Biology subjects

Sultan, M. M.

Publications and source records attributed to Sultan, M. M..

4 recordsLinked to original sources

bioRxiv2017

Towards simple kinetic models of functional dynamics for a kinase subfamily

Kinases are ubiquitous enzymes involved in the regulation of critical cellular pathways and have been implicated in several cancers. Consequently, the kinetics and thermodynamics of prototypical kinases are of interest and have been the subject of numerous experimental studies. In-silico modeling of the conformational ensembles of these enzymes, on the other hand, is lacking due to inherent computational limitations. Recent algorithmic advances combined with homology modeling and parallel simulations allow us to address this computational sampling bottleneck. Here, we present the results of molecular dynamics (MD) studies for seven Src family kinase (SFK) members Fyn, Lyn, Lck, Hck, Fgr, Yes, and Blk. We present a sequence invariant extension to Markov state models (MSMs), which allows us to quantitatively compare the structural ensembles of the seven kinases. Our findings indicate that in the absence of their regulatory partners, SFK members have similar in-silico dynamics with active state populations ranging from 4-40% and activation timescales in the hundreds of microseconds. Furthermore, we observe several potentially druggable intermediate states, including a pocket next to the ATP binding site that could be potentially targeted via a small molecule inhibitors. These results establish the utility of MSMs for studying protein families.

biophysics↗

bioRxiv2017

Transferable tICA-Metadynamics: Efficient sampling of protein mutants by transferring information from the wild type’s Markov state model

We recently showed that the time-structure based independent component analysis method from Markov state model literature provided a set of variationally optimal slow collective variables for Metadynamics (tICA-Metadynamics). In this paper, we extend the methodology towards efficient sampling of related mutants by borrowing ideas from transfer learning methods in machine learning. Our method explicitly assumes that a similar set of slow modes and metastable states are found in both the wild type (base line) and its mutants. Under this assumption, we describe a few simple techniques using sequence mapping for transferring the slow modes and structural information contained in the wild type simulation to a mutant model for performing enhanced sampling. The resulting simulations can then be reweighted onto the full-phase space using Multi-state Bennett Acceptance Ratio, allowing for thermodynamic comparison against the wild type. We first benchmark our methodology by re-capturing alanine dipeptide dynamics across a range of different atomistic force fields, including the polarizable Amoeba force field, after learning a set of slow modes using Amber ff99sb-ILDN. We next extend the method by including structural data from the wild type simulation and apply the technique to recapturing the affects of the GTT mutation on the FIP35 WW domain.

biophysics↗

bioRxiv2017

Millisecond Dynamics Of BTK Reveal Kinome-Wide Conformational Plasticity Within The Apo Kinase Domain

Bruton tyrosine kinase (BTK) is a key enzyme in B-cell development whose improper regulation causes severe immunodeficiency diseases. Design of selective BTK therapeutics would benefit from improved, in-silico structural modeling of the kinases solution ensemble. However, this remains challenging due to the immense computational cost of sampling events on biological timescales. In this work, we combine multi-millisecond molecular dynamics (MD) simulations with Markov state models (MSMs) to report on the thermodynamics, kinetics, and accessible states of BTKs kinase domain. Our conformational landscape links the active state to several inactive states, connected via a structurally diverse intermediate. Our calculations predict a kinome-wide conformational plasticity, and indicate the presence of several new potentially druggable BTK states. We further find that the population of these states and the kinetics of their inter-conversion are modulated by protonation of an aspartate residue, establishing the power of MD & MSMs in predicting effects of chemical perturbations.

biophysics↗

bioRxiv2016

MSMBuilder: Statistical Models for Biomolecular Dynamics

MSMBuilder is a software package for building statistical models of high-dimensional time-series data. It is designed with a particular focus on the analysis of atomistic simulations of biomolecular dynamics such as protein folding and conformational change. MSMBuilder is named for its ability to construct Markov State Models (MSMs), a class of models that has gained favor among computational biophysicists. In addition to both well-established and newer MSM methods, the package includes complementary algorithms for understanding time-series data such as hidden Markov models (HMMs) and time-structure based independent component analysis (tICA). MSMBuilder boasts an easy to use command-line interface, as well as clear and consistent abstractions through its Python API (application programming interface). MSMBuilder is developed with careful consideration for compatibility with the broader machine-learning community by following the design of scikit-learn. The package is used primarily by practitioners of molecular dynamics but is just as applicable to other computational or experimental time-series measurements. http://msmbuilder.org

biophysics↗