THOUSAND-GRAIN WEIGHT 6, which is an IAA-glucose hydrolase, preferentially recognizes the structure of the indole ring.

An indole-3-acetic acid (IAA)-glucose hydrolase, THOUSAND-GRAIN WEIGHT 6 (TGW6), negatively regulates the grain weight in rice. TGW6 has been used as a target for breeding increased rice yield. Moreover, the activity of TGW6 has been thought to involve auxin homeostasis, yet the details of this putative TGW6 activity remain unclear. Here, we show the three-dimensional structure and substrate preference of TGW6 using X-ray crystallography, thermal shift assays and fluorine nuclear magnetic resonance (19F NMR). The crystal structure of TGW6 was determined at 2.6 Å resolution and exhibited a six-bladed ß-propeller structure. Thermal shift assays revealed that TGW6 preferably interacted with indole compounds among the tested substrates, enzyme products and their analogs. Further analysis using 19F NMR with 1,134 fluorinated fragments emphasized the importance of indole fragments in recognition by TGW6. Finally, docking simulation analyses of the substrate and related fragments in the presence of TGW6 supported the interaction specificity for indole compounds. Herein, we describe the structure and substrate preference of TGW6 for interacting with indole fragments during substrate recognition. Uncovering the molecular details of TGW6 activity will stimulate the use of this enzyme for increasing crop yields and contributes to functional studies of IAA glycoconjugate hydrolases in auxin homeostasis.

A new workflow for the effective curation of membrane permeability data from open ADME information.

Membrane permeability is an in vitro parameter that represents the apparent permeability (Papp) of a compound, and is a key absorption, distribution, metabolism, and excretion parameter in drug development. Although the Caco-2 cell lines are the most used cell lines to measure Papp, other cell lines, such as the Madin-Darby Canine Kidney (MDCK), LLC-Pig Kidney 1 (LLC-PK1), and Ralph Russ Canine Kidney (RRCK) cell lines, can also be used to estimate Papp. Therefore, constructing in silico models for Papp estimation using the MDCK, LLC-PK1, and RRCK cell lines requires collecting extensive amounts of in vitro Papp data. An open database offers extensive measurements of various compounds covering a vast chemical space; however, concerns were reported on the use of data published in open databases without the appropriate accuracy and quality checks. Ensuring the quality of datasets for training in silico models is critical because artificial intelligence (AI, including deep learning) was used to develop models to predict various pharmacokinetic properties, and data quality affects the performance of these models. Hence, careful curation of the collected data is imperative. Herein, we developed a new workflow that supports automatic curation of Papp data measured in the MDCK, LLC-PK1, and RRCK cell lines collected from ChEMBL using KNIME. The workflow consisted of four main phases. Data were extracted from ChEMBL and filtered to identify the target protocols. A total of 1661 high-quality entries were retained after checking 436 articles. The workflow is freely available, can be updated, and has high reusability. Our study provides a novel approach for data quality analysis and accelerates the development of helpful in silico models for effective drug discovery. Scientific Contribution: The cost of building highly accurate predictive models can be significantly reduced by automating the collection of reliable measurement data. Our tool reduces the time and effort required for data collection and will enable researchers to focus on constructing high-performance in silico models for other types of analysis. To the best of our knowledge, no such tool is available in the literature.

PoSSuM v.3: A Major Expansion of the PoSSuM Database for Finding Similar Binding Sites of Proteins.

Information on structures of protein-ligand complexes, including comparisons of known and putative protein-ligand-binding pockets, is valuable for protein annotation and drug discovery and development. To facilitate biomedical and pharmaceutical research, we developed PoSSuM (https://possum.cbrc.pj.aist.go.jp/PoSSuM/), a database for identifying similar binding pockets in proteins. The current PoSSuM database includes 191 million similar pairs among almost 10 million identified pockets. PoSSuM drug search (PoSSuMds) is a resource for investigating ligand and receptor diversity among a set of pockets that can bind to an approved drug compound. The enhanced PoSSuMds covers pockets associated with both approved drugs and drug candidates in clinical trials from the latest release of ChEMBL. Additionally, we developed two new databases: PoSSuMAg for investigating antibody-antigen interactions and PoSSuMAF to simplify exploring putative pockets in AlphaFold human protein models.

Applying deep learning to iterative screening of medium-sized molecules for protein-protein interaction-targeted drug discovery.

We combined a library of medium-sized molecules with iterative screening using multiple machine learning algorithms that were ligand-based, which resulted in a large increase of the hit rate against a protein-protein interaction target. This was demonstrated by inhibition assays using a PPI target, Kelch-like ECH-associated protein 1/nuclear factor erythroid 2-related factor 2 (Keap1/Nrf2), and a deep neural network model based on the first-round assay data showed a highest hit rate of 27.3%. Using the models, we identified novel active and non-flat compounds far from public datasets, expanding the chemical space.

Effect of Water Adsorption Layers on the Friction Properties of Fluorinated Amorphous Carbon Films in Ambient Air.

Fluorinated amorphous carbon (a-C:F) films with different microstructures were prepared with bipolar-type plasma-based ion implantation and deposition method by changing the negative bias voltage, and the tribological properties were investigated in ambient air. Their surface chemistry and water adsorption properties were investigated to determine their friction properties. Microstructural analysis results showed that, as the negative bias voltage increased, the density of the films decreased with the promotion of graphitization. The water adsorption properties evaluated using a quartz crystal microbalance showed that a large adsorbed water weight was observed for the films deposited at high negative bias voltages. In contrast, these films exhibited thin water adsorption layers from the measurements with an ellipsometer. These results indicate that water molecules are adsorbed on the film surface and permeate into the films, particularly for the films deposited at high negative bias voltages. The friction properties in ambient air depend significantly on their microstructure and relative humidity (RH). Regardless of the RH, the higher the negative bias voltage during film deposition, the lower the friction coefficient. Since several water molecules existed on the surface of the film deposited with a low negative bias voltage, its surface was oxidized during sliding, which increased the friction coefficient. In addition, the friction coefficient of the films increased at high RH. The number of water molecules adsorbed on the film increased as the RH increased, causing a high shear force owing to many hydrogen bonds and/or high capillary force at the friction interface.

DLiP-PPI library: An integrated chemical database of small-to-medium-sized molecules targeting protein-protein interactions.

Protein-protein interactions (PPIs) are recognized as important targets in drug discovery. The characteristics of molecules that inhibit PPIs differ from those of small-molecule compounds. We developed a novel chemical library database system (DLiP) to design PPI inhibitors. A total of 32,647 PPI-related compounds are registered in the DLiP. It contains 15,214 newly synthesized compounds, with molecular weight ranging from 450 to 650, and 17,433 active and inactive compounds registered by extracting and integrating known compound data related to 105 PPI targets from public databases and published literature. Our analysis revealed that the compounds in this database contain unique chemical structures and have physicochemical properties suitable for binding to the protein-protein interface. In addition, advanced functions have been integrated with the web interface, which allows users to search for potential PPI inhibitor compounds based on types of protein-protein interfaces, filter results by drug-likeness indicators important for PPI targeting such as rule-of-4, and display known active and inactive compounds for each PPI target. The DLiP aids the search for new candidate molecules for PPI drug discovery and is available online (https://skb-insilico.com/dlip).

Comprehensive Approach of 19F Nuclear Magnetic Resonance, Enzymatic, and In Silico Methods for Site-Specific Hit Selection and Validation of Fragment Molecules that Inhibit Methionine γ-Lyase Activity.

Fragment-based screening using 19F NMR (19F-FS) is an efficient method for exploring seed and lead compounds for drug discovery. Here, we demonstrate the utility and merits of using 19F-FS for methionine γ-lyase-binding fragments, together with a 19F NMR-based competition and mutation assay, as well as enzymatic and in silico methods. 19F NMR-based assays provided useful information on binding between 19F-FS hit fragments and target proteins. Although the 19F-FS and enzymatic assay were weakly correlated, they show that the 19F-FS hit fragments contained compounds with inhibitory activity. Furthermore, we found that in silico calculations partially account for the differences in activity levels between the 19F-FS hits as per NMR analysis. A comprehensive approach combining the 19F-FS and other methods not only identified fragment hits but also distinguished structural differences in chemical groups with diverse activity levels.

Identification of novel inhibitors of Keap1/Nrf2 by a promising method combining protein-protein interaction-oriented library and machine learning.

Protein-protein interactions (PPIs) are prospective but challenging targets for drug discovery, because screening using traditional small-molecule libraries often fails to identify hits. Recently, we developed a PPI-oriented library comprising 12,593 small-to-medium-sized newly synthesized molecules. This study validates a promising combined method using PPI-oriented library and ligand-based virtual screening (LBVS) to discover novel PPI inhibitory compounds for Kelch-like ECH-associated protein 1 (Keap1) and nuclear factor erythroid 2-related factor 2 (Nrf2). We performed LBVS with two random forest models against our PPI library and the following time-resolved fluorescence resonance energy transfer (TR-FRET) assays of 620 compounds identified 15 specific hit compounds. The high hit rates for the entire PPI library (estimated 0.56-1.3%) and the LBVS (maximum 5.4%) compared to a conventional screening library showed the utility of the library and the efficiency of LBVS. All the hit compounds possessed novel structures with Tanimoto similarity ≤ 0.26 to known Keap1/Nrf2 inhibitors and aqueous solubility (AlogP < 5). Reasonable binding modes were predicted using 3D alignment of five hit compounds and a Keap1/Nrf2 peptide crystal structure. Our results represent a new, efficient method combining the PPI library and LBVS to identify novel PPI inhibitory ligands with expanded chemical space.

Chemical curation to improve data accuracy: recent development of the 3DMET database.

We have developed a three-dimensional structure database of natural metabolites (3DMET). Early development of the 3DMET database relied on content auto-generated from 2D-structures of other chemical databases. From 2009, we began manual curation, obtaining new compounds from published works. In the process of curation, problems of digitizing 3D-structures from structure drawings of documents were accumulated. As the same as auto-generation, structure drawings should be also payed attention about stereochemistry. Our experiences in manual curation of 3DMET, as described herein, may be useful to others in this field of research and for the development of supporting systems of a chemical structure database. Manual curation is still necessary for proper database entry of the 3D-configurations of chiral atoms, a problem encountered frequently among natural products.