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.

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.

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.

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.

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.

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.

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).

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.

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.

Substrate spectrum extension of PenA in Burkholderia thailandensis with a single amino acid deletion, Glu168del.

We describe a deletion mutation in a class A ß-lactamase, PenA, of Burkholderia thailandensis that extended the substrate spectrum of the enzyme to include ceftazidime. Glu168del was located in a functional domain called the omega loop causing expansion of the space in the loop, which in turn increased flexibility at the active site. This deletion mutation represents a rare but significant alternative mechanical path to substrate spectrum extension in PenA besides more common substitution mutations.

Development of surface-SFED models for polar solvents.

We developed surface grid-based solvation free energy density (Surface-SFED) models for 36 commonly used polar solvents. The parametrization was performed with a large and diverse set of experimental solvation free energies mainly consisting of combinations of polar solvent and multipolar solute. Therefore, the contribution of hydrogen bonds was dominant in the model. In order to increase the accuracy of the model, an elaborate version of a previous hydrogen bond acidity and basicity prediction model was introduced. We present two parametrizations for use with experimentally determined (Surface-SFED/HB(exp)) and empirical (Surface-SFED/HB(cal)) hydrogen bond acidity and basicity values. Our computational results agreed well with experimental results, and inaccuracy of empirical hydrogen bond acidity and basicity values was the main source of error in Surface-SFED/HB(cal). The mean absolute errors of Surface-SFED/HB(exp) and Surface-SFED/HB(cal) were 0.49 and 0.54 kcal/mol, respectively.

Ligand aligning method for molecular docking: alignment of property-weighted vectors.

To reduce searching effort in conformational space of ligand docking positions, we propose an algorithm that generates initial binding positions of the ligand in a target protein, based on the property-weighted vector (P-weiV), the three-dimensional orthogonal vector determined by the molecular property of hydration-free energy density. The alignment of individual P-weiVs calculated separately for the ligand and the protein gives the initial orientation of a given ligand conformation relative to an active site; these initial orientations are then ranked by simple energy functions, including solvation. Because we are using three-dimensional orthogonal vectors to be aligned, only four orientations of ligand positions are possible for each ligand conformation, which reduces the search space dramatically. We found that the performance of P-weiV compared favorably to the use of principle moment of inertia (PMI) as implemented in LigandFit when we tested the abilities of the two approaches to correctly predict 205 protein-ligand complex data sets from the PDBBind database. P-weiV correctly predicted the alignment of ligands (within rmsd of 2.5 Å) with 57.6% reliability (118/205) for the top 10 ranked conformations and with 74.1% reliability (152/205) for the top 50 ranked conformations of Catalyst-generated conformers, as compared to 22.9% (47/205) and 31.2% (64/205), respectively, in the case of PMI with the same conformer set.

A robust method for searching the smallest set of smallest rings with a path-included distance matrix.

The perception of rings in graphs is widely used in many fields of science and engineering. Algorithms developed in the chemistry community, called smallest set of smallest rings (SSSR), applicable only for simple graphs or chemical structures. In contrast, algorithms developed by the computer science community, called minimum cycle basis (MCB) are identical to SSSR yet exhibit greater robustness. MCB-based algorithms can correctly reveal all rings in any complex graph. However, they are slow when applied to large complex graphs due to the inherent limitations of the algorithms used. Here, we suggest a heuristic method called RP-Path. This method is a robust, simple, and fast search method with O(n(3)) runtime algorithm that correctly identifies the SSSR of all of the test case of complex graphs by using approach different from the MCB-based method. Both the robustness and improvement in speed are achieved by using a path-included distance matrix and describing the characteristic features of rings in the matrix. This method is accurate and faster than any other methods and may find many application in various fields of science and engineering that use complicated graphs with thousands of nodes.

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.

An insight into the interaction mode between CheB and chemoreceptor from two crystal structures of CheB methylesterase catalytic domain.

We have determined 2.2 Å resolution crystal structure of Thermotoga maritima CheB methylesterase domain to provide insight into the interaction mode between CheB and chemoreceptors. T. maritima CheB methylesterase domain has identical topology of a modified doubly-wound α/ß fold that was observed from the previously reported Salmonella typhimurium counterpart, but the analysis of the electrostatic potential surface near the catalytic triad indicated considerable charge distribution difference. As the CheB demethylation consensus sites of the chemoreceptors, the CheB substrate, are not uniquely conserved between T. maritima and S. typhimurium, such surfaces with differing electrostatic properties may reflect CheB regions that mediate protein-protein interaction. Via the computational docking of the two T. maritima and S. typhimurium CheB structures to the respective T. maritima and Escherichia coli chemoreceptors, we propose a CheB:chemoreceptor interaction mode.

Calculation of the solvation free energy of neutral and ionic molecules in diverse solvents.

The solvation free energy density (SFED) model was modified to extend its applicability and predictability. The parametrization process was performed with a large, diverse set of solvation free energies that included highly polar and ionic molecules. The mean absolute error for 1200 solvation free energies of the 379 neutral molecules in 9 organic solvents and water was 0.40 kcal/mol, and for 90 hydration free energies of ions was 1.7 kcal/mol. Overall, the calculated solvation free energies of a wide range of solute functional groups in diverse solvents were consistent with experimental data.

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.

Amide I bands of terminally blocked alanine in solutions investigated by infrared spectroscopy and density functional theory calculation: hydrogen-bonding interactions and solvent effects.

Structural aspects of terminally blocked alanine trans-N-acetyl-L-alanyl-trans-N'-methylamide (Ac-Ala-NHMe) in several different solvents were compared by attenuated total reflection infrared (ATR-IR) spectroscopy and density functional theory (DFT) calculations. The amide I bands between 1600 and 1700 cm(-1) appeared to change depending on media, indicating dissimilar hydrogen-bonding interactions among the peptides and solvent molecules. The minimum energy geometry in the isolated gas phase and aqueous environments were calculated at the B3LYP/6-311++G** theoretical level. In the solid state, Ac-Ala-NHMe is assumed to have an extended beta-stranded structure (C5), whereas it is assumed to have a cyclic structure (C7eq or alphaL) in a nonpolar tetrahydrofuran (THF) solvent. The optimized backbone dihedral angles (Phi, Psi) of Ac-Ala-NHMe plus four explicit water molecules were estimated to be -94 degrees and +133 degrees, respectively, indicating the polyproline II structure (PII). The energy differences between the most stable conformers were predicted to be larger for Ac-Ala-NHMe, which implies that more conformational ensemble structures should coexist for the gas phase than for the aqueous medium with explicit water molecules.