Computational and functional studies of the PI(4,5)P2 binding site of the TRPM3 ion channel reveal interactions with other regulators.

Transient receptor potential melastatin 3 (TRPM3) is a heat-activated ion channel expressed in peripheral sensory neurons and the central nervous system. TRPM3 activity depends on the membrane phospholipid phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2), but the molecular mechanism of activation by PI(4,5)P2 is not known. As no experimental structure of TRPM3 is available, we built a homology model of the channel in complex with PI(4,5)P2via molecular modeling. We identified putative contact residues for PI(4,5)P2 in the pre-S1 segment, the S4-S5 linker, and the proximal C-terminal TRP domain. Mutating these residues increased sensitivity to inhibition of TRPM3 by decreasing PI(4,5)P2 levels. Changes in ligand-binding affinities via molecular mechanics/generalized Born surface area (MM/GBSA) showed reduced PI(4,5)P2 affinity for the mutants. Mutating PI(4,5)P2-interacting residues also reduced sensitivity for activation by the endogenous ligand pregnenolone sulfate, pointing to an allosteric interaction between PI(4,5)P2 and pregnenolone sulfate. Similarly, mutating residues in the PI(4,5)P2 binding site in TRPM8 resulted in increased sensitivity to PI(4,5)P2 depletion and reduced sensitivity to menthol. Mutations of most PI(4,5)P2-interacting residues in TRPM3 also increased sensitivity to inhibition by Gßγ, indicating allosteric interaction between Gßγ and PI(4,5)P2 regulation. Disease-associated gain-of-function TRPM3 mutations on the other hand resulted in no change of PI(4,5)P2 sensitivity, indicating that mutations did not increase channel activity via increasing PI(4,5)P2 interactions. Our data provide insight into the mechanism of regulation of TRPM3 by PI(4,5)P2, its relationship to endogenous activators and inhibitors, as well as identify similarities and differences between PI(4,5)P2 regulation of TRPM3 and TRPM8.

Allosteric inhibitors of the main protease of SARS-CoV-2.

SARS-CoV-2 has raised the alarm to search for effective therapy for this virus. To date several vaccines have been approved but few available drugs reported recently still need approval from FDA. Remdesivir was approved for emergency use only. In this report, the SARS-CoV-2 3CLpro was expressed and purified. By using a FRET-based enzymatic assay, we have screened a library consisting of more than 300 different niclosamide derivatives and identified three molecules JMX0286, JMX0301, and JMX0941 as potent allosteric inhibitors against SARS-CoV-2 3CLpro, with IC50 values similar to that of known covalent inhibitor boceprevir. In a cell-based antiviral assay, these inhibitors can inhibit the virus growth with EC50 in the range of 2-3 µM. The mechanism of action of JMX0286, JMX0301, and JMX0941 were characterized by enzyme kinetics, affinity binding and protein-based substrate digestion. Molecular docking, molecular dynamics (MD) simulations and hydration studies suggested that JMX0286, JMX0301, JMX0941 bind specifically to an allosteric pocket of the SARS-CoV-2 3CL protease. This study provides three potent compounds for further studies.

Machine Learning at the Interface of Polymer Science and Biology: How Far Can We Go?

This Perspective outlines recent progress and future directions for using machine learning (ML), a data-driven method, to address critical questions in the design, synthesis, processing, and characterization of biomacromolecules. The achievement of these tasks requires the navigation of vast and complex chemical and biological spaces, difficult to accomplish with reasonable speed. Using modern algorithms and supercomputers, quantum physics methods are able to examine systems containing a few hundred interacting species and determine the probability of finding them in a particular region of phase space, thereby anticipating their properties. Likewise, modern approaches in chemistry and biomolecular simulation, supported by high performance computing, have culminated in producing data sets of escalating size and intrinsically high complexity. Hence, using ML to extract relevant information from these fields is of paramount importance to advance our understanding of chemical and biomolecular systems. At the heart of ML approaches lie statistical algorithms, which by evaluating a portion of a given data set, identify, learn, and manipulate the underlying rules that govern the whole data set. The assembly of a quality model to represent the data followed by the predictions and elimination of error sources are the key steps in ML. In addition to a growing infrastructure of ML tools to address complex problems, an increasing number of aspects related to our understanding of the fundamental properties of biomacromolecules are exposed to ML. These fields, including those residing at the interface of polymer science and biology (i.e., structure determination, de novo design, folding, and dynamics), strive to adopt and take advantage of the transformative power offered by approaches in the ML domain, which clearly has the potential of accelerating research in the field of biomacromolecules.

Dual regulation of TRPV1 channels by phosphatidylinositol via functionally distinct binding sites.

Regulation of the heat- and capsaicin-activated transient receptor potential vanilloid 1 (TRPV1) channel by phosphoinositides is complex and controversial. In the most recent TRPV1 cryo-EM structure, endogenous phosphatidylinositol (PtdIns) was detected in the vanilloid binding site, and phosphoinositides were proposed to act as competitive vanilloid antagonists. This model is difficult to reconcile with phosphatidylinositol 4,5-bisphosphate [PtdIns(4,5)P2] being a well-established positive regulator of TRPV1. Here we show that in the presence of PtdIns(4,5)P2 in excised patches, PtdIns, but not PtdIns(4)P, partially inhibited TRPV1 activity at low, but not at high capsaicin concentrations. This is consistent with PtdIns acting as a competitive vanilloid antagonist. However, in the absence of PtdIns(4,5)P2, PtdIns partially stimulated TRPV1 activity. We computationally identified residues, which are in contact with PtdIns, but not with capsaicin in the vanilloid binding site. The I703A mutant of TRPV1 showed increased sensitivity to capsaicin, as expected when removing the effect of an endogenous competitive antagonist. I703A was not inhibited by PtdIns in the presence of PtdIns(4,5)P2, but it was still activated by PtdIns in the absence of PtdIns(4,5)P2 indicating that inhibition, but not activation by PtdIns proceeds via the vanilloid binding site. In molecular dynamics simulations, PtdIns was more stable than PtdIns(4,5)P2 in this inhibitory site, whereas PtdIns(4,5)P2 was more stable than PtdIns in a previously identified, nonoverlapping, putative activating binding site. Our data indicate that phosphoinositides regulate channel activity via functionally distinct binding sites, which may explain some of the complexities of the effects of these lipids on TRPV1.

TRPA1 modulation by piperidine carboxamides suggests an evolutionarily conserved binding site and gating mechanism.

The transient receptor potential ankyrin 1 (TRPA1) channel functions as an irritant sensor and is a therapeutic target for treating pain, itch, and respiratory diseases. As a ligand-gated channel, TRPA1 can be activated by electrophilic compounds such as allyl isothiocyanate (AITC) through covalent modification or activated by noncovalent agonists through ligand binding. However, how covalent modification leads to channel opening and, importantly, how noncovalent binding activates TRPA1 are not well-understood. Here we report a class of piperidine carboxamides (PIPCs) as potent, noncovalent agonists of human TRPA1. Based on their species-specific effects on human and rat channels, we identified residues critical for channel activation; we then generated binding modes for TRPA1-PIPC interactions using structural modeling, molecular docking, and mutational analysis. We show that PIPCs bind to a hydrophobic site located at the interface of the pore helix 1 (PH1) and S5 and S6 transmembrane segments. Interestingly, this binding site overlaps with that of known allosteric modulators, such as A-967079 and propofol. Similar binding sites, involving π-helix rearrangements on S6, have been recently reported for other TRP channels, suggesting an evolutionarily conserved mechanism. Finally, we show that for PIPC analogs, predictions from computational modeling are consistent with experimental structure-activity studies, thereby suggesting strategies for rational drug design.

Suppression of Zika Virus Infection in the Brain by the Antiretroviral Drug Rilpivirine.

Zika virus (ZIKV) infection is associated with microcephaly in neonates and Guillain-Barré syndrome in adults. ZIKV produces a class of nonstructural (NS) regulatory proteins that play a critical role in viral transcription and replication, including NS5, which possesses RNA-dependent RNA polymerase (RdRp) activity. Here we demonstrate that rilpivirine (RPV), a non-nucleoside reverse transcriptase inhibitor (NNRTI) used in the treatment of HIV-1 infection, inhibits the enzymatic activity of NS5 and suppresses ZIKV infection and replication in primary human astrocytes. Similarly, other members of the NNRTI family, including etravirine and efavirenz, showed inhibitory effects on viral infection of brain cells. Site-directed mutagenesis identified 14 amino acid residues within the NS5 RdRp domain (AA265-903), which are important for the RPV interaction and the inhibition of NS5 polymerase activity. Administration of RPV to ZIKV-infected interferon-alpha/beta receptor (IFN-A/R) knockout mice improved the clinical outcome and prevented ZIKV-induced mortality. Histopathological examination of the brains from infected animals revealed that RPV reduced ZIKV RNA levels in the hippocampus, frontal cortex, thalamus, and cerebellum. Repurposing of NNRTIs, such as RPV, for the inhibition of ZIKV replication offers a possible therapeutic strategy for the prevention and treatment of ZIKV-associated disease.

Computational Approaches to Studying Voltage-Gated Ion Channel Modulation by General Anesthetics.

Voltage-gated ion channels (VGICs) are responsible for the propagation of electrical signals in excitable cells. Small-molecule modulation of VGICs affects transmission of action potentials in neurons and thus can modulate the activity of the central nervous system. For this reason, VGICs are considered key players in the medically induced state of general anesthesia. Consistently, VGICs have been shown to respond to several general anesthetics. However, in spite of extensive electrophysiological characterizations, modulation of VGICs by anesthetics is still only partially understood. Among the challenging aspects are the presence of multiple binding sites and the observation of paradoxical effects, i.e., evidence, for the same channel, of inhibition and potentiation. In this context, molecular simulations emerged in the recent past as the tool of choice to complement electrophysiology studies with a microscopic picture of binding and allosteric regulation. In this chapter, we describe the most effective computational techniques to study VGIC modulation by general anesthetics. We start by reviewing the VGIC conduction cycle, the corresponding set of channel conformations, and the approaches used to model them. We then review the most successful strategies to identify binding sites and estimate binding affinities.

Sites Contributing to TRPA1 Activation by the Anesthetic Propofol Identified by Photoaffinity Labeling.

In addition to inducing anesthesia, propofol activates a key component of the pain pathway, the transient receptor potential ankyrin 1 ion channel (TRPA1). Recent mutagenesis studies suggested a potential activation site within the transmembrane domain, near the A-967079 cavity. However, mutagenesis cannot distinguish between protein-based and ligand-based mechanisms, nor can this site explain the complex modulation by propofol. Thus more direct approaches are required to reveal potentially druggable binding sites. Here we apply photoaffinity labeling using a propofol derivative, meta-azipropofol, for direct identification of binding sites in mouse TRPA1. We confirm that meta-azipropofol activates TRPA1 like the parent anesthetic, and identify two photolabeled residues (V954 and E969) in the S6 helix. In combination with docking to closed and open state models of TRPA1, photoaffinity labeling suggested that the A-967079 cavity is a positive modulatory site for propofol. Further, the photoaffinity labeling of E969 indicated pore block as a likely mechanism for propofol inhibition at high concentrations. The direct identification of drug-binding sites clarifies the molecular mechanisms of important TRPA1 agonists, and will facilitate drug design efforts to modulate TRPA1.

Does Proton Conduction in the Voltage-Gated H+ Channel hHv1 Involve Grotthuss-Like Hopping via Acidic Residues?

Hv1s are ubiquitous highly selective voltage-gated proton channels involved in male fertility, immunology, and the invasiveness of certain forms of breast cancer. The mechanism of proton extrusion in Hv1 is not yet understood, while it constitutes the first step toward the design of high-affinity drugs aimed at this important pharmacological target. In this contribution, we explore the details of the mechanism via an integrative approach, using classical and QM/MM molecular dynamics simulations of a monomeric hHv1 model. We propose that protons localize in three binding sites along the channel lumen, formed by three pairs of conserved negatively charged residues lining the pore: D174/E153, D112/D185, and E119/D123. Local rearrangements, involving notably a dihedral transition of F150, a conserved phenylalanine lining the permeation pathway, appear to allow protons to hop from one acidic residue to the next through a bridging water molecule. These results constitute a first attempt at rationalizing hHv1 selectivity for H+ and the role played by D112 in this process. They pave the way for further quantitative characterization of H+ transport in hHv1.

On the role of water density fluctuations in the inhibition of a proton channel.

Hv1 is a transmembrane four-helix bundle that transports protons in a voltage-controlled manner. Its crucial role in many pathological conditions, including cancer and ischemic brain damage, makes Hv1 a promising drug target. Starting from the recently solved crystal structure of Hv1, we used structural modeling and molecular dynamics simulations to characterize the channel's most relevant conformations along the activation cycle. We then performed computational docking of known Hv1 inhibitors, 2-guanidinobenzimidazole (2GBI) and analogs. Although salt-bridge patterns and electrostatic potential profiles are well-defined and distinctive features of activated versus nonactivated states, the water distribution along the channel lumen is dynamic and reflects a conformational heterogeneity inherent to each state. In fact, pore waters assemble into intermittent hydrogen-bonded clusters that are replaced by the inhibitor moieties upon ligand binding. The entropic gain resulting from releasing these conformationally restrained waters to the bulk solvent is likely a major contributor to the binding free energy. Accordingly, we mapped the water density fluctuations inside the pore of the channel and identified the regions of maximum fluctuation within putative binding sites. Two sites appear as outstanding: One is the already known binding pocket of 2GBI, which is accessible to ligands from the intracellular side; the other is a site located at the exit of the proton permeation pathway. Our analysis of the waters confined in the hydrophobic cavities of Hv1 suggests a general strategy for drug discovery that can be applied to any ion channel.

Computational analysis of EBNA1 "druggability" suggests novel insights for Epstein-Barr virus inhibitor design.

The Epstein-Barr Nuclear Antigen 1 (EBNA1) is a critical protein encoded by the Epstein-Barr Virus (EBV). During latent infection, EBNA1 is essential for DNA replication and transcription initiation of viral and cellular genes and is necessary to immortalize primary B-lymphocytes. Nonetheless, the concept of EBNA1 as drug target is novel. Two EBNA1 crystal structures are publicly available and the first small-molecule EBNA1 inhibitors were recently discovered. However, no systematic studies have been reported on the structural details of EBNA1 "druggable" binding sites. We conducted computational identification and structural characterization of EBNA1 binding pockets, likely to accommodate ligand molecules (i.e. "druggable" binding sites). Then, we validated our predictions by docking against a set of compounds previously tested in vitro for EBNA1 inhibition (PubChem AID-2381). Finally, we supported assessments of pocket druggability by performing induced fit docking and molecular dynamics simulations paired with binding affinity predictions by Molecular Mechanics Generalized Born Surface Area calculations for a number of hits belonging to druggable binding sites. Our results establish EBNA1 as a target for drug discovery, and provide the computational evidence that active AID-2381 hits disrupt EBNA1:DNA binding upon interacting at individual sites. Lastly, structural properties of top scoring hits are proposed to support the rational design of the next generation of EBNA1 inhibitors.

An SH2 domain model of STAT5 in complex with phospho-peptides define "STAT5 Binding Signatures".

The signal transducer and activator of transcription 5 (STAT5) is a member of the STAT family of proteins, implicated in cell growth and differentiation. STAT activation is regulated by phosphorylation of protein monomers at conserved tyrosine residues, followed by binding to phospho-peptide pockets and subsequent dimerization. STAT5 is implicated in the development of severe pathological conditions, including many cancer forms. However, nowadays a few STAT5 inhibitors are known, and only one crystal structure of the inactive STAT5 dimer is publicly available. With a view to enabling structure-based drug design, we have: (1) analyzed phospho-peptide binding pockets on SH2 domains of STAT5, STAT1 and STAT3; (2) generated a model of STAT5 bound to phospho-peptides; (3) assessed our model by docking against a class of known STAT5 inhibitors (Müller et al. in ChemBioChem 9:723-727, 2008); (4) used molecular dynamics simulations to optimize the molecular determinants responsible for binding and (5) proposed unique "Binding Signatures" of STAT5. Our results put in place the foundations to address STAT5 as a target for rational drug design, from sequence, structural and functional perspectives.

Hydrogen-bonded water molecules in the M2 channel of the influenza A virus guide the binding preferences of ammonium-based inhibitors.

The tetrameric M2 proton channel of influenza A virus is an integral membrane protein responsible for the acidification of the viral interior. Drugs such as amantadine target the transmembrane region of wild type M2 by acting as pore blockers. However, a number of mutations affecting this domain confer drug resistance, prompting the need for alternative inhibitors. The availability of high-resolution structures of drug-bound M2, paired with computational investigations, revealed that inhibitors can bind at different sites, and provided useful insights in understanding the principles governing proton conduction. Here, we investigated by computation the energetic and geometric factors determining the relative stability of pore blockers at individual sites of different M2 strains. We found that local free energy minima along the translocation pathway of positively charged chemical species correspond to experimentally determined binding sites of inhibitors. Then, by examining the structure of water clusters hydrating each site, as well as of those displaced by binding of hydrophobic scaffolds, we predicted the binding preferences of M2 ligands. This information can be used to guide the identification of novel classes of inhibitors.

Flipping in the pore: discovery of dual inhibitors that bind in different orientations to the wild-type versus the amantadine-resistant S31N mutant of the influenza A virus M2 proton channel.

Influenza virus infections lead to numerous deaths and millions of hospitalizations each year. One challenge facing anti-influenza drug development is the heterogeneity of the circulating influenza viruses, which comprise several strains with variable susceptibility to antiviral drugs. For example, the wild-type (WT) influenza A viruses, such as the seasonal H1N1, tend to be sensitive to antiviral drugs, amantadine and rimantadine, while the S31N mutant viruses, such as the pandemic 2009 H1N1 (H1N1pdm09) and seasonal H3N2, are resistant to this class of drugs. Thus, drugs targeting both WT and the S31N mutant are highly desired. We report our design of a novel class of dual inhibitors along with their ion channel blockage and antiviral activities. The potency of the most active compound 11 in inhibiting WT and the S31N mutant influenza viruses is comparable with that of amantadine in inhibiting WT influenza virus. Solution NMR studies and molecular dynamics (MD) simulations of drug-M2 interactions supported our design hypothesis: namely, the dual inhibitor binds in the WT M2 channel with an aromatic group facing down toward the C-terminus, while the same drug binds in the S31N M2 channel with its aromatic group facing up toward the N-terminus. The flip-flop mode of drug binding correlates with the structure-activity relationship (SAR) and has paved the way for the next round of rational design of broad-spectrum antiviral drugs.

Fragment-based Shape Signatures: a new tool for virtual screening and drug discovery.

Since its introduction in 2003, the Shape Signatures method has been successfully applied in a number of drug design projects. Because it uses a ray-tracing approach to directly measure molecular shape and properties (as opposed to relying on chemical structure), it excels at scaffold hopping, and is extraordinarily easy to use. Despite its advantages, a significant drawback of the method has hampered its application to certain classes of problems; namely, when the chemical structures considered are large and contain heterogeneous ring-systems, the method produces descriptors that tend to merely measure the overall size of the molecule, and begin to lose selective power. To remedy this, the approach has been reformulated to automatically decompose compounds into fragments using ring systems as anchors, and to likewise partition the ray-trace in accordance with the fragment assignments. Subsequently, descriptors are generated that are fragment-based, and query and target molecules are compared by mapping query fragments onto target fragments in all ways consistent with the underlying chemical connectivity. This has proven to greatly extend the selective power of the method, while maintaining the ease of use and scaffold-hopping capabilities that characterized the original implementation. In this work, we provide a full conceptual description of the next generation Shape Signatures, and we underline the advantages of the method by discussing its practical applications to ligand-based virtual screening. The new approach can also be applied in receptor-based mode, where protein-binding sites (partitioned into subsites) can be matched against the new fragment-based Shape Signatures descriptors of library compounds.

Modeling androgen receptor flexibility: a binding mode hypothesis of CYP17 inhibitors/antiandrogens for prostate cancer therapy.

Prostate Cancer (PCa), a leading cause of cancer death worldwide (www.cancer.gov), is a complex malignancy where a spectrum of targets leads to a diversity of PCa forms. A widely pursued therapeutic target is the Androgen Receptor (AR). As a Steroid Hormone Receptor, AR serves as activator of transcription upon binding to androgens and plays a central role in the development of PCa. AR is a structurally flexible protein, and conformational plasticity of residues in the binding-pocket is a key to its ability to accommodate ligands from various chemical classes. Besides direct modulation of AR activity by antagonists, inhibition of cytochrome CYP17 (17α-hydroxylase/17,20-lyase), essential in androgen biosynthesis, has widely been considered an effective strategy against PCa. Interestingly, Handratta et al. (2005) discovered new, potent inhibitors of CYP17 (C-17 steroid derivatives) with pure AR antagonistic properties. Although the antiandrogenic activity of their lead compound (VN/124-1) has been experimentally proven both in vitro and in vivo, no structural data are currently available to elucidate the molecular determinants responsible for these desirable dual inhibitory properties. We implemented a Structure-based Drug Design (SBDD) approach to generate a valuable hypothesis as to the binding modes of steroidal CYP17 inhibitors/antiandrogens against the AR. To deal with the plasticity of residues buried in the Ligand Binding Domain (LBD), we developed a flexible-receptor Docking protocol based on Induced-Fit (IFD) methodology (www.schrodinger.com/). Our results constitute an ideal starting point for the rational design of next-generation analogues of CYP17 inhibitors/antiandrogens as well as an attractive tool to suggest novel chemical classes of AR antagonists.

Synthesis and biological evaluation of N-hydroxyphenylacrylamides and N-hydroxypyridin-2-ylacrylamides as novel histone deacetylase inhibitors.

The histone deacetylases (HDACs) are able to regulate gene expression, and histone deacetylase inhibitors (HDACi) emerged as a new class of agents in the treatment of cancer as well as other human disorders such as neurodegenerative diseases. In the present investigation, we report on the synthesis and biological evaluation of compounds derived from the expansion of a HDAC inhibitor scaffold having N-hydroxy-3-phenyl-2-propenamide and N-hydroxy-3-(pyridin-2-yl)-2-propenamide as core structures and containing a phenyloxopropenyl moiety, either unsubstituted or substituted by a 4-methylpiperazin-1-yl or 4-methylpiperazin-1-ylmethyl group. The compounds were evaluated for their ability to inhibit nuclear HDACs, as well as for their in vitro antiproliferative activity. Moreover, their metabolic stability in microsomes and aqueous solubility were studied and selected compounds were further characterized by in vivo pharmacokinetic experiments. These compounds showed a remarkable stability in vivo, compared to hydroxamic acid HDAC inhibitors that have already entered clinical trials. The representative compound 30b showed in vivo antitumor activity in a human colon carcinoma xenograft model.

Identification and selection of "privileged fragments" suitable for primary screening.

The use of small molecule libraries for fragment-based primary screening (FBS) is a well-known approach to identify protein binders in the low affinity range. However, the search, analysis, and selection of suitable screening fragments can be a lengthy process, because of the large number of compounds that must be analyzed for different levels of ring/substituents identification and submitted to selection/exclusion criteria based on their physicochemical properties. The purpose of the present work is to propose a strategy to identify substructures from databases of known drugs, which can be used as templates for the generation of libraries of "privileged fragments" that are able to provide high-quality hits. The entire process has been developed integrating Pipeline Pilot (Accelrys Inc., San Diego, CA; http://www.accelrys.com ) native components and user-defined molecular files containing ISIS-like substructure query features (Symyx, San Ramon, CA; http://www.symyx.com ). The method is effortless, easy to put in place, and fast enough to be iteratively applied to different sources of druglike compounds.

PoInTree: a polar and interactive phylogenetic tree.

PoInTree (Polar and Interactive Tree) is an application that allows to build, visualize and customize phylogenetic trees in a polar interactive and highly flexible view. It takes as input a FASTA file or multiple alignment formats. Phylogenetic tree calculation is based on a sequence distance method and utilizes the Neighbor Joining (NJ) algorithm. It also allows displaying precalculated trees of the major protein families based on Pfam classification. In PoInTree, nodes can be dynamically opened and closed and distances between genes are graphically represented. Tree root can be centered on a selected leaf. Text search mechanism, color-coding and labeling display are integrated. The visualizer can be connected to an Oracle database containing information on sequences and other biological data, helping to guide their interpretation within a given protein family across multiple species. The application is written in Borland Delphi and based on VCL Teechart Pro 6 graphical component (Steema software).