Biology subjects

Kasimova, M. A.

Publications and source records attributed to Kasimova, M. A..

7 recordsLinked to original sources

bioRxiv2022

Identifying endogenous peptide receptors bycombining structure and transmembrane topologyprediction

Many secreted endogenous peptides rely on signalling pathways to exert their function in the body. While peptides can be discovered through high throughput technologies, their cognate receptors typically cannot, hindering the understanding of their mode of action. We investigate the use of AlphaFold-Multimer for identifying the cognate receptors of secreted endogenous peptides in human receptor libraries without any prior knowledge about likely candidates. We find that AlphaFolds predicted confidence metrics have strong performance for prioritizing true peptide-receptor interactions. By applying transmembrane topology prediction using DeepTMHMM, we further improve performance by detecting and filtering biologically implausible predicted interactions. In a library of 1112 human receptors, the method ranks true receptors in the top percentile on average for 11 benchmark peptide-receptor pairs.

bioinformatics↗

bioRxiv2021

Modulation of the IKS channel by PIP2 requires two binding sites per monomer

The phosphatidyl-inositol-4,5-bisphosphate (PIP2) lipid has been shown to be crucial for the coupling between the voltage sensor and the pore of the potassium voltage-gated KV7 channel family, especially the KV7.1 channel. The latter, expressed in the myocardium membrane is complexed with its auxiliary subunits, KCNE1 to generate the so-called IKS current. We present here molecular models of transmembrane domain of this complex in its three known states, namely the Resting/Closed (RC), the Intermediate/Closed (IC), and the Activated/Open (AO), robustness of which is assessed by agreement with a range of biophysical data. Molecular Dynamics (MD) simulations of these models embedded in a lipid bilayer including phosphatidyl-inositol-4,5-bisphosphate (PIP2) lipids show that in presence of KCNE1, two PIP2 lipids are necessary to stabilize each state. The simulations also show that KCNE1 interacts with both PIP2 binding sites, forming a tourniquet around the pore and preventing its opening. The present investigation provides therefore key molecular elements that govern the role of PIP2 in KCNE1 modulation of IKS channels, possibly a common mechanism by which auxiliary KCNE subunits might modulate a variety of other ion channels.

biophysics↗

bioRxiv2019

Two-stage electro-mechanical coupling of a Kv channel in voltage-dependent activation

In voltage-gated potassium (KV) channels, the voltage-sensing domain (VSD) undergoes sequential activation from the resting state to the intermediate state and activated state to trigger pore opening via electro-mechanical (E-M) coupling. However, the spatial and temporal details underlying E-M coupling remain elusive. Here, we leverage KV7.1s unique two open states associated with the VSD adopting the intermediate and activated conformations to report a two-stage E-M coupling mechanism in voltage-dependent gating of KV7.1 as triggered by VSD activations to the intermediate and then activated state. When the S4 segment transitions to the intermediate state, the hand-like C-terminus of the VSD-pore linker (S4-S5L) interacts with the pore in the same subunit. When S4 then proceeds on to the fully-activated state, the elbow-like hinge between S4 and S4-S5L engages with the pore to activate conductance. This two-stage \"hand-and-elbow\" gating mechanism elucidates distinct tissue-specific modulations, pharmacology, and disease pathogenesis of KV7.1, and likely applies to numerous KV channels.

biophysics↗

bioRxiv2019

Extracting molecular insights from conformational ensembles using Machine Learning

Biomolecular simulations are intrinsically high dimensional and generate noisy datasets of ever increasing size. Extracting important features in the data is crucial for understanding the biophysical properties of molecular processes, but remains a big challenge. Machine learning (ML) provides powerful dimensionality reduction tools. However, such methods are often criticized to resemble black boxes with limited human-interpretable insight. We use methods from supervised and unsupervised ML to efficiently create interpretable maps of important features from molecular simulations. We benchmark the performance of several methods including neural networks, random forests and principal component analysis, using a toy model with properties reminiscent of macromolecular behavior. We then analyze three diverse biological processes: conformational changes within the soluble protein calmodulin, ligand binding to a G protein-coupled receptor and activation of an ion channel voltage-sensor domain, unravelling features critical for signal transduction, ligand binding and voltage sensing. This work demonstrates the usefulness of ML in understanding biomolecular states and demystifying complex simulations. STATEMENT OF SIGNIFICANCEUnderstanding how biomolecules function requires resolving the ensemble of structures they visit. Molecular dynamics simulations compute these ensembles and generate large amounts of data that can be noisy and need to be condensed for human interpretation. Machine learning methods are designed to process large amounts of data, but are often criticized for their black-box nature and have historically been modestly used in the analysis of biomolecular systems. We demonstrate how machine learning tools can provide an interpretable overview of important features in a simulation dataset. We develop a protocol to quickly perform data-driven analysis of molecular simulations. This protocol is applied to identify the molecular basis of ligand binding to a receptor and of voltage sensitivity of an ion channel.

biophysics↗

bioRxiv2019

Phosphoinositides regulate the TRPV1 channel via two functionally distinct binding sites

Regulation of the heat- and capsaicin-activated Transient Receptor Potential Vanilloid 1 (TRPV1) channel by phosphoinositides is controversial. In a recent cryoEM structure, an endogenous phosphoinositide was detected in the vanilloid binding site, and phosphoinositides were proposed to act as competitive vanilloid antagonists. This model is difficult to reconcile with phosphatidylinositol 4,5- bisphosphate [PtdIns(4,5)P2] being a well established positive regulator of TRPV1. To resolve this controversy, we propose that phosphoinositides regulate TRPV1 via two functionally distinct binding sites. Our molecular dynamics simulations show that phosphatidylinositol (PtdIns) is more stable in the vanilloid binding site, whereas a distinct site responsible for activation is preferentially occupied by PtdIns(4,5)P2. Consistently, we show that in the presence of PtdIns(4,5)P2 in excised patches PtdIns partially inhibited TRPV1 activity induced by low, but not high capsaicin concentrations. In the absence of PtdIns(4,5)P2 on the other hand, PtdIns partially stimulated TRPV1 activity presumably by binding to the activating site. Overall, our data resolve a major controversy in the regulation of TRPV1.

biophysics↗

bioRxiv2018

Molecular basis for voltage sensitivity in membrane proteins

Voltage-sensitive membrane proteins are united by the ability to transform changes in the membrane potential into mechanical work. They are responsible for a spectrum of key physiological processes in living organisms, including electric signaling and progression along the cell cycle. While the voltage-sensing mechanism has been well characterized for some membrane proteins such as voltage-gated ion channels, for others even the location of the voltage-sensing elements remains unknown. The detection of these elements using experimental techniques is complicated due to the large diversity of membrane proteins. Here, we suggest a computational approach to predict voltage-sensing elements in any membrane protein independent of structure or function. It relies on the estimation of the capacity of a protein to respond to changes in the membrane potential. We first show how this property correlates well with voltage sensitivity by applying our approach to a set of membrane proteins including voltage-sensitive and voltage-insensitive ones. We further show that it correctly identifies true voltage-sensitive residues in the voltage sensor domain of voltage-gated ion channels. Finally, we investigate six membrane proteins for which the voltage-sensing elements have not yet been characterized and identify residues and ions potentially involved in the response to voltage. The suggested approach is fast and simple and allows for characterization of voltage sensitivity that goes beyond mere identification of charges. We anticipate that its application prior to mutagenesis experiments will allow for significant reduction of the number of potential voltage-sensitive elements to be tested.

biophysics↗

bioRxiv2017

Conformational Dynamics In TRPV1 Channels Reported By An Encoded Coumarin Amino Acid

Transient Receptor Potential Vanilloid (TRPV1) channels support the detection and integration of nociceptive input. Currently available functional and structural data suggest that that TRPV1 channels have two potential gates within their cation selective permeation pathway: a barrier formed by a bundle crossing at the intracellular entrance and a second constriction created by the ion selectivity filter. To describe conformational changes associated with channel gating within the pore, the fluorescent non-canonical amino acid (f- ncAA) coumarin-tyrosine was genetically encoded at Y671, a residue proximal to the selectivity filter. TRPV1 channels expressing coumarin at either site displayed normal voltage- and agonist-dependent gating. Next, total internal reflection microscopy (TIRF) was performed to enable ultra-rapid, millisecond imaging of the conformational dynamics in single TRPV1 channels in live cells. Here, the data obtained from channels expressed in human derived cells show that optical fluctuations, photon counts, and variance of noise analysis from Y671 coumarin encoded in TRPV1 tetramers correlates closely with channel activation by capsaicin, thus providing an direct optical marker of channel activation at the selectivity filter. In companion molecular dynamics simulations, Y671 displays alternating solvent exposure between the closed and open states, giving support to the optical data. These calculations further suggest a direct involvement of Y671 in controlling the relative position of the pore helix and its role in supporting ionic conductance at the TRPV1 selectivity filter.

biophysics↗