Development of Drug-Induced Gene Expression Ranking Analysis (DIGERA) and Its Application to Virtual Screening for Poly (ADP-Ribose) Polymerase 1 Inhibitor.

Understanding drug-target interactions is crucial for identifying novel lead compounds, enhancing efficacy, and reducing toxicity. Phenotype-based approaches, like analyzing drug-induced gene expression changes, have shown effectiveness in drug discovery and precision medicine. However, experimentally determining gene expression for all relevant chemicals is impractical, limiting large-scale gene expression-based screening.

Interface-aware molecular generative framework for protein-protein interaction modulators.

Protein-protein interactions (PPIs) play a crucial role in numerous biochemical and biological processes. Although several structure-based molecular generative models have been developed, PPI interfaces and compounds targeting PPIs exhibit distinct physicochemical properties compared to traditional binding pockets and small-molecule drugs. As a result, generating compounds that effectively target PPIs, particularly by considering PPI complexes or interface hotspot residues, remains a significant challenge.

Pan-Transcriptional Enhanced Associated Domain Palmitoylation Pocket Covalent Inhibitor.

In the Hippo signaling pathway, the palmitoylated transcriptional enhanced associated domain (TEAD) protein interacts with the coactivator Yes-associated protein/PDZ-binding motif, leading to transcriptional upregulation of oncogenes such as Ctgf and Cyr61. Consequently, targeting the palmitoylation sites of TEAD has emerged as a promising strategy for treating TEAD-dependent cancers. Compound was identified using a structure-based drug design approach, leveraging the molecular insights gained from the known TEAD palmitoylation site inhibitor, K-975.

Identification of novel anti-obesity saponins from the ovary of sea cucumber ().

The potential anti-obesity effects of sea cucumber extract have been reported. However, the individual saponins responsible for these effects are yet to be isolated and characterized. This study aimed to identify the most effective sea cucumber body part for inhibiting lipid accumulation in adipocytes and to elucidate the compounds responsible for this effect using nuclear magnetic resonance (NMR) techniques.

Inhibition of IRP2-dependent reprogramming of iron metabolism suppresses tumor growth in colorectal cancer.

Background: Dysregulation of iron metabolism is implicated in malignant transformation, cancer progression, and therapeutic resistance. Here, we demonstrate that iron regulatory protein 2 (IRP2) preferentially regulates iron metabolism and promotes tumor growth in colorectal cancer (CRC).

Methods: IRP2 knockdown and knockout cells were generated using RNA interference and clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 methodologies, respectively.

The efficacy of the food-grade antimicrobial xanthorrhizol against is associated with McsL channel expression.

Background: The emergence and spread of multidrug-resistant strains demonstrates the urgent need for new antimicrobials. Xanthorrhizol, a plant-derived sesquiterpenoid compound, has a rapid killing effect on methicillin-susceptible strains and methicillin-resistant strains of achieving the complete killing of staphylococcal cells within 2 min using 64 μg/mL xanthorrhizol. However, the mechanism of its action is not yet fully understood.

Exploring the Conformational Ensembles of Protein-Protein Complex with Transformer-Based Generative Model.

Protein-protein interactions are the basis of many protein functions, and understanding the contact and conformational changes of protein-protein interactions is crucial for linking the protein structure to biological function. Although difficult to detect experimentally, molecular dynamics (MD) simulations are widely used to study the conformational ensembles and dynamics of protein-protein complexes, but there are significant limitations in sampling efficiency and computational costs. In this study, a generative neural network was trained on protein-protein complex conformations obtained from molecular simulations to directly generate novel conformations with physical realism.

Leveraging the Fragment Molecular Orbital and MM-GBSA Methods in Virtual Screening for the Discovery of Novel Non-Covalent Inhibitors Targeting the TEAD Lipid Binding Pocket.

The Hippo pathway controls organ size and homeostasis and is linked to numerous diseases, including cancer. The transcriptional enhanced associate domain (TEAD) family of transcription factors acts as a receptor for downstream effectors, namely yes-associated protein (YAP) and transcriptional co-activator with PDZ-binding motif (TAZ), which binds to various transcription factors and is essential for stimulated gene transcription. YAP/TAZ-TEAD facilitates the upregulation of multiple genes involved in evolutionary cell proliferation and survival.

Fragment molecular orbital-based variational quantum eigensolver for quantum chemistry in the age of quantum computing.

Quantum computers offer significant potential for complex system analysis, yet their application in large systems is hindered by limitations such as qubit availability and quantum hardware noise. While the variational quantum eigensolver (VQE) was proposed to address these issues, its scalability remains limited. Many efforts, including new ansätze and Hamiltonian modifications, have been made to overcome these challenges.

PhyloSophos: a high-throughput scientific name mapping algorithm augmented with explicit consideration of taxonomic science, and its application on natural product (NP) occurrence database processing.

Background: The standardization of biological data using unique identifiers is vital for seamless data integration, comprehensive interpretation, and reproducibility of research findings, contributing to advancements in bioinformatics and systems biology. Despite being widely accepted as a universal identifier, scientific names for biological species have inherent limitations, including lack of stability, uniqueness, and convertibility, hindering their effective use as identifiers in databases, particularly in natural product (NP) occurrence databases, posing a substantial obstacle to utilizing this valuable data for large-scale research applications.

Result: To address these challenges and facilitate high-throughput analysis of biological data involving scientific names, we developed PhyloSophos, a Python package that considers the properties of scientific names and taxonomic systems to accurately map name inputs to entries within a chosen reference database.

Leveraging the Fragment Molecular Orbital Method to Explore the PLK1 Kinase Binding Site and Polo-Box Domain for Potent Small-Molecule Drug Design.

Polo-like kinase 1 (PLK1) plays a pivotal role in cell division regulation and emerges as a promising therapeutic target for cancer treatment. Consequently, the development of small-molecule inhibitors targeting PLK1 has become a focal point in contemporary research. The adenosine triphosphate (ATP)-binding site and the polo-box domain in PLK1 present crucial interaction sites for these inhibitors, aiming to disrupt the protein's function.

Targeting FLT3-TAZ signaling to suppress drug resistance in blast phase chronic myeloid leukemia.

Background: Although the development of BCR::ABL1 tyrosine kinase inhibitors (TKIs) rendered chronic myeloid leukemia (CML) a manageable condition, acquisition of drug resistance during blast phase (BP) progression remains a critical challenge. Here, we reposition FLT3, one of the most frequently mutated drivers of acute myeloid leukemia (AML), as a prognostic marker and therapeutic target of BP-CML.

Methods: We generated FLT3 expressing BCR::ABL1 TKI-resistant CML cells and enrolled phase-specific CML patient cohort to obtain unpaired and paired serial specimens and verify the role of FLT3 signaling in BP-CML patients.

Intracellular Loop in the Brain Isoforms of Anoctamin 2 Channels Regulates Calcium-dependent Activation.

Anoctamin 2 (ANO2 or TMEM16B), a calcium-activated chloride channel (CaCC), performs diverse roles in neurons throughout the central nervous system. In hippocampal neurons, ANO2 narrows action potential width and reduces postsynaptic depolarization with high sensitivity to Ca at relatively fast kinetics. In other brain regions, including the thalamus, ANO2 mediates activity-dependent spike frequency adaptations with low sensitivity to Ca at relatively slow kinetics.

Transformer-Based Molecular Generative Model for Antiviral Drug Design.

Since the Simplified Molecular Input Line Entry System (SMILES) is oriented to the atomic-level representation of molecules and is not friendly in terms of human readability and editable, however, IUPAC is the closest to natural language and is very friendly in terms of human-oriented readability and performing molecular editing, we can manipulate IUPAC to generate corresponding new molecules and produce programming-friendly molecular forms of SMILES. In addition, antiviral drug design, especially analogue-based drug design, is also more appropriate to edit and design directly from the functional group level of IUPAC than from the atomic level of SMILES, since designing analogues involves altering the R group only, which is closer to the knowledge-based molecular design of a chemist. Herein, we present a novel data-driven self-supervised pretraining generative model called "TransAntivirus" to make select-and-replace edits and convert organic molecules into the desired properties for design of antiviral candidate analogues.

Prediction of polyreactive and nonspecific single-chain fragment variables through structural biochemical features and protein language-based descriptors.

Background: Monoclonal antibodies (mAbs) have been used as therapeutic agents, which must overcome many developability issues after the discovery from in vitro display libraries. Especially, polyreactive mAbs can strongly bind to a specific target and weakly bind to off-target proteins, which leads to poor antibody pharmacokinetics in clinical development. Although early assessment of polyreactive mAbs is important in the early discovery stage, experimental assessments are usually time-consuming and expensive.

Development of an innovative data-driven system to generate descriptive prediction equation of dielectric constant on small sample sets.

Dielectric constant (DC, ε) is a fundamental parameter in material sciences to measure polarizability of the system. In industrial processes, its value is an imperative indicator, which demonstrates the dielectric property of material and compiles information including separation information, chemical equilibrium, chemical reactivity analysis, and solubility modeling. Since, the available ε-prediction models are fairly primitive and frequently suffer from serious failures especially when deals with strong polar compounds.

De novo molecular design with deep molecular generative models for PPI inhibitors.

We construct a protein-protein interaction (PPI) targeted drug-likeness dataset and propose a deep molecular generative framework to generate novel drug-likeness molecules from the features of the seed compounds. This framework gains inspiration from published molecular generative models, uses the key features associated with PPI inhibitors as input and develops deep molecular generative models for de novo molecular design of PPI inhibitors. For the first time, quantitative estimation index for compounds targeting PPI was applied to the evaluation of the molecular generation model for de novo design of PPI-targeted compounds.

Quantum computational study of chloride attack on chloromethane for chemical accuracy and quantum noise effects with UCCSD and k-UpCCGSD ansatzes.

Quantum computing is expected to play an important role in solving the problem of huge computational costs in various applications by utilizing the collective properties of quantum states, including superposition, interference, and entanglement, to perform computations. Quantum mechanical (QM) methods are candidates for various applications and can provide accurate absolute energy calculations in structure-based methods. QM methods are powerful tools for describing reaction pathways and their potential energy surfaces (PES).

Identification of Novel Natural Product Inhibitors against Matrix Metalloproteinase 9 Using Quantum Mechanical Fragment Molecular Orbital-Based Virtual Screening Methods.

Matrix metalloproteinases (MMPs) are calcium-dependent zinc-containing endopeptidases involved in multiple cellular processes. Among the MMP isoforms, MMP-9 regulates cancer invasion, rheumatoid arthritis, and osteoarthritis by degrading extracellular matrix proteins present in the tumor microenvironment and cartilage and promoting angiogenesis. Here, we identified two potent natural product inhibitors of the non-catalytic hemopexin domain of MMP-9 using a novel quantum mechanical fragment molecular orbital (FMO)-based virtual screening workflow.

Evaluation of protein descriptors in computer-aided rational protein engineering tasks and its application in property prediction in SARS-CoV-2 spike glycoprotein.

The importance of protein engineering in the research and development of biopharmaceuticals and biomaterials has increased. Machine learning in computer-aided protein engineering can markedly reduce the experimental effort in identifying optimal sequences that satisfy the desired properties from a large number of possible protein sequences. To develop general protein descriptors for computer-aided protein engineering tasks, we devised new protein descriptors, one sequence-based descriptor (PCgrades), and three structure-based descriptors (PCspairs, 3D-SPIEs_5.

Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models.

Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory versatility and accuracy in fields such as drug discovery because they are based on traditional machine learning and interpretive expert features. The development of Big Data and deep learning technologies significantly improve the processing of unstructured data and unleash the great potential of QSAR. Here we discuss the integration of wet experiments (which provide experimental data and reliable verification), molecular dynamics simulation (which provides mechanistic interpretation at the atomic/molecular levels), and machine learning (including deep learning) techniques to improve QSAR models.

Hot Spot Analysis of YAP-TEAD Protein-Protein Interaction Using the Fragment Molecular Orbital Method and Its Application for Inhibitor Discovery.

The Hippo pathway is an important signaling pathway modulating growth control and cancer cell proliferation. Dysregulation of the Hippo pathway is a common feature of several types of cancer cells. The modulation of the interaction between yes-associated protein (YAP) and transcriptional enhancer associated domain (TEAD) in the Hippo pathway is considered an attractive target for cancer therapeutic development, although the inhibition of PPI is a challenging task.

Development of an Infinite Dilution Activity Coefficient Prediction Model for Organic Solutes in Ionic Liquids with Modified Partial Equalization Orbital Electronegativity Method Derived Descriptors.

The objective of this study was to develop a robust prediction model for the infinite dilution activity coefficients ( ) of organic molecules in diverse ionic liquid (IL) solvents. Electrostatic, hydrogen bond, polarizability, molecular structure, and temperature terms were used in model development. A feed-forward model based on artificial neural networks was developed with 34,754 experimental activity coefficients, a combination of 195 IL solvents (88 cations and 38 anions), and 147 organic solutes at a temperature range of 298 to 408 K.

Development of Natural Compound Molecular Fingerprint (NC-MFP) with the Dictionary of Natural Products (DNP) for natural product-based drug development.

Computer-aided research on the relationship between molecular structures of natural compounds (NC) and their biological activities have been carried out extensively because the molecular structures of new drug candidates are usually analogous to or derived from the molecular structures of NC. In order to express the relationship physically realistically using a computer, it is essential to have a molecular descriptor set that can adequately represent the characteristics of the molecular structures belonging to the NC's chemical space. Although several topological descriptors have been developed to describe the physical, chemical, and biological properties of organic molecules, especially synthetic compounds, and have been widely used for drug discovery researches, these descriptors have limitations in expressing NC-specific molecular structures.