Biology subjects

Smith, Z.

Publications and source records attributed to Smith, Z..

21 records · Page 2Linked to original sources

bioRxiv2019

Can one trust kinetic and thermodynamic observables from biased metadynamics simulations: detailed quantitative benchmarks on millimolar drug fragment dissociation

Obtaining atomistic resolution of ligand dissociation from a protein is a much sought after experimental and computational challenge. Structural details of the dissociation process are in general hard to capture in experiments, while the relevant timescales are far beyond molecular dynamics (MD) simulations even with the most powerful super-computers. As such many different specialized enhanced sampling methods have been proposed that make it possible to efficiently calculate the dissociation mechanisms in protein-ligand systems. However, accurate benchmarks against long unbiased MD simulations are either not reported yet or simply not feasible due to the extremely long timescales. In this manuscript, we consider one such recent method "infrequent metadynamics", and benchmark in detail the various thermodynamics and kinetic information obtained from this method against extensive unbiased MD simulations for the dissociation dynamics of two different millimolar fragments from the protein FKBP in explicit water with residence times in nanoseconds to microseconds regime. We find that the metadynamics approach gives the same binding free energy profile, dissociation pathway and ligand residence time as the unbiased MD, albeit using only 6 to 50 times lower computational resources. Furthermore, we demonstrate how the metadynamics approach can self-consistently be used to ascertain whether the reweighted kinetic constants are reliable or not. We thus conclude that the answer to the question posed in the title of this manuscript is: statistically speaking, yes.

biochemistry↗

bioRxiv2018

Multi-dimensional spectral gap optimization of order parameters (SGOOP) through conditional probability factorization

Spectral gap optimization of order parameters (SGOOP) (Tiwary and Berne, Proc. Natl. Acad. Sci. 113 2839 (2016)) is a method for constructing the reaction coordinate (RC) in molecular systems, especially when they are plagued with hard to sample rare events, given a larger dictionary of order parameters or basis functions, and limited static and dynamic information about the system. In its original formulation, SGOOP is designed to construct a 1-dimensional RC. Here we extend its scope by introducing a simple but powerful extension based on the notion of conditional probability factorization where known features are washed out to learn additional and possibly hidden features of the energy landscape. We show how SGOOP can be used to proceed in a sequential and bottom-up manner to (i) systematically probe the need for extending the dimensionality of the RC, and (ii) if such a need is identified, learn additional coordinates of the RC in a computationally efficient manner. We formulate the method and demonstrate its usefulness through three illustrative examples, including the challenging and important problem of calculating the kinetics of benzene unbinding from the protein T4L99A lysozyme, where we obtain excellent agreement in terms of dissociation pathway and kinetics with other sampling methods and experiments. In this last case, starting from a larger dictionary of fairly generic and arbitrarily chosen 11 order parameters, we demonstrate how to automatically learn a 2-dimensional RC, which we then use in the infrequent metadynamics protocol to obtain 16 independent unbinding trajectories. We believe our method will be a big step in increasing the usefulness of SGOOP in performing intuition-free sampling of complex systems. Finally, we believe that the usefulness of our protocol is amplified by its applicability to not just SGOOP but also other generic methods for constructing the RC.

biophysics↗

bioRxiv2017

Emerging patterns of plasmid-host coevolution that stabilize antibiotic resistance

Multidrug resistant bacterial pathogens have become a serious global human health threat, and conjugative plasmids are important drivers of the rapid spread of resistance to last-resort antibiotics. Whereas antibiotics have been shown to select for adaptation of resistance plasmids to their new bacterial hosts, or vice versa, a general evolutionary mechanism has not yet emerged. Here we conducted an experimental evolution study aimed at determining general patterns of plasmid-bacteria evolution. Specifically, we found that a large conjugative resistance plasmid follows the same evolutionary trajectories as its non-conjugative mini-replicon in the same and other species. Furthermore, within a single host-plasmid pair three distinct patterns of adaptive evolution led to increased plasmid persistence: i) mutations in the replication protein gene (trfA1); ii) the acquisition by the resistance plasmid of a transposon from a co-residing plasmid encoding a putative toxin-antitoxin system; iii) a mutation in the hosts global transcriptional regulator gene fur. Since each of these evolutionary solutions individually have been shown to increase plasmid persistence in other plasmid-host pairs, our work points towards common mechanisms of plasmid stabilization. These could become the targets of future alternative drug therapies to slow down the spread of antibiotic resistance.

evolutionary biology↗