Predicting protein-membrane interfaces of peripheral membrane proteins using ensemble machine learning.

Abnormal protein-membrane attachment is involved in deregulated cellular pathways and in disease. Therefore, the possibility to modulate protein-membrane interactions represents a new promising therapeutic strategy for peripheral membrane proteins that have been considered so far undruggable. A major obstacle in this drug design strategy is that the membrane-binding domains of peripheral membrane proteins are usually unknown. The development of fast and efficient algorithms predicting the protein-membrane interface would shed light into the accessibility of membrane-protein interfaces by drug-like molecules. Herein, we describe an ensemble machine learning methodology and algorithm for predicting membrane-penetrating amino acids. We utilize available experimental data from the literature for training 21 machine learning classifiers and meta-classifiers. Evaluation of the best ensemble classifier model accuracy yields a macro-averaged F1 score = 0.92 and a Matthews correlation coefficient = 0.84 for predicting correctly membrane-penetrating amino acids on unknown proteins of a validation set. The python code for predicting protein-membrane interfaces of peripheral membrane proteins is available at https://github.com/zoecournia/DREAMM.

Computational Investigation of BMAA and Its Carbamate Adducts as Potential GluR2 Modulators.

Beta-N-methylamino-l-alanine (BMAA) is a potential neurotoxic nonprotein amino acid, which can reach the human body through the food chain. When BMAA interacts with bicarbonate in the human body, carbamate adducts are produced, which share a high structural similarity with the neurotransmitter glutamate. It is believed that BMAA and its l-carbamate adducts bind in the glutamate binding site of ionotropic glutamate receptor 2 (GluR2). Chronic exposure to BMAA and its adducts could cause neurological illness such as neurodegenerative diseases. However, the mechanism of BMAA action and its carbamate adducts bound to GluR2 has not yet been elucidated. Here, we investigate the binding modes and the affinity of BMAA and its carbamate adducts to GluR2 in comparison to the natural agonist, glutamate, to understand whether these can act as GluR2 modulators. Initially, we perform molecular dynamics simulations of BMAA and its carbamate adducts bound to GluR2 to examine the stability of the ligands in the S1/S2 ligand-binding core of the receptor. In addition, we utilize alchemical free energy calculations to compute the difference in the free energy of binding of the beta-carbamate adduct of BMAA to GluR2 compared to that of glutamate. Our findings indicate that carbamate adducts of BMAA and glutamate remain stable in the binding site of the GluR2 compared to BMAA. Additionally, alchemical free energy results reveal that glutamate and the beta-carbamate adduct of BMAA have comparable binding affinity to the GluR2. These results provide a rationale that BMAA carbamate adducts may be, in fact, the modulators of GluR2 and not BMAA itself.

A Perspective on the Prospective Use of AI in Protein Structure Prediction.

AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.

DREAMM: a web-based server for drugging protein-membrane interfaces as a novel workflow for targeted drug design.

SUMMARY: The allosteric modulation of peripheral membrane proteins (PMPs) by targeting protein-membrane interactions with drug-like molecules represents a new promising therapeutic strategy for proteins currently considered undruggable. However, the accessibility of protein-membrane interfaces by small molecules has been so far unexplored, possibly due to the complexity of the interface, the limited protein-membrane structural information and the lack of computational workflows to study it. Herein, we present a pipeline for drugging protein-membrane interfaces using the DREAMM (Drugging pRotein mEmbrAne Machine learning Method) web server. DREAMM works in the back end with a fast and robust ensemble machine learning algorithm for identifying protein-membrane interfaces of PMPs. Additionally, DREAMM also identifies binding pockets in the vicinity of the predicted membrane-penetrating amino acids in protein conformational ensembles provided by the user or generated within DREAMM. AVAILABILITY AND IMPLEMENTATION: DREAMM web server is accessible via https://dreamm.ni4os.eu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Inactive-to-Active Transition of Human Thymidine Kinase 1 Revealed by Molecular Dynamics Simulations.

Despite its importance in the nucleoside (and nucleoside prodrug) metabolism, the structure of the active conformation of human thymidine kinase 1 (hTK1) remains elusive. We perform microsecond molecular dynamics simulations of the inactive enzyme form bound to a bisubstrate inhibitor that was shown experimentally to activate another TK1-like kinase, Thermotoga maritima TK (TmTK). Our results are in excellent agreement with the experimental findings for the TmTK closed-to-open state transition. We show that the inhibitor induces an increase of the enzyme radius of gyration due to the expansion on one of the dimer interfaces; the structural changes observed, including the active site pocket volume increase and the decrease in the monomer-monomer buried surface area and of the number of hydrogen bonds (as compared to the inactive enzyme control simulation), indicate that the catalytically competent (open) conformation of hTK1 can be assumed in the presence of an activating ligand.

Comparative Interaction Studies of Quercetin with 2-Hydroxyl-propyl-ß-cyclodextrin and 2,6-Methylated-ß-cyclodextrin.

Quercetin (QUE) is a well-known natural product that can exert beneficial properties on human health. However, due to its low solubility its bioavailability is limited. In the present study, we examine whether its formulation with two cyclodextrins (CDs) may enhance its pharmacological profile. Comparative interaction studies of quercetin with 2-hydroxyl-propyl-ß-cyclodextrin (2HP-ß-CD) and 2,6-methylated cyclodextrin (2,6Me-ß-CD) were performed using NMR spectroscopy, DFT calculations, and in silico molecular dynamics (MD) simulations. Using T1 relaxation experiments and 2D DOSY it was illustrated that both cyclodextrin vehicles can host quercetin. Quantum mechanical calculations showed the formation of hydrogen bonds between QUE with 2HP-ß-CD and 2,6Μe-ß-CD. Six hydrogen bonds are formed ranging between 2 to 2.8 Å with 2HP-ß-CD and four hydrogen bonds within 2.8 Å with 2,6Μe-ß-CD. Calculations of absolute binding free energies show that quercetin binds favorably to both 2,6Me-ß-CD and 2HP-ß-CD. MM/GBSA results show equally favorable binding of quercetin in the two CDs. Fluorescence spectroscopy shows moderate binding of quercetin in 2HP-ß-CD (520 M-1) and 2,6Me-ß-CD (770 M-1). Thus, we propose that both formulations (2HP-ß-CD:quercetin, 2,6Me-ß-CD:quercetin) could be further explored and exploited as small molecule carriers in biological studies.

ChemBioServer 2.0: an advanced web server for filtering, clustering and networking of chemical compounds facilitating both drug discovery and repurposing.

SUMMARY: ChemBioServer 2.0 is the advanced sequel of a web server for filtering, clustering and networking of chemical compound libraries facilitating both drug discovery and repurposing. It provides researchers the ability to (i) browse and visualize compounds along with their physicochemical and toxicity properties, (ii) perform property-based filtering of compounds, (iii) explore compound libraries for lead optimization based on perfect match substructure search, (iv) re-rank virtual screening results to achieve selectivity for a protein of interest against different protein members of the same family, selecting only those compounds that score high for the protein of interest, (v) perform clustering among the compounds based on their physicochemical properties providing representative compounds for each cluster, (vi) construct and visualize a structural similarity network of compounds providing a set of network analysis metrics, (vii) combine a given set of compounds with a reference set of compounds into a single structural similarity network providing the opportunity to infer drug repurposing due to transitivity, (viii) remove compounds from a network based on their similarity with unwanted substances (e.g. failed drugs) and (ix) build custom compound mining pipelines. AVAILABILITY AND IMPLEMENTATION: http://chembioserver.vi-seem.eu.

FEPrepare: A Web-Based Tool for Automating the Setup of Relative Binding Free Energy Calculations.

Relative binding free energy calculations in drug design are becoming a useful tool in facilitating lead binding affinity optimization in a cost- and time-efficient manner. However, they have been limited by technical challenges such as the manual creation of large numbers of input files to set up, run, and analyze free energy simulations. In this Application Note, we describe FEPrepare, a novel web-based tool, which automates the setup procedure for relative binding FEP calculations for the dual-topology scheme of NAMD, one of the major MD engines, using OPLS-AA force field topology and parameter files. FEPrepare provides the user with all necessary files needed to run a FEP/MD simulation with NAMD. FEPrepare can be accessed and used at https://feprepare.vi-seem.eu/.

Impact of the Journal of Chemical Information and Modeling Special Issue on Women in Computational Chemistry.

In this Viewpoint, we provide a commentary on the impact of the Journal of Chemical Information and Modeling Special Issue on Women in Computational Chemistry published in May 2019 and the feedback we received.

Rigorous Free Energy Simulations in Virtual Screening.

Virtual high throughput screening (vHTS) in drug discovery is a powerful approach to identify hits: when applied successfully, it can be much faster and cheaper than experimental high-throughput screening approaches. However, mainstream vHTS tools have significant limitations: ligand-based methods depend on knowledge of existing chemical matter, while structure-based tools such as docking involve significant approximations that limit their accuracy. Recent advances in scientific methods coupled with dramatic speedups in computational processing with GPUs make this an opportune time to consider the role of more rigorous methods that could improve the predictive power of vHTS workflows. In this Perspective, we assert that alchemical binding free energy methods using all-atom molecular dynamics simulations have matured to the point where they can be applied in virtual screening campaigns as a final scoring stage to prioritize the top molecules for experimental testing. Specifically, we propose that alchemical absolute binding free energy (ABFE) calculations offer the most direct and computationally efficient approach within a rigorous statistical thermodynamic framework for computing binding energies of diverse molecules, as is required for virtual screening. ABFE calculations are particularly attractive for drug discovery at this point in time, where the confluence of large-scale genomics data and insights from chemical biology have unveiled a large number of promising disease targets for which no small molecule binders are known, precluding ligand-based approaches, and where traditional docking approaches have foundered to find progressible chemical matter.

The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations.

Approaches for computing small molecule binding free energies based on molecular simulations are now regularly being employed by academic and industry practitioners to study receptor-ligand systems and prioritize the synthesis of small molecules for ligand design. Given the variety of methods and implementations available, it is natural to ask how the convergence rates and final predictions of these methods compare. In this study, we describe the concept and results for the SAMPL6 SAMPLing challenge, the first challenge from the SAMPL series focusing on the assessment of convergence properties and reproducibility of binding free energy methodologies. We provided parameter files, partial charges, and multiple initial geometries for two octa-acid (OA) and one cucurbit[8]uril (CB8) host-guest systems. Participants submitted binding free energy predictions as a function of the number of force and energy evaluations for seven different alchemical and physical-pathway (i.e., potential of mean force and weighted ensemble of trajectories) methodologies implemented with the GROMACS, AMBER, NAMD, or OpenMM simulation engines. To rank the methods, we developed an efficiency statistic based on bias and variance of the free energy estimates. For the two small OA binders, the free energy estimates computed with alchemical and potential of mean force approaches show relatively similar variance and bias as a function of the number of energy/force evaluations, with the attach-pull-release (APR), GROMACS expanded ensemble, and NAMD double decoupling submissions obtaining the greatest efficiency. The differences between the methods increase when analyzing the CB8-quinine system, where both the guest size and correlation times for system dynamics are greater. For this system, nonequilibrium switching (GROMACS/NS-DS/SB) obtained the overall highest efficiency. Surprisingly, the results suggest that specifying force field parameters and partial charges is insufficient to generally ensure reproducibility, and we observe differences between seemingly converged predictions ranging approximately from 0.3 to 1.0 kcal/mol, even with almost identical simulations parameters and system setup (e.g., Lennard-Jones cutoff, ionic composition). Further work will be required to completely identify the exact source of these discrepancies. Among the conclusions emerging from the data, we found that Hamiltonian replica exchange-while displaying very small variance-can be affected by a slowly-decaying bias that depends on the initial population of the replicas, that bidirectional estimators are significantly more efficient than unidirectional estimators for nonequilibrium free energy calculations for systems considered, and that the Berendsen barostat introduces non-negligible artifacts in expanded ensemble simulations.

Nurr1:RXRα heterodimer activation as monotherapy for Parkinson's disease.

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by the loss of dopaminergic (DAergic) neurons in the substantia nigra and the gradual depletion of dopamine (DA). Current treatments replenish the DA deficit and improve symptoms but induce dyskinesias over time, and neuroprotective therapies are nonexistent. Here we report that Nuclear receptor-related 1 (Nurr1):Retinoid X receptor α (RXRα) activation has a double therapeutic potential for PD, offering both neuroprotective and symptomatic improvement. We designed BRF110, a unique in vivo active Nurr1:RXRα-selective lead molecule, which prevents DAergic neuron demise and striatal DAergic denervation in vivo against PD-causing toxins in a Nurr1-dependent manner. BRF110 also protects against PD-related genetic mutations in patient induced pluripotent stem cell (iPSC)-derived DAergic neurons and a genetic mouse PD model. Remarkably, besides neuroprotection, BRF110 up-regulates tyrosine hydroxylase (TH), aromatic l-amino acid decarboxylase (AADC), and GTP cyclohydrolase I (GCH1) transcription; increases striatal DA in vivo; and has symptomatic efficacy in two postneurodegeneration PD models, without inducing dyskinesias on chronic daily treatment. The combined neuroprotective and symptomatic effects of BRF110 identify Nurr1:RXRα activation as a potential monotherapeutic approach for PD.

Effect of 5-trans Isomer of Arachidonic Acid on Model Liposomal Membranes Studied by a Combined Simulation and Experimental Approach.

Unsaturated fatty acids are found in humans predominantly in the cis configuration. Fatty acids in the trans configuration are primarily the result of human processing (trans fats), but can also be formed endogenously by radical stress. The cis-trans isomerization of fatty acids by free radicals could be connected to several pathologies. Trans fats have been linked to an increased risk of coronary artery disease; however, the reasons for the resulting pathogenesis remain unclear. Here, we investigate the effect of a mono-trans isomer of arachidonic acid (C20:4-5trans, 8cis, 11cis, 14cis) produced by free radicals in physiological concentration on a model erythrocyte membrane using a combined experimental and theoretical approach. Molecular Dynamics (MD) simulations of two model lipid bilayers containing arachidonic acid and its 5-trans isomer in 3 mol% were carried out for this purpose. The 5-trans isomer formation in the phospholipids was catalyzed by HOCH2CH2S· radicals, generated from the corresponding thiol by γ-irradiation, in multilamellar vesicles of SAPC. Large unilamellar vesicles were made by the extrusion method (LUVET) as a biomimetic model for cis-trans isomerization. Atomic Force Microscopy and Dynamic Light Scattering were used to measure the average size, morphology, and the z-potential of the liposomes. Both results from MD simulations and experiments are in agreement and indicate that the two model membranes display different physicochemical properties in that the bilayers containing the trans fatty acids were more ordered and more rigid than those containing solely the cis arachidonic acid. Correspondingly, the average size of the liposomes containing trans isomers was smaller than the ones without.

NanoCrystal: A Web-Based Crystallographic Tool for the Construction of Nanoparticles Based on Their Crystal Habit.

Modeling of nanoparticles is an essential first step to assess their capacities for different uses such as energy storage and drug delivery. However, creating an initial starting conformation for modeling and simulation is tedious because every crystalline material grows with a different crystal habit. In this application note, we describe NanoCrystal, a novel web-based crystallographic tool that creates nanoparticle models from any crystal structure guided by their preferred equilibrium shape under standard conditions according to the Wulff morphology (crystal habit). Users can upload a cif file, define the Miller indices and their corresponding minimum surface energies according to the Wulff construction of a particular crystal, and specify the size of the nanocrystal. As a result, the nanoparticle is constructed and visualized, and the coordinates of the atoms are output to the user. NanoCrystal can be accessed at http://nanocrystal.vi-seem.edu/ .

Using physics-based pose predictions and free energy perturbation calculations to predict binding poses and relative binding affinities for FXR ligands in the D3R Grand Challenge 2.

Computer-aided drug design has become an integral part of drug discovery and development in the pharmaceutical and biotechnology industry, and is nowadays extensively used in the lead identification and lead optimization phases. The drug design data resource (D3R) organizes challenges against blinded experimental data to prospectively test computational methodologies as an opportunity for improved methods and algorithms to emerge. We participated in Grand Challenge 2 to predict the crystallographic poses of 36 Farnesoid X Receptor (FXR)-bound ligands and the relative binding affinities for two designated subsets of 18 and 15 FXR-bound ligands. Here, we present our methodology for pose and affinity predictions and its evaluation after the release of the experimental data. For predicting the crystallographic poses, we used docking and physics-based pose prediction methods guided by the binding poses of native ligands. For FXR ligands with known chemotypes in the PDB, we accurately predicted their binding modes, while for those with unknown chemotypes the predictions were more challenging. Our group ranked #1st (based on the median RMSD) out of 46 groups, which submitted complete entries for the binding pose prediction challenge. For the relative binding affinity prediction challenge, we performed free energy perturbation (FEP) calculations coupled with molecular dynamics (MD) simulations. FEP/MD calculations displayed a high success rate in identifying compounds with better or worse binding affinity than the reference (parent) compound. Our studies suggest that when ligands with chemical precedent are available in the literature, binding pose predictions using docking and physics-based methods are reliable; however, predictions are challenging for ligands with completely unknown chemotypes. We also show that FEP/MD calculations hold predictive value and can nowadays be used in a high throughput mode in a lead optimization project provided that crystal structures of sufficiently high quality are available.

DPPC-cholesterol phase diagram using coarse-grained Molecular Dynamics simulations.

Cholesterol-phospholipid bilayers continue to be the current state of the art in membrane models and serve as representative systems for studying the effect of cholesterol on the cell membrane. As the mixing of different lipid species requires long spatio-temporal scales, coarse-grained models have gained increasing popularity in modeling such membrane systems. In this paper, a systematic study of the MARTINI coarse-grained model for the DPPC-cholesterol binary system has been performed. We construct the phase diagram of DPPC lipid bilayers in the presence of different cholesterol concentrations and at different temperatures using coarse-grained Molecular Dynamics (MD) simulations with the MARTINI force field. The phase diagram based on the condensation effect is directly comparable to available experimental data and demonstrates qualitative agreement over all cholesterol concentrations. Self-assembled bilayers quantitatively reproduce experimental observables, such as lateral diffusion of lipids, electron density, area per lipid and lipid order parameters. The phase diagram of the DPPC-cholesterol binary system also reveals the profound effect of cholesterol on the physical properties of phospholipid bilayers such lipid order, diffusion, and fluidity. Cholesterol induces the liquid-ordered phase, which increases the fluidity of the phospholipid hydrocarbon chains above the gel to liquid-crystalline phase transition temperature and decreases it below the phase transition. The present study suggests that the MARTINI force field can be successfully used to obtain molecular level insights into cholesterol-DPPC model membranes.

Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations.

Accurate in silico prediction of protein-ligand binding affinities has been a primary objective of structure-based drug design for decades due to the putative value it would bring to the drug discovery process. However, computational methods have historically failed to deliver value in real-world drug discovery applications due to a variety of scientific, technical, and practical challenges. Recently, a family of approaches commonly referred to as relative binding free energy (RBFE) calculations, which rely on physics-based molecular simulations and statistical mechanics, have shown promise in reliably generating accurate predictions in the context of drug discovery projects. This advance arises from accumulating developments in the underlying scientific methods (decades of research on force fields and sampling algorithms) coupled with vast increases in computational resources (graphics processing units and cloud infrastructures). Mounting evidence from retrospective validation studies, blind challenge predictions, and prospective applications suggests that RBFE simulations can now predict the affinity differences for congeneric ligands with sufficient accuracy and throughput to deliver considerable value in hit-to-lead and lead optimization efforts. Here, we present an overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results. We focus specifically on relative binding free energies because the calculations are less computationally intensive than absolute binding free energy (ABFE) calculations and map directly onto the hit-to-lead and lead optimization processes, where the prediction of relative binding energies between a reference molecule and new ideas (virtual molecules) can be used to prioritize molecules for synthesis. We describe the critical aspects of running RBFE calculations, from both theoretical and applied perspectives, using a combination of retrospective literature examples and prospective studies from drug discovery projects. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative binding free energy simulations, with a focus on real-world drug discovery applications. We offer guidelines for improving the accuracy of RBFE simulations, especially for challenging cases, and emphasize unresolved issues that could be improved by further research in the field.