Biology subjects

Shukla, D.

Publications and source records attributed to Shukla, D..

At least 73 records · Page 4Linked to original sources

bioRxiv2021

Comparison of seven modelling algorithms for GABA-edited 1H-MRS

Edited MRS sequences are widely used for studying GABA in the human brain. Several algorithms are available for modelling these data, deriving metabolite concentration estimates through peak fitting or a linear combination of basis spectra. The present study compares seven such algorithms, using data obtained in a large multi-site study. GABA-edited (GABA+, TE = 68 ms MEGA-PRESS) data from 222 subjects at 20 sites were processed via a standardised pipeline, before modelling with FSL-MRS, Gannet, AMARES, QUEST, LCModel, Osprey and Tarquin, using standardised vendor-specific basis sets (for GE, Philips and Siemens) where appropriate. After referencing metabolite estimates (to water or creatine), systematic differences in scale were observed between datasets acquired on different vendors hardware, presenting across algorithms. Scale differences across algorithms were also observed. Using the correlation between metabolite estimates and voxel tissue fraction as a benchmark, most algorithms were found to be similarly effective in detecting differences in GABA+. An inter-class correlation across all algorithms showed single-rater consistency for GABA+ estimates of around 0.38, indicating moderate agreement. Upon inclusion of a basis set component explicitly modelling the macromolecule signal underlying the observed 3.0 ppm GABA peaks, single-rater consistency improved to 0.44. Correlation between discrete pairs of algorithms varied, and was concerningly weak in some cases. Our findings highlight the need for consensus on appropriate modelling parameters across different algorithms, and for detailed reporting of the parameters adopted in individual studies to ensure reproducibility and meaningful comparison of outcomes between different studies. HighlightsO_LIGABA-edited MRS data from 222 healthy adults across 20 research sites were analyzed C_LIO_LIData were modelled using seven different algorithms, yielding GABA+ and Glx estimates C_LIO_LIModerate agreement was seen across all the tested algorithms C_LIO_LIAdding a component to represent co-edited macromolecule signals improved concordance C_LIO_LIBaseline modelling emerged as major factor differentiating outcomes C_LI

neuroscience↗

bioRxiv2021

Structural rearrangement of the intracellular gate of the serotonin transporter induced by Thr276 phosphorylation

The reuptake of the neurotransmitter serotonin from the synaptic cleft by the serotonin transporter, SERT, is essential for proper neurological signaling. Biochemical studies have shown Thr276 of transmembrane helix 5 is a site of PKG-mediated SERT phosphorylation, which has been proposed to shifts the SERT conformational equlibira to promote inward-facing states, thus enhancing 5HT transport. Recent structural and simulation studies have provided insights into the conformation transitions during substrate transport but have not shed light on SERT regulation via post-translational modifications. Using molecular dynamics simulations and Markov state models, we investigate how Thr276 phosphorylation impacts the SERT mechanism and its role in enhancing transporter stability and function. Our simulations show that Thr276 phosphorylation alters the hydrogen-bonding network involving residues on transmembrane helix 5. This in turn decreases the free energy barriers for SERT to transition to the inward-facing state, thus facilitating 5HT transport. The results provide atomistic insights into in vivo SERT regulation and can be extended to other pharmacologically important transporters in the solute carrier superfamily.

biophysics↗

bioRxiv2021

Intriguing Role of Water in Plant Hormone Perception

Plant hormones are small molecules that regulate plant growth, development, and responses to biotic and abiotic stresses. Plant hormones are specifically recognized by the binding site of their receptors. In this work, we investigated the role of water displacement and reorganization at the binding site of plant receptors on the binding of eight classes of phytohormones (auxin, jasmonate, gibberellin, strigolactone, brassinosteroid, cytokinin, salicylic acid, and abscisic acid) using extensive molecular dynamics simulations and inhomogeneous solvation theory. Our findings demonstrated that displacement of water molecules by phytohormones contributes to free energy of binding via entropy gain and is associated with free energy barriers. Also, our results have shown that displacement of unfavorable water molecules in the binding site can be exploited in rational agrochemical design. Overall, this study uncovers the role of water molecules in plant hormone perception, which creates new avenues for agrochemical design to target plant growth and development.

plant biology↗

bioRxiv2021

Predicting the Activities of Drug Excipients on Biological Targets using One-Shot Learning

Excipients are a major component of drugs and are used to improve drugs attributes such as stability and appearance. Excipients approved by Food and Drug Administration (FDA) are regarded as safe for human in allowed concentration, but their potential interaction with drug targets have not been investigated systematically, which might influence drugs efficacy. Deep learning models have been used for identification of ligands that could bind to the drug targets. However, due to the limited available data, it is challenging to reliably estimate the likelihood of a ligand-protein interaction. One-shot learning techniques provide a potential approach to address this low-data problem as these techniques require only one or a few examples to classify the new data. In this study, we apply one-shot learning models on datasets that include ligands binding to G-Protein Coupled Receptors (GPCRs) and Kinases. The predicted results suggest that one-shot learning models could be used for predicting ligand-protein interaction and the models attain better performance when protein targets contain conserved binding pockets. The trained models are also used to predict interactions between excipients and drug targets, which provides a potential efficient strategy to explore the activities of drug excipients. We find that a large number of drug excipients could interact with biological targets and influence their function. The results demonstrate how one-shot learning models can be used to make accurate prediction for excipient-protein interactions and these methods could be used for selecting excipients with limited drug-protein interactions.

biochemistry↗

bioRxiv2021

Investigation of gating in outer membrane porins provides new perspectives on antibiotic resistance mechanisms

Gram-negative bacteria pose a serious public health concern, primarily due to a higher frequency of antibiotic resistance conferred to them as a result of low permeability of their outer membrane (OM). Antibiotics capable of traversing the OM typically permeate through OM porins; thus, understanding the permeation properties of these porins is instrumental to the development of new antibiotics. A common macroscopic feature of many OM porins is their ability to transition between functionally distinct open and closed states that regulate transport properties and rate. To obtain a molecular basis for these processes, we performed tens of microseconds of molecular dynamics simulations of E. coli OM porin, OmpF. We observed that large-scale motion of the internal loop, L3, leads to widening and narrowing of the pore, suggesting its potential role in gating. Furthermore, Markov state analysis revealed multiple energetically stable conformations of L3 corresponding to open and closed states of the porin. Dynamics between these functional states occurs on the time scale of tens of microseconds and are mediated by the movement of highly conserved acidic residues of L3 to form H-bonds with opposing sides of the barrel wall of the pore. To validate our mechanism, we mutated key residues involved in the gating process that alter the H-bond pattern in the open/closed states and performed additional simulations. These mutations shifted the dynamic equilibrium of the pore towards open or closed states. Complementarily, the mutations favoring the open/closed states lead to increased/decreased accumulation of multiple antibiotics in our whole-cell accumulation assays. Notably, porins containing one of the mutations favoring the closed state has previously been found in antibiotic resistant bacterial strains. Overall, our 180 {micro}s of simulation data (wild type and mutants) with concerted experiments suggests that regulation of the dynamic equilibrium between open and closed states of OM porins could be a mechanism by which Gram-negative bacteria acquire antibiotic resistance.

biophysics↗

bioRxiv2021

Activation Mechanism of Strigolactone Receptors And Its Impact On Ligand Selectivity Between Host And Parasitic Plants

Parastic weeds such as Striga have led to significant losses in agricultural productivity worldwide. These weeds use the plant hormone strigolactone as a germination stimulant. Strigolactone signaling involves substrate binding and hydrolysis followed by a large conformational change of the receptor to a "closed" or "active" state that is able to associate with a downstream signaling partner MAX2/D3. The crystal structure of the active and inactive AtD14 receptor have helped in elucidating the structural changes involved in activation. However, the mechanism by which the receptor activates remains unknown. The ligand dependence of AtD14 activation has been disputed by mutagenesis studies showing that enzymatically inactive receptors are able to form a complex with MAX2 proteins. Furthermore, activation differences between strigolactone receptor in Striga, ShHTL7 and textitAtD14 could contribute to the high sensitivity to strigolactones exhibited by parasitic plants. Using molecular dynamics simulations, we demonstrate that both AtD14 and ShHTL7 could adopt an active conformation in absence of ligand. However, the ShHTL7 receptor exhibits higher population in the inactive apo state as compared to the AtD14 receptor. We demonstrate that this difference in inactive state population is caused by sequence differences between their D-loops and its interactions with the catalytic histidine that prevents full binding pocket closure in ShHTL7. These results indicate that hydrolysis of a strigolactone ligand would enhance the active state population by destabilizing the inactive state in ShHTL7 as compared to AtD14. We also show that the mechanism of activation is more concerted in AtD14 than in ShHTL7 and that the main barrier to activation in ShHTL7 is closing of the binding pocket.

plant biology↗

bioRxiv2021

Why does Δ9-Tetrahydrocannabinol act as a partial agonist of cannabinoid receptors?

Cannabinoid receptor 1 (CB1) is a therapeutically relevant drug target for controlling pain, obesity, and other central nervous system disorders. However, full agonists and antagonists of CB1 have been reported to cause serious side effects in patients. Therefore, partial agonists have emerged as a viable alternative to full agonists and antagonists as they avoid overstimulation and side effects. One of the key bottlenecks in the design of partial agonists is the lack of understanding of the molecular mechanism of partial agonism. In this study, we examine two mechanistic hypotheses for the origin of partial agonism in cannabinoid receptors and explain the mechanistic basis of partial agonism exhibited by {Delta}9-Tetrahydrocannabinol (THC). In particular, we inspect whether partial agonism emerges from the ability of THC to bind in both agonist and antagonist binding pose or from its ability to only partially activate the receptor. Extensive molecular dynamics simulations and the Markov state model capture the THC binding in both antagonist, and agonist binding poses in CB1 receptor. Furthermore, we observe that binding of THC in the agonist binding pose leads to rotation of toggle switch residues and causes partial outward movement of intracellular transmembrane helix 6 (TM6). Our simulations also suggest that the alkyl side chain of THC plays a crucial role in determining partial agonism by stabilizing the ligand in the agonist and antagonist-like poses within the pocket. This study provides us fundamental insights into the mechanistic origin of the partial agonism of THC.

biophysics↗

bioRxiv2021

Deep Mutagenesis of a Transporter for Uptake of a Non-Native Substrate Identifies Conformationally Dynamic Regions

The serotonin transporter, SERT, catalyzes serotonin reuptake at the synapse to terminate neurotransmission via an alternating access mechanism, and SERT inhibitors are the most widely prescribed antidepressants. Here, deep mutagenesis is used to determine the effects of nearly all amino acid substitutions on human SERT surface expression and transport of the fluorescent substrate APP+, identifying many mutations that enhance APP+ import. Comprehensive simulations of the entire ion-coupled import process reveal that while binding of the native substrate, serotonin, reduces free energy barriers between conformational states to promote SERT dynamics, the conformational free energy landscape in the presence of APP+ instead resembles Na+ bound-SERT, with a higher free energy barrier for transitioning to an inward-facing state. The deep mutational scan for SERT-catalyzed import of APP+ finds mutations that promote the necessary conformational changes that would otherwise be facilitated by the native substrate. Indeed, hundreds of gain-of-function mutations for APP+ import are found along the permeation pathway, most notably mutations that favor the formation of a solvent-exposed intracellular vestibule. The mutagenesis data support the simulated mechanism in which the neurotransmitter and a symported sodium share a common cytosolic exit pathway to achieve coupling. Furthermore, the mutational landscape for SERT surface expression, which likely filters out misfolded sequences, reveals that residues along the permeation pathway are mutationally tolerant, providing plausible evolutionary pathways for changes in transporter properties while maintaining folded structure.

biochemistry↗

bioRxiv2021

Distinct Binding Mechanisms for Allosteric Sodium Ion In Cannabinoid Receptors

The therapeutical potential of Cannabinoid receptors is not fully explored due to psychoactive side-effects and lack of selectivity associated with the orthosteric ligands. Allosteric modulators have the potential to become selective therapeutics for cannabinoid receptors. Biochemical experiments have shown the effects of the allosteric Na+ binding on cannabinoid receptor activity. However, the Na+ coordination site, and binding pathway are still unknown. Here, we perform molecular dynamic simulations to explore Na+ binding in the cannabinoid receptors, CB1 and CB2. Simulations reveal that Na+ binds to the primary binding site from different extracellular sites for CB1 and CB2. A distinct secondary Na+ coordinate site is identified that is not present in CB2. Furthermore, simulations also show that intracellular Na+ could bind to the Na+ binding site in CB1. Constructed Markov state models show that the standard free energy of Na+ binding is similar to the previously calculated free energy for other class A GPCRs.

biophysics↗

bioRxiv2021

Impact of increased membrane realism on conformational sampling of proteins

The realism and accuracy of lipid bilayer simulations through molecular dynamics (MD) is heavily dependent on the lipid composition. While the field is pushing towards implementing more heterogeneous and realistic membrane compositions, a lack of high-resolution lipidomic data prevents some membrane protein systems from being modeled with the highest level of realism. Given the additional diversity of real-world cellular membranes and protein-lipid interactions, it is still not fully understood how altering membrane complexity affects modeled membrane protein function or if it matters over long timescale simulations. This is especially true for organisms whose membrane environments have little to no computational study, such as the plant plasma membrane. Tackling these issues in tandem, a generalized, realistic, and asymmetric plant plasma with more than 10 different lipid species membrane is constructed herein. Classical MD simulations of pure membrane constructs were performed to evaluate how altering the compositional complexity of the membrane impacted the plant membrane properties. The apo form of a plant sugar transporter, OsSWEET2b, was inserted into membrane models where lipid diversity was calculated in either a size-dependent or -independent manner. An adaptive sampling simulation regime validated by Markov-state models was performed to capture the gating dynamics of OsSWEET2b in each of these membrane constructs. In comparison to previous OsSWEET2b simulations performed in a pure POPC bilayer, we confirm that simulations performed within a native-like membrane composition alter the stabilization of apo OsSWEET2b conformational states by ~1 kcal/mol. The free energy barriers of intermediate conformational states decrease when realistic membrane complexity is simplified, albeit roughly within sampling error, suggesting that protein-specific responses to membranes differ due to altered packing caused by compositional fluctuations. This work serves as a case study where a more realistic bilayer composition makes unbiased conformational sampling easier to achieve than with simplified bilayers.

biophysics↗

bioRxiv2020

Reconciling Membrane Protein Simulations with Experimental DEER Spectroscopy Data

Spectroscopy experiments are crucial to study membrane proteins for which traditional structure determination methods still prove challenging. Double electron-electron resonance (DEER) spectroscopy experiments provide protein residue-pair distance distributions that are indicative of their conformational heterogeneity. Atomistic molecular dynamics (MD) simulations are another tool that have proved vital to study the structural dynamics of membrane proteins such as to identify inward-open, occluded, and outward-open conformations of transporter membrane proteins, among other partially open or closed states of the protein. Yet, studies have reported that there is no direct consensus between distributional data from DEER experiments and MD simulations, which has challenged validation of structures obtained from long-timescale simulations and using simulations to design experiments. Current coping strategies for comparisons rely on heuristics, such as mapping nearest matching peaks between two ensembles or biased simulations. Here we examine the differences in residue-pair distance distributions arising due to choice of membrane around the protein and covalent modification of a pair of residues to nitroxide spin labels in DEER experiments. Through comparing MD simulations of two proteins, PepTSo and LeuT - both of which have been characterized using DEER experiments previously - we show that the proteins dynamics are similar despite the choice of the detergent micelle as a membrane mimetic in DEER experiments. On the other hand, covalently modified residues show slight local differences in their dynamics and a huge divergence when the spin labels anointed oxygen atom pair distances are measured rather than protein backbone distances. Given the computational expense associated with pairwise MTSSL labeled MD simulations, we examine the use of biased simulations to explore the conformational dynamics of the spin labels only to reveal that such simulations alter the underlying protein dynamics. Our study identifies the main cause for the mismatch between DEER experiments and MD simulations and will accelerate developing potential mitigation strategies to improve simulation observables match with DEER spectroscopy experiments.

biophysics↗

bioRxiv2020

Ancestral class-promiscuity as a driver of functional diversity in the BAHD acyltransferase family in plants

Large enzyme families catalyze metabolic diversification by virtue of their ability to use diverse chemical scaffolds. How enzyme families attain such functional diversity is not clear. Here, we addressed this question using BAHD acyltransferases as a model, and identified the routes by which duplication, promiscuity and sequence changes influenced BAHD diversification. This fast-evolving family expanded drastically during land plant evolution from 1-5 copies in algae to [~]100 copies in diploid angiosperm genomes. In vitro characterization of fourteen BAHDs against a substrate panel and compilation of >160 published activities revealed the wide prevalence of promiscuity among BAHDs. Using phylogenetic analysis, we predicted the substrate classes that the ancestral enzymes were likely capable of using prior to land plant origins. While the anthocyanin acylation activity was fixed in BAHDs later near the origin of angiosperms, in vitro testing of BAHDs from non-seed plant lineages suggested that the ability to acylate anthocyanins likely existed promiscuously millions of years prior to its fixation. Motif enrichment analysis in anthocyanin-acylating BAHDs identified two motifs fixed in the largest anthocyanin acylating clade. Molecular dynamic simulations and enzyme kinetics revealed the important role of an active site tryptophan, whose bulkiness, hydrophobicity and aromaticity are critical for anthocyanin acylation. Our results thus describe the molecular processes in robust, evolvable enzymes that drive emergence of functional diversity in enzyme families. One sentence summaryUsing a combination of phylogenetics, biochemistry and protein structure analysis, we investigated how the BAHD acyltransferase family evolved to use a structurally diverse array of substrates.

plant biology↗

bioRxiv2020

mTORC2 combats cellular stress and potentiates immunity during viral infection

Herpes simplex virus type 1 (HSV-1) causes ocular and orofacial infections, which are generally well controlled by the host and nonlethal. In rare cases, HSV-1 causes encephalitis, which leads to permanent brain injuries, memory loss or even death. Host factors protect the organism from viral infections by activating the immune response. However, the factors that confer neuroprotection during viral encephalitis are unknown. Here we show that mammalian target of rapamycin complex 2 (mTORC2) is essential for the host survival of ocular HSV-1 infections in vivo. We found that the loss of mTORC2 causes systemic HSV-1 infection not only because of weak innate and adaptive immune responses but also due to increased ocular and neuronal cell death, which becomes lethal over time. Furthermore, we found that mTORC2 mediates cell survival channels through the inactivation of the proapoptotic factor FoxO3a. Our results demonstrate how mTORC2 potentiates host defenses against viral infections as well as implicating mTORC2 as a necessary host factor for survival. We anticipate our findings may help develop new therapeutic window for severe HSV-1 infections, such as herpes simplex encephalitis.

cell biology↗

bioRxiv2020

Disruption of innate defense responses by endoglycosidase HPSE promotes cell survival

The drive to withstand environmental stresses and defend against invasion is a universal trait extant in all forms of life. While numerous canonical signaling cascades have been characterized in detail, it remains unclear how these pathways interface to generate coordinated responses to diverse stimuli. To dissect these connections, we follow heparanase (HPSE), a protein best known for its endoglycosidic activity at the extracellular matrix but recently recognized to drive various forms of late stage disease through unknown mechanisms. Using herpes simplex virus-1 (HSV-1) infection as a model cellular perturbation, we demonstrate that HPSE acts beyond its established enzymatic role to restrict multiple forms of cell-intrinsic defense and facilitate host cell reprogramming by the invading pathogen. We reveal that cells devoid of HPSE are innately resistant to infection and counteract viral takeover through multiple amplified defense mechanisms. With a unique grasp of the fundamental processes of transcriptional regulation and cell death, HPSE represents a potent cellular intersection with broad therapeutic potential.

cell biology↗

bioRxiv2020

Role of substrate recognition in modulating strigolactone receptor selectivity in witchweed.

Witchweed, or Striga hermonthica, is a parasitic weed that destroys billions of dollars worth of crops globally every year. Its germination is stimulated by strigolactones exuded by its host plants. Despite high sequence, structure, and ligand binding site conservation across different plant species, one strigolactone receptor in witchweed (Sh HTL7) uniquely exhibits a picomolar EC50 for downstream signaling. Previous biochemical and structural analyses have hypothesized that this unique ligand sensitivity can be attributed to a large binding pocket volume in Sh HTL7 resulting in enhanced ability to bind substrates. Additional structural details of the substrate binding process can help explain its role in modulating the ligand selectivity. Using long-timescale molecular dynamics simulations, we demonstrate that mutations at the entrance of the binding pocket facilitate a more direct ligand binding pathway to Sh HTL7, whereas hydrophobicity at the binding pocket entrance results in a stable "anchored" state. We also demonstrate that several residues on the D-loop of At D14 stabilize catalytically inactive conformations. Finally, we show that strigolactone selectivity is not modulated by binding pocket volume. Our results indicate that while ligand binding is not the sole modulator of strigolactone receptor selectivity, it is a significant contributing factor. These results can be used to inform the design of selective antagonists for strigolactone receptors in witchweed.

plant biology↗

bioRxiv2020

How antiporters exchange substrates across the cell membrane? An atomic-level description of the complete exchange cycle in NarK

Major facilitator superfamily (MFS) proteins operate via three different mechanisms: uniport, symport, and antiport. Despite extensive investigations, molecular understanding of antiporters is less advanced than other transporters due to the complex coupling between two substrates and the lack of distinct structures. We employ extensive (~300 s) all-atom molecular dynamics simulations to dissect the complete substrate exchange cycle of the bacterial [Formula] antiporter, NarK. We show that paired basic residues in the binding site prevent the closure of unbound protein and ensure the exchange of two substrates. Conformational transition only occurs in the presence of substrate, which weakens the electrostatic repulsion and stabilizes the transporter by ~1.5 {+/-} 0.1 kcal/mol. Furthermore, we propose a state-dependent substrate exchange model, in which the relative spacing between the paired basic residues determines whether [Formula] and [Formula] bind simultaneously or sequentially. Overall, this work presents a general working model for the antiport mechanism within MFS family.

biophysics↗

bioRxiv2020

Structural Basis for Negative Regulation of ABA Signaling by ROP11 GTPase

Abscisic acid (ABA) is an essential plant hormone responsible for plant development and stress responses. Recent structural and biochemical studies have identified the key components involved in ABA signaling cascade, including PYR/PYL/RCAR receptors, protein phosphatases PP2C, and protein kinases SnRK2. The plant-specific, Roh-like (ROPs) small GTPases are negative regulators of ABA signal transduction by interacting with PP2C, which can shut off "leaky" ABA signal transduction caused by constitutive activity of monomeric PYR/PYL/RCAR receptors. However, the structural basis for negative regulation of ABA signaling by ROP GTPases remains elusive. In this study, we have utilized large-scale coarse-grained (10.05 milliseconds) and allatom molecular dynamics simulations and standard protein-protein binding free energy calculations to predict the complex structure of AtROP11 and phosphatase AtABI1. In addition, we have predicted the detailed complex association pathway and identified the critical residue pairs in AtROP11 and AtABI1 for complex stability. Overall, this study has established a powerful framework of using large-scale molecular simulations to predict unknown protein complex structures and suggested the molecular mechanism of the negative regulation of ABA signal transduction by small GTPases.

plant biology↗

bioRxiv2020

FingerprintContacts: Predicting Alternative Conformations of Proteins from Coevolution

Proteins are dynamic molecules which perform diverse molecular functions by adopting different three-dimensional structures. Recent progress in residue-residue contacts prediction opens up new avenues for the de novo protein structure prediction from sequence information. However, it is still difficult to predict more than one conformation from residue-residue contacts alone. This is due to the inability to deconvolve the complex signals of residue-residue contacts, i.e. spatial contacts relevant for protein folding, conformational diversity, and ligand binding. Here, we introduce a machine learning based method, called FingerprintContacts, for extending the capabilities of residue-residue contacts. This algorithm leverages the features of residue-residue contacts, that is, (1) a single conformation outperforms the others in the structural prediction using all the top ranking residue-residue contacts as structural constraints, and (2) conformation specific contacts rank lower and constitute a small fraction of residue-residue contacts. We demonstrate the capabilities of FingerprintContacts on eight ligand binding proteins with varying conformational motions. Furthermore, FingerprintContacts identifies small clusters of residue-residue contacts which are preferentially located in the dynamically fluctuating regions. With the rapid growth in protein sequence information, we expect FingerprintContacts to be a powerful first step in structural understanding of protein functional mechanisms.

bioinformatics↗