Correction to "Discovery of a Novel Class of Dual GPBAR1 Agonists-RORγt Inverse Agonists for the Treatment of IL-17-Mediated Disorders".

[This corrects the article DOI: 10.1021/acsomega.2c07907.].

Conformational plasticity and allosteric communication networks explain Shelterin protein TPP1 binding to human telomerase.

The Shelterin complex protein TPP1 interacts with human telomerase (TERT) by means of the TEL-patch region, controlling telomere homeostasis. Aberrations in the TPP1-TERT heterodimer formation might lead to short telomeres and severe diseases like dyskeratosis congenita and Hoyeraal-Hreidarsson syndrome. In the present study, we provide a thorough characterization of the structural properties of the TPP1's OB-domain by combining data coming from microsecond-long molecular dynamics calculations, time-series analyses, and graph-based networks. Our results show that the TEL-patch conformational freedom is influenced by a network of long-range amino acid communications that together determine the proper TPP1-TERT binding. Furthermore, we reveal that in TPP1 pathological variants Glu169Δ, Lys170Δ and Leu95Gln, the TEL-patch plasticity is reduced, affecting the correct binding to TERT and, in turn, telomere processivity, which eventually leads to accelerated aging of affected cells. Our study provides a structural basis for the design of TPP1-targeting ligands with therapeutic potential against cancer and telomeropathies.

Structural basis of dimerization of chemokine receptors CCR5 and CXCR4.

G protein-coupled receptors (GPCRs) are prominent drug targets responsible for extracellular-to-intracellular signal transduction. GPCRs can form functional dimers that have been poorly characterized so far. Here, we show the dimerization mechanism of the chemokine receptors CCR5 and CXCR4 by means of an advanced free-energy technique named coarse-grained metadynamics. Our results reproduce binding events between the GPCRs occurring in the minute timescale, revealing a symmetric and an asymmetric dimeric structure for each of the three investigated systems, CCR5/CCR5, CXCR4/CXCR4, and CCR5/CXCR4. The transmembrane helices TM4-TM5 and TM6-TM7 are the preferred binding interfaces for CCR5 and CXCR4, respectively. The identified dimeric states differ in the access to the binding sites of the ligand and G protein, indicating that dimerization may represent a fine allosteric mechanism to regulate receptor activity. Our study offers structural basis for the design of ligands able to modulate the formation of CCR5 and CXCR4 dimers and in turn their activity, with therapeutic potential against HIV, cancer, and immune-inflammatory diseases.

Perspectives on Ligand/Protein Binding Kinetics Simulations: Force Fields, Machine Learning, Sampling, and User-Friendliness.

Computational techniques applied to drug discovery have gained considerable popularity for their ability to filter potentially active drugs from inactive ones, reducing the time scale and costs of preclinical investigations. The main focus of these studies has historically been the search for compounds endowed with high affinity for a specific molecular target to ensure the formation of stable and long-lasting complexes. Recent evidence has also correlated the in vivo drug efficacy with its binding kinetics, thus opening new fascinating scenarios for ligand/protein binding kinetic simulations in drug discovery. The present article examines the state of the art in the field, providing a brief summary of the most popular and advanced ligand/protein binding kinetics techniques and evaluating their current limitations and the potential solutions to reach more accurate kinetic models. Particular emphasis is put on the need for a paradigm change in the present methodologies toward ligand and protein parametrization, the force field problem, characterization of the transition states, the sampling issue, and algorithms' performance, user-friendliness, and data openness.

Uracil/H+ Symport by FurE Refines Aspects of the Rocking-bundle Mechanism of APC-type Transporters.

Transporters mediate the uptake of solutes, metabolites and drugs across the cell membrane. The eukaryotic FurE nucleobase/H+ symporter of Aspergillus nidulans has been used as a model protein to address structure-function relationships in the APC transporter superfamily, members of which are characterized by the LeuT-fold and seem to operate by the so-called 'rocking-bundle' mechanism. In this study, we reveal the binding mode, translocation and release pathway of uracil/H+ by FurE using path collective variable, funnel metadynamics and rational mutational analysis. Our study reveals a stepwise, induced-fit, mechanism of ordered sequential transport of proton and uracil, which in turn suggests that FurE, functions as a multi-step gated pore, rather than employing 'rocking' of compact domains, as often proposed for APC transporters. Finally, our work supports that specific residues of the cytoplasmic N-tail are involved in substrate translocation, in line with their essentiality for FurE function.

Expanding the Library of 1,2,4-Oxadiazole Derivatives: Discovery of New Farnesoid X Receptor (FXR) Antagonists/Pregnane X Receptor (PXR) Agonists.

Compounds featuring a 1,2,4-oxadiazole core have been recently identified as a new chemotype of farnesoid X receptor (FXR) antagonists. With the aim to expand this class of compounds and to understand the building blocks necessary to maintain the antagonistic activity, we describe herein the synthesis, the pharmacological evaluation, and the in vitro pharmacokinetic properties of a novel series of 1,2,4-oxadiazole derivatives decorated on the nitrogen of the piperidine ring with different N-alkyl and N-aryl side chains. In vitro pharmacological evaluation showed compounds 5 and 11 as the first examples of nonsteroidal dual FXR/Pregnane X receptor (PXR) modulators. In HepG2 cells, these compounds modulated PXR- and FXR-regulated genes, resulting in interesting leads in the treatment of inflammatory disorders. Moreover, molecular docking studies supported the experimental results, disclosing the ligand binding mode and allowing rationalization of the activities of compounds 5 and 11.

Discovery of a Novel Class of Dual GPBAR1 Agonists-RORγt Inverse Agonists for the Treatment of IL-17-Mediated Disorders.

Retinoic acid receptor-related orphan receptor γ-t (RORγt) and GPBAR1, a transmembrane G-protein-coupled receptor for bile acids, are attractive drug targets to develop clinically relevant small modulators as potent therapeutics for autoimmune diseases. Herein, we designed, synthesized, and evaluated several new bile acid-derived ligands with potent dual activity. Furthermore, we performed molecular docking and MD calculations of the best dual modulators in the two targets to identify the binding modes as well as to better understand the molecular basis of the inverse agonism of RORγt by bile acid derivatives. Among these compounds, 7 was identified as a GPBAR1 agonist (EC50 5.9 µM) and RORγt inverse agonist (IC50 0.107 µM), with excellent pharmacokinetic properties. Finally, the most promising ligand displayed robust anti-inflammatory activity in vitro and in vivo in a mouse model of 2,4,6-trinitrobenzene sulfonic acid (TNBS)-induced colitis.

Transferring chemical and energetic knowledge between molecular systems with machine learning.

Predicting structural and energetic properties of a molecular system is one of the fundamental tasks in molecular simulations, and it has applications in chemistry, biology, and medicine. In the past decade, the advent of machine learning algorithms had an impact on molecular simulations for various tasks, including property prediction of atomistic systems. In this paper, we propose a novel methodology for transferring knowledge obtained from simple molecular systems to a more complex one, endowed with a significantly larger number of atoms and degrees of freedom. In particular, we focus on the classification of high and low free-energy conformations. Our approach relies on utilizing (i) a novel hypergraph representation of molecules, encoding all relevant information for characterizing multi-atom interactions for a given conformation, and (ii) novel message passing and pooling layers for processing and making free-energy predictions on such hypergraph-structured data. Despite the complexity of the problem, our results show a remarkable Area Under the Curve of 0.92 for transfer learning from tri-alanine to the deca-alanine system. Moreover, we show that the same transfer learning approach can also be used in an unsupervised way to group chemically related secondary structures of deca-alanine in clusters having similar free-energy values. Our study represents a proof of concept that reliable transfer learning models for molecular systems can be designed, paving the way to unexplored routes in prediction of structural and energetic properties of biologically relevant systems.

Phosphorylation of FAM134C by CK2 controls starvation-induced ER-phagy.

Selective degradation of the endoplasmic reticulum (ER) via autophagy (ER-phagy) is initiated by ER-phagy receptors, which facilitate the incorporation of ER fragments into autophagosomes. FAM134 reticulon family proteins (FAM134A, FAM134B, and FAM134C) are ER-phagy receptors with structural similarities and nonredundant functions. Whether they respond differentially to the stimulation of ER-phagy is unknown. Here, we describe an activation mechanism unique to FAM134C during starvation. In fed conditions, FAM134C is phosphorylated by casein kinase 2 (CK2) at critical residues flanking the LIR domain. Phosphorylation of these residues negatively affects binding affinity to the autophagy proteins LC3. During starvation, mTORC1 inhibition limits FAM134C phosphorylation by CK2, hence promoting receptor activation and ER-phagy. Using a novel tool to study ER-phagy in vivo and FAM134C knockout mice, we demonstrated the physiological relevance of FAM134C phosphorylation during starvation-induced ER-phagy in liver lipid metabolism. These data provide a mechanistic insight into ER-phagy regulation and an example of autophagy selectivity during starvation.

Discovery of a Potent and Orally Active Dual GPBAR1/CysLT1R Modulator for the Treatment of Metabolic Fatty Liver Disease.

Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) are two highly prevalent human diseases caused by excessive fat deposition in the liver. Although multiple approaches have been suggested, NAFLD/NASH remains an unmet clinical need. Here, we report the discovery of a novel class of hybrid molecules designed to function as cysteinyl leukotriene receptor 1 (CysLT1R) antagonists and G protein bile acid receptor 1 (GPBAR1/TGR5) agonists for the treatment of NAFLD/NASH. The most potent of these compounds generated by harnessing the scaffold of the previously described CystLT1R antagonists showed efficacy in reversing liver histopathology features in a preclinical model of NASH, reshaping the liver transcriptome and the lipid and energy metabolism in the liver and adipose tissues. In summary, the present study described a novel orally active dual CysLT1R antagonist/GPBAR1 agonist that effectively protects against the development of NAFLD/NASH, showing promise for further development.

Discovery of Bile Acid Derivatives as Potent ACE2 Activators by Virtual Screening and Essential Dynamics.

The angiotensin-converting enzyme II (ACE2) is a key molecular player in the regulation of vessel contraction, inflammation, and reduction of oxidative stress. In addition, ACE2 has assumed a prominent role in the fight against the COVID-19 pandemic-causing virus SARS-CoV-2, as it is the very first receptor in the host of the viral spike protein. The binding of the spike protein to ACE2 triggers a cascade of events that eventually leads the virus to enter the host cell and initiate its life cycle. At the same time, SARS-CoV-2 infection downregulates ACE2 expression especially in the lung, altering the biochemical signals regulated by the enzyme and contributing to the poor clinical prognosis characterizing the late stage of the COVID-19 disease. Despite its important biological role, a very limited number of ACE2 activators are known. Here, using a combined in silico and experimental approach, we show that ursodeoxycholic acid (UDCA) derivatives work as ACE2 activators. In detail, we have identified two potent ACE2 ligands, BAR107 and BAR708, through a docking virtual screening campaign and elucidated their mechanism of action from essential dynamics of the enzyme observed during microsecond molecular dynamics calculations. The in silico results were confirmed by in vitro pharmacological assays with the newly identified compounds showing ACE2 activity comparable to that of DIZE, the most potent ACE2 activator known so far. Our work provides structural insight into ACE2/ligand-binding interaction useful for the design of compounds with therapeutic potential against SARS-CoV-2 infection, inflammation, and other ACE2-related diseases.

Improving Small-Molecule Force Field Parameters in Ligand Binding Studies.

Small molecules are major players of many chemical processes in diverse fields, from material science to biology. They are made by a combination of carbon and heteroatoms typically organized in system-specific structures of different complexity. This peculiarity hampers the application of standard force field parameters and their in silico study by means of atomistic simulations. Here, we combine quantum-mechanics and atomistic free-energy calculations to achieve an improved parametrization of the ligand torsion angles with respect to the state-of-the-art force fields in the paradigmatic molecular binding system benzamidine/trypsin. Funnel-Metadynamics calculations with the new parameters greatly reproduced the high-resolution crystallographic ligand binding mode and allowed a more accurate description of the binding mechanism, when the ligand might assume specific conformations to cross energy barriers. Our study impacts on future drug design investigations considering that the vast majority of marketed drugs are small-molecules.

Structural Basis for Developing Multitarget Compounds Acting on Cysteinyl Leukotriene Receptor 1 and G-Protein-Coupled Bile Acid Receptor 1.

G-protein-coupled receptors (GPCRs) are the molecular target of 40% of marketed drugs and the most investigated structures to develop novel therapeutics. Different members of the GPCRs superfamily can modulate the same cellular process acting on diverse pathways, thus representing an attractive opportunity to achieve multitarget drugs with synergic pharmacological effects. Here, we present a series of compounds with dual activity toward cysteinyl leukotriene receptor 1 (CysLT1R) and G-protein-coupled bile acid receptor 1 (GPBAR1). They are derivatives of REV5901─the first reported dual compound─with therapeutic potential in the treatment of colitis and other inflammatory processes. We report the binding mode of the most active compounds in the two GPCRs, revealing unprecedented structural basis for future drug design studies, including the presence of a polar group opportunely spaced from an aromatic ring in the ligand to interact with Arg792.60 of CysLT1R and achieve dual activity.

Drug Repurposing on G Protein-Coupled Receptors Using a Computational Profiling Approach.

G protein-coupled receptors (GPCRs) are the largest human membrane receptor family regulating a wide range of cell signaling. For this reason, GPCRs are highly desirable drug targets, with approximately 40% of prescribed medicines targeting a member of this receptor family. The structural homology of GPCRs and the broad spectrum of applications of GPCR-acting drugs suggest an investigation of the cross-activity of a drug toward different GPCR receptors with the aim of rationalizing drug side effects, designing more selective and less toxic compounds, and possibly proposing off-label therapeutic applications. Herein, we present an original in silico approach named "Computational Profiling for GPCRs" (CPG), which is able to represent, in a one-dimensional (1D) string, the physico-chemical properties of a ligand-GPCR binding interaction and, through a tailored alignment algorithm, repurpose the ligand for a different GPCR. We show three case studies where docking calculations and pharmacological data confirm the drug repurposing findings obtained through CPG on 5-hydroxytryptamine receptor 2B, beta-2 adrenergic receptor, and M2 muscarinic acetylcholine receptor. The CPG code is released as a user-friendly graphical user interface with numerous options that make CPG a powerful tool to assist the drug design of GPCR ligands.

International Scientific Collaboration Is Needed to Bridge Science to Society: USERN2020 Consensus Statement.

Scientific collaboration has been a critical aspect of the development of all fields of science, particularly clinical medicine. It is well understood that myriads of benefits can be yielded by interdisciplinary and international collaboration. For instance, our rapidly growing knowledge on COVID-19 and vaccine development could not be attained without expanded collaborative activities. However, achieving fruitful results requires mastering specific tactics in collaborative efforts. These activities can enhance our knowledge, which ultimately benefits society. In addition to tackling the issue of the invisible border between different countries, institutes, and disciplines, the border between the scientific community and society needs to be addressed as well. International and transdisciplinary approaches can potentially be the best solution for bridging science and society. The Universal Scientific Education and Research Network (USERN) is a non-governmental, non-profit organization and network to promote professional, scientific research and education worldwide. The fifth annual congress of USERN was held in Tehran, Iran, in a hybrid manner on November 7-10, 2020, with key aims of bridging science to society and facilitating borderless science. Among speakers of the congress, a group of top scientists unanimously agreed on The USERN 2020 consensus, which is drafted with the goal of connecting society with scientific scholars and facilitating international and interdisciplinary scientific activities in all fields, including clinical medicine.

Perspectives on High-Throughput Ligand/Protein Docking With Martini MD Simulations.

Molecular docking is central to rational drug design. Current docking techniques suffer, however, from limitations in protein flexibility and solvation models and by the use of simplified scoring functions. All-atom molecular dynamics simulations, on the other hand, feature a realistic representation of protein flexibility and solvent, but require knowledge of the binding site. Recently we showed that coarse-grained molecular dynamics simulations, based on the most recent version of the Martini force field, can be used to predict protein/ligand binding sites and pathways, without requiring any a priori information, and offer a level of accuracy approaching all-atom simulations. Given the excellent computational efficiency of Martini, this opens the way to high-throughput drug screening based on dynamic docking pipelines. In this opinion article, we sketch the roadmap to achieve this goal.

Ligand binding free-energy calculations with funnel metadynamics.

The accurate resolution of the binding mechanism of a ligand to its molecular target is fundamental to develop a successful drug design campaign. Free-energy calculations, which provide the energy value of the ligand-protein binding complex, are essential for resolving the binding mode of the ligand. The accuracy of free-energy calculation methods is counteracted by their poor user-friendliness, which hampers their broad application. Here we present the Funnel-Metadynamics Advanced Protocol (FMAP), which is a flexible and user-friendly graphical user interface (GUI)-based protocol to perform funnel metadynamics, a binding free-energy method that employs a funnel-shape restraint potential to reveal the ligand binding mode and accurately calculate the absolute ligand-protein binding free energy. FMAP guides the user through all phases of the free-energy calculation process, from preparation of the input files, to production simulation, to analysis of the results. FMAP delivers the ligand binding mode and the absolute protein-ligand binding free energy as outputs. Alternative binding modes and the role of waters are also elucidated, providing a detailed description of the ligand binding mechanism. The entire protocol on the paradigmatic system benzamidine-trypsin, composed of ~105 k atoms, took ~2.8 d using the Cray XC50 piz Daint cluster at the Swiss National Supercomputing Centre.

Protein-ligand binding with the coarse-grained Martini model.

The detailed understanding of the binding of small molecules to proteins is the key for the development of novel drugs or to increase the acceptance of substrates by enzymes. Nowadays, computer-aided design of protein-ligand binding is an important tool to accomplish this task. Current approaches typically rely on high-throughput docking essays or computationally expensive atomistic molecular dynamics simulations. Here, we present an approach to use the recently re-parametrized coarse-grained Martini model to perform unbiased millisecond sampling of protein-ligand interactions of small drug-like molecules. Remarkably, we achieve high accuracy without the need of any a priori knowledge of binding pockets or pathways. Our approach is applied to a range of systems from the well-characterized T4 lysozyme over members of the GPCR family and nuclear receptors to a variety of enzymes. The presented results open the way to high-throughput screening of ligand libraries or protein mutations using the coarse-grained Martini model.

Binding of the Anti-FIV Peptide C8 to Differently Charged Membrane Models: From First Docking to Membrane Tubulation.

Gp36 is the virus envelope glycoproteins catalyzing the fusion of the feline immunodeficiency virus with the host cells. The peptide C8 is a tryptophan-rich peptide corresponding to the fragment 770W-I777 of gp36 exerting antiviral activity by binding the membrane cell and inhibiting the virus entry. Several factors, including the membrane surface charge, regulate the binding of C8 to the lipid membrane. Based on the evidence that imperceptible variation of membrane charge may induce a dramatic effect in several critical biological events, in the present work we investigate the effect induced by systematic variation of charge in phospholipid bilayers on the aptitude of C8 to interact with lipid membranes, the tendency of C8 to assume specific conformational states and the re-organization of the lipid bilayer upon the interaction with C8. Accordingly, employing a bottom-up multiscale protocol, including CD, NMR, ESR spectroscopy, atomistic molecular dynamics simulations, and confocal microscopy, we studied C8 in six membrane models composed of different ratios of zwitterionic/negatively charged phospholipids. Our data show that charge content modulates C8-membrane binding with significant effects on the peptide conformations. C8 in micelle solution or in SUV formed by DPC or DOPC zwitterionic phospholipids assumes regular ß-turn structures that are progressively destabilized as the concentration of negatively charged SDS or DOPG phospholipids exceed 40%. Interaction of C8 with zwitterionic membrane surface is mediated by Trp1 and Trp4 that are deepened in the membrane, forming H-bonds and cation-π interactions with the DOPC polar heads. Additional stabilizing salt bridge interactions involve Glu2 and Asp3. MD and ESR data show that the C8-membrane affinity increases as the concentration of zwitterionic phospholipid increases. In the lipid membrane characterized by an excess of zwitterionic phospholipids, C8 is adsorbed at the membrane interface, inducing a stiffening of the outer region of the DOPC bilayer. However, the bound of C8 significantly perturbs the whole organization of lipid bilayer resulting in membrane remodeling. These events, measurable as a variation of the bilayer thickness, are the onset mechanism of the membrane fusion and vesicle tubulation observed in confocal microscopy by imaging zwitterionic MLVs in the presence of C8 peptide.

Bioinformatics and Biosimulations as Toolbox for Peptides and Peptidomimetics Design: Where Are We?

Peptides and peptidomimetics are strongly re-emerging as amenable candidates in the development of therapeutic strategies against a plethora of pathologies. In particular, these molecules are extremely suitable to treat diseases in which a major role is played by protein-protein interactions (PPIs). Unlike small organic compounds, peptides display both a high degree of specificity avoiding secondary off-targets effects and a relatively low degree of toxicity. Further advantages are provided by the possibility to easily conjugate peptides to functionalized nanoparticles, so improving their delivery and cellular uptake. In many cases, such molecules need to assume a specific three-dimensional conformation that resembles the bioactive one of the endogenous ligand. To this end, chemical modifications are introduced in the polypeptide chain to constrain it in a well-defined conformation, and to improve the drug-like properties. In this context, a successful strategy for peptide/peptidomimetics design and optimization is to combine different computational approaches ranging from structural bioinformatics to atomistic simulations. Here, we review the computational tools for peptide design, highlighting their main features and differences, and discuss selected protocols, among the large number of methods available, used to assess and improve the stability of the functional folding of the peptides. Finally, we introduce the simulation techniques employed to predict the binding affinity of the designed peptides for their targets.