Periodic Reporting for period 1 - CryoLigate (Characterizing ligand-protein interactions with a cryo-EM data-driven modeling approach)
Reporting period: 2023-11-01 to 2025-10-31
During the CryoLigate fellowship, I successfully visualized protein–ligand interactions at atomic detail, a key step in understanding how ligands regulate macromolecular function. The acquired knowledge has direct implications for structure-based drug discovery (SBDD) by providing accurate structural frameworks for rational drug design. Traditionally, experimental determination of protein–ligand complex structures using X-ray crystallography has been limited by the need to obtain suitable crystals. In this project, I overcame these limitations by leveraging recent advances in single-particle cryo-electron microscopy (cryo-EM), which now enables near-atomic resolution imaging of complex biomolecular assemblies. However, cryo-EM density maps often suffer from low-resolution ligand regions, which limits their direct use for SBDD. To address this, I developed and implemented a cryo-EM–guided computational modeling framework that integrates molecular dynamics (MD) simulations, machine learning, and refinement algorithms to accurately model ligand conformations in low-resolution EM densities. This novel computational approach was tested on a large dataset of protein–ligand complexes and successfully applied to membrane proteins, allowing identification of ligand binding sites and elucidation of how ligand binding regulates the functional energy landscape of target proteins. The outcomes of this work provide a new methodological pipeline that bridges cryo-EM data with computational chemistry, offering a practical route toward accurate modeling of ligand–protein complexes for drug discovery applications. The project has therefore opened new avenues for SBDD and strengthened my expertise in machine learning, molecular dynamics simulations, cryo-EM data processing, membrane protein dynamics, and drug–protein interaction modeling.
Explanation of the work carried out per Work Packages
Work Package 1 - [WP1: Characterizing ligand binding in Cryo-EM density maps]
Understanding how proteins interact with small molecules, such as drugs, has long been a central challenge in structural biology. Visualising these interactions at the atomic level can reveal critical details of protein function and empower drug design. Despite recent advances in imaging such as cryogenic electron microscopy (cryo-EM) and computational predictions using artificial intelligence (AI), many important protein-drug complexes remain difficult to decipher by either method in isolation. In this study, we show how combining AI-driven structure prediction with cryo-EM imaging data and molecular simulations can enable accurate modeling of protein-drug complexes with minimal system knowledge or structural biology expertise. Our proposed pipeline demonstrates the power of integrating experimental and computational methods to decode the complex language of molecular recognition, and holds promise for advancing both basic science and pharmaceutical innovation.
Outcome and results: We present an approach that integrates artificial intelligence (AI) with cryo-EM density-guided simulations to fit ligands into experimental maps. Using three inputs: 1) a protein amino acid sequence, 2) a ligand specification, and 3) an experimental cryo-EM map, we validated our approach on a set of biomedically relevant protein-ligand complexes including kinases, GPCRs, and solute transporters, none of which were present in the AI training data. In cases for which AI was not sufficient to predict experimental poses outright, integration of flexible fitting into molecular dynamics simulations improved ligand model-to-map cross-correlation relative to the deposited structure from 40-71% to 82-95%. This work offers a straightforward pipeline for integrating AI and density-guided simulations to model building in cryo-EM maps of ligand-protein complexes.
Achievement of scientific deliverables and milestones: Our work has resulted in an open-source peer-reviewed publication: N. Haloi, R. J. Howard, and E. Lindahl, "Cryo-EM ligand building using AlphaFold3-like model and molecular dynamics "PLOS Computational Biology, 21(8): e1013367. Also, we make our data and code publicly available in Zenodo: 10.5281/zenodo.14842872
Figure 1: A pipeline to modeling of ligand-protein complexes. First, the protein sequence and ligand SMILES information were provided in Chai-1 to predict the protein-ligand complex structure. Then, rigid body alignment followed by molecular dynamics simulation-based flexible fitting were performed.
Work Package 2 - [WP2: Identifying agonist, modulator and lipid binding in new cryo-EM maps of GABAA receptor]
A diverse set of modulators, including stimulants and anesthetics, regulates ion channel function in our nervous system. However, structures of ligand-bound complexes can be difficult to capture by experimental methods, particularly when binding is dynamic. We used computational methods and electrophysiology to identify a possible bound state of a modulatory stimulant derivative in a cryptic vestibular pocket of a mammalian serotonin-3 receptor (a structurally and functionally related protein to GABAA receptor in our brain).
Outcome and results: We first applied a molecular dynamics simulation–based goal-oriented adaptive sampling method to identify possible open-pocket conformations, followed by Boltzmann docking that combines traditional docking with Markov state modeling. Clustering and analysis of stability and accessibility of docked poses supported a preferred binding site; we further validated this site by mutagenesis and electrophysiology, suggesting a mechanism of potentiation by stabilizing intersubunit contacts. Given the pharmaceutical relevance of serotonin-3 receptors in emesis, psychiatric, and gastrointestinal diseases, characterizing relatively unexplored modulatory sites such as these could open valuable avenues to understanding conformational cycling and designing state-dependent drugs.
Achievement of scientific deliverables and milestones: Our work has resulted in an open-source peer-reviewed publication: N. Haloi, E. Karlsson, M. Delarue, R. J. Howard, E. Lindahl, "Discovering cryptic pocket opening and binding of a stimulant derivative in a vestibular site of the 5-HT3A receptor" Science Advances, 11, eadr0797. Also, we make our simulation data publicly available in Zenodo: 10.5281/zenodo.10812994.
Figure 2: Overview of 5-HT3AR structure and pharmacology explored in this work. (A) Architecture of a representative 5-HT3AR, colored by subunit, viewed from the membrane plane (left) and from the extracellular side (right). 5-HT bound at the orthosteric site is represented in yellow. The vestibular site is colored blue. (B) ECD of a single 5-HT3AR subunit, with the omega-loop (Ω-loop) in blue. (C) Representative current trace from a 5-HT3AR–expressing X. laevis oocyte in the absence and presence of BrAmp during 5-HT (at EC20) pulses. (D) Pocket volumes at the 5-HT3AR vestibular site, generated in Fpocket, show a superficial cavity (volume, 524 Å3) in the experimental structure (left) but a clearly opened pocket (volume, 740 Å3) surrounding the docked pose of BrAmp (right), as found in our study. Pocket volumes are shown in red mesh, the ligand in van der Waals, and the receptor in gray surface representations.
Work Package 3 - [WP3: Characterizing protein functional modulations by ligand binding in GABAA receptor]
γ-Aminobutyric acid type A (GABAA) receptors are ligand-gated ion channels in the central nervous system with largely inhibitory function. Despite being a target for drugs including general anesthetics and benzodiazepines, experimental structures have yet to capture an open state of classical synaptic α1β2γ2 GABAA receptors. We use a goal-oriented adaptive sampling strategy in molecular dynamics simulations followed by Markov state modeling to capture an energetically stable putative open state of the receptor.
Outcome and results: The model conducts chloride ions with conductance comparable as in electrophysiology measurements. Relative to experimental structures, our open model is relatively expanded at both the cytoplasmic (−2′) and central (9′) gates, coordinated with distinctive rearrangements at the transmembrane αβ subunit interface. Consistent with previous experiments, targeted substitutions disrupting interactions at this interface slowed the open-to-desensitized transition rate. This work demonstrates the capacity of advanced simulation techniques to investigate a computationally and experimentally plausible functionally critical of a complex membrane protein yet to be resolved by experimental methods.
Achievement of scientific deliverables and milestones (fully achieved): Our work has resulted in an open-source peer-reviewed publication: N. Haloi, S. E. Lidbrink, R. J. Howard, E. Lindahl, "Adaptive sampling-based structural prediction reveals opening of a GABAA receptor through the αβ interface" Science Advances, 11, eadq3788. Also, we make our simulation data publicly available in Zenodo: 10.5281/zenodo.10964268.
Figure 3: Overview of structural, energetic, and kinetic aspects of GABAA receptors explored in this work. (A, left) Transmembrane domain of an experimental desensitized structure of GABAA. Surface shows permeation pathway, colored green where narrower than a water molecule. (A, right) Open model discovered in our study. 8 A chloride ion (green) can readily permeate the pore. (B) Free energy landscape derived from my MSM analysis. (C) Rates for the open-to-desensitized transitions in the WT and mutant systems, calculated using MSM, with thicker lines indicating faster rates.
Work Package 1 - [WP1: Characterizing ligand binding in Cryo-EM density maps]
Understanding how proteins interact with small molecules, such as drugs, has long been a central challenge in structural biology. Visualising these interactions at the atomic level can reveal critical details of protein function and empower drug design. Despite recent advances in imaging such as cryogenic electron microscopy (cryo-EM) and computational predictions using artificial intelligence (AI), many important protein-drug complexes remain difficult to decipher by either method in isolation. In this study, we show how combining AI-driven structure prediction with cryo-EM imaging data and molecular simulations can enable accurate modeling of protein-drug complexes with minimal system knowledge or structural biology expertise. Our proposed pipeline demonstrates the power of integrating experimental and computational methods to decode the complex language of molecular recognition, and holds promise for advancing both basic science and pharmaceutical innovation.
Outcome and results: We present an approach that integrates artificial intelligence (AI) with cryo-EM density-guided simulations to fit ligands into experimental maps. Using three inputs: 1) a protein amino acid sequence, 2) a ligand specification, and 3) an experimental cryo-EM map, we validated our approach on a set of biomedically relevant protein-ligand complexes including kinases, GPCRs, and solute transporters, none of which were present in the AI training data. In cases for which AI was not sufficient to predict experimental poses outright, integration of flexible fitting into molecular dynamics simulations improved ligand model-to-map cross-correlation relative to the deposited structure from 40-71% to 82-95%. This work offers a straightforward pipeline for integrating AI and density-guided simulations to model building in cryo-EM maps of ligand-protein complexes.
Achievement of scientific deliverables and milestones: Our work has resulted in an open-source peer-reviewed publication: N. Haloi, R. J. Howard, and E. Lindahl, "Cryo-EM ligand building using AlphaFold3-like model and molecular dynamics "PLOS Computational Biology, 21(8): e1013367. Also, we make our data and code publicly available in Zenodo: 10.5281/zenodo.14842872
Figure 1: A pipeline to modeling of ligand-protein complexes. First, the protein sequence and ligand SMILES information were provided in Chai-1 to predict the protein-ligand complex structure. Then, rigid body alignment followed by molecular dynamics simulation-based flexible fitting were performed.
Work Package 2 - [WP2: Identifying agonist, modulator and lipid binding in new cryo-EM maps of GABAA receptor]
A diverse set of modulators, including stimulants and anesthetics, regulates ion channel function in our nervous system. However, structures of ligand-bound complexes can be difficult to capture by experimental methods, particularly when binding is dynamic. We used computational methods and electrophysiology to identify a possible bound state of a modulatory stimulant derivative in a cryptic vestibular pocket of a mammalian serotonin-3 receptor (a structurally and functionally related protein to GABAA receptor in our brain).
Outcome and results: We first applied a molecular dynamics simulation–based goal-oriented adaptive sampling method to identify possible open-pocket conformations, followed by Boltzmann docking that combines traditional docking with Markov state modeling. Clustering and analysis of stability and accessibility of docked poses supported a preferred binding site; we further validated this site by mutagenesis and electrophysiology, suggesting a mechanism of potentiation by stabilizing intersubunit contacts. Given the pharmaceutical relevance of serotonin-3 receptors in emesis, psychiatric, and gastrointestinal diseases, characterizing relatively unexplored modulatory sites such as these could open valuable avenues to understanding conformational cycling and designing state-dependent drugs.
Achievement of scientific deliverables and milestones: Our work has resulted in an open-source peer-reviewed publication: N. Haloi, E. Karlsson, M. Delarue, R. J. Howard, E. Lindahl, "Discovering cryptic pocket opening and binding of a stimulant derivative in a vestibular site of the 5-HT3A receptor" Science Advances, 11, eadr0797. Also, we make our simulation data publicly available in Zenodo: 10.5281/zenodo.10812994.
Figure 2: Overview of 5-HT3AR structure and pharmacology explored in this work. (A) Architecture of a representative 5-HT3AR, colored by subunit, viewed from the membrane plane (left) and from the extracellular side (right). 5-HT bound at the orthosteric site is represented in yellow. The vestibular site is colored blue. (B) ECD of a single 5-HT3AR subunit, with the omega-loop (Ω-loop) in blue. (C) Representative current trace from a 5-HT3AR–expressing X. laevis oocyte in the absence and presence of BrAmp during 5-HT (at EC20) pulses. (D) Pocket volumes at the 5-HT3AR vestibular site, generated in Fpocket, show a superficial cavity (volume, 524 Å3) in the experimental structure (left) but a clearly opened pocket (volume, 740 Å3) surrounding the docked pose of BrAmp (right), as found in our study. Pocket volumes are shown in red mesh, the ligand in van der Waals, and the receptor in gray surface representations.
Work Package 3 - [WP3: Characterizing protein functional modulations by ligand binding in GABAA receptor]
γ-Aminobutyric acid type A (GABAA) receptors are ligand-gated ion channels in the central nervous system with largely inhibitory function. Despite being a target for drugs including general anesthetics and benzodiazepines, experimental structures have yet to capture an open state of classical synaptic α1β2γ2 GABAA receptors. We use a goal-oriented adaptive sampling strategy in molecular dynamics simulations followed by Markov state modeling to capture an energetically stable putative open state of the receptor.
Outcome and results: The model conducts chloride ions with conductance comparable as in electrophysiology measurements. Relative to experimental structures, our open model is relatively expanded at both the cytoplasmic (−2′) and central (9′) gates, coordinated with distinctive rearrangements at the transmembrane αβ subunit interface. Consistent with previous experiments, targeted substitutions disrupting interactions at this interface slowed the open-to-desensitized transition rate. This work demonstrates the capacity of advanced simulation techniques to investigate a computationally and experimentally plausible functionally critical of a complex membrane protein yet to be resolved by experimental methods.
Achievement of scientific deliverables and milestones (fully achieved): Our work has resulted in an open-source peer-reviewed publication: N. Haloi, S. E. Lidbrink, R. J. Howard, E. Lindahl, "Adaptive sampling-based structural prediction reveals opening of a GABAA receptor through the αβ interface" Science Advances, 11, eadq3788. Also, we make our simulation data publicly available in Zenodo: 10.5281/zenodo.10964268.
Figure 3: Overview of structural, energetic, and kinetic aspects of GABAA receptors explored in this work. (A, left) Transmembrane domain of an experimental desensitized structure of GABAA. Surface shows permeation pathway, colored green where narrower than a water molecule. (A, right) Open model discovered in our study. 8 A chloride ion (green) can readily permeate the pore. (B) Free energy landscape derived from my MSM analysis. (C) Rates for the open-to-desensitized transitions in the WT and mutant systems, calculated using MSM, with thicker lines indicating faster rates.
The expertise and experience acquired during the CryoLigate project have significantly advanced my professional goal of becoming an independent researcher and future Principal Investigator (PI). The fellowship has allowed me to gain the scientific, technical, and practical competencies required to establish and lead my own research group.
Scientific Impact: Through the CryoLigate project, I successfully proposed and implemented an independent research project, marking the first major step in my career trajectory toward research independence. The project focused on visualizing ligand–protein interactions relevant to drug discovery and provided me with in-depth experience in integrating Machine Learning, Molecular Dynamics (MD) Simulations, and Cryo-EM Data Processing for biological insight. I have developed expertise in investigating membrane protein function and dynamics and understanding drug–protein interactions at the molecular level. This experience has positioned me to pursue long-standing biological questions, such as elucidating how the same allosteric modulators can elicit different functional effects across GABA receptor subtypes - knowledge that can guide subtype-specific drug design. Furthermore, the software and computational tools developed in Work Package 1 (WP1) have direct applications in lead compound optimization and can be extended to other ion channels and membrane proteins. These outcomes have opened new opportunities for academic–industrial collaborations in the field of computational biophysics and drug discovery.
Technical Impact: Leading computational biophysics research requires the ability to address complex methodological and technical challenges. During the fellowship, I acquired advanced skills in method development and software implementation, benefitting from the host laboratory’s strong background in computational tool creation (e.g. RELION and eBDIMS). This experience has enabled me to design and execute novel computational pipelines combining structural data analysis, MD simulations, and machine learning-based prediction models, thereby strengthening my technical foundation for future independent research.
Practical Impact: Beyond scientific and technical expertise, I have developed several practical and transferable skills essential for a successful academic career. Working at SciLifeLab provided a unique opportunity to interact and exchange ideas with cross-disciplinary researchers from its constituent universities, fostering my ability to network and collaborate across scientific domains. My professional network has expanded significantly through international collaborations and participation in major conferences in Europe and abroad, enhancing my visibility within the structural biology and computational biophysics communities. During the fellowship, I also gained valuable leadership and mentoring experience by supervising undergraduate, master’s, and PhD students in topics such as protein conformational sampling and free-energy calculations. These experiences have strengthened my team management, communication, and research dissemination skills, preparing me to effectively lead an independent research group.
Scientific Impact: Through the CryoLigate project, I successfully proposed and implemented an independent research project, marking the first major step in my career trajectory toward research independence. The project focused on visualizing ligand–protein interactions relevant to drug discovery and provided me with in-depth experience in integrating Machine Learning, Molecular Dynamics (MD) Simulations, and Cryo-EM Data Processing for biological insight. I have developed expertise in investigating membrane protein function and dynamics and understanding drug–protein interactions at the molecular level. This experience has positioned me to pursue long-standing biological questions, such as elucidating how the same allosteric modulators can elicit different functional effects across GABA receptor subtypes - knowledge that can guide subtype-specific drug design. Furthermore, the software and computational tools developed in Work Package 1 (WP1) have direct applications in lead compound optimization and can be extended to other ion channels and membrane proteins. These outcomes have opened new opportunities for academic–industrial collaborations in the field of computational biophysics and drug discovery.
Technical Impact: Leading computational biophysics research requires the ability to address complex methodological and technical challenges. During the fellowship, I acquired advanced skills in method development and software implementation, benefitting from the host laboratory’s strong background in computational tool creation (e.g. RELION and eBDIMS). This experience has enabled me to design and execute novel computational pipelines combining structural data analysis, MD simulations, and machine learning-based prediction models, thereby strengthening my technical foundation for future independent research.
Practical Impact: Beyond scientific and technical expertise, I have developed several practical and transferable skills essential for a successful academic career. Working at SciLifeLab provided a unique opportunity to interact and exchange ideas with cross-disciplinary researchers from its constituent universities, fostering my ability to network and collaborate across scientific domains. My professional network has expanded significantly through international collaborations and participation in major conferences in Europe and abroad, enhancing my visibility within the structural biology and computational biophysics communities. During the fellowship, I also gained valuable leadership and mentoring experience by supervising undergraduate, master’s, and PhD students in topics such as protein conformational sampling and free-energy calculations. These experiences have strengthened my team management, communication, and research dissemination skills, preparing me to effectively lead an independent research group.