VMD as a Platform for Interactive Small Molecule Preparation and Visualization in Quantum and Classical Simulations

The mycobacterium lipid transporter MmpL3 is dimeric in detergent solution, SMALPs and reconstituted nanodiscs

The mycobacterial membrane protein large 3 (MmpL3) transports key precursor lipids to the outer membrane of Mycobacterium species. Multiple structures of MmpL3 from both M. tuberculosis and M. smegmatis in various conformational states indicate that the protein is both structurally and functionally monomeric. However, most other resistance, nodulation and cell division (RND) transporters structurally characterised to date are either dimeric or trimeric. Here we present an in depth biophysical and computational analysis revealing that MmpL3 from M. smegmatis exists as a dimer in a variety of membrane mimetic systems (SMALPs, detergent-based solution and nanodiscs). Sucrose gradient separation of MmpL3 populations from M. smegmatis, reconstituted into nanodiscs, identified monomeric and dimeric populations of the protein using laser induced liquid bead ion desorption (LILBID), a native mass spectrometry technique. Preliminary cryo-EM analysis confirmed that MmpL3 forms physiological dimers. Untargeted lipidomics experiments on membrane protein co-purified lipids revealed PE and PG lipid classes were predominant. Molecular dynamics (MD) simulations, in the presence of physiologically-relevant lipid compositions revealed the likely dimer interface.

Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions

Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure are long-standing goals with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost-intensive. Computational methods have potential to accelerate epitope predictions; however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologues. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis were ineffective in distinguishing between two proposed binding models, parallel and perpendicular. However, our integrated approach, utilizing dynamic network analysis, demonstrated that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols, including cross-linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Conversely, AlphaFold3 failed to predict a structure bound in the perpendicular pose, highlighting the necessity for exploratory research in the search for binding epitopes and challenging the notion that AI-generated protein structures can be accepted without scrutiny. Our research underscores the potential of employing dynamic network analysis to enhance AI-based structure predictions for more accurate identification of protein-protein interaction interfaces.

Adaptation of turnip mosaic virus to Arabidopsis thaliana involves rewiring of VPg-host proteome interactions

The outcome of a viral infection depends on a complex interplay between the host physiology and the virus, mediated through numerous protein-protein interactions. In a previous study, we used high-throughput yeast two-hybrid (HT-Y2H) to identify proteins in Arabidopsis thaliana that bind to the proteins encoded by the turnip mosaic virus (TuMV) genome. Furthermore, after experimental evolution of TuMV lineages in plants with mutations in defense-related or proviral genes, most mutations observed in the evolved viruses affected the VPg cistron. Among these mutations, D113G was a convergent mutation selected in many lineages across different plant genotypes, including cpr5-2 with constitutive expression of systemic acquired resistance. In contrast, mutation R118H specifically emerged in the jin1 mutant with affected jasmonate signaling. Using the HT-Y2H system, we analyzed the impact of these two mutations on VPg's interaction with plant proteins. Interestingly, both mutations severely compromised the interaction of VPg with the translation initiation factor eIF(iso)4E, a crucial interactor for potyvirus infection. Moreover, mutation D113G, but not R118H, adversely affected the interaction with RHD1, a zinc-finger homeodomain transcription factor involved in regulating DNA demethylation. Our results suggest that RHD1 enhances plant tolerance to TuMV infection. We also discuss our findings in a broad virus evolution context.

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.

Prediction of Pesticide Interactions with Proteins Involved in Human Reproduction by Using a Virtual Screening Approach: A Case Study of Famoxadone Binding CRBP-III and Izumo

In recent years, the awareness that pesticides can have other effects apart from generic toxicity is growing. In particular, several pieces of evidence highlight their influence on human fertility. In this study, we investigated, by a virtual screening approach, the binding between pesticides and proteins present in human gametes or associated with reproduction, in order to identify new interactions that could affect human fertility. To this aim, we prepared ligand (pesticides) and receptor (proteins) 3D structure datasets from online structural databases (such as PubChem and RCSB), and performed a virtual screening analysis using Autodock Vina. In the comparison of the predicted interactions, we found that famoxadone was predicted to bind Cellular Retinol Binding Protein-III in the retinol-binding site with a better minimum energy value of -10.4 Kcal/mol and an RMSD of 3.77 with respect to retinol (-7.1 Kcal/mol). In addition to a similar network of interactions, famoxadone binding is more stabilized by additional hydrophobic patches including L20, V29, A33, F57, L117, and L118 amino acid residues and hydrogen bonds with Y19 and K40. These results support a possible competitive effect of famoxadone on retinol binding with impacts on the ability of developing the cardiac tissue, in accordance with the literature data on zebrafish embryos. Moreover, famoxadone binds, with a minimum energy value between -8.3 and -8.0 Kcal/mol, to the IZUMO Sperm-Egg Fusion Protein, interacting with a network of polar and hydrophobic amino acid residues in the cavity between the 4HB and Ig-like domains. This binding is more stabilized by a predicted hydrogen bond with the N185 residue of the protein. A hindrance in this position can probably affect the conformational change for JUNO binding, avoiding the gamete membrane fusion to form the zygote. This work opens new interesting perspectives of study on the effects of pesticides on fertility, extending the knowledge to other typologies of interaction which can affect different steps of the reproductive process.

A Perspective on Protein Structure Prediction Using Quantum Computers

Despite the recent advancements by deep learning methods such as AlphaFold2, in silico protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum computers can offer some meaningful benefits for approaching this problem. Yet, identifying specific problem instances amenable to quantum advantage and estimating the quantum resources required are equally challenging tasks. Here, we share our perspective on how to create a framework for systematically selecting protein structure prediction problems that are amenable for quantum advantage, and estimate quantum resources for such problems on a utility-scale quantum computer. As a proof-of-concept, we validate our problem selection framework by accurately predicting the structure of a catalytic loop of the Zika Virus NS3 Helicase, on quantum hardware.

Proteomics Platform Reveals Broad-Spectrum Nanobodies for SARS-CoV-2 Variant Neutralization

The ongoing evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the emergence of different variants of concerns with immune evasion that have been prevalent over the past three years. Nanobodies, the functional variable regions of camelid heavy-chain-only antibodies, have garnered interest in developing neutralizing antibodies due to their smaller size, structural stability, ease of production, high affinity, and low immunogenicity, among other characteristics. In this work, we describe an integrated proteomics platform for the high-throughput screening of nanobodies against different SARS-CoV-2 spike variants. To demonstrate this platform, we immunized a camel with subunit 1 (S1) of the wild-type spike protein and constructed a nanobody phage library. The binding and neutralizing activities of the nanobodies against 72 spike variants were then measured, resulting in the identification of two nanobodies (C-282 and C-39) with broad neutralizing activity against six non-Omicron variants (D614G, Alpha, Beta, Gamma, Delta, Kappa) and five Omicron variants (BA.1-5). Their neutralizing capability was validated using in vitro pseudovirus-based neutralization assays. All these results demonstrate the utility of our proteomics platform to identify new nanobodies with broad neutralizing capability and to develop a treatment for patients with SARS-CoV-2 variant infection in the future.

Design and Mechanism Study of High-Safety and Long-Life Electrolyte for High-Energy-Density Lithium-Ion Batteries

Although nonflammable electrolytes are beneficial for battery safety, they often adversely affect the electrochemical performance of lithium-ion batteries due to their poor compatibility with electrodes. Herein, we design a nonflammable electrolyte consisting of cyclic carbonate and 2,2-difluoroethyl acetate (DFEA) solvents paired with several surface-film-forming additives, significantly improving the safety and cycling performance of NMC811||SiOx/graphite pouch cells. The DFEA solvent exhibits not only good flame retardancy but also lower lowest unoccupied molecular orbital (LUMO) energy, promoting the formation of a robust inorganic-rich and gradient-architecture hybrid interface between the SiOx/graphite anode and electrolyte. The double insurance of good flame retardancy of the DFEA solvent and decreased exothermic effects of both bulk electrolyte and DFEA-derived solid electrolyte interphase (SEI) can ensure the high safety of the pouch cell. Moreover, the highly robust SEI can prevent the excessive reduction decomposition of the electrolyte and alleviate the structural decay of the anode, which can restrain the formation of lithium deposition on the anode surface and further suppress the structural decay of NMC materials. This contributes to the unprecedented cycling performance of the NMC811||SiOx/graphite pouch cells with a capacity retention of 80% after 1000 cycles at a 0.33C rate.

An ensemble docking-based virtual screening and molecular dynamics simulation of phytochemical compounds from Malaysian Kelulut Honey (KH) against SARS-CoV-2 target enzyme, human angiotensin-converting enzyme 2 (ACE-2)

The human angiotensin-converting enzyme 2 (ACE-2) receptor is a metalloenzyme that plays an important role in regulating blood pressure by modulating angiotensin II. This receptor facilitates SARS-CoV-2 entry into human cells via receptor-mediated endocytosis, causing the global COVID-19 pandemic and a major health crisis. Kelulut honey (KH), one of Malaysian honey recently gained attention for its distinct flavour and taste while having many nutritional and medicinal properties. Recent study demonstrates the antiviral potential of KH against SARS-CoV-2 by inhibiting ACE-2 in vitro, but the bioactive compound pertaining to the ACE-2 inhibition is yet unknown. An ensemble docking-based virtual screening was employed to screen the phytochemical compounds from KH with high binding affinity against the 10 best representative structures of ACE-2 that mostly formed from MD simulation. From 110 phytochemicals previously identified in KH, 27 compounds passed the ADMET analysis and proceeded to docking. Among the docked compound, SDC and FMN consistently exhibited strong binding to ACE-2's active site (-9.719 and -9.473 kcal/mol) and allosteric site (-7.305 and -7.464 kcal/mol) as compared to potent ACE-2 inhibitor, MLN 4760. Detailed trajectory analysis of MD simulation showed stable binding interaction towards active and allosteric sites of ACE-2. KH's compounds show promise in inhibiting SARS-CoV-2 binding to ACE-2 receptors, indicating potential for preventive use or as a supplement to other COVID-19 treatments. Additional research is needed to confirm KH's antiviral effects and its role in SARS-CoV-2 therapy, including prophylaxis and adjuvant treatment with vaccination.Communicated by Ramaswamy H. Sarma.

Computational methods in glaucoma research: Current status and future outlook

Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics integrates genomic and clinical data, aiding in identifying drug targets, comprehending disease mechanisms, and personalizing treatment strategies for glaucoma. Molecular dynamics simulations offer valuable molecular-level insights into glaucoma-related biomolecule behavior and drug interactions, guiding experimental studies and drug discovery efforts. Artificial intelligence (AI) technologies hold promise in revolutionizing glaucoma research, enhancing disease diagnosis, target identification, and drug candidate selection. The generalized protocols for systems genetics, MD simulations, and AI model development are included as a guide for glaucoma researchers. These computational methods, however, are not separate and work harmoniously together to discover novel ways to combat glaucoma. Ongoing research and progresses in genomics technologies, MD simulations, and AI methodologies project computational methods to become an integral part of glaucoma research in the future.

Effect of Fragment 1 on the Binding of Epigallocatechin Gallate to the PD-L1 Dimer Explored by Molecular Dynamics

Blocking the interaction between programmed cell death-1 (PD-1) and programmed cell death-ligand 1 (PD-L1) by directly targeting the PD-L1 dimer has emerged as a hot topic in the field of cancer immunotherapy. Epigallocatechin gallate (EGCG), a natural product, has been demonstrated binding to the PD-L1 dimer in our previous study, but has a weaker binding capacity, moreover, EGCG is located at the end of the binding pocket of the PD-L1 dimer. The inhibitor fragment 1 (FRA) lies at the other end. So, we proposed that the introduction of FRA might be able to improve the binding ability. To illuminate this issue, molecular dynamics (MD) simulation was performed in the present study. Binding free energy calculations show that the binding affinity is significantly increased by 17 kcal/mol upon the introduction of FRA. It may be due to the energy contributions of emerging key residues ATyr56, AMet115, BTyr123, AIle54 and the enhanced contributions of initial key residues ATyr123 and BVal68. Binding mode and non-bonded interaction results indicate that FRA_EGCG (EGCG in combination with FRA) binds to the C-, F- and G-sheet of the PD-L1 dimer. Importantly, the introduction of FRA mainly strengthened the nonpolar interactions. The free energy landscape and secondary structure results further show that FRA_EGCG can interact with the PD-L1 dimer more stably. These data demonstrated here provide the theoretical basis for screening two or more natural products with additive inhibitory effect on this pathway and therefore exerting more effective anticancer immunity.