About

Professor Taras V Pogorelov leads research at the intersection of biophysics, chemistry, biology, and computer science, focusing on the physical chemistry of complex cellular environments. His work addresses critical questions in the mechanisms of cell signaling in both health and disease. By developing multiscale computational methods and workflows, his research integrates molecular dynamics, quantum chemistry, and molecular evolution frameworks with multi-resolution experimental data. The current emphasis of his research includes plasma membrane cell signaling, protein dynamics within complex environments, and the role of functional lipids in membrane-associated phenomena. Professor Pogorelov's research is highly collaborative, involving experimental colleagues to verify and extend findings, and spans diverse topics such as transmembrane signaling proteins, functional lipid signatures, in silico cellular dynamics, mechanisms of fast protein folding, and photosynthetic membrane protein systems. His work contributes to a deeper understanding of molecular interactions and dynamics that underpin cellular function and signaling.

Research topics

• Biochemistry
• Biology
• Chemistry
• Stereochemistry
• Computational biology
• Microbiology
• Pharmacology

Selected publications

• BPS2026 – Lipid asymmetry and signaling activation of receptor tyrosine kinases

  Biophysical Journal · 2026-02-01

  articleSenior author
  PublisherDOI

• BPS2026 – Modeling-informed experiments capture Arc-lipid interactions critical to intercellular communication

  Biophysical Journal · 2026-02-01

  article
  PublisherDOI

• BPS2026 – Dynamic ensembles of a non-canonical receptor tyrosine kinase heterodimer captured with multiscale modeling

  Biophysical Journal · 2026-02-01

  articleSenior author
  PublisherDOI

• BPS2026 – Interpretable machine learning reveals structural determinants of protein-protein interactions from extended atomistic simulations

  Biophysical Journal · 2026-02-01

  article
  PublisherDOI

• Interpretable machine learning uncovers structural determinants of Wnt-Wntless binding specificity from atomistic simulations

  Communications Chemistry · 2026-04-11

  articleOpen access

  The Wnt protein family plays a critical role in cell development, with each Wnt protein interacting differently with the Wntless (Wls) membrane protein through distinct binding residues. A direct comparison and elucidation of the molecular mechanisms underlying Wnt–Wls binding across the diverse Wnt family remain challenging, owing to variations in sequence length and amino acid composition among Wnt proteins, which can affect their binding affinity and trafficking efficiency via Wls. Here we combine atomistic molecular dynamics simulations with supervised machine learning to elucidate binding specificity among four Wnt proteins, selected based on experimental structure availability and scientific relevance. We implement a local structure alignment algorithm to enable cross-system matching and comparison of residue interactions, and we apply a two-stage clustering strategy to reduce feature redundancy and facilitate robust feature selection. After training a Random Forest classifier that achieved high predicting accuracy, our feature importance analysis reveals both previously known and novel key residue pairs responsible for distinguishing among the Wnt systems. Our findings highlight that the binding specificity across different systems arises from the distributed nature of interactions across the protein binding surface and demonstrate how interpretable machine learning can effectively uncover crucial biophysical interactions. Importantly, our integrated strategy is generalizable to other systems and provides a data-driven approach for analyzing protein–protein interactions and guiding experimental validation or therapeutic targeting. Variations in how Wnt proteins interact with the membrane protein Wntless (Wls) make it difficult to define binding principles across the Wnt family because of sequence divergence and differences in binding modes. Here, the authors combine molecular dynamics simulations with machine learning to reveal key residue interactions among the Wnt systems, offering insights into the Wnt–Wls binding specificity.

  PublisherOA PDFDOI

• Molecular prosthetics for CFTR designed for anion selectivity outperform amphotericin B in cultured cystic fibrosis airway epithelia

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-08-28

  preprintOpen access

  The ion channel-forming natural product amphotericin B (AmB) can serve as a molecular prosthetic for the cystic fibrosis transmembrane conductance regulator (CFTR) anion channel and thereby restore host defenses in cultured cystic fibrosis (CF) airway epithelia. This is despite the fact that the permeability of AmB-based channels favors cations, and these channels lose their capacity to increase airway surface liquid (ASL) pH in CF airway epithelia at high concentrations. We hypothesize that modifying such channels to favor anion permeability would make them more CFTR-like and thus increase their potential therapeutic effects compared to AmB. Here we show that a synthetic derivative of AmB, AmB-AA, which has an added positively charged appendage and forms ion channels with an improved relative permeability to anions, outperformed AmB in increasing the ASL pH in CF airway epithelia at both low and high concentrations. Further modifications led to another AmB derivative, C2'epiAmB-AA, that also minimized cholesterol binding and thus toxicity to cultured CF airway epithelia and was an effective surrogate for CFTR in primary cultured airway epithelia from people with CF.

  PublisherOA PDFDOI

• Charge, Hydrophobicity, and Lipid Type Drive Antimicrobial Peptides’ Unique Perturbation Ensembles

  Biochemistry · 2025-03-19 · 3 citations

  articleOpen accessSenior authorCorresponding

  Antimicrobial peptides (AMPs) have emerged as a promising solution to the escalating public health threat caused by multidrug-resistant bacteria. Although ongoing research efforts have established AMP's role in membrane permeabilization and leakage, the precise mechanisms driving these disruption patterns remain unclear. We leverage molecular dynamics (MD) simulations enhanced by membrane mimetic (HMMM) to systematically investigate how the physiochemical properties of magainin (+3) and pexiganan (+9) affect their localization, insertion, curvature perturbation, and membrane binding ensemble. Building on existing microbiology, NMR, circular dichroism, and fluorescence data, our analysis reveals that the lipid makeup is a key determinant in the binding dynamics and structural conformation of AMPs. We find that phospholipid type is crucial for peptide localization, demonstrated through magainin's predominant interaction with lipid tails and pexiganan's with polar headgroups in POPC/POPS membranes. The membrane curvature changes induced by pexiganan relative to magainin suggest that AMPs with larger charges have more potential in modulating bilayer bending. These insights advance our understanding of AMP-membrane interactions at the molecular level, offering guidance for the design of targeted antimicrobial therapies.

  PublisherOA PDFDOI

• BPS2025 - A machine learning approach for identifying determinants of WNT protein-protein interactions

  Biophysical Journal · 2025-02-01

  article
  PublisherDOI

• Interpretable Machine Learning Uncovers Structural Determinants of Wnt-Wls Binding Specificity from Extended Atomistic Simulations

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-08-22

  preprintOpen access

  Abstract The Wnt protein family plays a critical role in cell development, with each Wnt protein interacting differently with the Wls membrane protein through distinct binding residues. A direct comparison and elucidation of the molecular mechanisms underlying Wnt–Wls binding across the diverse Wnt family remain challenging, owing to variations in sequence length and amino acid composition among Wnt proteins, which can affect their binding affinity and trafficking efficiency via Wls. Here we combine extended atomistic molecular dynamics simulations with supervised machine learning to elucidate binding specificity among four Wnt proteins, selected based on experimental structure availability and scientific relevance. We implement a local structure alignment algorithm to enable cross-system matching and comparison of residue interactions, and we apply a two-stage clustering strategy to reduce feature redundancy and facilitate robust feature selection. After training a Random Forest classifier that achieved high predicting accuracy, our feature importance analysis reveals both previously known and novel key residue pairs responsible for distinguishing among the Wnt systems. Our findings highlight that the binding specificity across different systems arises from the distributed nature of interactions across the protein binding surface and demonstrate how interpretable machine learning can effectively uncover crucial biophysical interactions. Importantly, our integrated strategy is generalizable to other systems and provides a data-driven approach for analyzing protein– protein interactions and guiding experimental validation or therapeutic targeting.

  PublisherOA PDFDOI

• BPS2025 - Effects of membrane composition on the binding and insertion of the influenza virus fusion peptide

  Biophysical Journal · 2025-02-01

  articleSenior author
  PublisherDOI

Recent grants

• Capturing structure and dynamics of transmembrane signaling proteins

  NIH · $1.3M · 2021–2026

Frequent coauthors

• Martin Gruebele

  66 shared

• Dorothy Mary Crowfoot Hodgkin

  Harvard University Press

  30 shared

• Anthony A. Kossiakoff

  University of Chicago

  30 shared

• Emil Thomas

  Harvard University Press

  30 shared

• Minoru Kanehisa

  Kyoto University

  30 shared

• Hao Wu

  Ningbo No.6 Hospital

  30 shared

• D. Thirumalai

  30 shared

• Shahriar Mobashery

  University of Notre Dame

  30 shared

Labs

• Pogorelov LabPI

Awards & honors

• RSOCR Award, Office of Vice-Chancellor for Research, Univers…
• Faculty Fellow, National Center for Supercomputing Applicati…
• Illinois Research Board Award, University of Illinois (2012-…
• Richard I. Gumport Travel Award in Biochemistry, University…
• NIH Travel Award to attend the NIGMS Workshop for Postdocs T…