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In addition to vaccines, antiviral drugs are essential for suppressing COVID-19. Although several inhibitor candidates were reported for SARS-CoV-2 main protease, most are highly polar peptidomimetics with poor oral bioavailability and cell membrane permeability. Here, we conducted structure-based virtual screening and in vitro assays to obtain hit compounds belonging to a new chemical space, excluding peptidyl secondary amides. In total, 180 compounds were subjected to the primary assay at 20 μM, and nine compounds with inhibition rates of >5% were obtained. The IC50 of six compounds was determined in dose–response experiments, with the values on the order of 10–4 M. Although nitro groups were enriched in the substructure of the hit compounds, they did not significantly contribute to the binding interaction in the predicted docking poses. Physicochemical properties prediction showed good oral absorption. These new scaffolds are promising candidates for future optimization.

An active structural-control strategy has been widely studied to improve the control performance. Most studies used the linear quadratic-regulator (LQR) method to design the state-feedback controller. The LQR method requires to tune many weights in the cost function to design the controller. Moreover, various earthquake waves have to be considered. Thus, it is difficult to determine the weights. This paper determines the weights by using the Bayesian optimization method with multiple earthquake waves to reduces the burden of tuning the weights.

The number of cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (COVID-19) has reached over 114,000. SARS-CoV-2 caused a pandemic in Wuhan, China, in December 2019 and is rapidly spreading globally. It has been reported that peptide-like anti-HIV-1 drugs are effective against SARS-CoV Main protease (Mpro). Due to the close phylogenetic relationship between SARS-CoV and SARS-CoV-2, their main proteases share many structural and functional features. Thus, these drugs are also regarded as potential drug candidates targeting SARS-CoV-2 Mpro. However, the mechanism of action of SARS-CoV-2 Mpro at the atomic-level is unknown. In the present study, we revealed key interactions between SARS-CoV-2 Mpro and three drug candidates by performing pharmacophore modeling and 1μs molecular dynamics (MD) simulations. His41, Gly143, and Glu166 formed interactions with the functional groups that were common among peptide-like inhibitors in all MD simulations. These interactions are important targets for potential drugs against SARS-CoV-2 Mpro.

Background Chagas disease is caused by the parasite Trypanosoma cruzi and is one of the neglected tropical diseases. Although two types of drugs are currently available, new drugs are still required because they have serious side effects. To develop a therapeutic agent for trypanosomiasis, we focused on spermidine synthase (SpdSyn) as the target protein and determined the hidden binding site which was not identified in the X-ray structure for obtaining seed compounds using a computational simulation. Methodology/Principal Findings Molecular dynamics (MD) simulation was performed for TcSpdSyn to predict new binding sites. These results indicated that the highly druggable binding site was discovered around Glu22. We also conducted docking simulation for the new binding site and in vitro assay to determine half-maximal inhibitory concentration (IC50) value. Furthermore, to confirm ligand of binding site and pose, we conducted X-ray crystallographic studies. As a result, two compounds were discovered as inhibitors of TcSpdSyn with IC50 values of 82.27 and 43.41 μM, respectively. X-ray crystallographic analysis shows that two inhibitors are bound to the hidden binding site which is detected by computational simulation. Conclusions/Significance MD simulation revealed that there are new sites in the TcSpdSyn that are not an active site. This site exists near Glu22 and Asp77, and crystal structures revealed that compounds 1 and 2 are bound to the hidden binding site, as predicted by MD simulations, and interacts with Glu22 and Asp77 through hydrogen bonds. 4MCHA which has been reported as known inhibitor binds to the TcSpdSyn active site while interacting with Asp171. Therefore, these inhibitors we discovered differs in binding mode from a known inhibitor and this new binding site is useful for antitrypanosomiasis target.

Neglected tropical diseases (NTDs) are parasitic and bacterial infections that are widespread, especially in the tropics, and cause health problems for about one billion people over 149 countries worldwide. However, in terms of therapeutic agents, for example, nifurtimox and benznidazole were developed in the 1960s to treat Chagas disease, but new drugs are desirable because of their side effects. Drug discovery takes 12 to 14 years and costs $2.6 billon dollars, and hence, computer aided drug discovery (CADD) technology is expected to reduce the time and cost. This paper describes our methods and results based on CADD, mainly for NTDs. An overview of databases, molecular simulation and pharmacophore modeling, contest-based drug discovery, and machine learning and their results are presented herein.

In addition to vaccines, antiviral drugs are essential for suppressing COVID-19. Although several inhibitor candidates were reported for SARS-CoV-2 main protease, most are highly polar peptidomimetics with poor oral bioavailability and cell membrane permeability. Here, we conducted structure-based virtual screening and in vitro assays to obtain hit compounds belonging to a new chemical space excluding secondary amides. In total, 180 compounds were subjected to the primary assay at 20 μM, and nine compounds with inhibition rates higher than 5% were obtained. The IC50 of six compounds was determined in dose-response experiments, with the values on the order of 10-4 μM. Although nitro groups were enriched in the substructure of the hit compounds, they did not significantly contribute to the binding interaction in the predicted docking poses. Physicochemical properties prediction showed good oral absorption. These new scaffolds are promising candidates for future optimization.

Aggregation of therapeutic monoclonal antibodies (mAbs) can negatively affect their chemistry, manufacturing and control attributes and lead to undesirable immune responses in patients. Therefore, optimization of lead Monoclonal antibody (mAb) drug candidates during discovery stages to mitigate aggregation is increasingly becoming an integral part of their developability assessments. The disruption of short sequence motifs called Aggregation prone regions (APRs) found in amino acid sequences of mAb candidates can potentially mitigate their aggregation. In this work, we have performed Molecular Dynamics (MD) simulations to study the aggregation of an APR (VLVIY) found in λ light chains of human antibodies and its single point mutant KLVIY. Eighteen different multi-copy peptide simulation systems of 'VLVIY' and 'KLVIY' were constructed by varying their concentrations, temperatures, termini capping, and flanking gate-keeper regions. Within 20 ns of the simulation, peptide 'VLVIY' formed an aggregate of 100 peptides at ~0.1 M concentration with a 60% reduction in solvent accessible surface area (SASA). Further, analysis of the SASA change, peptide cluster distribution, and water residence time demonstrated how Val➔Lys mutation resists aggregation and improves solubility. Presence of Lys slows down aggregation kinetics via charge-charge repulsions and by raising the kinetic barrier to formation of large oligomers. However, the effect of the Val ➔ Lys mutation is dependent on sequence and structural contexts around the APR. This mutation also alters the solvation shell around the peptide by favoring solute-solvent interactions, thereby increasing its solubility. This work has provided a detailed mechanistic explanation of how APR disruption can mitigate aggregation in biotherapeutics and improve their developability. This article is protected by copyright. All rights reserved.

Characterizing RNA-protein interactions remains an important endeavor, complicated by the difficulty in obtaining the relevant structures. Evaluating model structures via statistical potentials is in principle straight-forward and effective. However, given the relatively small size of the existing learning set of RNA-protein complexes optimization of such potentials continues to be problematic. Notably, interaction-based statistical potentials have problems in addressing large RNA-protein complexes. In this study, we adopted a novel strategy with covariance matrix adaptation (CMA-ES) to calculate statistical potentials, successfully identifying native docking poses.

Baloxavir marboxil (BXM), an antiviral drug for influenza virus, inhibits RNA replication by binding to RNA replication cap-dependent endonuclease (CEN) of influenza A and B viruses. Although this drug was only approved by the FDA in October 2018, drug resistant viruses have already been detected from clinical trials owing to an I38 mutation of CEN. To investigate the reduction of drug sensitivity by the I38 mutant variants, we performed a molecular dynamics (MD) simulation on the CEN-BXM complex structure to analyze variations in the mode of interaction. Our simulation results suggest that the side chain methyl group of I38 in CEN engages in a CH-pi interaction with the aromatic ring of BXM. This interaction is abolished in various I38 mutant variants. Moreover, MD simulation on various mutation models and binding free energy prediction by MM/GBSA method suggest that the I38 mutation precludes any interaction with the aromatic ring of BXA and thereby reduces BXA sensitivity.

Perovskite solar cells have attracted much attention as a new type of solar cell that can be smaller and thinner than conventional silicon solar cells. However, the development of lead-free perovskite solar cells is required because currently most of them contain lead, which is harmful to the human body and the environment. In addition, the field of materials informatics, which combines materials development with information technology and computational science, has become active in recent years. Research on materials development that incorporates machine learning methods has become common in order to develop better materials quicker. In this paper, we aim to predict the band gap, one of the properties of unknown lead-free perovskite materials, by using machine learning methods. We focused on an element and constructed a prediction model to evaluate the case where the element is not included in the training data.

This paper presents a method for the automatic selection of weighting matrices for a linear-quadratic regulator (LQR) in order to design an optimal active structural control system. The weighting matrices of a control performance index, which are used to design optimal state-feedback gains, are usually determined by rule of thumb or exhaustive search approaches. To explore an easy way to select optimal parameters, this paper presents a method based on Bayesian optimization (BO). A 10-degree-of-freedom (DOF) shear building model that has passive-base isolation (PBI) under the building is used as an example to explain the method. A control performance index that contains the absolute acceleration, along with the inter-story drift and velocity of each story, is chosen for the design of the controller. An objective function that contains the maximum absolute acceleration of the building is chosen for BO to produce optimal weighting matrices. In the numerical example, a restriction on the displacement of the PBI is used as a constraint for the selection of weighting matrices. First, the BO method is compared to the exhaustive search method using two parameters in the weighting matrices to illustrate the validity of the BO method. Then, thirty-three parameters (which are automatically optimized by the BO method) in the weighting matrices are used to elaborately tune the controller. The control results are compared to those for the exhaustive search method and conventional optimal control, in terms of the control performance of the relative displacement, absolute acceleration, inter-story-drift angle, and the story-shear coefficient of each story. The damping ratio for each mode, and the control energy and power are also compared. The comparison demonstrates the validity of the method.

B-cell lymphoma 2 (Bcl-2) family proteins are potential drug targets in cancer and have a relatively flat and flexible binding site. ABT-199 is one of the most promising selective Bcl-2 inhibitors, and A-1155463 selectively inhibits Bcl-XL. Although the amino acid sequences of the binding sites of these two inhibitors are similar, the inhibitors selectively bind the target protein. In order to determine the origin of the selectivity of these inhibitors, we conducted molecular dynamics simulations using protein-inhibitor modeling. We confirmed that ASP103 of Bcl-2 is a key residue and that hydrogen bonding between ASP103 and ABT-199 confers the Bcl-2 selectivity of this inhibitor. For Bcl-XL selectivity, the secondary structure of α-helix 3 is a key factor. PHE105, SER106, and LEU108 in the loose α-helix 3 interact with A-1155463 to confer Bcl-XL selectivity. These findings provide important insights into the molecular mechanisms of selective inhibitors of Bcl-2 family proteins.

Virtual screening is a promising method for obtaining novel hit compounds in drug discovery. It aims to enrich potentially active compounds from a large chemical library for further biological experiments. However, the accuracy of current virtual screening methods is insufficient. In this study, we develop a new virtual screening method named Similarity of Interaction Energy VEctor Score (SIEVE-Score), in which protein-ligand interaction energies are extracted to represent docking poses for machine learning. SIEVE-Score offers substantial improvements compared to other state-of-the-art virtual screening methods, namely, other machine-learning-based scoring functions, interaction fingerprints, and docking software, for the enrichment factor 1% results on the Directory of Useful Decoys, Enhanced (DUD-E). The screening results are also human-interpretable in the form of important interactions for distinguishing between active and inactive compounds. The source code is available at https://github.com/sekijima-lab/SIEVE-Score .

AC impedance spectroscopy is an important method for evaluating ionic, electronic, and dielectric properties of materials. In conventional analysis of AC impedance spectra, the selection of an equivalent circuit model and its initial parameters are visually determined from a Nyquist plot; this visual determination can be both inefficient and inaccurate. Thus, analysis based on a rigorous mathematical method is highly desirable. Here, we demonstrate the analysis of AC impedance spectra using Bayesian statistics. We apply the method to artificial AC impedance spectra generated from resistance (R) and capacitance (C) circuits, obtaining a high accuracy ratio (>90%) in model selection when the ratio of the time constants of two RC parallel circuits exceeds 3. Furthermore, this method is applied to an actual electrical circuit comprising a resistance and two RC parallel circuits, yielding highly accurate model selection and parameter estimation. The results demonstrate the effectiveness of the proposed method for AC impedance spectra.

Potential inhibitors of a target biomolecule, NAD-dependent deacetylase Sirtuin 1, were identified by a contest-based approach, in which participants were asked to propose a prioritized list of 400 compounds from a designated compound library containing 2.5 million compounds using in silico methods and scoring. Our aim was to identify target enzyme inhibitors and to benchmark computer-aided drug discovery methods under the same experimental conditions. Collecting compound lists derived from various methods is advantageous for aggregating compounds with structurally diversified properties compared with the use of a single method. The inhibitory action on Sirtuin 1 of approximately half of the proposed compounds was experimentally accessed. Ultimately, seven structurally diverse compounds were identified.

During drug discovery, drug candidates are narrowed down over several steps to develop pharmaceutical products. The theoretical chemical space in such steps is estimated to be [Formula: see text]. To cover that space, extensive virtual compound libraries have been developed; however, the compilation of extensive libraries comes at large computational cost. Thus, to reduce the computational cost, researchers have constructed custom-made virtual compound libraries that focus on target diseases. In this study, we develop a system that generates virtual compound libraries from input compounds. When a user inputs a compound, the system recursively applies virtual synthetic reaction rules to the compound to improve its properties. The synthetic pathway can also be traced by the user because the reaction rules in this system are based on real organic synthesis reactions. This system has useful functions for effective drug design, such as structural preservation, allowing the substructures necessary for potency to be maintained. In this paper, to confirm the effect of directional reaction sets, we applied the reaction sets to 100 compounds. Moreover, to confirm that the system can reproduce real synthetic pathways, the synthetic pathways of Ibuprofen and Ofloxacin were explored by inputting isobutyl benzene and 7,8-difluoro-2,3-dihydro-3-methyl-4H-benzoxazine. This application is available at the following URL: http://enh.sekijima-lab.org .

We propose a new iterative screening contest method to identify target protein inhibitors. After conducting a compound screening contest in 2014, we report results acquired from a contest held in 2015 in this study. Our aims were to identify target enzyme inhibitors and to benchmark a variety of computer-aided drug discovery methods under identical experimental conditions. In both contests, we employed the tyrosine-protein kinase Yes as an example target protein. Participating groups virtually screened possible inhibitors from a library containing 2.4 million compounds. Compounds were ranked based on functional scores obtained using their respective methods, and the top 181 compounds from each group were selected. Our results from the 2015 contest show an improved hit rate when compared to results from the 2014 contest. In addition, we have successfully identified a statistically-warranted method for identifying target inhibitors. Quantitative analysis of the most successful method gave additional insights into important characteristics of the method used.

Chagas disease results from infection by Trypanosoma cruzi and is a neglected tropical disease (NTD). Although some treatment drugs are available, their use is associated with severe problems, including adverse effects and limited effectiveness during the chronic disease phase. To develop a novel anti-Chagas drug, we virtually screened 4.8 million small molecules against spermidine synthase (SpdSyn) as the target protein using our super computer “TSUBAME2.5” and conducted in vitro enzyme assays to determine the half-maximal inhibitory concentration values. We identified four hit compounds that inhibit T. cruzi SpdSyn (TcSpdSyn) by in silico and in vitro screening. We also determined the TcSpdSyn–hit compound complex structure using X-ray crystallography, which shows that the hit compound binds to the putrescine-binding site and interacts with Asp171 through a salt bridge.

A search of broader range of chemical space is important for drug discovery. Different methods of computer-aided drug discovery (CADD) are known to propose compounds in different chemical spaces as hit molecules for the same target protein. This study aimed at using multiple CADD methods through open innovation to achieve a level of hit molecule diversity that is not achievable with any particular single method. We held a compound proposal contest, in which multiple research groups participated and predicted inhibitors of tyrosine-protein kinase Yes. This showed whether collective knowledge based on individual approaches helped to obtain hit compounds from a broad range of chemical space and whether the contest-based approach was effective.

Background Chagas disease, caused by the parasite Trypanosoma cruzi, is a neglected tropical disease that causes severe human health problems. To develop a new chemotherapeutic agent for the treatment of Chagas disease, we predicted a pharmacophore model for T. cruzi dihydroorotate dehydrogenase (TcDHODH) by fragment molecular orbital (FMO) calculation for orotate, oxonate, and 43 orotate derivatives. Methodology/Principal Findings Intermolecular interactions in the complexes of TcDHODH with orotate, oxonate, and 43 orotate derivatives were analyzed by FMO calculation at the MP2/6-31G level. The results indicated that the orotate moiety, which is the base fragment of these compounds, interacts with the Lys43, Asn67, and Asn194 residues of TcDHODH and the cofactor flavin mononucleotide (FMN), whereas functional groups introduced at the orotate 5-position strongly interact with the Lys214 residue. Conclusions/Significance FMO-based interaction energy analyses revealed a pharmacophore model for TcDHODH inhibitor. Hydrogen bond acceptor pharmacophores correspond to Lys43 and Lys214, hydrogen bond donor and acceptor pharmacophores correspond to Asn67 and Asn194, and the aromatic ring pharmacophore corresponds to FMN, which shows important characteristics of compounds that inhibit TcDHODH. In addition, the Lys214 residue is not conserved between TcDHODH and human DHODH. Our analysis suggests that these orotate derivatives should preferentially bind to TcDHODH, increasing their selectivity. Our results obtained by pharmacophore modeling provides insight into the structural requirements for the design of TcDHODH inhibitors and their development as new anti-Chagas drugs.

Protein-ligand docking is an important method in Structure-based Drug Discovery [1]. Although many programs have been developed for docking [2], the accuracy is still insufficient due to the difficulty in the scoring function [3]. Interaction fingerprint is one of the solutions, which generate fingerprints of ligands using the interactions between the ligand and the protein. Interaction fingerprints use the information of known compounds so that compounds that have similar interaction to the known active ligands are expected to find through the virtual screening. However, existing interaction fingerprints such as SIFt [4] and SPLIF [5] only assess the existence or the distance of the interactions and do not consider the strength correctly. In this study, we made a new scoring function of protein-ligand docking called SIEVE-Score (Similarity of Interaction Energy VEctor-Score), which can consider the strength of each interaction explicitly. SIEVE-Score is calculated based on the similarity of the interaction energy vector, which is the list of interaction energy between the ligand and each residue of the protein. We also evaluate the accuracy of virtual screening using SIEVE-Score after the docking by Glide [6].[1] Chiba, S., et al. Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target. Scientific reports 5:17209, 2015.[2] Elizabeth Y., Jessica H., and Paul A. R., Improvements, trends, and new ideas in molecular docking: 2012–2013 in review. Journal of Molecular Recognition, 28(10):581–604, 2015.[3] Yan L., Li H., Zhihai L., and Renxiao W., Comparative assessment of scoring functions on an updated benchmark: 2. evaluation methods and general results. Journal of Chemical Information and Modeling, 54(6):1717–1736, 2014.[4] Zhan D., Claudio C., and Juswinder S., Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions. Journal of Medicinal Chemistry, 47(2):337–344, 2004.[5] Da C., and Kireev D., Structural protein–ligand interaction fingerprints (SPLIF) for structure- based virtual screening: Method and benchmark study. Journal of Chemical Information and Modeling, 54(9):2555–2561, 2014.[6] Richard A. F., et al., Glide: a new approach for rapid, accurate docking and scoring. 1. method and assessment of docking accuracy. Journal of Medicinal Chemistry, 47(7):1739–1749, 2004.