Water Pharmacophore: Designing Ligands using Molecular Dynamics Simulations with Water

Peptide-to-Small Molecule: Discovery of Non-Covalent, Active-Site Inhibitors of β-Herpesvirus Proteases

Viral proteases, the key enzymes that regulate viral replication and assembly, are promising targets for antiviral drug discovery. Herpesvirus proteases are enzymes with no crystallographically confirmed noncovalent active-site binders, owing to their shallow and polar substrate-binding pockets. Here, we applied our previously reported "Peptide-to-Small Molecule" strategy to generate novel inhibitors of β-herpesvirus proteases. Rapid selection with a display technology was used to identify macrocyclic peptide 1 bound to the active site of human cytomegalovirus protease (HCMVPro) with high affinity, and pharmacophore queries were defined based on the results of subsequent intermolecular interaction analyses. Membrane-permeable small molecule 19, designed de novo according to this hypothesis, exhibited enzyme inhibitory activity (IC50 = 10-6 to 10-7 M) against β-herpesvirus proteases, and the design concept was proved by X-ray cocrystal analysis.

The Advances and Limitations of the Determination and Applications of Water Structure in Molecular Engineering

Water is a key actor of various processes of nature and, therefore, molecular engineering has to take the structural and energetic consequences of hydration into account. While the present review focuses on the target-ligand interactions in drug design, with a focus on biomolecules, these methods and applications can be easily adapted to other fields of the molecular engineering of molecular complexes, including solid hydrates. The review starts with the problems and solutions of the determination of water structures. The experimental approaches and theoretical calculations are summarized, including conceptual classifications. The implementations and applications of water models are featured for the calculation of the binding thermodynamics and computational ligand docking. It is concluded that theoretical approaches not only reproduce or complete experimental water structures, but also provide key information on the contribution of individual water molecules and are indispensable tools in molecular engineering.

WaterKit: Thermodynamic Profiling of Protein Hydration Sites

Water desolvation is one of the key components of the free energy of binding of small molecules to their receptors. Thus, understanding the energetic balance of solvation and desolvation resulting from individual water molecules can be crucial when estimating ligand binding, especially when evaluating different molecules and poses as done in High-Throughput Virtual Screening (HTVS). Over the most recent decades, several methods were developed to tackle this problem, ranging from fast approximate methods (usually empirical functions using either discrete atom-atom pairwise interactions or continuum solvent models) to more computationally expensive and accurate ones, mostly based on Molecular Dynamics (MD) simulations, such as Grid Inhomogeneous Solvation Theory (GIST) or Double Decoupling. On one hand, MD-based methods are prohibitive to use in HTVS to estimate the role of waters on the fly for each ligand. On the other hand, fast and approximate methods show an unsatisfactory level of accuracy, with low agreement with results obtained with the more expensive methods. Here we introduce WaterKit, a new grid-based sampling method with explicit water molecules to calculate thermodynamic properties using the GIST method. Our results show that the discrete placement of water molecules is successful in reproducing the position of crystallographic waters with very high accuracy, as well as providing thermodynamic estimates with accuracy comparable to more expensive MD simulations. Unlike these methods, WaterKit can be used to analyze specific regions on the protein surface, (such as the binding site of a receptor), without having to hydrate and simulate the whole receptor structure. The results show the feasibility of a general and fast method to compute thermodynamic properties of water molecules, making it well-suited to be integrated in high-throughput pipelines such as molecular docking.

Inhibiting Leishmania donovani Sterol Methyltransferase to Identify Lead Compounds Using Molecular Modelling

The recent outlook of leishmaniasis as a global public health concern coupled with the reportage of resistance and lack of efficacy of most antileishmanial drugs calls for a concerted effort to find new leads. The study combined In silico and in vitro approaches to identify novel potential synthetic small-molecule inhibitors targeting the Leishmania donovani sterol methyltransferase (LdSMT). The LdSMT enzyme in the ergosterol biosynthetic pathway is required for the parasite’s membrane fluidity, distribution of membrane proteins, and control of the cell cycle. The lack of LdSMT homologue in the human host and its conserved nature among all Leishmania parasites makes it a viable target for future antileishmanial drugs. Initially, six known inhibitors of LdSMT with IC50 < 10 μM were used to generate a pharmacophore model with a score of 0.9144 using LigandScout. The validated model was used to screen a synthetic library of 95,630 compounds obtained from InterBioScreen limited. Twenty compounds with pharmacophore fit scores above 50 were docked against the modelled three-dimensional structure of LdSMT using AutoDock Vina. Consequently, nine compounds with binding energies ranging from −7.5 to −8.7 kcal/mol were identified as potential hit molecules. Three compounds comprising STOCK6S-06707, STOCK6S-84928, and STOCK6S-65920 with respective binding energies of −8.7, −8.2, and −8.0 kcal/mol, lower than 22,26-azasterol (−7.6 kcal/mol), a known LdSMT inhibitor, were selected as plausible lead molecules. Molecular dynamics simulation studies and molecular mechanics Poisson–Boltzmann surface area calculations showed that the residues Asp25 and Trp208 were critical for ligand binding. The compounds were also predicted to have antileishmanial activity with reasonable pharmacological and toxicity profiles. When the antileishmanial activity of the three hits was evaluated in vitro against the promastigotes of L. donovani, mean half-maximal inhibitory concentrations (IC50) of 21.9 ± 1.5 μM (STOCK6S-06707), 23.5 ± 1.1 μM (STOCK6S-84928), and 118.3 ± 5.8 μM (STOCK6S-65920) were obtained. Furthermore, STOCK6S-84928 and STOCK6S-65920 inhibited the growth of Trypanosoma brucei, with IC50 of 14.3 ± 2.0 μM and 18.1 ± 1.4 μM, respectively. The identified compounds could be optimised to develop potent antileishmanial therapeutic agents.

Peptide-to-Small Molecule: A Pharmacophore-Guided Small Molecule Lead Generation Strategy from High-Affinity Macrocyclic Peptides

Recent technological innovations have led to the development of methods for the rapid identification of high-affinity macrocyclic peptides for a wide range of targets; however, it is still challenging to achieve the desired activity and membrane permeability at the same time. Here, we propose a novel small molecule lead discovery strategy, ″Peptide-to-Small Molecule″, which is a combination of rapid identification of high-affinity macrocyclic peptides via peptide display screening followed by pharmacophore-guided de novo design of small molecules, and demonstrate the applicability using nicotinamide N-methyltransferase (NNMT) as a target. Affinity selection by peptide display technology identified macrocyclic peptide 1 that exhibited good enzymatic inhibitory activity but no cell-based activity. Thereafter, a peptide pharmacophore-guided de novo design and further structure-based optimization resulted in highly potent and cell-active small molecule 14 (cell-free IC₅₀ = 0.0011 μM, cell-based IC₅₀ = 0.40 μM), indicating that this strategy could be a new option for drug discovery.

Computational Modelling and Sustainable Synthesis of a Highly Selective Electrochemical MIP-Based Sensor for Citalopram Detection

A novel molecularly imprinted polymer (MIP) has been developed based on a simple and sustainable strategy for the selective determination of citalopram (CTL) using screen-printed carbon electrodes (SPCEs). The MIP layer was prepared by electrochemical in situ polymerization of the 3-amino-4 hydroxybenzoic acid (AHBA) functional monomer and CTL as a template molecule. To simulate the polymerization mixture and predict the most suitable ratio between the template and functional monomer, computational studies, namely molecular dynamics (MD) simulations, were carried out. During the experimental preparation process, essential parameters controlling the performance of the MIP sensor, including CTL:AHBA concentration, number of polymerization cycles, and square wave voltammetry (SWV) frequency were investigated and optimized. The electrochemical characteristics of the prepared MIP sensor were evaluated by both cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) techniques. Based on the optimal conditions, a linear electrochemical response of the sensor was obtained by SWV measurements from 0.1 to 1.25 µmol L-1 with a limit of detection (LOD) of 0.162 µmol L-1 (S/N = 3). Moreover, the MIP sensor revealed excellent CTL selectivity against very close analogues, as well as high imprinting factor of 22. Its applicability in spiked river water samples demonstrated its potential for adequate monitoring of CTL. This sensor offers a facile strategy to achieve portability while expressing a willingness to care for the environment.

Docking Paradigm in Drug Design

Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins.

An online repository of solvation thermodynamic and structural maps of SARS-CoV-2 targets

SARS-CoV-2 recently jumped species and rapidly spread via human-to-human transmission to cause a global outbreak of COVID-19. The lack of effective vaccine combined with the severity of the disease necessitates attempts to develop small molecule drugs to combat the virus. COVID19_GIST_HSA is a freely available online repository to provide solvation thermodynamic maps of COVID-19-related protein small molecule drug targets. Grid inhomogeneous solvation theory maps were generated using AmberTools cpptraj-GIST, 3D reference interaction site model maps were created with AmberTools rism3d.snglpnt and hydration site analysis maps were created using SSTMap code. The resultant data can be applied to drug design efforts: scoring solvent displacement for docking, rational lead modification, prioritization of ligand- and protein- based pharmacophore elements, and creation of water-based pharmacophores. Herein, we demonstrate the use of the solvation thermodynamic mapping data. It is hoped that this freely provided data will aid in small molecule drug discovery efforts to defeat SARS-CoV-2.

Target-Specific Drug Design Method Combining Deep Learning and Water Pharmacophore

Following identification of a target protein, hit identification, which finds small organic molecules that bind to the target, is an important first step of a structure-based drug design project. In this study, we demonstrate a target-specific drug design method that can autonomously generate a series of target-favorable compounds. This method utilizes the seq2seq model based on a deep learning algorithm and a water pharmacophore. Water pharmacophore models are used to screen compounds that are favorable to a given target in a large compound database, and seq2seq compound generators are used to train the screened compounds and generate entirely new compounds based on the training model. Our method was tested through binding energy calculation studies of six pharmaceutically relevant targets in the directory of useful decoys (DUD) set with docking. The compounds generated by our method had lower average binding energies than decoy compounds in five out of six cases and included a number of compounds that had lower binding energies than the average binding energies of the active compounds in four cases. The generated compound lists for these four cases featured compounds with lower binding energies than even the most active compounds.

An online repository of solvation thermodynamic and structural maps of SARS-CoV-2 targets

SARS-CoV-2 recently jumped species and rapidly spread via human-to-human transmission to cause a global outbreak of COVID-19. The lack of effective vaccine combined with the severity of the disease necessitates attempts to develop small molecule drugs to combat the virus. COVID19_GIST_HSA is a freely available online repository to provide solvation thermodynamic maps of COVID-19-related protein small molecule drug targets. Grid Inhomogeneous Solvation Theory maps were generated using AmberTools cpptraj-GIST and Hydration Site Analysis maps were created using SSTmap code. The resultant data can be applied to drug design efforts: scoring solvent displacement for docking, rational lead modification, prioritization of ligand- and protein- based pharmacophore elements, and creation of water-based pharmacophores. Herein, we demonstrate the use of the solvation thermodynamic mapping data. It is hoped that this freely provided data will aid in small molecule drug discovery efforts to defeat SARS-CoV-2.

Fragment-based discovery of the first nonpeptidyl inhibitor of an S46 family peptidase

Antimicrobial resistance is a global public threat and raises the need for development of new antibiotics with a novel mode of action. The dipeptidyl peptidase 11 from Porphyromonas gingivalis (PgDPP11) belongs to a new class of serine peptidases, family S46. Because S46 peptidases are not found in mammals, these enzymes are attractive targets for novel antibiotics. However, potent and selective inhibitors of these peptidases have not been developed to date. In this study, a high-resolution crystal structure analysis of PgDPP11 using a space-grown crystal enabled us to identify the binding of citrate ion, which could be regarded as a lead fragment mimicking the binding of a substrate peptide with acidic amino acids, in the S1 subsite. The citrate-based pharmacophore was utilized for in silico inhibitor screening. The screening resulted in an active compound SH-5, the first nonpeptidyl inhibitor of S46 peptidases. SH-5 and a lipophilic analog of SH-5 showed a dose-dependent inhibitory effect against the growth of P. gingivalis. The binding mode of SH-5 was confirmed by crystal structure analysis. Thus, these compounds could be lead structures for the development of selective inhibitors of PgDPP11.

PyRod: Tracing Water Molecules in Molecular Dynamics Simulations

Ligands entering a protein binding pocket essentially compete with water molecules for binding to the protein. Hence, the location and thermodynamic properties of water molecules in protein structures have gained increased attention in the drug design community. Including corresponding data into 3D pharmacophore modeling is essential for efficient high throughput virtual screening. Here, we present PyRod, a free and open-source Python software that allows for visualization of pharmacophoric binding pocket characteristics, identification of hot spots for ligand binding, and subsequent generation of pharmacophore features for virtual screening. The implemented routines analyze the protein environment of water molecules in molecular dynamics (MD) simulations and can differentiate between hydrogen bonded waters as well as waters in a protein environment of hydrophobic, charged, or aromatic atom groups. The gathered information is further processed to generate dynamic molecular interaction fields (dMIFs) for visualization and pharmacophoric features for virtual screening. The described software was applied to 5 therapeutically relevant drug targets, and generated pharmacophores were evaluated using DUD-E benchmarking sets. The best performing pharmacophore was found for the HIV1 protease with an early enrichment factor of 54.6. PyRod adds a new perspective to structure-based screening campaigns by providing easy-to-interpret dMIFs and purely protein-based pharmacophores that are solely based on tracing water molecules in MD simulations. Since structural information about cocrystallized ligands is not needed, screening campaigns can be followed, for which less or no ligand information is available. PyRod is freely available at https://github.com/schallerdavid/pyrod .