About

George Khelashvili, Ph.D., is an Associate Professor of Physiology and Biophysics and an Associate Professor of Computational Biophysics in the Institute for Computational Biomedicine at Weill Cornell Medicine. His research focuses on uncovering dynamic mechanisms in fundamental biological processes of signal transduction by cell surface proteins, including receptors such as G protein-coupled receptors (GPCRs), transporters in the family of Neurotransmitter:Sodium-Symporters (NSS), and lipid scramblases. His work emphasizes understanding how the spatial organization and function of these molecular machines are regulated by the cell membrane, its components like cholesterol and various lipids, and interactions with other proteins within the cell environment. Dr. Khelashvili approaches these topics using advanced quantitative methods of theoretical and computational biophysics, integrating biophysical theory and computation with biophysical measurements and molecular cell biology experimentation. His interdisciplinary and multi-scale strategies interpret experimental insights into membrane-associated signaling proteins within a novel quantitative framework, providing mechanistic insights into how membrane properties and remodeling influence protein function, organization, and signaling, which are of major importance to cell physiology.

Research topics

• Chemistry
• Biochemistry
• Biology
• Biophysics
• Cell biology
• Materials science

Selected publications

• Structure and function of the human apoptotic scramblase Xkr4

  Nature Communications · 2025-08-08 · 1 citations

  articleOpen access

  Phosphatidylserine externalization on the surface of dying cells is a key signal for their recognition and clearance by macrophages and is mediated by members of the X-Kell related (Xkr) protein family. Defective Xkr-mediated scrambling impairs clearance, leading to inflammation. It was proposed that activation of the Xkr4 apoptotic scramblase requires caspase cleavage, followed by dimerization and ligand binding. Here, using a combination of biochemical approaches we show that purified monomeric, full-length human Xkr4 (hXkr4) scrambles lipids. CryoEM imaging shows that hXkr4 adopts a novel conformation, where three conserved acidic residues create a negative electrostatic surface embedded in the membrane. Molecular dynamics simulations show this conformation induces membrane thinning, which could promote scrambling. Thinning is ablated or reduced in conditions where scrambling is abolished or reduced. Our work provides insights into the molecular mechanisms of hXkr4 scrambling and suggests the ability to thin membranes might be a general property of active scramblases. Xkr4 apoptotic scramblase activation is thought to involve caspase cleavage and dimerization to expose phosphatidylserine on dying cells. The authors show that full-length Xkr4 is an active monomeric scramblase. CryoEM reveals a conformation that promotes membrane thinning and scrambling, enhancing understanding of hXkr4 activity.

  PublisherOA PDFDOI

• Cholesterol modulates membrane elasticity via unified biophysical laws

  Nature Communications · 2025-07-31 · 18 citations

  articleOpen access

  Cholesterol and lipid unsaturation underlie a balance of opposing forces that features prominently in adaptive cell responses to diet and environmental cues. These competing factors have resulted in contradictory observations of membrane elasticity across different measurement scales, requiring chemical specificity to explain incompatible structural and elastic effects. Here, we demonstrate that - unlike macroscopic observations - lipid membranes exhibit a unified elastic behavior in the mesoscopic regime between molecular and macroscopic dimensions. Using nuclear spin techniques and computational analysis, we find that mesoscopic bending moduli follow a universal dependence on the lipid packing density regardless of cholesterol content, lipid unsaturation, or temperature. Our observations reveal that compositional complexity can be explained by simple biophysical laws that directly map membrane elasticity to molecular packing associated with biological function, curvature transformations, and protein interactions. The obtained scaling laws closely align with theoretical predictions based on conformational chain entropy and elastic stress fields. These findings provide unique insights into the membrane design rules optimized by nature and unlock predictive capabilities for guiding the functional performance of lipid-based materials in synthetic biology and real-world applications.

  PublisherOA PDFDOI

• BPS2025 - Bridging membrane simulations and NMR spectroscopy: The importance of orientational anisotropy for lipid dynamics

  Biophysical Journal · 2025-02-01

  article
  PublisherDOI

• BPS2025 - Molecular mechanisms of MFSD2A-mediated lysolipid transport across the blood-brain barrier

  Biophysical Journal · 2025-02-01

  articleSenior author
  PublisherDOI

• Automated Collective Variable Discovery for MFSD2A transporter from molecular dynamics simulations

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-04-25

  preprintOpen accessSenior authorCorresponding

  ABSTRACT Biomolecules often exhibit complex free energy landscapes in which long-lived metastable states are separated by large energy barriers. Overcoming these barriers to robustly sample transitions between the metastable states with classical molecular dynamics (MD) simulations presents a challenge. To circumvent this issue, collective variable (CV)-based enhanced sampling MD approaches are often employed. Traditional CV selection relies on intuition and prior knowledge of the system. This approach introduces bias, which can lead to incomplete mechanistic insights. Thus, automated CV detection is desired to gain a deeper understanding of the system/process. Analysis of MD data with various machine learning algorithms, such as Principal Component Analysis (PCA), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA)-based approaches have been implemented for automated CV detection. However, their performance has not been systematically evaluated on structurally and mechanistically complex biological systems. Here, we applied these methods to MD simulations of the MFSD2A (Major Facilitator Superfamily Domain 2A) lysolipid transporter in multiple functionally relevant metastable states with the goal of identifying optimal CVs that would structurally discriminate these states. Specific emphasis was on the automated detection and interpretive power of LDA-based CVs. We found that LDA methods, which included a novel gradient descent-based multiclass harmonic variant, termed GDHLDA, we developed here, outperform PCA in class separation, exhibiting remarkable consistency in extracting CVs critical for distinguishing metastable states. Furthermore, the identified CVs included features previously associated with conformational transitions in MFSD2A. Specifically, conformational shifts in transmembrane helix 7 and in residue Y294 on this helix emerged as critical features discriminating the metastable states in MFSD2A. This highlights the effectiveness of LDA-based approaches in automatically extracting from MD trajectories CVs of functional relevance that can be used to drive biased MD simulations to efficiently sample conformational transitions in the molecular system. STATEMENT OF SIGNIFICANCE To elucidate the biological mechanisms of pertinent biomolecules, it is crucial to understand their complex free energy landscapes. Such landscapes are often constructed from molecular dynamics (MD) simulations using collective variable (CV)-guided enhanced sampling methods. Identifying proper CVs for this task is critical but can be challenging with traditional intuition-based approaches. Here we propose an automated protocol for CV discovery which is based on linear discriminant analysis (LDA) for dimensionality reduction of MD data. By applying the protocol to MD simulations of the MFSD2A lysolipid transporter, a structurally and mechanistically complex biological system, we show that LDA-based methods efficiently detect system-specific CVs that accurately classify different metastable states of MFSD2A and are highly interpretable in a detailed structural context.

  PublisherOA PDFDOI

• Sampling caveats in validating simulated lipid dynamics

  Biophysical Journal · 2024-02-01

  articleOpen access
  PublisherOA PDFDOI

• Structure and function of the human apoptotic scramblase Xkr4

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-08-09 · 3 citations

  preprintOpen access

  Phosphatidylserine externalization on the surface of dying cells is a key signal for their recognition and clearance by macrophages and is mediated by members of the X-Kell related (Xkr) protein family. Defective Xkr-mediated scrambling impairs clearance, leading to inflammation. It was proposed that activation of the Xkr4 apoptotic scramblase requires caspase cleavage, followed by dimerization and ligand binding. Here, using a combination of biochemical approaches we show that purified monomeric, full-length human Xkr4 (hXkr4) scrambles lipids. CryoEM imaging shows that hXkr4 adopts a novel conformation, where three conserved acidic residues create an electronegative surface embedded in the membrane. Molecular dynamics simulations show this conformation induces membrane thinning, which could promote scrambling. Thinning is ablated or reduced in conditions where scrambling is abolished or reduced. Our work provides insights into the molecular mechanisms of hXkr4 scrambling and suggests the ability to thin membranes might be a general property of active scramblases.

  PublisherOA PDFDOI

• Cholesterol-induced membrane elasticity of lipid membranes

  Biophysical Journal · 2024-02-01

  article
  PublisherDOI

• A cholesterol switch controls phospholipid scrambling by G protein–coupled receptors

  Journal of Biological Chemistry · 2024-01-16 · 24 citations

  articleOpen access

  Class A G protein-coupled receptors (GPCRs), a superfamily of cell membrane signaling receptors, moonlight as constitutively active phospholipid scramblases. The plasma membrane of metazoan cells is replete with GPCRs yet has a strong resting trans-bilayer phospholipid asymmetry, with the signaling lipid phosphatidylserine confined to the cytoplasmic leaflet. To account for the persistence of this lipid asymmetry in the presence of GPCR scramblases, we hypothesized that GPCR-mediated lipid scrambling is regulated by cholesterol, a major constituent of the plasma membrane. We now present a technique whereby synthetic vesicles reconstituted with GPCRs can be supplemented with cholesterol to a level similar to that of the plasma membrane and show that the scramblase activity of two prototypical GPCRs, opsin and the β1-adrenergic receptor, is impaired upon cholesterol loading. Our data suggest that cholesterol acts as a switch, inhibiting scrambling above a receptor-specific threshold concentration to disable GPCR scramblases at the plasma membrane.

  PublisherOA PDFDOI

• A Mechanistic Understanding of the Modes of Ca²⁺ Ion Binding to the SARS-CoV-1 Fusion Peptide and Their Role in the Dynamics of Host Membrane Penetration

  ACS Infectious Diseases · 2024-01-25 · 3 citations

  articleOpen access

  The SARS-CoV-1 spike glycoprotein contains a fusion peptide (FP) segment that mediates the fusion of the viral and host cell membranes. Calcium ions are thought to position the FP optimally for membrane insertion by interacting with negatively charged residues in this segment (E801, D802, D812, E821, D825, and D830); however, which residues bind to calcium and in what combinations supportive of membrane insertion are unknown. Using biological assays and molecular dynamics studies, we have determined the functional configurations of FP-Ca2+ binding that likely promote membrane insertion. We first individually mutated the negatively charged residues in the SARS CoV-1 FP to assay their roles in cell entry and syncytia formation, finding that charge loss in the D802A or D830A mutants greatly reduced syncytia formation and pseudoparticle transduction of VeroE6 cells. Interestingly, one mutation (D812A) led to a modest increase in cell transduction, further indicating that FP function likely depends on calcium binding at specific residues and in specific combinations. To interpret these results mechanistically and identify specific modes of FP-Ca2+ binding that modulate membrane insertion, we performed molecular dynamics simulations of the SARS-CoV-1 FP and Ca2+ions. The preferred residue pairs for Ca2+ binding we identified (E801/D802, E801/D830, and D812/E821) include the two residues found to be essential for S function in our biological studies (D802 and D830). The three preferred Ca2+ binding pairs were also predicted to promote FP membrane insertion. We also identified a Ca2+ binding pair (E821/D825) predicted to inhibit FP membrane insertion. We then carried out simulations in the presence of membranes and found that binding of Ca2+ to SARS-CoV-1 FP residue pairs E801/D802 and D812/E821 facilitates membrane insertion by enabling the peptide to adopt conformations that shield the negative charges of the FP to reduce repulsion by the membrane phospholipid headgroups. This calcium binding mode also optimally positions the hydrophobic LLF region of the FP for membrane penetration. Conversely, Ca2+ binding to the FP E801/D802 and D821/D825 pairs eliminates the negative charge screening and instead creates a repulsive negative charge that hinders membrane penetration of the LLF motif. These computational results, taken together with our biological studies, provide an improved and nuanced mechanistic understanding of the dymanics of SARS-CoV-1 calcium binding and their potential effects on host cell entry.

  PublisherOA PDFDOI

Recent grants

• Rhodopsin dimerization: mechanistic basis and functional consequences

  NIH · $2.2M · 2017–2022

• NIH Grant R21EY028314

  NIH · $466k · 2020

Frequent coauthors

• Harel Weinstein

  Cornell University

  141 shared

• Jonathan A. Javitch

  New York State Psychiatric Institute

  71 shared

• Michael V. Levine

  Cornell University

  43 shared

• Michel Bouvier

  Université de Montréal

  42 shared

• Jufang Shan

  42 shared

• Jean‐Philippe Pin

  Université de Montpellier

  42 shared

• Marta Filizola

  Icahn School of Medicine at Mount Sinai

  41 shared

• Milka Doktorova

  University of Virginia

  39 shared