Search

Among the factors affecting biological processes such as protein folding and ligand binding, hydration, which is represented by a three-dimensional water site distribution function around the protein, is crucial. The typical methods for computing the distribution functions, including molecular dynamics simulations and the three-dimensional reference interaction site model (3D-RISM) theory, require a long computation time ranging from hours to tens of hours. Here, we propose a deep learning (DL) model that rapidly estimates the distribution functions around proteins obtained using the 3D-RISM theory from the protein 3D structure. The distribution functions predicted using our DL model are in good agreement with those obtained using the 3D-RISM theory. Particularly, the coefficient of determination between the distribution function obtained by the DL model and that obtained using the 3D-RISM theory is approximately 0.98. Furthermore, using a graphics processing unit, the prediction by the DL model is completed in less than 1 min, more than 2 orders of magnitude faster than the calculation time of the 3D-RISM theory. The position of water molecules around the protein was estimated based on the distribution function obtained by our DL model, and the position of waters estimated by our DL model was in good agreement with that of water molecules estimated using the 3D-RISM theory and of crystallographic waters. Therefore, our DL model provides a practical and efficient way to calculate the three-dimensional water site distribution functions and to estimate the position of water molecules around the protein. The program called "gr Predictor" is available under the GNU General Public License from https://github.com/YoshidomeGroup-Hydration/gr-predictor.

Hydration is a critical factor in the ligand binding process. Herein, to examine the hydration states of ligand binding sites, the three‐dimensional distribution function for the water oxygen site, gO(r), is computed for 3,706 ligand‐free protein structures based on the corresponding small molecule–protein complexes using the 3D‐RISM theory. For crystallographic waters (CWs) close to the ligand, gO(r) reveals that several CWs are stabilized by interaction networks formed between the ligand, CW, and protein. Based on the gO(r) for the crystallographic binding pose of the ligand, hydrogen bond interactions are dominant in the highly hydrated regions while weak interactions such as CH‐O are dominant in the moderately hydrated regions. The polar heteroatoms of the ligand occupy the highly hydrated and moderately hydrated regions in the crystallographic (correct) and wrongly docked (incorrect) poses, respectively. Thus, the gO(r) of polar heteroatoms may be used to distinguish the correct binding poses., The hydration states of ligand binding sites are comprehensively analyzed using a theory of solvation. An analysis of the hydration states at the positions of ligand heavy atoms indicates that the polar heteroatoms of the ligand tend to occupy highly hydrated regions in the correct ligand poses and moderately hydrated regions in the incorrect ligand poses, suggesting a way to distinguish these two poses.

Here, we have constructed neural network-based models that predict atomic partial charges with high accuracy at low computational cost. The models were trained using high-quality data acquired from quantum mechanics calculations using the fragment molecular orbital method. We have succeeded in obtaining highly accurate atomic partial charges for three representative molecular systems of proteins, including one large biomolecule (approx. 2000 atoms). The novelty of our approach is the ability to take into account the electronic polarization in the system, which is a system-dependent phenomenon, being important in the field of drug design. Our high-precision models are useful for the prediction of atomic partial charges and expected to be widely applicable in structure-based drug designs such as structural optimization, high-speed and high-precision docking, and molecular dynamics calculations.

During the course of derivatization of HTS hit 4a, we have identified a novel small-molecule hPTHR1 agonist, 1-(3,5-dimethyl-4-(2-((2-((1R,4R)-4-methylcyclohexyl)-4-oxo-1,3,8-triazaspiro[4.5]dec-1-en-8-yl)sulfonyl)ethyl)phenyl)-1-methylurea (CH5447240, 14l). Compound 14l exhibited a potent in vitro hPTHR1 agonist effect with EC20 of 3.0 μM and EC50 of 12 μM and showed excellent physicochemical properties, such as high solubility in fasted state simulated intestinal fluid and good metabolic stability in human liver microsomes. Importantly, 14l showed 55% oral bioavailability and a significantly elevated serum calcium level in hypocalcemic model rats.

A series of 5,5-dimethylthiohydantoin derivatives were synthesized and evaluated for androgen receptor pure antagonistic activities for the treatment of castration-resistant prostate cancer. Since CH4933468, which we reported previously, had a problem with agonist metabolites, novel thiohydantoin derivatives were identified by applying two strategies. One was the replacement of the alkylsulfonamide moiety by a phenylsulfonamide to avoid the production of agonist metabolites. The other was the replacement of the phenyl ring with a pyridine ring to improve in vivo potency and reduce hERG affinity. Pharmacological assays indicated that CH5137291 (17b) was a potent AR pure antagonist which did not produce the agonist metabolite. Moreover, CH5137291 completely inhibited in vivo tumor growth of LNCaP-BC2, a castration-resistant prostate cancer model.

A series of 5,5-dimethylthiohydantoin derivatives were synthesized and evaluated for androgen receptor pure antagonistic activities for the treatment of hormone refractory prostate cancer. CH4933468 (32d) with a sulfonamide side chain not only exhibited antagonistic activity with no agonistic activity in the reporter gene assay but also inhibited the growth of bicalutamide-resistant cell lines. This compound also inhibited tumor growth of the LNCaP xenograft in mice dose-dependently.

Selective factor VIIa-tissue factor complex (FVIIa/TF) inhibition is regarded as a promising target for developing new anticoagulant drugs. Compound 1 was discovered from focused screening of serine protease-directed compounds from our internal collection. Using parallel synthesis supported by structure-based drug design, we identified peptidemimetic FVIIa/TF inhibitors (compounds 4-11) containing L-Gln or L-Met as the P2 moiety. However, these compounds lacked the selectivity of other serine proteases in the coagulation cascade, especially thrombin. Further optimization of these compounds was carried out with a focus on the P4 moiety. Among the optimized compounds, 12b-f showed improved selectivity.

The 3D structure of human factor VIIa/soluble tissue factor in complex with a peptide mimetic inhibitor, propylsulfonamide-D-Thr-Met-p-aminobenzamidine, is determined by X-ray crystallography. As compared with the interactions between thrombin and thrombin inhibitors, the interactions at S2 and S3 sites characteristic of factor VIIa and factor VIIa inhibitors are revealed. The S2 site has a small pocket, which is filled by the hydrophobic methionine side chain in P2. The small S3 site fits the small size residue, D-threonine in P3. The structural data and SAR data of the peptide mimetic inhibitor show that these interactions in the S2 and S3 sites play an important role for the improvement of selectivity versus thrombin. The results will provide valuable information for the structure-based drug design of specific inhibitors for FVIIa/TF.