Fungi Tryptophan Synthases: What Is the Role of the Linker Connecting the α and ß Structural Domains in Hemileia vastatrix TRPS? A Molecular Dynamics Investigation.

Tryptophan synthase (TRPS) is a complex enzyme responsible for tryptophan biosynthesis. It occurs in bacteria, plants, and fungi as an αßßα heterotetramer. Although encoded by independent genes in bacteria and plants, in fungi, TRPS is generated by a single gene that concurrently expresses the α and ß entities, which are linked by an elongated peculiar segment. We conducted 1 µs all-atom molecular dynamics simulations on Hemileia vastatrix TRPS to address two questions: (i) the role of the linker segment and (ii) the comparative mode of action. Since there is not an experimental structure, we started our simulations with homology modeling. Based on the results, it seems that TRPS makes use of an already-existing tunnel that can spontaneously move the indole moiety from the α catalytic pocket to the ß one. Such behavior was completely disrupted in the simulation without the linker. In light of these results and the αß dimer's low stability, the full-working TRPS single genes might be the result of a particular evolution. Considering the significant losses that Hemileia vastatrix causes to coffee plantations, our next course of action will be to use the TRPS to look for substances that can block tryptophan production and therefore control the disease.

Singular Interface Dynamics of the SARS-CoV-2 Delta Variant Explained with Contact Perturbation Analysis.

Emerging SARS-CoV-2 variants raise concerns about our ability to withstand the Covid-19 pandemic, and therefore, understanding mechanistic differences of those variants is crucial. In this study, we investigate disparities between the SARS-CoV-2 wild type and five variants that emerged in late 2020, focusing on the structure and dynamics of the spike protein interface with the human angiotensin-converting enzyme 2 (ACE2) receptor, by using crystallographic structures and extended analysis of microsecond molecular dynamics simulations. Dihedral angle principal component analysis (PCA) showed the strong similarities in the spike receptor binding domain (RBD) dynamics of the Alpha, Beta, Gamma, and Delta variants, in contrast with those of WT and Epsilon. Dynamical perturbation networks and contact PCA identified the peculiar interface dynamics of the Delta variant, which cannot be directly imputable to its specific L452R and T478K mutations since those residues are not in direct contact with the human ACE2 receptor. Our outcome shows that in the Delta variant the L452R and T478K mutations act synergistically on neighboring residues to provoke drastic changes in the spike/ACE2 interface; thus a singular mechanism of action eventually explains why it dominated over preceding variants.

Computational Strategy for Minimizing Mycotoxins in Cereal Crops: Assessment of the Biological Activity of Compounds Resulting from Virtual Screening.

Cereal crops are frequently affected by toxigenic Fusarium species, among which the most common and worrying in Europe are Fusarium graminearum and Fusarium culmorum. These species are the causal agents of grain contamination with type B trichothecene (TCTB) mycotoxins. To help reduce the use of synthetic fungicides while guaranteeing low mycotoxin levels, there is an urgent need to develop new, efficient and environmentally-friendly plant protection solutions. Previously, F. graminearum proteins that could serve as putative targets to block the fungal spread and toxin production were identified and a virtual screening undertaken. Here, two selected compounds, M1 and M2, predicted, respectively, as the top compounds acting on the trichodiene synthase, a key enzyme of TCTB biosynthesis, and the 24-sterol-C-methyltransferase, a protein involved in ergosterol biosynthesis, were submitted for biological tests. Corroborating in silico predictions, M1 was shown to significantly inhibit TCTB yield by a panel of strains. Results were less obvious with M2 that induced only a slight reduction in fungal biomass. To go further, seven M1 analogs were assessed, which allowed evidencing of the physicochemical properties crucial for the anti-mycotoxin activity. Altogether, our results provide the first evidence of the promising potential of computational approaches to discover new anti-mycotoxin solutions.

Structural insight into the catalytic mechanism and inhibitor binding of aminopeptidase A.

Aminopeptidase A (APA) is a membrane-bound monozinc aminopeptidase. In the brain, APA generates angiotensin III which exerts a tonic stimulatory effect on the control of blood pressure (BP) in hypertensive animals. The oral administration of RB150 renamed firibastat by WHO, an APA inhibitor prodrug, targeting only the S1 subsite, decreases BP in hypertensive patients from various ethnic origins. To identify new families of potent and selective APA inhibitors, we explored the organization of the APA active site, especially the S2' subsite. By molecular modeling, docking, molecular dynamics simulations and site-directed mutagenesis, we revealed that Arg368 and Arg386, in the S2' subsite of human APA established various types of interactions in major part with the P2' residue but also with the P1' residue of APA inhibitors, required for their nanomolar inhibitory potency. We also demonstrated an important role for Arg368 in APA catalysis, in maintaining the structural integrity of the GAMEN motif, a conserved sequence involved in exopeptidase specificity and optimal positioning of the substrate in monozinc aminopeptidases. This arginine together with the GAMEN motif are key players for the catalytic mechanism of these enzymes.

Large-Scale Virtual Screening Against the MET Kinase Domain Identifies a New Putative Inhibitor Type.

By using an ensemble-docking strategy, we undertook a large-scale virtual screening campaign in order to identify new putative hits against the MET kinase target. Following a large molecular dynamics sampling of its conformational space, a set of 45 conformers of the kinase was retained as docking targets to take into account the flexibility of the binding site moieties. Our screening funnel started from about 80,000 chemical compounds to be tested in silico for their potential affinities towards the kinase binding site. The top 100 molecules selected-thanks to the molecular docking results-were further analyzed for their interactions, and 25 of the most promising ligands were tested for their ability to inhibit MET activity in cells. F0514-4011 compound was the most efficient and impaired this scattering response to HGF (Hepatocyte Growth Factor) with an IC 50 of 7.2 µ M. Interestingly, careful docking analysis of this molecule with MET suggests a possible conformation halfway between classical type-I and type-II MET inhibitors, with an additional region of interaction. This compound could therefore be an innovative seed to be repositioned from its initial antiviral purpose towards the field of MET inhibitors. Altogether, these results validate our ensemble docking strategy as a cost-effective functional method for drug development.

Repurposing approach identifies new treatment options for invasive fungal disease.

Drug repositioning is the process of discovery, validation and marketing of previously approved drugs for new indications. Our aim was drug repositioning, using ligand-based and structure-based computational methods, of compounds that are similar to two hit compounds previously selected by our group that show promising antifungal activity. Through the ligand-based method, 100 compounds from each of three databases (MDDR, DrugBank and TargetMol) were selected by the Tanimoto coefficient, as similar to LMM5 or LMM11. These compounds were analyzed by the scaffold trees, and up to 10 compounds from each database were selected. The structure-based method (molecular docking) using thioredoxin reductase as the target drug was performed as a complementary approach, resulting in six compounds that were tested in an in vitro assay. All compounds, particularly raltegravir, showed antifungal activity against the genus Paracoccidioides. Raltegravir, an antiviral drug, showed promising antifungal activity against the experimental murine paracoccidioidomycosis, with significant reduction of the fungal burden and decreased alterations in the lung structure of mice treated with 1 mg/kg of raltegravir. In conclusion, the combination of two in silico methods for drug repositioning was able to select an antiviral drug with promising antifungal activity for treatment of paracoccidioidomycosis.

Selection of potential anti-adhesion drugs by in silico approaches targeted to ALS3 from Candida albicans.

OBJECTIVE: To select potential ligands of ALS3 for drug development with anti-adhesion and/or anti-biofilm activities. METHODOLOGY: ALS3 model was considered stable by DM. The main features of protein flexibility were represented by two conformers which were used in the virtual screening. Twenty-four small molecules were selected for in vitro assays. Five of them presented the best biological activity with ability to inhibit the adhesion and C. albicans biofilm formation on abiotic surface. RESULTS: To select potential ligands of ALS3 for drug development with anti-adhesion and/or anti-biofilm activities. CONCLUSION: In silico tools application was able to select promising compounds with anti-adhesion activity, opening a new perspective of medical device treatment.

A Chemosensory GPCR as a Potential Target to Control the Root-Knot Nematode Meloidogyne incognita Parasitism in Plants.

Root-knot nematodes (RKN), from the Meloidogyne genus, have a worldwide distribution and cause severe economic damage to many life-sustaining crops. Because of their lack of specificity and danger to the environment, most chemical nematicides have been banned from use. Thus, there is a great need for new and safe compounds to control RKN. Such research involves identifying beforehand the nematode proteins essential to the invasion. Since G protein-coupled receptors GPCRs are the target of a large number of drugs, we have focused our research on the identification of putative nematode GPCRs such as those capable of controlling the movement of the parasite towards (or within) its host. A datamining procedure applied to the genome of Meloidogyne incognita allowed us to identify a GPCR, belonging to the neuropeptide GPCR family that can serve as a target to carry out a virtual screening campaign. We reconstructed a 3D model of this receptor by homology modeling and validated it through extensive molecular dynamics simulations. This model was used for large scale molecular dockings which produced a filtered limited set of putative antagonists for this GPCR. Preliminary experiments using these selected molecules allowed the identification of an active compound, namely C260-2124, from the ChemDiv provider, which can serve as a starting point for further investigations.

Modeling and minimizing CAPRI round 30 symmetrical protein complexes from CASP-11 structural models.

Many of the modeling targets in the blind CASP-11/CAPRI-30 experiment were protein homo-dimers and homo-tetramers. Here, we perform a retrospective docking-based analysis of the perfectly symmetrical CAPRI Round 30 targets whose crystal structures have been published. Starting from the CASP "stage-2" fold prediction models, we show that using our recently developed "SAM" polar Fourier symmetry docking algorithm combined with NAMD energy minimization often gives acceptable or better 3D models of the target complexes. We also use SAM to analyze the overall quality of all CASP structural models for the selected targets from a docking-based perspective. We demonstrate that docking only CASP "center" structures for the selected targets provides a fruitful and economical docking strategy. Furthermore, our results show that many of the CASP models are dockable in the sense that they can lead to acceptable or better models of symmetrical complexes. Even though SAM is very fast, using docking and NAMD energy minimization to pull out acceptable docking models from a large ensemble of docked CASP models is computationally expensive. Nonetheless, thanks to our SAM docking algorithm, we expect that applying our docking protocol on a modern computer cluster will give us the ability to routinely model 3D structures of symmetrical protein complexes from CASP-quality models. Proteins 2017; 85:463-469. © 2016 Wiley Periodicals, Inc.

GPCRs from fusarium graminearum detection, modeling and virtual screening - the search for new routes to control head blight disease.

BACKGOUND: Fusarium graminearum (FG) is one of the major cereal infecting pathogens causing high economic losses worldwide and resulting in adverse effects on human and animal health. Therefore, the development of new fungicides against FG is an important issue to reduce cereal infection and economic impact. In the strategy for developing new fungicides, a critical step is the identification of new targets against which innovative chemicals weapons can be designed. As several G-protein coupled receptors (GPCRs) are implicated in signaling pathways critical for the fungi development and survival, such proteins could be valuable efficient targets to reduce Fusarium growth and therefore to prevent food contamination. RESULTS: In this study, GPCRs were predicted in the FG proteome using a manually curated pipeline dedicated to the identification of GPCRs. Based on several successive filters, the most appropriate GPCR candidate target for developing new fungicides was selected. Searching for new compounds blocking this particular target requires the knowledge of its 3D-structure. As no experimental X-Ray structure of the selected protein was available, a 3D model was built by homology modeling. The model quality and stability was checked by 100 ns of molecular dynamics simulations. Two stable conformations representative of the conformational families of the protein were extracted from the 100 ns simulation and were used for an ensemble docking campaign. The model quality and stability was checked by 100 ns of molecular dynamics simulations previously to the virtual screening step. The virtual screening step comprised the exploration of a chemical library with 11,000 compounds that were docked to the GPCR model. Among these compounds, we selected the ten top-ranked nontoxic molecules proposed to be experimentally tested to validate the in silico simulation. CONCLUSIONS: This study provides an integrated process merging genomics, structural bioinformatics and drug design for proposing innovative solutions to a world wide threat to grain producers and consumers.

Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment.

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323-348. © 2016 Wiley Periodicals, Inc.

New structural insights into the apelin receptor: identification of key residues for apelin binding.

Apelin is the endogenous ligand of the orphan 7-transmembrane domain GPCR APJ, now named the apelin receptor (ApelinR). Apelin plays a prominent role in body fluid and cardiovascular homeostasis. To better understand the structural organization of the ApelinR, we built 3 homology 3-dimensional (3D) models of the human ApelinR using the validated cholecystokinin receptor-1 3D model or the X-ray structures of the ß2-adrenergic and CXCR4 receptors as templates. Docking of the pyroglutamyl form of apelin 13 (pE13F) into these models revealed the conservation at the bottom of the binding site of a hydrophobic cavity in which the C-terminal Phe of pE13F was embedded. In contrast, at the top of the binding site, depending on the model, different interactions were visualized between acidic residues of the ApelinR and the basic residues of pE13F. Using site-directed mutagenesis, we showed that Asp 92, Glu 172, and Asp 282 of rat ApelinR are key residues in apelin binding by interacting with Lys 8, Arg 2, and Arg 4 of pE13F, respectively. These residues are only seen in the CXCR4-based ApelinR 3D model, further validating this model. These findings bring new insights into the structural organization of the ApelinR and the mode of apelin binding.

Identification of novel Trypanosoma cruzi prolyl oligopeptidase inhibitors by structure-based virtual screening.

We have previously demonstrated that the secreted prolyl oligopeptidase of Trypanosoma cruzi (POPTc80) is involved in the infection process by facilitating parasite migration through the extracellular matrix. We have built a 3D structural model where POPTc80 is formed by a catalytic α/ß-hydrolase domain and a ß-propeller domain, and in which the substrate docks at the inter-domain interface, suggesting a "jaw opening" gating access mechanism. This preliminary model was refined by molecular dynamics simulations and next used for a virtual screening campaign, whose predictions were tested by standard binding assays. This strategy was successful as all 13 tested molecules suggested from the in silico calculations were found out to be active POPTc80 inhibitors in the micromolar range (lowest K i at 667 nM). This work paves the way for future development of innovative drugs against Chagas disease.

Carbohydrate-based peptidomimetics targeting neuropilin-1: Synthesis, molecular docking study and in vitro biological activities.

Neuropilin-1 (NRP-1), a transmembrane glycoprotein acting as a co-receptor of VEGF-A, is expressed by cancer and angiogenic endothelial cells and is involved in the angiogenesis process. Taking advantage of functionalities and stereodiversities of sugar derivatives, the design and the synthesis of carbohydrate based peptidomimetics are here described. One of these compounds (56) demonstrated inhibition of VEGF-A165 binding to NRP-1 (IC50=39µM) and specificity for NRP-1 over VEGF-R2. Biological evaluations were performed on human umbilical vein endothelial cells (HUVECs) through activation of downstream proteins (AKT and ERK phosphorylation), viability/proliferation assays and in vitro measurements of anti-angiogenic abilities.

Molecular-evaluated and explainable drug repurposing for COVID-19 using ensemble knowledge graph embedding.

The search for an effective drug is still urgent for COVID-19 as no drug with proven clinical efficacy is available. Finding the new purpose of an approved or investigational drug, known as drug repurposing, has become increasingly popular in recent years. We propose here a new drug repurposing approach for COVID-19, based on knowledge graph (KG) embeddings. Our approach learns "ensemble embeddings" of entities and relations in a COVID-19 centric KG, in order to get a better latent representation of the graph elements. Ensemble KG-embeddings are subsequently used in a deep neural network trained for discovering potential drugs for COVID-19. Compared to related works, we retrieve more in-trial drugs among our top-ranked predictions, thus giving greater confidence in our prediction for out-of-trial drugs. For the first time to our knowledge, molecular docking is then used to evaluate the predictions obtained from drug repurposing using KG embedding. We show that Fosinopril is a potential ligand for the SARS-CoV-2 nsp13 target. We also provide explanations of our predictions thanks to rules extracted from the KG and instanciated by KG-derived explanatory paths. Molecular evaluation and explanatory paths bring reliability to our results and constitute new complementary and reusable methods for assessing KG-based drug repurposing.

Identification of new putative inhibitors of Mycobacterium tuberculosis 3-dehydroshikimate dehydratase from a combination of ligand- and structure-based and deep learning in silico approaches.

The development of new drugs against Mycobacterium tuberculosis is an essential strategy for fighting drug resistance. Although 3-dehydroquinate dehydratase (MtDHQ) is known to be a highly relevant target for M. tuberculosis, current research shows new putative inhibitors of MtDHQ selected by a large-scale ensemble-docking strategy combining ligand- and target-based chemoinformatic methods to deep learning. Initial chemical library was reduced from 216 million to approximately 460 thousand after pharmacophore, toxicity and molecular weight filters. Final library was subjected to an ensemble-docking protocol in GOLD which selected the top 300 molecules (GHITS). GHITS displayed different structures and characteristics when compared to known inhibitors (KINH). GHITS were further screened by post-docking analysis in AMMOS2 and deep learning virtual screening in DeepPurpose. DeepPurpose predicted that a number of GHITS had comparable or better affinity for the target than KINH. The best molecule was selected by consensus ranking using GOLD, AMMOS2 and DeepPurpose scores. Molecular dynamics revealed that the top hit displayed consistent and stable binding to MtDHQ, making strong interactions with active-site loop residues. Results forward new putative inhibitors of MtDHQ and reinforce the potential application of artificial intelligence methods for drug design. This work represents the first step in the validation of these molecules as inhibitors of MtDHQ.

A new class of peptides from wasp venom: a pathway to antiepileptic/neuroprotective drugs.

The ability of venom-derived peptides to disrupt physiological processes in mammals provides an exciting source for pharmacological development. Our research group has identified a new class of neuroactive peptides from the venom of a Brazilian social wasp, Polybia occidentalis, with the potential pharmacological profile to treat epilepsies. The study was divided into five phases: Phase 1 concerned the extraction, isolation and purification of Occidentalin-1202(n) from the crude venom, followed by the synthesis of an identical analogue peptide, named Occidentalin-1202(s). In Phase 2, we described the effects of both peptides in two acute models of epilepsy-kainic acid and pentylenetetrazole-induced model of seizures-and measured estimated ED50 and therapeutic index values, electroencephalographic studies and C-fos evaluation. Phase 3 was a compilation of advanced tests performed with Occidentalin-1202(s) only, reporting histopathological features and its performance in the pilocarpine-induced status epilepticus. After the determination of the antiepileptic activity of Occidentalin-1202(s), Phase 4 consisted of evaluating its potential adverse effects, after chronic administration, on motor coordination (Rotarod) and cognitive impairment (Morris water maze) tests. Finally, in Phase 5, we proposed a mechanism of action using computational models with kainate receptors. The new peptide was able to cross the blood-brain barrier and showed potent antiseizure effects in acute (kainic acid and pentylenetetrazole) and chronic (temporal lobe epilepsy model induced by pilocarpine) models. Motor and cognitive behaviour were not adversely affected, and a potential neuroprotective effect was observed. Occidentalin-1202 can be a potent blocker of the kainate receptor, as assessed by computational analysis, preventing glutamate and kainic acid from binding to the receptor's active site. Occidentalin-1202 is a peptide with promising applicability to treat epilepsy and can be considered an interesting drug model for the development of new medicines.

Exploring c-Met kinase flexibility by sampling and clustering its conformational space.

It is now widely recognized that the flexibility of both partners has to be considered in molecular docking studies. However, the question how to handle the best the huge computational complexity of exploring the protein binding site landscape is still a matter of debate. Here we investigate the flexibility of c-Met kinase as a test case for comparing several simulation methods. The c-Met kinase catalytic site is an interesting target for anticancer drug design. In particular, it harbors an unusual plasticity compared with other kinases ATP binding sites. Exploiting this feature may eventually lead to the discovery of new anticancer agents with exquisite specificity. We present in this article an extensive investigation of c-Met kinase conformational space using large-scale computational simulations in order to extend the knowledge already gathered from available X-ray structures. In the process, we compare the relevance of different strategies for modeling and injecting receptor flexibility information into early stage in silico structure-based drug discovery pipeline. The results presented here are currently being exploited in on-going virtual screening investigations on c-Met.

By interacting with the C-terminal Phe of apelin, Phe255 and Trp259 in helix VI of the apelin receptor are critical for internalization.

Apelin is the endogenous ligand of the orphan seven-transmembrane domain (TM) G protein-coupled receptor APJ. Apelin is involved in the regulation of body fluid homeostasis and cardiovascular functions. We previously showed the importance of the C-terminal Phe of apelin 17 (K17F) in the hypotensive activity of this peptide. Here, we show either by deleting the Phe residue (K16P) or by substituting it by an Ala (K17A), that it plays a crucial role in apelin receptor internalization but not in apelin binding or in Gα(i)-protein coupling. Then we built a homology three-dimensional model of the human apelin receptor using the cholecystokinin receptor-1 model as a template, and we subsequently docked K17F into the binding site. We visualized a hydrophobic cavity at the bottom of the binding pocket in which the C-terminal Phe of K17F was embedded by Trp(152) in TMIV and Trp(259) and Phe(255) in TMVI. Using molecular modeling and site-directed mutagenesis studies, we further showed that Phe(255) and Trp(259) are key residues in triggering receptor internalization without playing a role in apelin binding or in Gα(i)-protein coupling. These findings bring new insights into apelin receptor activation and show that Phe(255) and Trp(259), by interacting with the C-terminal Phe of the pyroglutamyl form of apelin 13 (pE13F) or K17F, are crucial for apelin receptor internalization.

Comparative genomics allowed the identification of drug targets against human fungal pathogens.

BACKGROUND: The prevalence of invasive fungal infections (IFIs) has increased steadily worldwide in the last few decades. Particularly, there has been a global rise in the number of infections among immunosuppressed people. These patients present severe clinical forms of the infections, which are commonly fatal, and they are more susceptible to opportunistic fungal infections than non-immunocompromised people. IFIs have historically been associated with high morbidity and mortality, partly because of the limitations of available antifungal therapies, including side effects, toxicities, drug interactions and antifungal resistance. Thus, the search for alternative therapies and/or the development of more specific drugs is a challenge that needs to be met. Genomics has created new ways of examining genes, which open new strategies for drug development and control of human diseases. RESULTS: In silico analyses and manual mining selected initially 57 potential drug targets, based on 55 genes experimentally confirmed as essential for Candida albicans or Aspergillus fumigatus and other 2 genes (kre2 and erg6) relevant for fungal survival within the host. Orthologs for those 57 potential targets were also identified in eight human fungal pathogens (C. albicans, A. fumigatus, Blastomyces dermatitidis, Paracoccidioides brasiliensis, Paracoccidioides lutzii, Coccidioides immitis, Cryptococcus neoformans and Histoplasma capsulatum). Of those, 10 genes were present in all pathogenic fungi analyzed and absent in the human genome. We focused on four candidates: trr1 that encodes for thioredoxin reductase, rim8 that encodes for a protein involved in the proteolytic activation of a transcriptional factor in response to alkaline pH, kre2 that encodes for α-1,2-mannosyltransferase and erg6 that encodes for Δ(24)-sterol C-methyltransferase. CONCLUSIONS: Our data show that the comparative genomics analysis of eight fungal pathogens enabled the identification of four new potential drug targets. The preferred profile for fungal targets includes proteins conserved among fungi, but absent in the human genome. These characteristics potentially minimize toxic side effects exerted by pharmacological inhibition of the cellular targets. From this first step of post-genomic analysis, we obtained information relevant to future new drug development.