AI-driven molecular generation of not-patented pharmaceutical compounds using world open patent data.

Developing compounds with novel structures is important for the production of new drugs. From an intellectual perspective, confirming the patent status of newly developed compounds is essential, particularly for pharmaceutical companies. The generation of a large number of compounds has been made possible because of the recent advances in artificial intelligence (AI). However, confirming the patent status of these generated molecules has been a challenge because there are no free and easy-to-use tools that can be used to determine the novelty of the generated compounds in terms of patents in a timely manner; additionally, there are no appropriate reference databases for pharmaceutical patents in the world. In this study, two public databases, SureChEMBL and Google Patents Public Datasets, were used to create a reference database of drug-related patented compounds using international patent classification. An exact structure search system was constructed using InChIKey and a relational database system to rapidly search for compounds in the reference database. Because drug-related patented compounds are a good source for generative AI to learn useful chemical structures, they were used as the training data. Furthermore, molecule generation was successfully directed by increasing and decreasing the number of generated patented compounds through incorporation of patent status (i.e., patented or not) into learning. The use of patent status enabled generation of novel molecules with high drug-likeness. The generation using generative AI with patent information would help efficiently propose novel compounds in terms of pharmaceutical patents. Scientific contribution: In this study, a new molecule-generation method that takes into account the patent status of molecules, which has rarely been considered but is an important feature in drug discovery, was developed. The method enables the generation of novel molecules based on pharmaceutical patents with high drug-likeness and will help in the efficient development of effective drug compounds.

Improving taxonomic classification of marine zooplankton by molecular approach: registration of taxonomically verified 18S and 28S rRNA gene sequences.

Background: Zooplankton plays an important role in the marine ecosystem. A high level of taxonomic expertise is necessary for accurate species identification based on morphological characteristics. As an alternative method to morphological classification, we focused on a molecular approach using 18S and 28S ribosomal RNA (rRNA) gene sequences. This study investigates how the accuracy of species identification by metabarcoding improves when taxonomically verified sequences of dominant zooplankton species are added to the public database. The improvement was tested by using natural zooplankton samples. Methods: rRNA gene sequences were obtained from dominant zooplankton species from six sea areas around Japan and registered in the public database for improving the accuracy of taxonomic classifications. Two reference databases with and without newly registered sequences were created. Comparison of detected OTUs associated with single species between the two references was done using field-collected zooplankton samples from the Sea of Okhotsk for metabarcoding analysis to verify whether or not the newly registered sequences improved the accuracy of taxonomic classifications. Results: A total of 166 sequences in 96 species based on the 18S marker and 165 sequences in 95 species based on the 28S marker belonging to Arthropoda (mostly Copepoda) and Chaetognatha were registered in the public database. The newly registered sequences were mainly composed of small non-calanoid copepods, such as species belonging to Oithona and Oncaea. Based on the metabarcoding analysis of field samples, a total of 18 out of 92 OTUs were identified at the species level based on newly registered sequences in the data obtained by the 18S marker. Based on the 28S marker, 42 out of 89 OTUs were classified at the species level based on taxonomically verified sequences. Thanks to the newly registered sequences, the number of OTUs associated with a single species based on the 18S marker increased by 16% in total and by 10% per sample. Based on the 28S marker, the number of OTUs associated with a single species increased by 39% in total and by 15% per sample. The improved accuracy of species identification was confirmed by comparing different sequences obtained from the same species. The newly registered sequences had higher similarity values (mean >0.003) than the pre-existing sequences based on both rRNA genes. These OTUs were identified at the species level based on sequences not only present in the Sea of Okhotsk but also in other areas. Discussion: The results of the registration of new taxonomically verified sequences and the subsequent comparison of databases based on metabarcoding data of natural zooplankton samples clearly showed an increase in accuracy in species identification. Continuous registration of sequence data covering various environmental conditions is necessary for further improvement of metabarcoding analysis of zooplankton for monitoring marine ecosystems.

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.

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).

Electrocatalytic hydrogenation of benzoic acids in a proton-exchange membrane reactor.

The highly efficient chemoselective electrocatalytic hydrogenation of benzoic acids (BAs) to cyclohexanecarboxylic acids (CCAs) was carried out in a proton-exchange membrane reactor under mild conditions without hydrogenation of the carboxyl group. Among the investigated catalysts, the PtRu alloy catalyst was found to be the most suitable for achieving high current efficiencies for production of CCAs. An electrochemical spillover mechanism on the PtRu alloy catalyst was also proposed.

Partial structural order of gel-forming material detected as multimodal subdiffusion by logarithmic measure.

Fibrous nanomaterials suspended in liquid form gel structures when the binding sites between the components reach sufficient number densities. Cellulose nanofibers (CNFs) are one of such nanomaterials, and transparent papers are fabricated by drying their aqueous dispersions. It is therefore important to characterize the wet state, but the specific fluorescent marker molecules are not available for arbitrary CNFs. We report an approach based on the single particle tracking of Brownian probe particles. We focus on the nonuniformity in the Brownian motion to detect the partial structural order between sol and gel, which is nontrivial to characterize. The simple logarithmic measure of diffusive behavior reveals the multimodal nature of Brownian motion depending on the CNF concentration. The subdiffusive behavior by the overall mean squared displacements alone does not tell whether it is caused by confinement in the local environment by CNFs, or binding to single CNFs possibly diffusing in the dispersion. However, the particle-size dependence clarifies that it is not caused by binding but the confinement effect. Furthermore, the logarithmic measure approach enables the detection of overlapping distributions through their heads rather than tails. The detection of partial structural order by rheological non-uniformity of the system with a simple approach will contribute to the further understanding of gel forming materials in general.

Large-scale distribution and composition of floating plastic debris in the transition region of the North Pacific.

Floating plastic debris was investigated in the transition region in the North Pacific between 141°E and 165°W to understand its transportation process from Asian coast to central subtropical Pacific. Distribution was influenced primarily by the current system and the generation process of the high concentration area differed between the western and eastern areas. West of 180°, debris largely accumulated around nearshore convergent area and was transported by eddies and quasi-stationary jet from south to the subarctic region. The average was 15% higher than that previously reported in 1989, suggesting an increase in plastic debris in 30 years. East of 180°, debris concentrated in the calm water downstream of the Kuroshio Extension Bifurcation with considerably high concentration (505,032 ± 991,989 pieces km-2), due to the accumulation of small transparent film caused by calm weather conditions, suggesting a further investigation on small plastic (<1 mm) in the subsurface depth in the subtropical North Pacific.

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.

Discriminating the reaction types of plant type III polyketide synthases.

MOTIVATION: Functional prediction of paralogs is challenging in bioinformatics because of rapid functional diversification after gene duplication events combined with parallel acquisitions of similar functions by different paralogs. Plant type III polyketide synthases (PKSs), producing various secondary metabolites, represent a paralogous family that has undergone gene duplication and functional alteration. Currently, there is no computational method available for the functional prediction of type III PKSs. RESULTS: We developed a plant type III PKS reaction predictor, pPAP, based on the recently proposed classification of type III PKSs. pPAP combines two kinds of similarity measures: one calculated by profile hidden Markov models (pHMMs) built from functionally and structurally important partial sequence regions, and the other based on mutual information between residue positions. pPAP targets PKSs acting on ring-type starter substrates, and classifies their functions into four reaction types. The pHMM approach discriminated two reaction types with high accuracy (97.5%, 39/40), but its accuracy decreased when discriminating three reaction types (87.8%, 43/49). When combined with a correlation-based approach, all 49 PKSs were correctly discriminated, and pPAP was still highly accurate (91.4%, 64/70) even after adding other reaction types. These results suggest pPAP, which is based on linear discriminant analyses of similarity measures, is effective for plant type III PKS function prediction. AVAILABILITY AND IMPLEMENTATION: pPAP is freely available at ftp://ftp.genome.jp/pub/tools/ppap/. CONTACT: goto@kuicr.kyoto-u.ac.jp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Type†III Polyketide Synthases: Functional Classification and Phylogenomics.

Polyketide synthases (PKSs) catalyze the sequential condensation of simple acetate units to produce a large class of natural products, including pharmacologically valuable compounds. PKSs are classified into three types on the basis of their domain structures; type III PKSs have the simplest domain structure, although their products have various structures and functions. The sequence-function relationship is fundamental for predicting enzyme functions, but it has not been well investigated in type III PKSs to date. Consequently, the current methods for predicting type III PKS functions are still immature in comparison with those that target type I/II PKSs. In this review we summarize the current functional and phylogenomic knowledge about type III PKSs and propose a new classification of their enzymatic reactions. We also discuss possible directions for the development of better computational tools for functional prediction of type III PKS homologues.

Linking Virus Genomes with Host Taxonomy.

Environmental genomics can describe all forms of organisms--cellular and viral--present in a community. The analysis of such eco-systems biology data relies heavily on reference databases, e.g., taxonomy or gene function databases. Reference databases of symbiosis sensu lato, although essential for the analysis of organism interaction networks, are lacking. By mining existing databases and literature, we here provide a comprehensive and manually curated database of taxonomic links between viruses and their cellular hosts.

Intrusion of Fukushima-derived radiocaesium into subsurface water due to formation of mode waters in the North Pacific.

The Fukushima Dai-ichi Nuclear Power Plant accident in March 2011 released radiocaesium ((137)Cs and (134)Cs) into the North Pacific Ocean. Meridional transects of the vertical distribution of radiocaesium in seawater were measured along 147 °E and 155 °E in October-November 2012, 19 months after the accident. These measurements revealed subsurface peaks in radiocaesium concentrations at locations corresponding to two mode waters, Subtropical Mode Water and Central Mode Water. Mode water is a layer of almost vertically homogeneous water found over a large geographical area. Here we show that repeated formation of mode water during the two winter seasons after the Fukushima accident and subsequent outcropping into surface water transported radiocaesium downward and southward to subtropical regions of the North Pacific. The total amount of Fukushima-derived (134)Cs within Subtropical Mode Water, decay-corrected to April 2011, was estimated to be 4.2 ± 1.1 PBq in October-November 2012. This amount of (134)Cs corresponds to 22-28% of the total amount of (134)Cs released to the Pacific Ocean.

Radioactive cesium dynamics derived from hydrographic observations in the Abukuma River Estuary, Japan.

Large quantities of radioactive materials were released into the air and the ocean as a result of the Fukushima Dai-ichi Nuclear Power Plant (FDNPP) accident, caused by the 2011 Tohoku earthquake and the subsequent major tsunami off the Pacific coast. There is much concern about radioactive contamination in both the watershed of the Abukuma River, which flows through Fukushima Prefecture, and its estuary, where it discharges into the sea in Miyagi Prefecture. We investigated radioactive cesium dynamics using mixing diagrams obtained from hydrographic observations of the Abukuma River Estuary. Particulate radioactive cesium dominates the cesium load in the river, whereas the dissolved form dominates in the sea. As the salinity increased from <0.1 to 0.1-2.3, the mixing diagram showed that dissolved radioactive cesium concentrations increased, because of desorption. Desorption from suspended particles explained 36% of dissolved radioactive cesium in estuarine water. However, the dissolved and particulate radioactive cesium concentrations in the sea decreased sharply because of dilution. It is thought that more than 80% of the discharged particulate radioactive cesium was deposited off the river mouth, where the radioactive cesium concentrations in sediment were relatively high (217-2440 Bq kg(-1)). Radioactive cesium that was discharged to the sea was transported southward by currents driven by the density distribution.

Southwest intrusion of 134Cs and 137Cs derived from the Fukushima Dai-ichi nuclear power plant accident in the Western North Pacific.

Enormous quantities of radionuclides were released into the ocean via both atmospheric deposition and direct release as a result of the Fukushima Dai-ichi Nuclear Power Plant (FNPP) accident. This study discusses the southward dispersion of FNPP-derived radioactive cesium (Cs) in subsurface waters. The southernmost point where we found the FNPP-derived (134)Cs (1.5-6.8 Bq m(-3)) was 18 °N, 135 °E, in September 2012. The potential density at the subsurface peaks of (134)Cs (100-500 m) and the increased water column inventories of (137)Cs between 0 and 500 m after the winter of 2011-2012 suggested that the main water mass containing FNPP-derived radioactive Cs was the North Pacific Subtropical Mode Water (NPSTMW), formed as a result of winter convection. We estimated the amount of (134)Cs in core waters of the western part of the NPSTMW to be 0.99 PBq (decay-corrected on 11 March 2011). This accounts for 9.0% of the (134)Cs released from the FNPP, with our estimation revealing that a considerable amount of FNPP-derived radioactive Cs has been transported to the subtropical region by the formation and circulation of the mode water.

Generalized reaction patterns for prediction of unknown enzymatic reactions.

Prediction of unknown enzymatic reactions is useful for understanding biological processes such as reactions to external substances like endocrine disrupters. To create an accurate prediction, we need to define a similarity measure in the reaction. We have developed the KEGG RPAIR database which is a collection of chemical structure transformation patterns, called RDM patterns, for substrate-product pairs of enzymatic reactions. In this study, we compared RDM patterns with EC numbers which are the well-known hierarchical classification scheme for enzymes. Additionally, we performed hierarchical clustering of RDM patterns using the information stating whether each sub-subclass of EC has a particular RDM patterns or not. To represent the variation of RDM patterns in a cluster, we generalized RDM patterns in the same cluster using the hierarchy of KEGG Atomtypes, which are the components of RDM patterns. Using this generalized pattern, we can predict which cluster includes a given RDM pattern even if the reaction of the pattern has not been assigned any EC numbers. Thus we will be able to define the similarity between enzymatic reactions by using this cluster information.

A line-scanning semi-confocal multi-photon fluorescence microscope with a simultaneous broadband spectral acquisition and its application to the study of the thylakoid membrane of a cyanobacterium Anabaena PCC7120.

We describe the construction and characterization of a laser-line-scanning microscope capable of detection of broad fluorescence spectra with a resolution of 1 nm. A near-infrared femtosecond pulse train at 800 nm was illuminated on a line (one lateral axis, denoted as X axis) in a specimen by a resonant scanning mirror oscillating at 7.9 kHz, and total multi-photon-induced fluorescence from the linear region was focused on the slit of an imaging polychromator. An electron-multiplying CCD camera was used to resolve fluorescence of different colours at different horizontal pixels and fluorescence of different spatial positions in a specimen at different vertical pixels. Scanning on the other two axes (Y and Z) was achieved by a closed-loop controlled sample scanning stage and a piezo-driven objective actuator. The full widths at half maximum of the point-spread function of the system were estimated to be 0.39-0.40, 0.33 and 0.56-0.59 mum for the X (lateral axis along the line-scan), Y (the other lateral axis) and Z axes (the axial direction), respectively, at fluorescence wavelengths between 644 and 690 nm. A biological application of this microscope was demonstrated in a study of the sub-cellular fluorescence spectra of thylakoid membranes in a cyanobacterium, Anabaena PCC7120. It was found that the fluorescence intensity ratio between chlorophyll molecules mainly of photosystem II and phycobilin molecules of phycobilisome (chlorophyll/phycobilin), in the thylakoid membranes, became lower as one probed deeper inside the cells. This was attributable not to position dependence of re-absorption or scattering effects, but to an intrinsic change in the local physiological state of the thylakoid membrane, with the help of a transmission spectral measurement of sub-cellular domains. The efficiency of the new line-scanning spectromicroscope was estimated in comparison with our own point-by-point scanning spectromicroscope. Under typical conditions of observing cyanobacterial cells, the total exposure time became shorter by about 50 times for a constant excitation density. The improvement factor was proportional to the length of the line-scanned region, as expected.