Biology subjects

Pande, V.

Publications and source records attributed to Pande, V..

3 recordsLinked to original sources

bioRxiv2018

CFTR misfolds during native-centric simulations due to entropic penalties of native state formation

Cystic fibrosis (CF) is a common genetic disorder that affects approximately 70,000 people worldwide. It is caused by mutation-induced defects in synthesis, folding, processing, or function of the Cystic Fibrosis Transmembrane conductance Regulator protein (CFTR), a chloride-selective ion channel required for the proper functioning of secretory epithelia in tissues such as the lung, pancreas, and skin. The most common cause of CF is the single-residue deletion of F508 (F508del), a mutation present in one or both alleles in 90% of patients that induces severe folding defects and results in greatly reduced expression of the protein. Despite its medical importance, high-resolution mechanistic information about CFTR folding is lacking. In this study, we used molecular dynamics simulation with a native-centric force field to examine the folding and assembly of both full-length CFTR and the isolated first nucleotide-binding domain (NBD1). We observed that the protein was capable of substantial misfolding on both the intradomain and interdomain scale due to entropically favorable kinetic traps that exist on CFTRs folding free energy surface. These results suggest that even wild type CFTR, in the absence of any disease-related mutations, has suboptimal folding efficiency. We speculate that such entropically-driven misfolding also occurs in disease-prone mutants such as F508del and contributes to the proteins poor in vivo activity.

molecular biology↗

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↗