Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment.

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.

Gargoyles: An Open Source Graph-Based Molecular Optimization Method Based on Deep Reinforcement Learning.

Automatic optimization methods for compounds in the vast compound space are important for drug discovery and material design. Several machine learning-based molecular generative models for drug discovery have been proposed, but most of these methods generate compounds from scratch and are not suitable for exploring and optimizing user-defined compounds. In this study, we developed a compound optimization method based on molecular graphs using deep reinforcement learning. This method searches for compounds on a fragment-by-fragment basis and at high density by generating fragments to be added atom by atom. Experimental results confirmed that the quantum electrodynamics (QED), the optimization target set in this study, was enhanced by searching around the starting compound. As a use case, we successfully enhanced the activity of a compound by targeting dopamine receptor D2 (DRD2). This means that the generated compounds are not structurally dissimilar from the starting compounds, as well as increasing their activity, indicating that this method is suitable for optimizing molecules from a given compound. The source code is available at https://github.com/sekijima-lab/GARGOYLES.

Discovery of a Hidden Trypanosoma cruzi Spermidine Synthase Binding Site and Inhibitors through In Silico, In Vitro, and X-ray Crystallography.

In drug discovery research, the selection of promising binding sites and understanding the binding mode of compounds are crucial fundamental studies. The current understanding of the proteins-ligand binding model extends beyond the simple lock and key model to include the induced-fit model, which alters the conformation to match the shape of the ligand, and the pre-existing equilibrium model, selectively binding structures with high binding affinity from a diverse ensemble of proteins. Although methods for detecting target protein binding sites and virtual screening techniques using docking simulation are well-established, with numerous studies reported, they only consider a very limited number of structures in the diverse ensemble of proteins, as these methods are applied to a single structure. Molecular dynamics (MD) simulation is a method for predicting protein dynamics and can detect potential ensembles of protein binding sites and hidden sites unobservable in a single-point structure. In this study, to demonstrate the utility of virtual screening with protein dynamics, MD simulations were performed on Trypanosoma cruzi spermidine synthase to obtain an ensemble of dominant binding sites with a high probability of existence. The structure of the binding site obtained through MD simulation revealed pockets in addition to the active site that was present in the initial structure. Using the obtained binding site structures, virtual screening of 4.8 million compounds by docking simulation, in vitro assays, and X-ray analysis was conducted, successfully identifying two hit compounds.

Multidimensional analyses of the pathomechanism caused by the non-catalytic GNE variant, c.620A>T, in patients with GNE myopathy.

GNE myopathy is a distal myopathy caused by biallelic variants in GNE, which encodes a protein involved in sialic acid biosynthesis. Compound heterozygosity of the second most frequent variant among Japanese GNE myopathy patients, GNE c.620A>T encoding p.D207V, occurs in the expected number of patients; however, homozygotes for this variant are rare; three patients identified while 238 homozygotes are estimated to exist in Japan. The aim of this study was to elucidate the pathomechanism caused by c.620A>T. Identity-by-descent mapping indicated two distinct c.620A>T haplotypes, which were not correlated with age onset or development of myopathy. Patients homozygous for c.620A>T had mildly decreased sialylation, and no additional pathogenic variants in GNE or abnormalities in transcript structure or expression of other genes related to sialic acid biosynthesis in skeletal muscle. Structural modeling of full-length GNE dimers revealed that the variant amino acid localized close to the monomer interface, but far from catalytic sites, suggesting functions in enzymatic product transfer between the epimerase and kinase domains on GNE oligomerization. In conclusion, homozygotes for c.620A>T rarely develop myopathy, while symptoms occur in compound heterozygotes, probably because of mildly decreased sialylation, due to partial defects in oligomerization and product trafficking by the mutated GNE protein.

Dephospho-Coenzyme A Kinase Is an Exploitable Drug Target against Plasmodium falciparum: Identification of Selective Inhibitors by High-Throughput Screening of a Large Chemical Compound Library.

Malaria is a mosquito-borne fatal infectious disease that affects humans and is caused by Plasmodium parasites, primarily Plasmodium falciparum. Widespread drug resistance compels us to discover novel compounds and alternative drug discovery targets. The coenzyme A (CoA) biosynthesis pathway is essential for the malaria parasite P. falciparum. The last enzyme in CoA biosynthesis, dephospho-CoA kinase (DPCK), is essential to the major life cycle development stages but has not yet been exploited as a drug target in antimalarial drug discovery. We performed a high-throughput screen of a 210,000-compound library using recombinant P. falciparum DPCK (PfDPCK). A high-throughput enzymatic assay using a 1,536-well platform was developed to identify potential PfDPCK inhibitors. PfDPCK inhibitors also inhibited parasite growth in a P. falciparum whole-cell asexual blood-stage assay in both drug-sensitive and drug-resistant strains. Hit compounds were selected based on their potency in cell-free (PfDPCK) and whole-cell (Pf3D7 and PfDd2) assays, selectivity over the human orthologue (HsCOASY) and no cytotoxicity (HepG2). The compounds were ranked using a multiparameter optimization (MPO) scoring model, and the specific binding and the mechanism of inhibition were investigated for the most promising compounds.

Novel Calcium-Binding Ablating Mutations Induce Constitutive RET Activity and Drive Tumorigenesis.

Distinguishing oncogenic mutations from variants of unknown significance (VUS) is critical for precision cancer medicine. Here, computational modeling of 71,756 RET variants for positive selection together with functional assays of 110 representative variants identified a three-dimensional cluster of VUSs carried by multiple human cancers that cause amino acid substitutions in the calmodulin-like motif (CaLM) of RET. Molecular dynamics simulations indicated that CaLM mutations decrease interactions between Ca2+ and its surrounding residues and induce conformational distortion of the RET cysteine-rich domain containing the CaLM. RET-CaLM mutations caused ligand-independent constitutive activation of RET kinase by homodimerization mediated by illegitimate disulfide bond formation. RET-CaLM mutants possessed oncogenic and tumorigenic activities that could be suppressed by tyrosine kinase inhibitors targeting RET. This study identifies calcium-binding ablating mutations as a novel type of oncogenic mutation of RET and indicates that in silico-driven annotation of VUSs of druggable oncogenes is a promising strategy to identify targetable driver mutations. SIGNIFICANCE: Comprehensive proteogenomic and in silico analyses of a vast number of VUSs identify a novel set of oncogenic and druggable mutations in the well-characterized RET oncogene.

Toxicokinetic analysis of the anticoagulant rodenticides warfarin & diphacinone in Egyptian fruit bats (Rousettus aegyptiacus) as a comparative sensitivity assessment for Bonin fruit bats (Pteropus pselaphon).

Anticoagulant rodenticides have been widely used to eliminate wild rodents, which as invasive species on remote islands can disturb ecosystems. Since rodenticides can cause wildlife poisoning, it is necessary to evaluate the sensitivity of local mammals and birds to the poisons to ensure the rodenticides are used effectively. The Bonin Islands are an archipelago located 1000 km southeast of the Japanese mainland and are famous for the unique ecosystems. Here the first-generation anticoagulant rodenticide diphacinone has been used against introduced black rats (Rattus rattus). The only land mammal native to the archipelago is the Bonin fruit bat (Pteropus pselaphon), but little is known regarding its sensitivity to rodenticides. In this study, the Egyptian fruit bats (Rousettus aegyptiacus) was used as a model animal for in vivo pharmacokinetics and pharmacodynamics analysis and in vitro enzyme kinetics using their hepatic microsomal fractions. The structure of vitamin K epoxide reductase (VKORC1), the target protein of the rodenticide in the Bonin fruit bat, was predicted from its genome and its binding affinity to rodenticides was evaluated. The Egyptian fruit bats excreted diphacinone slowly and showed similar sensitivity to rats. In contrast, they excreted warfarin, another first-generation rodenticide, faster than rats and recovered from the toxic effect faster. An in silico binding study also indicated that the VKORC1 of fruit bats is relatively tolerant to warfarin, but binds strongly to diphacinone. These results suggest that even chemicals with the same mode of action display different sensitivities in different species: fruit bat species are relatively resistant to warfarin, but vulnerable to diphacinone.

Screening for Inhibitors of Main Protease in SARS-CoV-2: In Silico and In Vitro Approach Avoiding Peptidyl Secondary Amides.

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.

Statistical potentials for RNA-protein interactions optimized by CMA-ES.

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.

Effect of charged mutation on aggregation of a pentapeptide: Insights from molecular dynamics simulations.

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 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 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 multicopy 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). Furthermore, 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.

MERMAID: an open source automated hit-to-lead method based on deep reinforcement learning.

The hit-to-lead process makes the physicochemical properties of the hit molecules that show the desired type of activity obtained in the screening assay more drug-like. Deep learning-based molecular generative models are expected to contribute to the hit-to-lead process. The simplified molecular input line entry system (SMILES), which is a string of alphanumeric characters representing the chemical structure of a molecule, is one of the most commonly used representations of molecules, and molecular generative models based on SMILES have achieved significant success. However, in contrast to molecular graphs, during the process of generation, SMILES are not considered as valid SMILES. Further, it is quite difficult to generate molecules starting from a certain molecule, thus making it difficult to apply SMILES to the hit-to-lead process. In this study, we have developed a SMILES-based generative model that can be generated starting from a certain molecule. This method generates partial SMILES and inserts it into the original SMILES using Monte Carlo Tree Search and a Recurrent Neural Network. We validated our method using a molecule dataset obtained from the ZINC database and successfully generated molecules that were both well optimized for the objectives of the quantitative estimate of drug-likeness (QED) and penalized octanol-water partition coefficient (PLogP) optimization. The source code is available at https://github.com/sekijima-lab/mermaid .

Biochemical Studies of Mitochondrial Malate: Quinone Oxidoreductase from Toxoplasma gondii.

Toxoplasma gondii is a protozoan parasite that causes toxoplasmosis and infects almost one-third of the global human population. A lack of effective drugs and vaccines and the emergence of drug resistant parasites highlight the need for the development of new drugs. The mitochondrial electron transport chain (ETC) is an essential pathway for energy metabolism and the survival of T. gondii. In apicomplexan parasites, malate:quinone oxidoreductase (MQO) is a monotopic membrane protein belonging to the ETC and a key member of the tricarboxylic acid cycle, and has recently been suggested to play a role in the fumarate cycle, which is required for the cytosolic purine salvage pathway. In T. gondii, a putative MQO (TgMQO) is expressed in tachyzoite and bradyzoite stages and is considered to be a potential drug target since its orthologue is not conserved in mammalian hosts. As a first step towards the evaluation of TgMQO as a drug target candidate, in this study, we developed a new expression system for TgMQO in FN102(DE3)TAO, a strain deficient in respiratory cytochromes and dependent on an alternative oxidase. This system allowed, for the first time, the expression and purification of a mitochondrial MQO family enzyme, which was used for steady-state kinetics and substrate specificity analyses. Ferulenol, the only known MQO inhibitor, also inhibited TgMQO at IC50 of 0.822 µM, and displayed different inhibition kinetics compared to Plasmodium falciparum MQO. Furthermore, our analysis indicated the presence of a third binding site for ferulenol that is distinct from the ubiquinone and malate sites.

Tackling Covid-19 using disordered-to-order transition of residues in the spike protein upon angiotensin-converting enzyme 2 binding.

The 2019-novel coronavirus also known as severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) is a common threat to animals and humans, and is responsible for the human SARS pandemic in 2019 to 2021. The infection of SARS-CoV-2 in humans involves a viral surface glycoprotein named as spike proteins, which bind to the human angiotensin-converting enzyme 2 (ACE2) proteins. Particularly, the receptor binding domains (RBDs) mediate the interaction and contain several disordered regions, which help in the binding. Investigations on the influence of disordered residues/regions in stability and binding of spike protein with ACE2 help to understand the disease pathogenesis, which has not yet been studied. In this study, we have used molecular-dynamics simulations to characterize the structural changes in disordered regions of the spike protein that result from ACE2 binding. We observed that the disordered regions undergo disorder-to-order transition (DOT) upon binding with ACE2, and the DOT residues are located at functionally important regions of RBD. Although the RBD is having rigid structure, DOT residues make conformational rearrangements for the spike protein to attach with ACE2. The binding is strengthened via hydrophilic and aromatic amino acids mainly present in the DOTs. The positively correlated motions of the DOT residues with its nearby residues also explain the binding profile of RBD with ACE2, and the residues are observed to be contributing more favorable binding energies for the spike-ACE2 complex formation. This study emphasizes that intrinsically disordered residues in the RBD of spike protein may provide insights into its etiology and be useful for drug and vaccine discovery.

Computer aided drug discovery review for infectious diseases with case study of anti-Chagas project.

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.

Identification of key interactions between SARS-CoV-2 main protease and inhibitor drug candidates.

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 1 µs 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.

Membrane Phospholipid Analogues as Molecular Rulers to Probe the Position of the Hydrophobic Contact Point of Lysophospholipid Ligands on the Surface of G-Protein-Coupled Receptor during Membrane Approach.

When lipid mediators bind to G-protein-coupled receptors (GPCRs), the ligand first enters the lipid bilayer, then diffuses laterally in the cell membrane to make hydrophobic contact with the receptor protein, and finally enters the receptor's binding pocket. In this process, the location of the hydrophobic contact point on the surface of the receptor has been little discussed even in cases in which the crystal structure has been determined, because the ligand binding pocket is buried inside the transmembrane (TM) domains. Here, we coupled an activator ligand to a series of membrane phospholipid surrogates, which constrain the depth of entry of the ligand into the lipid bilayer. Consequently, via measurement of the receptor-activating activity as a function of the depth of entry into the membrane, these surrogates can be used as molecular rulers to estimate the location of the hydrophobic contact point on the surface of GPCR. We focused on lysophosphatidylserine (LysoPS) receptor GPR34 and prepared a series of simplified membrane-lipid-surrogate-conjugated lysophospholipid analogues by attaching alkoxy amine chains of varying lengths to the hydrophobic tail of a potent GPR34 agonist. As expected, the activity of these lipid-conjugated LysoPS analogues was dependent on chain length. The predicted contact position matches the position of the terminal benzene ring of a nonlipidic ligand that protrudes between TMs 4 and 5 of the receptor. We further found that the nature of the terminal hydrophilic functional group of the conjugated membrane lipid surrogate strongly influences the activity, suggesting that lateral hydrophilic contact of LysoPS analogues with the receptor's surface is also crucial for ligand-GPCR binding.

ProAffiMuSeq: sequence-based method to predict the binding free energy change of protein-protein complexes upon mutation using functional classification.

MOTIVATION: Protein-protein interactions are essential for the cell and mediate various functions. However, mutations can disrupt these interactions and may cause diseases. Currently available computational methods require a complex structure as input for predicting the change in binding affinity. Further, they have not included the functional class information for the protein-protein complex. To address this, we have developed a method, ProAffiMuSeq, which predicts the change in binding free energy using sequence-based features and functional class. RESULTS: Our method shows an average correlation between predicted and experimentally determined ΔΔG of 0.73 and mean absolute error (MAE) of 0.86 kcal/mol in 10-fold cross-validation and correlation of 0.75 with MAE of 0.94 kcal/mol in the test dataset. ProAffiMuSeq was also tested on an external validation set and showed results comparable to structure-based methods. Our method can be used for large-scale analysis of disease-causing mutations in protein-protein complexes without structural information. AVAILABILITY AND IMPLEMENTATION: Users can access the method at https://web.iitm.ac.in/bioinfo2/proaffimuseq/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Molecular Dynamics Simulation reveals the mechanism by which the Influenza Cap-dependent Endonuclease acquires resistance against Baloxavir marboxil.

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.

Improved Method of Structure-Based Virtual Screening via Interaction-Energy-Based Learning.

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 .

Compound property enhancement by virtual compound synthesis.

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 .