Secondary structure determines electron transport in peptides.

Proteins play a key role in biological electron transport, but the structure-function relationships governing the electronic properties of peptides are not fully understood. Despite recent progress, understanding the link between peptide conformational flexibility, hierarchical structures, and electron transport pathways has been challenging. Here, we use single-molecule experiments, molecular dynamics (MD) simulations, nonequilibrium Green's function-density functional theory (NEGF-DFT), and unsupervised machine learning to understand the role of secondary structure on electron transport in peptides. Our results reveal a two-state molecular conductance behavior for peptides across several different amino acid sequences. MD simulations and Gaussian mixture modeling are used to show that this two-state molecular conductance behavior arises due to the conformational flexibility of peptide backbones, with a high-conductance state arising due to a more defined secondary structure (beta turn or 310 helices) and a low-conductance state occurring for extended peptide structures. These results highlight the importance of helical conformations on electron transport in peptides. Conformer selection for the peptide structures is rationalized using principal component analysis of intramolecular hydrogen bonding distances along peptide backbones. Molecular conformations from MD simulations are used to model charge transport in NEGF-DFT calculations, and the results are in reasonable qualitative agreement with experiments. Projected density of states calculations and molecular orbital visualizations are further used to understand the role of amino acid side chains on transport. Overall, our results show that secondary structure plays a key role in electron transport in peptides, which provides broad avenues for understanding the electronic properties of proteins.

GOLEM: Automated and Robust Cryo-EM-Guided Ligand Docking with Explicit Water Molecules.

A detailed understanding of ligand-protein interaction is essential for developing rational drug-design strategies. In recent years, technological advances in cryo-electron microscopy (cryo-EM) brought a new era to the structural determination of biological macromolecules and assemblies at high resolution, marking cryo-EM as a promising tool for studying ligand-protein interactions. However, even in high-resolution cryo-EM results, the densities for the bound small-molecule ligands are often of lower quality due to their relatively dynamic and flexible nature, frustrating their accurate coordinate assignment. To address the challenge of ligand modeling in cryo-EM maps, here we report the development of GOLEM (Genetic Optimization of Ligands in Experimental Maps), an automated and robust ligand docking method that predicts a ligand's pose and conformation in cryo-EM maps. GOLEM employs a Lamarckian genetic algorithm to perform a hybrid global/local search for exploring the ligand's conformational, orientational, and positional space. As an important feature, GOLEM explicitly considers water molecules and places them at optimal positions and orientations. GOLEM takes into account both molecular energetics and the correlation with the cryo-EM maps in its scoring function to optimally place the ligand. We have validated GOLEM against multiple cryo-EM structures with a wide range of map resolutions and ligand types, returning ligand poses in excellent agreement with the densities. As a VMD plugin, GOLEM is free of charge and accessible to the community. With these features, GOLEM will provide a valuable tool for ligand modeling in cryo-EM efforts toward drug discovery.

Atomistic Characterization of ß2-Glycoprotein I Domain V Interaction with Anionic Membranes.

BACKGROUND: Interaction of ß2-glycoprotein I (ß2GPI) with anionic membranes is crucial in antiphospholipid syndrome (APS), implicating the role of its membrane binding domain, Domain V (DV). The mechanism of DV binding to anionic lipids is not fully understood. OBJECTIVES: This study aims to elucidate the molecular details of ß2GPI DV binding to anionic membranes. METHODS: We utilized molecular dynamics (MD) simulations to investigate the structural basis of anionic lipid recognition by DV. To corroborate the membrane-binding mode identified in the HMMM simulations, we conducted additional simulations using a full membrane model. RESULTS: The study identified critical regions in DV, namely the lysine-rich loop and the hydrophobic loop, essential for membrane association via electrostatic and hydrophobic interactions, respectively. A novel lysine pair contributing to membrane binding was also discovered, providing new insights into ß2GPI's membrane interaction. Simulations revealed two distinct binding modes of DV to the membrane, with mode 1 characterized by the insertion of the hydrophobic loop into the lipid bilayer, suggesting a dominant mechanism for membrane association. This interaction is pivotal for the pathogenesis of APS, as it facilitates the recognition of ß2GPI by antiphospholipid antibodies. CONCLUSION: The study advances our understanding of the molecular interactions between ß2GPI's DV and anionic membranes, which are crucial for APS pathogenesis. It highlights the importance of specific regions in DV for membrane binding and reveals a predominant binding mode. These findings have significant implications for APS diagnostics and therapeutics, offering a deeper insight into the molecular basis of the syndrome.

Broadening access to small-molecule parameterization with the force field toolkit.

Access to accurate force-field parameters for small molecules is crucial for computational studies of their interactions with proteins. Although a number of general force fields for small molecules exist, e.g., CGenFF, GAFF, and OPLS, they do not cover all common chemical groups and their combinations. The Force Field Toolkit (ffTK) provides a comprehensive graphical interface that streamlines the development of classical parameters for small molecules directly from quantum mechanical (QM) calculations, allowing for force-field generation for almost any chemical group and validation of the fit relative to the target data. ffTK relies on supported external software for the QM calculations, but it can generate the necessary QM input files and parse and analyze the QM output. In previous ffTK versions, support for Gaussian and ORCA QM packages was implemented. Here, we add support for Psi4, an open-source QM package free for all users, thereby broadening user access to ffTK. We also compare the parameter sets obtained with the new ffTK version using Gaussian, ORCA, and Psi4 for three molecules: pyrrolidine, n-propylammonium cation, and chlorobenzene. Despite minor differences between the resulting parameter sets for each compound, most prominently in the dihedral and improper terms, we show that conformational distributions sampled in molecular dynamics simulations using these parameter sets are quite comparable.

Improved Highly Mobile Membrane Mimetic Model for Investigating Protein-Cholesterol Interactions.

Cholesterol (CHL) plays an integral role in modulating the function and activity of various mammalian membrane proteins. Due to the slow dynamics of lipids, conventional computational studies of protein-CHL interactions rely on either long-time scale atomistic simulations or coarse-grained approximations to sample the process. A highly mobile membrane mimetic (HMMM) has been developed to enhance lipid diffusion and thus used to facilitate the investigation of lipid interactions with peripheral membrane proteins and, with customized in silico solvents to replace phospholipid tails, with integral membrane proteins. Here, we report an updated HMMM model that is able to include CHL, a nonphospholipid component of the membrane, henceforth called HMMM-CHL. To this end, we had to optimize the effect of the customized solvents on CHL behavior in the membrane. Furthermore, the new solvent is compatible with simulations using force-based switching protocols. In the HMMM-CHL, both improved CHL dynamics and accelerated lipid diffusion are integrated. To test the updated model, we have applied it to the characterization of protein-CHL interactions in two membrane protein systems, the human ß2-adrenergic receptor (ß2AR) and the mitochondrial voltage-dependent anion channel 1 (VDAC-1). Our HMMM-CHL simulations successfully identified CHL binding sites and captured detailed CHL interactions in excellent consistency with experimental data as well as other simulation results, indicating the utility of the improved model in applications where an enhanced sampling of protein-CHL interactions is desired.

Topological Learning Approach to Characterizing Biological Membranes.

Biological membranes play key roles in cellular compartmentalization, structure, and its signaling pathways. At varying temperatures, individual membrane lipids sample from different configurations, a process that frequently leads to higher-order phase behavior and phenomena. Here, we present a persistent homology (PH)-based method for quantifying the structural features of individual and bulk lipids, providing local and contextual information on lipid tail organization. Our method leverages the mathematical machinery of algebraic topology and machine learning to infer temperature-dependent structural information on lipids from static coordinates. To train our model, we generated multiple molecular dynamics trajectories of dipalmitoyl-phosphatidylcholine membranes at varying temperatures. A fingerprint was then constructed for each set of lipid coordinates by PH filtration, in which interaction spheres were grown around the lipid atoms while tracking their intersections. The sphere filtration formed a simplicial complex that captures enduring key topological features of the configuration landscape using homology, yielding persistence data. Following fingerprint extraction for physiologically relevant temperatures, the persistence data were used to train an attention-based neural network for assignment of effective temperature values to selected membrane regions. Our persistence homology-based method captures the local structural effects, via effective temperature, of lipids adjacent to other membrane constituents, e.g., sterols and proteins. This topological learning approach can predict lipid effective temperatures from static coordinates across multiple spatial resolutions. The tool, called MembTDA, can be accessed at https://github.com/hyunp2/Memb-TDA.

A Gram-negative-selective antibiotic that spares the gut microbiome.

Infections caused by Gram-negative pathogens are increasingly prevalent and are typically treated with broad-spectrum antibiotics, resulting in disruption of the gut microbiome and susceptibility to secondary infections1-3. There is a critical need for antibiotics that are selective both for Gram-negative bacteria over Gram-positive bacteria, as well as for pathogenic bacteria over commensal bacteria. Here we report the design and discovery of lolamicin, a Gram-negative-specific antibiotic targeting the lipoprotein transport system. Lolamicin has activity against a panel of more than 130 multidrug-resistant clinical isolates, shows efficacy in multiple mouse models of acute pneumonia and septicaemia infection, and spares the gut microbiome in mice, preventing secondary infection with Clostridioides difficile. The selective killing of pathogenic Gram-negative bacteria by lolamicin is a consequence of low sequence homology for the target in pathogenic bacteria versus commensals; this doubly selective strategy can be a blueprint for the development of other microbiome-sparing antibiotics.

Generating Concentration Gradients across Membranes for Molecular Dynamics Simulations of Periodic Systems.

The plasma membrane forms the boundary between a living entity and its environment and acts as a barrier to permeation and flow of substances. Several computational means of calculating permeability have been implemented for molecular dynamics (MD) simulations-based approaches. Except for double bilayer systems, most permeability studies have been performed under equilibrium conditions, in large part due to the challenges associated with creating concentration gradients in simulations utilizing periodic boundary conditions. To enhance the scientific understanding of permeation and complement the existing computational means of characterizing membrane permeability, we developed a non-equilibrium method that enables the generation and maintenance of steady-state gradients in MD simulations. We utilize PBCs advantageously by imposing a directional bias to the motion of permeants so that their crossing of the boundary replenishes the gradient, like a previous study on ions. Under these conditions, a net flow of permeants across membranes may be observed to determine bulk permeability by a direct application of J=PΔc. In the present study, we explore the results of its application to an exemplary O2 and POPC bilayer system, demonstrating accurate and precise permeability measurements. In addition, we illustrate the impact of permeant concentration and the choice of thermostat on the permeability. Moreover, we demonstrate that energetics of permeation can be closely examined by the dissipation of the gradient across the membrane to gain nuanced insights into the thermodynamics of permeability.

Beyond nothingness in the formation and functional relevance of voids in polymer films.

Voids-the nothingness-broadly exist within nanomaterials and impact properties ranging from catalysis to mechanical response. However, understanding nanovoids is challenging due to lack of imaging methods with the needed penetration depth and spatial resolution. Here, we integrate electron tomography, morphometry, graph theory and coarse-grained molecular dynamics simulation to study the formation of interconnected nanovoids in polymer films and their impacts on permeance and nanomechanical behaviour. Using polyamide membranes for molecular separation as a representative system, three-dimensional electron tomography at nanometre resolution reveals nanovoid formation from coalescence of oligomers, supported by coarse-grained molecular dynamics simulations. Void analysis provides otherwise inaccessible inputs for accurate fittings of methanol permeance for polyamide membranes. Three-dimensional structural graphs accounting for the tortuous nanovoids within, measure higher apparent moduli with polyamide membranes of higher graph rigidity. Our study elucidates the significance of nanovoids beyond the nothingness, impacting the synthesis‒morphology‒function relationships of complex nanomaterials.

Atomistic Characterization of Beta-2-Glycoprotein I Domain V Interaction with Anionic Membranes.

Background: Interaction of beta-2-glycoprotein I ( ß 2 GPI) with anionic membranes is crucial in antiphospholipid syndrome (APS), implicating the role of it's membrane bind-ing domain, Domain V (DV). The mechanism of DV binding to anionic lipids is not fully understood. Objectives: This study aims to elucidate the mechanism by which DV of ß 2 GPI binds to anionic membranes. Methods: We utilized molecular dynamics (MD) simulations to investigate the struc-tural basis of anionic lipid recognition by DV. To corroborate the membrane-binding mode identified in the HMMM simulations, we conducted additional simulations using a full mem-brane model. Results: The study identified critical regions in DV, namely the lysine-rich loop and the hydrophobic loop, essential for membrane association via electrostatic and hydrophobic interactions, respectively. A novel lysine pair contributing to membrane binding was also discovered, providing new insights into ß 2 GPI's membrane interaction. Simulations revealed two distinct binding modes of DV to the membrane, with mode 1 characterized by the insertion of the hydrophobic loop into the lipid bilayer, suggesting a dominant mechanism for membrane association. This interaction is pivotal for the pathogenesis of APS, as it facilitates the recognition of ß 2 GPI by antiphospholipid antibodies. Conclusion: The study advances our understanding of the molecular interactions be-tween ß 2 GPI's DV and anionic membranes, crucial for APS pathogenesis. It highlights the importance of specific regions in DV for membrane binding and reveals a predominant bind-ing mode. These findings have significant implications for APS diagnostics and therapeutics, offering a deeper insight into the molecular basis of the syndrome.

WSB1/2 target chromatin-bound lysine-methylated RelA for proteasomal degradation and NF-κB termination.

Proteasome-mediated degradation of chromatin-bound NF-κB is critical in terminating the transcription of pro-inflammatory genes and can be triggered by Set9-mediated lysine methylation of the RelA subunit. However, the E3 ligase targeting methylated RelA remains unknown. Here, we find that two structurally similar substrate-recognizing components of Cullin-RING E3 ligases, WSB1 and WSB2, can recognize chromatin-bound methylated RelA for polyubiquitination and proteasomal degradation. We showed that WSB1/2 negatively regulated a subset of NF-κB target genes via associating with chromatin where they targeted methylated RelA for ubiquitination, facilitating the termination of NF-κB-dependent transcription. WSB1/2 specifically interacted with methylated lysines (K) 314 and 315 of RelA via their N-terminal WD-40 repeat (WDR) domains, thereby promoting ubiquitination of RelA. Computational modeling further revealed that a conserved aspartic acid (D) at position 158 within the WDR domain of WSB2 coordinates K314/K315 of RelA, with a higher affinity when either of the lysines is methylated. Mutation of D158 abolished WSB2's ability to bind to and promote ubiquitination of methylated RelA. Together, our study identifies a novel function and the underlying mechanism for WSB1/2 in degrading chromatin-bound methylated RelA and preventing sustained NF-κB activation, providing potential new targets for therapeutic intervention of NF-κB-mediated inflammatory diseases.

IHMCIF: An Extension of the PDBx/mmCIF Data Standard for Integrative Structure Determination Methods.

IHMCIF (github.com/ihmwg/IHMCIF) is a data information framework that supports archiving and disseminating macromolecular structures determined by integrative or hybrid modeling (IHM), and making them Findable, Accessible, Interoperable, and Reusable (FAIR). IHMCIF is an extension of the Protein Data Bank Exchange/macromolecular Crystallographic Information Framework (PDBx/mmCIF) that serves as the framework for the Protein Data Bank (PDB) to archive experimentally determined atomic structures of biological macromolecules and their complexes with one another and small molecule ligands (e.g., enzyme cofactors and drugs). IHMCIF serves as the foundational data standard for the PDB-Dev prototype system, developed for archiving and disseminating integrative structures. It utilizes a flexible data representation to describe integrative structures that span multiple spatiotemporal scales and structural states with definitions for restraints from a variety of experimental methods contributing to integrative structural biology. The IHMCIF extension was created with the benefit of considerable community input and recommendations gathered by the Worldwide Protein Data Bank (wwPDB) Task Force for Integrative or Hybrid Methods (wwpdb.org/task/hybrid). Herein, we describe the development of IHMCIF to support evolving methodologies and ongoing advancements in integrative structural biology. Ultimately, IHMCIF will facilitate the unification of PDB-Dev data and tools with the PDB archive so that integrative structures can be archived and disseminated through PDB.

Elucidating the Mechanism of Metabolism of Cannabichromene by Human Cytochrome P450s.

Cannabichromene (CBC) is a nonpsychoactive phytocannabinoid well-known for its wide-ranging health advantages. However, there is limited knowledge regarding its human metabolism following CBC consumption. This research aimed to explore the metabolic pathways of CBC by various human liver cytochrome P450 (CYP) enzymes and support the outcomes using in vivo data from mice. The results unveiled two principal CBC metabolites generated by CYPs: 8'-hydroxy-CBC and 6',7'-epoxy-CBC, along with a minor quantity of 1″-hydroxy-CBC. Notably, among the examined CYPs, CYP2C9 demonstrated the highest efficiency in producing these metabolites. Moreover, through a molecular dynamics simulation spanning 1 µs, it was observed that CBC attains stability at the active site of CYP2J2 by forming hydrogen bonds with I487 and N379, facilitated by water molecules, which specifically promotes the hydroxy metabolite's formation. Additionally, the presence of cytochrome P450 reductase (CPR) amplified CBC's binding affinity to CYPs, particularly with CYP2C8 and CYP3A4. Furthermore, the metabolites derived from CBC reduced cytokine levels, such as IL6 and NO, by approximately 50% in microglia cells. This investigation offers valuable insights into the biotransformation of CBC, underscoring the physiological importance and the potential significance of these metabolites.

Structure of alpha-synuclein fibrils derived from human Lewy body dementia tissue.

The defining feature of Parkinson disease (PD) and Lewy body dementia (LBD) is the accumulation of alpha-synuclein (Asyn) fibrils in Lewy bodies and Lewy neurites. Here we develop and validate a method to amplify Asyn fibrils extracted from LBD postmortem tissue samples and use solid state nuclear magnetic resonance (SSNMR) studies to determine atomic resolution structure. Amplified LBD Asyn fibrils comprise a mixture of single protofilament and two protofilament fibrils with very low twist. The protofilament fold is highly similar to the fold determined by a recent cryo-electron microscopy study for a minority population of twisted single protofilament fibrils extracted from LBD tissue. These results expand the structural characterization of LBD Asyn fibrils and approaches for studying disease mechanisms, imaging agents and therapeutics targeting Asyn.

Integrative analysis of pathogenic variants in glucose-6-phosphatase based on an AlphaFold2 model.

Mediating the terminal reaction of gluconeogenesis and glycogenolysis, the integral membrane protein glucose-6-phosphate catalytic subunit 1 (G6PC1) regulates hepatic glucose production by catalyzing hydrolysis of glucose-6-phosphate (G6P) within the lumen of the endoplasmic reticulum. Consistent with its vital contribution to glucose homeostasis, inactivating mutations in G6PC1 causes glycogen storage disease (GSD) type 1a characterized by hepatomegaly and severe hypoglycemia. Despite its physiological importance, the structural basis of G6P binding to G6PC1 and the molecular disruptions induced by missense mutations within the active site that give rise to GSD type 1a are unknown. In this study, we determine the atomic interactions governing G6P binding as well as explore the perturbations imposed by disease-linked missense variants by subjecting an AlphaFold2 G6PC1 structural model to molecular dynamics simulations and in silico predictions of thermodynamic stability validated with robust in vitro and in situ biochemical assays. We identify a collection of side chains, including conserved residues from the signature phosphatidic acid phosphatase motif, that contribute to a hydrogen bonding and van der Waals network stabilizing G6P in the active site. The introduction of GSD type 1a mutations modified the thermodynamic landscape, altered side chain packing and substrate-binding interactions, and induced trapping of catalytic intermediates. Our results, which corroborate the high quality of the AF2 model as a guide for experimental design and to interpret outcomes, not only confirm the active-site structural organization but also identify previously unobserved mechanistic contributions of catalytic and noncatalytic side chains.

Tumor-acquired somatic mutation affects conformation to abolish ABCG2-mediated drug resistance.

ABCG2 is an important ATP-binding cassette transporter impacting the absorption and distribution of over 200 chemical toxins and drugs. ABCG2 also reduces the cellular accumulation of diverse chemotherapeutic agents. Acquired somatic mutations in the phylogenetically conserved amino acids of ABCG2 might provide unique insights into its molecular mechanisms of transport. Here, we identify a tumor-derived somatic mutation (Q393K) that occurs in a highly conserved amino acid across mammalian species. This ABCG2 mutant seems incapable of providing ABCG2-mediated drug resistance. This was perplexing because it is localized properly and retained interaction with substrates and nucleotides. Using a conformationally sensitive antibody, we show that this mutant appears "locked" in a non-functional conformation. Structural modeling and molecular dynamics simulations based on ABCG2 cryo-EM structures suggested that the Q393K interacts with the E446 to create a strong salt bridge. The salt bridge is proposed to stabilize the inward-facing conformation, resulting in an impaired transporter that lacks the flexibility to readily change conformation, thereby disrupting the necessary communication between substrate binding and transport.

A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture.

Metal-organic frameworks (MOFs) exhibit great promise for CO2 capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we introduce GHP-MOFassemble, a generative artificial intelligence (AI), high performance framework for the rational and accelerated design of MOFs with high CO2 adsorption capacity and synthesizable linkers. GHP-MOFassemble generates novel linkers, assembled with one of three pre-selected metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer) into MOFs in a primitive cubic topology. GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity, uses molecular dynamics simulations to study their stability and chemical consistency, and crystal graph neural networks and Grand Canonical Monte Carlo simulations to quantify their CO2 adsorption capacities. We present the top six AI-generated MOFs with CO2 capacities greater than 2m mol g-1, i.e., higher than 96.9% of structures in the hypothetical MOF dataset.

The carboxy terminus causes interfacial assembly of oleate hydratase on a membrane bilayer.

The soluble flavoprotein oleate hydratase (OhyA) hydrates the 9-cis double bond of unsaturated fatty acids. OhyA substrates are embedded in membrane bilayers; OhyA must remove the fatty acid from the bilayer and enclose it in the active site. Here, we show that the positively charged helix-turn-helix motif in the carboxy terminus (CTD) is responsible for interacting with the negatively charged phosphatidylglycerol (PG) bilayer. Super-resolution microscopy of Staphylococcus aureus cells expressing green fluorescent protein fused to OhyA or the CTD sequence shows subcellular localization along the cellular boundary, indicating OhyA is membrane-associated and the CTD sequence is sufficient for membrane recruitment. Using cryo-electron microscopy, we solved the OhyA dimer structure and conducted 3D variability analysis of the reconstructions to assess CTD flexibility. Our surface plasmon resonance experiments corroborated that OhyA binds the PG bilayer with nanomolar affinity and we found the CTD sequence has intrinsic PG binding properties. We determined that the nuclear magnetic resonance structure of a peptide containing the CTD sequence resembles the OhyA crystal structure. We observed intermolecular NOE from PG liposome protons next to the phosphate group to the CTD peptide. The addition of paramagnetic MnCl2 indicated the CTD peptide binds the PG surface but does not insert into the bilayer. Molecular dynamics simulations, supported by site-directed mutagenesis experiments, identify key residues in the helix-turn-helix that drive membrane association. The data show that the OhyA CTD binds the phosphate layer of the PG surface to obtain bilayer-embedded unsaturated fatty acids.

A Rigorous Framework for Calculating Protein-Protein Binding Affinities in Membranes.

Calculating the binding free energy of integral transmembrane (TM) proteins is crucial for understanding the mechanisms by which they recognize one another and reversibly associate. The glycophorin A (GpA) homodimer, composed of two α-helical segments, has long served as a model system for studying TM protein reversible association. The present work establishes a methodological framework for calculating the binding affinity of the GpA homodimer in the heterogeneous environment of a membrane. Our investigation carefully considered a variety of protocols, including the appropriate choice of the force field, rigorous standardization reflecting the experimental conditions, sampling algorithm, anisotropic environment, and collective variables, to accurately describe GpA dimerization via molecular dynamics-based approaches. Specifically, two strategies were explored: (i) an unrestrained potential mean force (PMF) calculation, which merely enhances sampling along the separation of the two binding partners without any restraint, and (ii) a so-called "geometrical route", whereby the α-helices are progressively separated with imposed restraints on their orientational, positional, and conformational degrees of freedom to accelerate convergence. Our simulations reveal that the simplified, unrestrained PMF approach is inadequate for the description of GpA dimerization. Instead, the geometrical route, tailored specifically to GpA in a membrane environment, yields excellent agreement with experimental data within a reasonable computational time. A dimerization free energy of -10.7 kcal/mol is obtained, in fairly good agreement with available experimental data. The geometrical route further helps elucidate how environmental forces drive association before helical interactions stabilize it. Our simulations also brought to light a distinct, long-lived spatial arrangement that potentially serves as an intermediate state during dimer formation. The methodological advances in the generalized geometrical route provide a powerful tool for accurate and efficient binding-affinity calculations of intricate TM protein complexes in inhomogeneous environments.

Topological Learning Approach to Characterizing Biological Membranes.

Biological membranes play key roles in cellular compartmentalization, structure, and its signaling pathways. At varying temperatures, individual membrane lipids sample from different configurations, a process that frequently leads to higher-order phase behavior and phenomena. Here we present a persistent homology-based method for quantifying the structural features of individual and bulk lipids, providing local and contextual information on lipid tail organization. Our method leverages the mathematical machinery of algebraic topology and machine learning to infer temperature-dependent structural information of lipids from static coordinates. To train our model, we generated multiple molecular dynamics trajectories of DPPC membranes at varying temperatures. A fingerprint was then constructed for each set of lipid coordinates by a persistent homology filtration, in which interactions spheres were grown around the lipid atoms while tracking their intersections. The sphere filtration formed a simplicial complex that captures enduring key topological features of the configuration landscape, using homology, yielding persistence data. Following fingerprint extraction for physiologically relevant temperatures, the persistence data were used to train an attention-based neural network for assignment of effective temperature values to selected membrane regions. Our persistence homology-based method captures the local structural effects, via effective temperature, of lipids adjacent to other membrane constituents, e.g. sterols and proteins. This topological learning approach can predict lipid effective temperatures from static coordinates across multiple spatial resolutions. The tool, called MembTDA, can be accessed at https://github.com/hyunp2/Memb-TDA.