share high sequence similarity with numerous Gram-negative and Gram-positive bacterial proteins/domains. Nearly all of these bacterial homologs are toxins associated with virulence and bacterial competition, suggesting that the RNase A superfamily has a distinct bacterial subfamily branch, which likely arose by way of convergent evolution. Finally, RNase A interacts directly with the immunity protein of CdiA-CTYkris, thus the cognate immunity protein for the bacterial RNase A member could be engineered as a new eukaryotic RNase A inhibitor

    Enhancing Sidechain Rotamer Sampling Using Non-Equilibrium Candidate Monte Carlo

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    Molecular simulations are a valuable tool for studying biomolecular motions and thermodynamics. However, such motions can be slow compared to simulation timescales, yet critical. Specifically, adequate sampling of sidechain motions in protein binding pockets proves crucial for obtaining accurate estimates of ligand binding free energies from molecular simulations. The timescale of sidechain rotamer flips can range from a few ps to several hundred ns or longer, particularly in crowded environments like the interior of proteins. Here, we apply a mixed non-equilibrium candidate Monte Carlo (NCMC)/molecular dynamics (MD) method to enhance sampling of sidechain rotamers. The NCMC portion of our method applies a switching protocol wherein the steric and electrostatic interactions between target sidechain atoms and the surrounding environment are cycled off and then back on during the course of a move proposal. Between NCMC move proposals, simulation of the system continues via traditional molecular dynamics. Here, we first validate this approach on a simple, solvated valine-alanine dipeptide system and then apply it to a well-studied model ligand binding site in T4 lysozyme L99A. We compute the rate of rotamer transitions for a valine sidechain using our approach and compare it to that of traditional molecular dynamics simulations. Here, we show that our NCMC/MD method substantially enhances sidechain sampling, especially in systems where the torsional barrier to rotation is high (>10 kcal/mol). These barriers can be intrinsic torsional barriers or steric barriers imposed by the environment.Overall, this may provide a promising strategy to selectively improve sidechain sampling in molecular simulations.</div

    Blind prediction of cyclohexane-water distribution coefficients from the SAMPL5 challenge.

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    In the recent SAMPL5 challenge, participants submitted predictions for cyclohexane/water distribution coefficients for a set of 53 small molecules. Distribution coefficients (log D) replace the hydration free energies that were a central part of the past five SAMPL challenges. A wide variety of computational methods were represented by the 76 submissions from 18 participating groups. Here, we analyze submissions by a variety of error metrics and provide details for a number of reference calculations we performed. As in the SAMPL4 challenge, we assessed the ability of participants to evaluate not just their statistical uncertainty, but their model uncertainty-how well they can predict the magnitude of their model or force field error for specific predictions. Unfortunately, this remains an area where prediction and analysis need improvement. In SAMPL4 the top performing submissions achieved a root-mean-squared error (RMSE) around 1.5 kcal/mol. If we anticipate accuracy in log D predictions to be similar to the hydration free energy predictions in SAMPL4, the expected error here would be around 1.54 log units. Only a few submissions had an RMSE below 2.5 log units in their predicted log D values. However, distribution coefficients introduced complexities not present in past SAMPL challenges, including tautomer enumeration, that are likely to be important in predicting biomolecular properties of interest to drug discovery, therefore some decrease in accuracy would be expected. Overall, the SAMPL5 distribution coefficient challenge provided great insight into the importance of modeling a variety of physical effects. We believe these types of measurements will be a promising source of data for future blind challenges, especially in view of the relatively straightforward nature of the experiments and the level of insight provided
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