Shashank Pant- Doctor of Philosophy
- Scientist at Loxo Oncology, Inc.
Shashank Pant
- Doctor of Philosophy
- Scientist at Loxo Oncology, Inc.
About
51
Publications
39,896
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Citations
Introduction
I develop and use classical mechanical simulation methods to study the kinetics and thermodynamics of complex biological processes. I am specifically interested in performing all-atom simulations that can reach timescales of milliseconds and longer, with the aim to bridge gap between theory and experiments. As these timescales are far beyond the reach of the fastest supercomputers, my work entails the use of variety enhanced sampling algorithms to understand the protein dynamics.
Current institution
Additional affiliations
August 2016 - present
May 2015 - July 2016
Position
- Research Assistant
Description
- Coarse graining, SMD, Metadynamics, Developing Enhanced Sampling methods, Protein folding
August 2010 - June 2015
Education
December 2010
HBCSE
Field of study
- NATIONAL INITIATIVE FOR UNDERGRADUATE SCIENCES
August 2010 - August 2015
August 2009
IIIT- A
Field of study
- SCIENCE CONCLAVE
Publications
Publications (51)
ADP-ribosylation factor 1 (Arf1) interacts with multiple cellular partners and membranes to regulate intracellular traffic, organelle structure and actin dynamics. Defining the dynamic conformational landscape of Arf1 in its active form, when bound to the membrane, is of high functional relevance and key to understanding how Arf1 can alter diverse...
Phospholipid extrusion by ABC subfamily A (ABCA) exporters is central to cellular physiology, although the specifics of the underlying substrate interactions and transport mechanisms remain poorly resolved at the molecular level. Here we report cryo-EM structures of lipid-embedded human ABCA7 in an open state and in a nucleotide-bound, closed state...
Membrane lipids control the cellular activity of kinases containing the Src homology 2 (SH2) domain through direct lipid–SH2 domain interactions. Here we report development of new nonlipidic small molecule inhibitors of the lipid–SH2 domain interaction that block the cellular activity of their host proteins. As a pilot study, we evaluated the effic...
Glutamate is the predominant excitatory neurotransmitter in the mammalian central nervous system (CNS). Excitatory Amino Acid Transporters (EAATs) regulate extracellular glutamate by transporting it into cells, mostly glia, to terminate neurotransmission and to avoid neurotoxicity. EAATs are also chloride (Cl-) channels, but the physiological role...
Fatty acid (FA) transfer proteins extract FA from membranes and sequester them to facilitate their movement through the cytosol. Detailed structural information is available for these soluble protein-FA complexes, but the structure of the protein conformation responsible for FA exchange at the membrane is unknown. Staphylococcus aureus FakB1 is a p...
An effective implementation of enhanced sampling algorithms for molecular dynamics simulations requires a priori knowledge of the approximate reaction coordinate describing the relevant mechanisms in the system. Here we demonstrate how the artificial intelligence based recent State Predictive Information Bottleneck (SPIB) approach can learn such a...
Excitatory amino acid transporters (EAATs) are glutamate transporters that belong to the solute carrier 1A (SLC1A) family. They couple glutamate transport to the co-transport of three sodium (Na ⁺ ) ions and one proton (H ⁺ ) and the counter-transport of one potassium (K ⁺ ) ion. In addition to this coupled transport, binding of substrate and Na ⁺...
Phosphatidylinositol-4,5-bisphosphate (PIP2) is a signaling lipid which regulates voltage-gated Kv7/KCNQ potassium channels. Altered PIP2 sensitivity of neuronal Kv7.2 channel is involved in KCNQ2 epileptic encephalopathy. However, the molecular action of PIP2 on Kv7.2 gating remains largely elusive. Here, we use molecular dynamics simulations and...
Glutamate is the predominant excitatory neurotransmitter in the mammalian central nervous system (CNS). Excitatory Amino Acid Transporters (EAATs) regulate extracellular glutamate by transporting it into cells, mostly glia, to terminate neurotransmission and to avoid neurotoxicity. EAATs are also chloride (Cl-) channels, but the physiological role...
Fatty acid (FA) transfer proteins extract FA from membranes and sequester their ligand to facilitate its movement through the cytosol. While detailed views of soluble protein-FA complexes are available, how FA exchange occurs at the membrane has remained unknown. Staphylococcus aureus FakB1 is a prototypical bacterial FA transfer protein that binds...
Phosphatidylinositol-4,5-bisphosphate (PIP 2 ) is a signaling lipid which regulates voltage-gated K v 7/KCNQ potassium channels. Altered PIP2 sensitivity of neuronal K v 7.2 channel is involved in KCNQ2 epileptic encephalopathy. However, the molecular action of PIP 2 on K v 7.2 gating remains largely elusive. Here, we use molecular dynamics simulat...
P-glycoprotein (Pgp) is a major efflux pump in humans, overexpressed in a variety of cancers and associated with the development of multi-drug resistance. Allosteric modulation by various ligands (e.g., transport...
Glutamate is the most abundant excitatory neurotransmitter in the central nervous system, and its precise control is vital to maintain normal brain function and to prevent excitotoxicity. The removal of extracellular glutamate is achieved by plasma-membrane-bound transporters, which couple glutamate transport to sodium, potassium and pH gradients u...
Artificial intelligence (AI)-based approaches have had indubitable impact across the sciences through the ability to extract relevant information from raw data. Recently, AI has also found use in enhancing the efficiency of molecular simulations, wherein AI derived slow modes are used to accelerate the simulation in targeted ways. However, while ty...
Proton-coupled transporters use transmembrane proton gradients to power active transport of nutrients inside the cell. High-resolution structures often fail to capture the coupling between proton and ligand binding, and conformational changes associated with transport. We combine HDX-MS with mutagenesis and MD simulations to dissect the molecular m...
P-glycoprotein (Pgp) is a major efflux pump in humans, overexpressed in a variety of cancers and associated with the development of multi-drug resistance. Allosteric modulation induced by binding of various ligands (e.g., transport substrates, inhibitors, and ATP) has been bio-chemically shown to directly influence the function of Pgp. However, the...
Adenosine diphosphate–ribosylation factor (Arf) guanosine triphosphatase–activating proteins (GAPs) are enzymes that need to bind to membranes to catalyze the hydrolysis of guanosine triphosphate (GTP) bound to the small GTP-binding protein Arf. Binding of the pleckstrin homology (PH) domain of the ArfGAP With SH3 domain, ankyrin repeat and PH doma...
Artificial intelligence (AI) based approaches have had indubitable impact across the sciences through the ability to make sense of data. Recently AI has also seen use for enhancing the efficiency of molecular simulations, wherein AI derived slow modes are used to accelerate the simulation in targeted ways. However, while typical fields where AI is...
Glutamate is the most abundant excitatory neurotransmitter in the central nervous system, therefore its precise control is vital for maintaining normal brain function and preventing excitotoxicity1. Removal of extracellular glutamate is achieved by plasma membrane-bound transporters, which couple glutamate transport to sodium, potassium and pH grad...
Proton-coupled transporters use transmembrane proton gradients to power active transport of nutrients inside the cell. High-resolution structures often fail to capture the coupling between proton and ligand binding, and conformational changes associated with transport. We combine HDX-MS with mutagenesis and MD simulations to dissect the molecular m...
Kv7 channels are enriched at the axonal plasma membrane where their voltage-dependent potassium currents suppress neuronal excitability. Mutations in Kv7.2 and Kv7.3 subunits cause epileptic encephalopathy (EE), yet the underlying pathogenetic mechanism is unclear. Here, we used novel statistical algorithms and structural modeling to identify EE mu...
Membrane transporters are key gatekeeper proteins at cellular membranes that closely control the traffic of materials. Their function relies on structural rearrangements of varying degrees that facilitate substrate translocation across the membrane. Characterizing these functionally important molecular events at a microscopic level is key to our un...
The pleckstrin homology (PH) domain of general receptor for phosphoionositides 1 (GRP1-PHD) binds specifically to phosphatidylinositol (3,4,5)-triphosphate (PIP3 ), and acts as a second messenger. Using an extensive array of molecular dynamics (MD) simulations employing highly mobile membrane mimetic (HMMM) model as well as complementary full membr...
The cellular membrane constitutes one of the most fundamental compartments of a living cell, where key processes such as selective transport of material and exchange of information between the cell and its environment are mediated by proteins that are closely associated with the membrane. The heterogeneity of lipid composition of biological membran...
We investigate the role of the length scale of attraction on the dynamical heterogeneity in three-dimensional core-softened (CS) model liquid having interaction potential of two different length scales. Using the simple CS model, we have shown that the presence of heterogeneity is attributed to the existence of attractive interactions in a system....
Using Molecular Dynamics approach we investigated the structure, dynamics of water confined inside pristine and charged 6,6 carbon nanotubes (CNTs). This study reports the breakdown of 1D water wires and the emergence of triangular faced water on incorporating charges in 6,6 CNTs. Incorporation of charges results in high potential barriers to the f...
Thermodynamic and structural properties of core-softened fluid inside a (6,6) carbon nanotube (CNT) nanopore with tunable well depth, temperature and density have been carried out by molecular dynamic simulations. Methods of analysis include the radial distribution function, translational order parameter, excess entropy and derivative of excess ent...
We present a detailed molecular dynamics simulation investigation on hydration of C60 fullerene in a coarse-
grained water-like solvent. Based on our recent study (J. Chem. Phys. 2013), which has demonstrated the capability of a
coarse-grained, core-soft model of water to describe water-like anomalies, we report here the applicability of this model...
It is now well established that water-like anomalies can be reproduced by a spherically symmetric potential with two length scales, popularly known as core-softened potential. In the present study we aim to investigate the effect of attractive interactions among the particles in a model fluid interacting with core-softened potential on the existenc...
Reversible addition-fragmentation chain transfer (RAFT) polymerization was employed to afford two
different chiral copolymer series having opposite chiroptical properties, based on 2-(2-
methoxyethoxy)ethyl methacrylate (MEO2MA) and Boc-L/D-leucine methacryloyloxyethyl ester (Boc-
L/D-Leu-HEMA) with varying co-monomer proportions. The random nature...
Leucine/isoleucine side chain polymers are of interest due to their hydrophobicity and reported role in the formation of α-helical structures. The synthesis and reversible addition-fragmentation chain transfer (RAFT) polymerization of amino acid based chiral monomers, namely Boc-L-leucine methacryloyloxyethyl ester (Boc-L-Leu-HEMA, 1a), Boc-L-leuci...
Understanding the origin of anomalous properties of water is of prime importance. A pertinent question in
this respect is: whether tetrahedral orientational interaction is a prerequisite for the manifestation of water-like
anomalies. Recently it is shown that spherically symmetric potentials with two length scales, popularly known as coresoftened p...
We report a simple, rapid and green route for synthesis of fluorescent carbon quantum dots (CQDs) by
microwave assisted pyrolysis method using polyleucine polymer (Boc-L-Leu-HEMA) as precursor and self-passivating
agent. The as synthesized CQDs were found to possess low cytotoxicity, thus making them suitable candidates for bio-
imaging and bio-lab...
Questions
Questions (14)
I have been trying to calculate pressure profile of a lipid bilayers in NAMD. Although everything works fine but I was curious about which atoms do we generally consider while calculating pressure profile? Does it make sense to just calculate for Phosphate of the lipid bilayer?
I am working with Well-tempered Metadynamics and many times my simulations comes out showing rattle algorithm failure (which I suppose is due to the very fast movement of atoms). I tried to decrease the biasing factor and also play with the timestep of my simulation although sometimes this work but many times it doesn't. Any suggestions ?
P.S. : Using Colvar + NAMD
We usually come across the term acceleration factor in metadynamics. What is the physical significance of acceleration factor ? Suppose if for a particular system its high and for other its low then what are the factors affecting these change ?
I am performing Steered molecular dynamics simulations and trying to get PMF using work distribution curves. The problem is when i am tried to look into literature for such calculations, everywhere i found work distribution for backward sampling (say refolding of a protein) lags behind (average work value is lesser than forward average value) the forward sampling (say unfolding of protein). On the other hand in my system which is simple NaCl in water (test system) i found at certain distances forward work distribution is ahead of backward and vice versa. Can i get some help ?
I am new to SMD (Steered Molecular Dynamics) and trying to calculate PMF. Using lammps colvar package. After simulations i can see trajectories coming out along with the accumulated work. But i have a doubt about the accumulated work. Is it the net work done of the entire system or the work done due to the movement of spring (manual says its work in moving the bias). Further how can we use jarzynski equality to calculate PMF from these work values?
I am trying to calculate the PMF of NaCl dissociation by doing Umbrella Sampling and further for removing bias using WHAM, Umbrella Integration (This is just for testing the codes). I checked the overlaps of umbrellas at various positions and they are in good overlap. When i tried to use WHAM, i found that the PMF generated is not in correspondence with the reported literature. Although i am getting the CIP, SSIP positions perfect but the plot shows ions like to stay in SSIP region rather than CIP which is in-correct. Further when i tried to use umbrella integration then it worked. Now, i am confused why WHAM is not working properly.
I have seen authors running simulations in reduced units like T* = KT/epsilon, \rho* = \rho * sigma^3 etc .. Is there any added advantage by describing quantities in reduced units rather than using real units like Kelvin, g/cc etc.
Calculating structure factor from radial distribution curves is normal practice but in a publication is it just the real part of the structure factor which is reported or is it the magnitude i.e. (if x+iy is the S(q) corresponding to a particular q then x**2+y**2).
I want to differentiate between ice (mainly want to see which type of ice is being formed) and liquid water. Is there anything like ordered water ? Is yes, then how is is different from ice ?
I want to calculate the structure of water on the surface of non-polar solute (not for bulk system). For this calculation I cannot use radial distribution curve (g(r)) method because there will be a break in the radial symmetry if I consider a shell around the solute and calculate g(r) of water inside that shell. For the particles which at the interface (boundary of the shell) we don't have radial symmetry. I dont want to use angle dependence for these calculations. Are there any other methods for finding the structure?
