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. The results indicated that TransAntivirus is significantly superior to the control models in terms of novelty, validity, uniqueness, and diversity. TransAntivirus showed excellent performance in the design and optimization of nucleoside and non-nucleoside analogues by chemical space analysis and property prediction analysis. Furthermore, to validate the applicability of TransAntivirus in the design of antiviral drugs, we conducted two case studies on the design of nucleoside analogues and non-nucleoside analogues and screened four candidate lead compounds against anticoronavirus disease (COVID-19). Finally, we recommend this framework for accelerating antiviral drug discovery.

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. We illustrate the importance of assessing multiple taxonomic databases and considering taxonomic syntax-based pre-processing using NP occurrence databases as an example, with the ultimate goal of integrating heterogeneous information into a single, unified dataset. CONCLUSIONS: We anticipate PhyloSophos to significantly aid in the systematic processing of poorly digitized and curated biological data, such as biodiversity information and ethnopharmacological resources, enabling full-scale bioinformatics analysis using these valuable data resources.

KinScan: AI-based rapid profiling of activity across the kinome.

Kinases play a vital role in regulating essential cellular processes, including cell cycle progression, growth, apoptosis, and metabolism, by catalyzing the transfer of phosphate groups from adenosing triphosphate to substrates. Their dysregulation has been closely associated with numerous diseases, including cancer development, making them attractive targets for drug discovery. However, accurately predicting the binding affinity between chemical compounds and kinase targets remains challenging due to the highly conserved structural similarities across the kinome. To address this limitation, we present KinScan, a novel computational approach that leverages large-scale bioactivity data and integrates the Multi-Scale Context Aware Transformer framework to construct a virtual profiling model encompassing 391 protein kinases. The developed model demonstrates exceptional prediction capability, distinguishing between kinases by utilizing structurally aligned kinase binding site features derived from multiple sequence alignment for fast and accurate predictions. Through extensive validation and benchmarking, KinScan demonstrated its robust predictive power and generalizability for large-scale kinome-wide profiling and selectivity, uncovering associations with specific diseases and providing valuable insights into kinase activity profiles of compounds. Furthermore, we deployed a web platform for end-to-end profiling and selectivity analysis, accessible at https://kinscan.drugonix.com/softwares/kinscan.

AiKPro: deep learning model for kinome-wide bioactivity profiling using structure-based sequence alignments and molecular 3D conformer ensemble descriptors.

The discovery of selective and potent kinase inhibitors is crucial for the treatment of various diseases, but the process is challenging due to the high structural similarity among kinases. Efficient kinome-wide bioactivity profiling is essential for understanding kinase function and identifying selective inhibitors. In this study, we propose AiKPro, a deep learning model that combines structure-validated multiple sequence alignments and molecular 3D conformer ensemble descriptors to predict kinase-ligand binding affinities. Our deep learning model uses an attention-based mechanism to capture complex patterns in the interactions between the kinase and the ligand. To assess the performance of AiKPro, we evaluated the impact of descriptors, the predictability for untrained kinases and compounds, and kinase activity profiling based on odd ratios. Our model, AiKPro, shows good Pearson's correlation coefficients of 0.88 and 0.87 for the test set and for the untrained sets of compounds, respectively, which also shows the robustness of the model. AiKPro shows good kinase-activity profiles across the kinome, potentially facilitating the discovery of novel interactions and selective inhibitors. Our approach holds potential implications for the discovery of novel, selective kinase inhibitors and guiding rational drug design.

Discovery of New Quinolone-Based Diarylamides as Potent B-RAFV600E/C-RAF Kinase Inhibitors Endowed with Promising In Vitro Anticancer Activity.

The emergence of cancer resistance to targeted therapy represents a significant challenge in cancer treatment. Therefore, identifying new anticancer candidates, particularly those addressing oncogenic mutants, is an urgent medical demand. A campaign of structural modifications has been conducted to further optimize our previously reported 2-anilinoquinoline-diarylamides conjugate VII as a B-RAFV600E/C-RAF inhibitor. Considering the incorporation of a methylene bridge between the terminal phenyl and cyclic diamine, focused quinoline-based arylamides have been tailored, synthesized, and biologically evaluated. Among them, the 5/6-hydroxyquinolines 17b and 18a stood out as the most potent members, with IC50 values of 0.128 µM, 0.114 µM against B-RAFV600E, and 0.0653 µM, 0.0676 µM against C-RAF. Most importantly, 17b elicited remarkable inhibitory potency against the clinically resistant B-RAFV600K mutant with an IC50 value of 0.0616 µM. The putative binding mode of 17b and 18a were studied by molecular docking and molecular dynamics (MD). Moreover, the antiproliferative activity of all target compounds has been examined over a panel of NCI-60 human cancer cell lines. In agreement with cell-free assays, the designed compounds exerted superior anticancer impact over the lead quinoline VII against all cell lines at a 10 µM dose. Notably, both 17b and 18b showed highly potent antiproliferative activity against melanoma cell lines with growth percent under -90% (SK-MEL-29, SK-MEL-5, and UACC-62) at a single dose, while 17b maintained potency with GI50 values of 1.60-1.89 µM against melanoma cell lines. Taken together, 17b, a promising B-RAFV600E/V600K and C-RAF kinase inhibitor, may serve as a valuable candidate in the arsenal of anticancer chemotherapeutics.

A Two-Component-System-Governed Regulon That Includes a ß-Lactamase Gene is Responsive to Cell Envelope Disturbance.

ß-Lactamase production facilitates bacterial survival in nature and affects many infection therapies. However, much of its regulation remains unexplored. We used a genetics-based approach to identify a two-component system (TCS) present in a strain of Burkholderia thailandensis essential for the regulated expression of a class A ß-lactamase gene, penL, by sensing subtle envelope disturbance caused by ß-lactams, polymyxin B, or other chemical agents. The genes encoding stress responses and resistance to various antibiotics were coregulated, as were the catabolic genes that enabled the B. thailandensis strain to grow on penicillin G or phenylacetate, a degradation product of penicillin G. This regulon has likely evolved to facilitate bacterial survival in the soil microbiome that contains a multitude of antibiotic producers. Practically, this regulatory system makes this TCS, which we named BesRS, an excellent drug target for the purpose of increasing antibiotic efficacy in combination therapies for Burkholderia infections. IMPORTANCE ß-lactam antibiotics are the most frequently used drugs to treat infectious diseases. Although the production of ß-lactamases by bacteria is the main cause of treatments being compromised, much of the gene regulation mechanism governing the levels of these enzymes has not been fully explored. In this study, we report a novel ß-lactamase gene regulation mechanism that is governed by a two-component system responding to disturbances in the cell envelope. We showed gene regulation is a part of a regulon that includes genes involved in stress responses, resistance to various antibiotics, and a catabolic pathway for ß-lactams. This regulon may have been evolved to facilitate bacterial survival in the soil niches, which are highly competitive environments because of the presence of various antibiotic-producing microbes. The discovery of the ß-lactamase gene regulation mechanism opens new avenues for developing therapeutic strategies in the fight against antibiotic resistance.

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. Our results estimated that the generated molecules had better PPI-targeted drug-likeness and drug-likeness. Additionally, our model also exhibits comparable performance to other several state-of-the-art molecule generation models. The generated molecules share chemical space with iPPI-DB inhibitors as demonstrated by chemical space analysis. The peptide characterization-oriented design of PPI inhibitors and the ligand-based design of PPI inhibitors are explored. Finally, we recommend that this framework will be an important step forward for the de novo design of PPI-targeted therapeutics.

Mutations in ArgS Arginine-tRNA Synthetase Confer Additional Antibiotic Tolerance Protection to Extended-Spectrum-ß-Lactamase-Producing Burkholderia thailandensis.

Highly conserved PenI-type class A ß-lactamase in pathogenic members of Burkholderia species can evolve to extended-spectrum ß-lactamase (ESBL), which exhibits hydrolytic activity toward third-generation cephalosporins, while losing its activity toward the original penicillin substrates. We describe three single-amino-acid-substitution mutations in the ArgS arginine-tRNA synthetase that confer extra antibiotic tolerance protection to ESBL-producing Burkholderia thailandensis This pathway can be exploited to evade antibiotic tolerance induction in developing therapeutic measures against Burkholderia species, targeting their essential aminoacyl-tRNA synthetases.

PMFF: Development of a Physics-Based Molecular Force Field for Protein Simulation and Ligand Docking.

The physics-based molecular force field (PMFF) was developed by integrating a set of potential energy functions in which each term in an intermolecular potential energy function is derived based on experimental values, such as the dipole moments, lattice energy, proton transfer energy, and X-ray crystal structures. The term "physics-based" is used to emphasize the idea that the experimental observables that are considered to be the most relevant to each term are used for the parameterization rather than parameterizing all observables together against the target value. PMFF uses MM3 intramolecular potential energy terms to describe intramolecular interactions and includes an implicit solvation model specifically developed for the PMFF. We evaluated the PMFF in three ways. We concluded that the PMFF provides reliable information based on the structure in a biological system and interprets the biological phenomena accurately by providing more accurate evidence of the biological phenomena.

Mutations in MetG (methionyl-tRNA synthetase) and TrmD [tRNA (guanine-N1)-methyltransferase] conferring meropenem tolerance in Burkholderia thailandensis.

Objectives: Although meropenem is widely used to treat Burkholderia infections, the response of Burkholderia pathogens to this antibiotic is largely unexplored. Methods: Burkholderia thailandensis, a model for Burkholderia spp., particularly Burkholderia mallei and Burkholderia pseudomallei, was challenged with a lethal level of meropenem and survivors were isolated. The genomes of two of the isolates were analysed to identify mutated genes and these genes were then specifically examined in more isolates to profile mutation diversity. Mutants were characterized to investigate the biological basis underlying survival against meropenem. Results: One of two genes associated with tRNA metabolism [metG or trmD, encoding methionyl-tRNA synthetase or tRNA (guanine-N1)-methyltransferase, respectively] was found to be mutated in the two survivors. A single nucleotide substitution and a frameshift mutation were found in metG and trmD, respectively. Five different substitution mutations affecting methionine- or tRNA-binding sites were found in metG during further screening. The mutants exhibited slowed growth and increased tolerance not only to meropenem but also various other antibiotics. This tolerance required intact RelA, a key stringent response. Conclusions: Specific mutations affecting the tRNA pool, particularly those in metG, play a pivotal role in the B. thailandensis response to meropenem challenge. This mechanism of antibiotic tolerance is important because it can reduce the effectiveness of meropenem and thereby facilitate chronic infection by Burkholderia pathogens. In addition, specific mutations found in MetG will prove useful in the effort to develop new drugs to completely inhibit this essential enzyme, while preventing stringent-response-mediated antibiotic tolerance in pathogens.

Luminescent properties of 4-aminobenzo-15-crown-5 after preferential binding of ferric ions in aqueous solutions.

We report a combined approach that introduces the use of 4-aminobenzo-15-crown-5 (4AB15C5) for the detection of ferric(III) ions by colorimetric, ultraviolet (UV)-visible light absorption, fluorescence, and live-cell imaging techniques along with density functional theory (DFT) calculations. We have found that 4AB15C5 is sensitive and selective for binding ferric(III) ions in aqueous solutions. DFT calculations using the polarizable continuum model have been used to explain the strong binding of the ferric ion by 4AB15C5 in aqueous solutions. The detection limit in the fluorescence quenching measurements was found to be as low as 50 µM for the ferric ion with a determined Stern-Volmer constant of 1.52 × 104  M-1 . Fluorescence intensity did not change for other ions tested, Fe2+ , Co2+ , Mn2+ , Mg2+ , Zn2+ , Ca2+ , NH4+ , Na+ , and K+ ions. Live-cell fluorescence imaging was also used to check the intracellular variations in ferric ion levels. Our spectroscopic data indicated that 4AB15C5 can bind ferric ions selectively in aqueous solutions.

Computational classification models for predicting the interaction of compounds with hepatic organic ion importers.

Hepatic transporters, a major determinant of pharmacokinetics, have been used to profile drug properties like efficacy. Among hepatic transporters, importers alter the concentration of the drug by facilitating the transport of a drug into a cell. Despite vast pharmacokinetic studies, the interacting mechanisms of the importers with its substrates or inhibitors are not well understood. Hence, we developed compound binary classification models of whether a compound is binder or nonbinder to a hepatic transporter with experimental data of 284 compounds for four representative hepatic importers, OATP1B1, OATP1B3, OAT2, and OCT1. Support Vector Machine (SVM) along with Genetic Algorithm (GA) was used to construct the classification models of binder versus nonbinder for each target importer. To construct the models, we prepared two data sets, a training data set from Fujitsu database (284 compounds) and an external validation data set from ChEMBL database (1738 compounds). Since an experimental classification criterion between binder and nonbinder has some ambiguity, there is an intrinsic limitation to expect high predictability of the binary classification models developed with the experimental data. The predictability of the classification models calculated with external validation sets were obtained as 77.72%, 84.31%, 84.21%, and 76.38 for OATP1B1, OATP1B3, OAT2, and OCT1, respectively.

Energetic stabilities of thiolated pyrimidines on gold nanoparticles investigated by Raman spectroscopy and density functional theory calculations.

The adsorption structures of 2-thiocytosine (2TC) on gold surfaces were examined by means of vibrational Raman spectroscopy and quantum mechanical density functional theory calculations. The 1H-thione-amino form was calculated to be most stable among the six examined tautomers. The three plausible binding geometries of sulfur, pyrimidine nitrogen, and amino group binding modes were calculated to estimate the binding energies of the 1H-thione-amino form with six gold cluster atoms. Thiouracils including 2-thiouracil (2TU), 4-thiouracil (4TU), and 6-methyl-2-thiouracil (6M2TU) were also studied to compare their relative binding energies on gold atoms. The intracellular localization of a DNA base analog of 2TC on gold nanoparticles (AuNPs) in HeLa cells was identified by means of surface-enhanced Raman scattering. AuNPs were modified with 2TC by self-assembly. Our dark-field microscopy and z-depth-dependent confocal Raman spectroscopy indicated that 2TC-assembled AuNPs could be found inside cancer cells. On the other hand, we did not observe noticeably strong Raman peaks in the cases of thiouracils including 2TU, 4TU, and 6M2TU. This may be due to the additional amino group of 2TC, which can lead to a stronger binding of adsorbates on AuNPs.

Colloidal gold nanoparticle conjugates of gefitinib.

Gefitinib (GF) is a US Food and Drug Administration-approved epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor for treating the lung cancers. We fabricated colloidal gold nanoparticle (AuNP) conjugates of the GF anticancer drug by self-assembly to test their potency against A549, NCI-H460, and NCI-H1975 lung cancer cells. GF adsorption on AuNP surfaces was examined by UV-vis absorption spectra and surface-enhanced Raman scattering. Density functional theory calculations were performed to estimate the energetic stabilities of the drug-AuNP composites. The N1 nitrogen atom of the quinazoline ring of GF was calculated to be more stable than the N3 in binding Au cluster atoms. The internalizations of GF-coated AuNPs were examined by transmission electron and dark-field microscopy. A cell viability test of AuNP-GF conjugates with the EGFR antibody exhibited much higher reductions than free GF for A549, NCI-H460, and NCI-H1975 lung cancer cells after treatment for 48.

Deletion mutations conferring substrate spectrum extension in the class A ß-lactamase.

We describe four new deletion mutations in a class A ß-lactamase PenA in Burkholderia thailandensis, each conferring an extended substrate spectrum. Single-amino-acid deletions T171del, I173del, and P174del and a two-amino-acid deletion, R165_T167delinsP, occurred in the omega loop, increasing the flexibility of the binding cavity. This rare collection of mutations has significance, allowing exploration of the diverse evolutionary trajectories of ß-lactamases and as potential future mutations conferring high-level ceftazidime resistance on isolates from clinical settings, compared with amino acid substitution mutations.

Adsorption and desorption of tyrosine kinase inhibitor erlotinib on gold nanoparticles.

We investigated interfacial behaviors of erlotinib (EL) on gold nanoparticles (AuNPs) by means of Raman spectroscopy. The adsorption reactions and structures of EL on AuNP surfaces were examined by UV-Vis absorption spectroscopy and surface-enhanced Raman scattering (SERS). Density functional theory calculations were performed to estimate the energetic stabilities of the drug-AuNP composites. Among the binding units in EL, the acetylenic C≡C group was calculated to be the most strongly binding on the AuNP cluster atoms, consistent with the SERS spectra. The concentration-dependent SERS spectra indicated that ∼10(-5) M of EL exhibited the highest SERS signals. The attached EL appeared to desorb more efficiently with 2mM glutathione than with cell culture media. The lack of a strong SERS signal of EL in the dark-field microscopy images of AuNP-EL complexes suggested almost complete desorption of EL inside cells.

A recurrent inactivating mutation in RHOA GTPase in angioimmunoblastic T cell lymphoma.

The molecular mechanisms underlying angioimmunoblastic T cell lymphoma (AITL), a common type of mature T cell lymphoma of poor prognosis, are largely unknown. Here we report a frequent somatic mutation in RHOA (encoding p.Gly17Val) using exome and transcriptome sequencing of samples from individuals with AITL. Further examination of the RHOA mutation encoding p.Gly17Val in 239 lymphoma samples showed that the mutation was specific to T cell lymphoma and was absent from B cell lymphoma. We demonstrate that the RHOA mutation encoding p.Gly17Val, which was found in 53.3% (24 of 45) of the AITL cases examined, is oncogenic in nature using multiple molecular assays. Molecular modeling and docking simulations provided a structural basis for the loss of GTPase activity in the RHOA Gly17Val mutant. Our experimental data and modeling results suggest that the RHOA mutation encoding p.Gly17Val is a driver mutation in AITL. On the basis of these data and through integrated pathway analysis, we build a comprehensive signaling network for AITL oncogenesis.

Detection of the mycotoxin citrinin using silver substrates and Raman spectroscopy.

We detected a trace amount of the mycotoxin citrinin using surface-enhanced Raman scattering (SERS) on silver nanoparticle (Ag NP) surfaces. The SERS substrate on hydrophobic Teflon films was also introduced to observe the citrinin peaks. A broad band at ∼1382cm(-1), which was ascribed to the symmetric carboxylate stretching mode, was observed in addition to an antisymmetric carboxylate stretching mode at ∼1568cm(-1) in the Raman spectra. The spectral feature indicated that citrinin would adsorb on Ag NPs via its carboxylate form. Based on density functional theory (DFT) calculations, vibrational mode analysis was performed to compare the Raman spectra of citrinin. DFT calculations also predicted that a bidentate bridge configuration through O15 and O16 atoms in citrinin would be the most stable on three Ag atoms. After treating with Ag NPs, observation of citrinin peaks was attempted in fungal cells of Penicillium citrinum. This work may provide useful insights into the direct observation of the hazardous citrinin mycotoxin using SERS by understanding its adsorption behaviors on Ag surfaces.

A generalized G-SFED continuum solvation free energy calculation model.

An empirical continuum solvation model, solvation free energy density (SFED), has been developed to calculate solvation free energies of a molecule in the most frequently used solvents. A generalized version of the SFED model, generalized-SFED (G-SFED), is proposed here to calculate molecular solvation free energies in virtually any solvent. G-SFED provides an accurate and fast generalized framework without a complicated description of a solution. In the model, the solvation free energy of a solute is represented as a linear combination of empirical functions of the solute properties representing the effects of solute on various solute-solvent interactions, and the complementary solvent effects on these interactions were reflected in the linear expansion coefficients with a few solvent properties. G-SFED works well for a wide range of sizes and polarities of solute molecules in various solvents as shown by a set of 5,753 solvation free energies of diverse combinations of 103 solvents and 890 solutes. Octanol-water partition coefficients of small organic compounds and peptides were calculated with G-SFED with accuracy within 0.4 log unit for each group. The G-SFED computation time depends linearly on the number of nonhydrogen atoms (n) in a molecule, O(n).

Twelve positions in a ß-lactamase that can expand its substrate spectrum with a single amino acid substitution.

The continuous evolution of ß-lactamases resulting in bacterial resistance to ß-lactam antibiotics is a major concern in public health, and yet the underlying molecular basis or the pattern of such evolution is largely unknown. We investigated the mechanics of the substrate fspectrum expansion of the class A ß-lactamase using PenA of Burkholderia thailandensis as a model. By analyzing 516 mutated enzymes that acquired the ceftazidime-hydrolyzing activity, we found twelve positions with single amino acid substitutions (altogether twenty-nine different substitutions), co-localized at the active-site pocket area. The ceftazidime MIC (minimum inhibitory concentration) levels and the relative frequency in the occurrence of substitutions did not correlate well with each other, and the latter appeared be largely influenced by the intrinsic mutational biases present in bacteria. Simulation studies suggested that all substitutions caused a congruent effect, expanding the space in a conserved structure called the omega loop, which in turn increased flexibility at the active site. A second phase of selection, in which the mutants were placed under increased antibiotic pressure, did not result in a second mutation in the coding region, but a mutation that increased gene expression arose in the promoter. This result suggests that the twelve amino acid positions and their specific substitutions in PenA may represent a comprehensive repertoire of the enzyme's adaptability to a new substrate. These mapped substitutions represent a comprehensive set of general mechanical paths to substrate spectrum expansion in class A ß-lactamases that all share a functional evolutionary mechanism using common conserved residues.