Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment

A comprehensive review of computational methods for Protein-DNA binding site prediction

Accurately identifying protein-DNA binding sites is essential for understanding the molecular mechanisms underlying biological processes, which in turn facilitates advancements in drug discovery and design. While biochemical experiments provide the most accurate way to locate DNA-binding sites, they are generally time-consuming, resource-intensive, and expensive. There is a pressing need to develop computational methods that are both efficient and accurate for DNA-binding site prediction. This study thoroughly reviews and categorizes major computational approaches for predicting DNA-binding sites, including template detection, statistical machine learning, and deep learning-based methods. The 14 state-of-the-art DNA-binding site prediction models have been benchmarked on 136 non-redundant proteins, where the deep learning-based, especially pre-trained large language model-based, methods achieve superior performance over the other two categories. Applications of these DNA-binding site prediction methods are also involved.

Allele-specific detection of isoniazid metabolism modulating variants of N-acetyltransferase 2 enzyme and their frequencies in the Bangladeshi population

Tuberculosis is one of the oldest diseases that still affects millions of people worldwide and remains a significant public health challenge. The N-acetyltransferase 2 (NAT2) enzyme metabolizes isoniazid (INH), a primary antibiotic in tuberculosis treatment. The single nucleotide polymorphisms (SNPs) of NAT2 affect the metabolism and function of isoniazid. The rs1801280 (T341C) and rs1208 (G803A) variants of NAT2 are associated with INH drug responses. Individuals with the slow-metabolizing rs1801280 variant of the NAT2 enzyme are at a higher risk of INH-induced liver damage and require lower doses or longer treatment regimens. At the same time, individuals with the fast-metabolizing rs1208 variant are at risk of treatment failure due to rapid drug metabolism. Genotyping of the NAT2 variants can help clinicians personalize tuberculosis treatment, optimize drug doses, and thus minimize adverse effects. Under this study, an allele-specific PCR (ASPCR) method was developed for genotyping the NAT2 variants, and the results were validated through targeted sequencing. The allele frequencies at the rs1801280 locus were 0.60 for the T allele and 0.40 for the C. For rs1208, the participants' allele frequencies were 0.27 for the G allele and 0.73 for the A allele. This ASPCR method is quick, affordable, and could be used in routine genotyping to personalize the treatment for tuberculosis patients, leading to more effective and safer treatments. We also used molecular docking to study how the rs1801280 and rs1208 variants affect the interaction between the NAT2 enzyme and drugs. A slight change was visible in the flexibility of the amino acid residues. However, those amino acids were not involved in the ligand binding mechanism.

Hydrolysis of myofibrillar proteins by protease AprA secreted from Pseudomonas fragi: Preference for degrading Ala-linked peptide bonds

Extracellular proteases of bacteria have attracted attention in recent years. Alkaline protease AprA secreted from Pseudomonas fragi has been shown to cause spoilage in chilled meat and to degrade myofibrillar proteins (MPs), but the spoilage mechanism was unknown. AprA possessed a high affinity for substrate proteins (Km = 1.40 mg/mL, Vmax = 11.20 mg/mL/min) and was controlled by metal ions and inhibitors. AprA exhibited strong hydrolytic activity on MPs, with alterations in secondary structure, tertiary structure and sulfhydryl content. Molecular docking and molecular dynamics revealed that AprA bound to actin and myosin heavy chain (MHC) through hydrogen bonds, hydrophobic interaction and salt bridges, respectively. AprA exhibited a broad spectrum of cleavage, with a relative preference for Ala-linked peptide bonds, according to peptide release kinetics. The above results reveal the mechanism of bacterial spoilage of chilled meat at low temperatures. Of course, this provides a theoretical basis for targeted control of the meat spoilage caused by AprA.

In-silico analysis of deleterious non-synonymous SNPs in the human AVPR1a gene linked to autism

Single nucleotide polymorphisms are the most prevalent type of DNA variation occurring at a single nucleotide within the genomic sequence. The AVPR1a gene exhibits genetic polymorphism and is linked to neurological and developmental problems, including autism spectrum disorder. Due to the difficulties of studying all non-synonymous single nucleotide polymorphisms (nsSNPs) of the AVPR1a gene in the general population, our goal is to use a computational approach to identify the most detrimental nsSNPs of the AVPR1a gene. We employed several bioinformatics tools, such as SNPnexus, PROVEAN, PANTHER, PhD-SNP, SNP & GO, and I-Mutant2.0, to detect the 23 most detrimental mutants (R85H, D202N, E54G, H92P, D148Y, C203G, V297M, D148V, S182N, Q108L, R149C, G212V, M145T, G212S, Y140S, F207V, Q108H, W219G, R284W, L93F, P156R, F136C, P107L). Later, we used other bioinformatics tools to perform domain and conservation analysis. We analyzed the consequences of high‑risk nsSNPs on active sites, post-translational modification (PTM) sites, and their functional effects on protein stability. 3D modeling, structure validation, protein-ligand binding affinity prediction, and Protein-protein docking were conducted to verify the presence of five significant substitutions (R284W, Y140S, P107L, R149C, and F207V) and explore the modifications induced due to these mutants. These non-synonymous single nucleotide polymorphisms can potentially be the focus of future investigations into various illnesses caused by AVPR1a malfunction. Employing in-silico methodologies to evaluate AVPR1a gene variants will facilitate the coordination of extensive investigations and the formulation of specific therapeutic approaches for diseases associated with these variations.

Bioinformatics approach reveals the modulatory role of JUN in atorvastatin-mediated anti-breast cancer effects

Atorvastatin, a widely prescribed cholesterol-lowering drug, has recently shown potential anticancer effects. However, its influence on gene expression and its biological functions in cancer, in particular breast cancer, still unclear. We aim to identify the dysregulated genes associated with atorvastatin treatment and the main players in their biological network. A total of 103 differentially expressed genes (DEGs) in the unified signature were identified, and the functional enrichment analysis suggested their relation to multiple cancer-related pathways. JUN was identified as the hub gene in the protein-protein interaction (PPI) network and was shown to be responsive to atorvastatin in breast cancer. Atorvastatin exhibited notable predicted cytotoxicity against breast cancer lines, with the activity positively correlated with JUN expression. JUN was significantly downregulated in breast cancer expression inversely correlated with cancer progression, whereas higher JUN expression was linked with better survival outcomes. Atorvastatin may directly interact with JUN protein forming a more compact and stable conformation. These findings demystify the potential therapeutic mechanism of atorvastatin in breast cancer, possibly by fine tuning of JUN expression. As such, JUN might serve as a valuable prognostic biomarker in breast cancer.

Analysis of natural compounds identifies potential inhibitors for phosphoglucomutase of Acinetobacter baumannii: a computational approach

Acinetobacter baumannii has become resistant to almost all available antibiotics in the market, emphasizing the need to develop novel antibiotics against this pathogen. The present study aims to identify potential inhibitors for phosphoglucomutase (Pgm) of A. baumannii by screening natural compounds. Pgm, a key enzyme involved in bacterial cell wall biosynthesis, is identified as a promising drug target. The study first employed various computational modeling tools to predict the structure of Pgm protein as its experimental structure was unavailable. After a thorough evaluation, the AlphaFold2 model (Rank 4) was chosen and energy-minimized for molecular docking study with its natural substrates, glucose-1-phosphate (G1P) and glucose-6-phosphate (G6P). Virtual screening of the natural compounds from LOTUS and CMNPD databases against Pgm identified top five compounds DMA, DPD, 2-DPD, HAP, and DTP, which exhibited better docking scores (- 8.287 kcal/mol, - 8.082 kcal/mol, - 8.082 kcal/mol, - 8.081 kcal/mol and - 7.97 kcal/mol) compared to the natural substrates G6P and G1P (- 6.225 kcal/mol, - 5.959 kcal/mol). The drug-likeness assessment of these compounds revealed that DPD had favorable pharmacokinetic profiles and was non-carcinogenic, non-irritating to the eyes, non-corrosive, and free of respiratory toxicity, representing it as a promising drug candidate. Molecular dynamics simulations and binding free energy calculations confirmed the stable interactions of DPD with Pgm, further supporting its potential as an inhibitor. Thus, the present study elucidated a natural compound as potential inhibitor against a vital protein Pgm. Further experimental studies can be carried out to understand its antibacterial properties for developing novel drugs against A. baumannii infections.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-025-00360-2.

Bioinformatics analyses of lignin peroxidases from the smoky bracket fungi Bjerkandera adusta for endocrine disrupting chemical bioremediation

Lignin peroxidases (LiP; EC 1.11.1.14) are important oxidative enzymes involved in lignin biodegradation, a key constituent of plant cell walls. Despite their environmental and industrial potential, fungal LiPs are difficult to express and purify. Bjerkandera adusta is a white-rot fungus that secretes LiPs, the three-dimensional structure of which remains unknown. In this study, two LiPs from B. adusta were subjected to various bioinformatics tools to determine their physio-chemical, structural, and functional properties. Their 3D structure was modeled and molecular dynamic simulations were performed to assess their binding to endocrine disrupting chemicals (EDCs). Moreover, molecular docking analysis revealed that among the model lignin compounds, the dimer guaiacyl 4-O-5 guaiacyl exhibited the lowest binding energy with the EDC ligands, estrone (E1) and bisphenol A showing the strongest binding affinity for LiP 588479560 and LiP 444058, respectively. Molecular dynamics simulations further confirmed the stability of these complexes, with bisphenol A exhibiting particularly high stability as indicated by its low RMSD (≤2 Å) and favorable RoG values, reflecting a strong fit within the enzyme's active site. Additionally, the binding free energy calculations showed the substrate dimer had the most favorable binding energy, driven primarily by Van der Waals and lipophilic interactions, suggesting its intrinsic compatibility with B. adusta LiPs. This in silico characterization advances the understanding of LiP structure-function relationships and bioremediation potential. B. adusta LiPs demonstrate promising capacity to target persistent EDCs, offering solutions for environmental pollution mitigation.

Extracellular TatD from Listeria monocytogenes displays DNase activity and contributes to biofilm dispersion

TatD is evolutionarily conserved in a variety of organisms and has been implicated in DNA repair, apoptosis, and the disruption of extracellular traps. The aim of our study was to investigate the effects of TatD on L. monocytogenes biofilms. In our previous study, the deletion of the TatD gene from L. monocytogenes (named LmTatD) increased biofilm formation. However, the underlying mechanism remains unclear. In this study, we present a detailed analysis of the structural characteristics of TatD. Bioinformatic analysis revealed that the amino acid residues DPGEGDQHEDP are fully conserved. LmTatD belongs to the Class II TatD family (TATDN3) and contains a signal peptide. Recombinant LmTatD exhibited DNase activity regardless of the DNA substrate. Mutagenesis experiments confirmed the importance of glutamic acid, histidine, and aspartic acid residues in enzymatic activity. Biofilm formation was evaluated via a crystal violet assay, confocal laser scanning microscopy, and scanning electron microscopy. rLmTatD impaired biofilm formation and reduced eDNA levels without disrupting the integrity of the bacteria within biofilms. Moreover, deficiency of LmTatD led to a significant decrease in the DNase activity of the extracellular proteins from L. monocytogenes, whereas there was an increase in biofilm formation and eDNA production during the dispersion stage. However, no significant change in the total number of biofilm or planktonic bacteria was observed at any of the time points. Additionally, the mRNA level of LmTatD in the biofilm formed by the wild-type strain at the dispersion stage was greater than that at the attachment and maturation stages. The number of planktonic bacteria for the wild-type strain at the dispersion stage was significantly greater than that for the ΔLmTatD mutant. Collectively, these data suggest that LmTatD exhibits extracellular DNase activity and regulates L. monocytogenes biofilm dispersion.

Systems Biology-Driven Discovery of Host-Targeted Therapeutics for Oropouche Virus: Integrating Network Pharmacology, Molecular Docking, and Drug Repurposing

Background: Oropouche virus (OROV), part of the Peribunyaviridae family, is an emerging pathogen causing Oropouche fever, a febrile illness endemic in South and Central America. Transmitted primarily through midge bites (Culicoides paraensis), OROV has no specific antiviral treatment or vaccine. This study aims to identify host-targeted therapeutics against OROV using computational approaches, offering a potential strategy for sustainable antiviral drug discovery. Methods: Virus-associated host targets were identified using the OMIM and GeneCards databases. The Enrichr and DSigDB platforms were used for drug prediction, filtering compounds based on Lipinski's rule for drug likeness. A protein-protein interaction (PPI) network analysis was conducted using the STRING database and Cytoscape 3.10.3 software. Four key host targets-IL10, FASLG, PTPRC, and FCGR3A-were prioritized based on their roles in immune modulation and OROV pathogenesis. Molecular docking simulations were performed using the PyRx software to evaluate the binding affinities of selected small-molecule inhibitors-Acetohexamide, Deptropine, Methotrexate, Retinoic Acid, and 3-Azido-3-deoxythymidine-against the identified targets. Results: The PPI network analysis highlighted immune-mediated pathways such as Fc-gamma receptor signaling, cytokine control, and T-cell receptor signaling as critical intervention points. Molecular docking revealed strong binding affinities between the selected compounds and the prioritized targets, suggesting their potential efficacy as host-targeting antiviral candidates. Acetohexamide and Deptropine showed strong binding to multiple targets, indicating broad-spectrum antiviral potential. Further in vitro and in vivo validations are needed to confirm these findings and translate them into clinically relevant treatments. Conclusions: This study highlights the potential of using computational approaches to identify host-targeted therapeutics for Oropouche virus (OROV). By targeting key host proteins involved in immune modulation-IL10, FASLG, PTPRC, and FCGR3A-the selected compounds, Acetohexamide and Deptropine, demonstrate strong binding affinities, suggesting their potential as broad-spectrum antiviral candidates. Further experimental validation is needed to confirm their efficacy and potential for clinical application, offering a promising strategy for sustainable antiviral drug discovery.

cAMP-independent Crp homolog adds to the multi-layer regulatory network in Porphyromonas gingivalis

Introduction

Porphyromonas gingivalis encodes three CRP/FNR superfamily proteins: HcpR, PgRsp, and CrpPg, with CrpPg similar to cAMP-sensing proteins but not classified into known families. This study investigates the role of CrpPg in regulating the expression of factors essential for P. gingivalis virulence in A7436 and ATCC 33277 strains.

Methods

The role of CrpPg protein in P. gingivalis was determined using the ΔcrpPg mutant strains to characterize their phenotype and to assess the impact of crpPg inactivation on gene expression using RNA-seq and RT-qPCR. Additionally, the CrpPg protein was purified and characterized.

Results

Key findings in the ΔcrpPg mutant strain include up-regulated mfa1-5 and rgpA genes and down-regulated trxA, soxR, and ustA genes. While crpPg inactivation does not affect growth in liquid culture media, it impairs biofilm formation and enhances adhesion to and invasion of gingival keratinocytes. CrpPg binds directly to its own and mfa promoters without interacting with cyclic nucleotides or di-nucleotides. Its three-dimensional structure, resembling E. coli Crp in complex with cAMP and DNA, suggests that CrpPg functions as a global regulator independently of cAMP binding. The highest crpPg expression in the early exponential growth phase declines as cell density and metabolic conditions change over time, suggesting a regulatory function depending on the CrpPg protein amount.

Conclusions

By controlling the shift from planktonic to biofilm lifestyle, CrpPg may play a role in pathogenicity. Regulating the expression of virulence factors required for host cell invasion and intracellular replication, CrpPg may help P. gingivalis evade immune responses.

ATP-Pred: Prediction of Protein-ATP Binding Residues via Fusion of Residue-Level Embeddings and Kolmogorov-Arnold Network

Accurately identifying protein-ATP binding residues is essential for understanding biological processes and designing drugs. However, current sequence-based methods have limitations, such as difficulties in extracting discriminative features and the need for more efficient algorithms. Additionally, methods based on multiple sequence alignments often face challenges in handling large-scale predictions. To address these issues, we developed ATP-Pred, a sequence-based method for predicting ATP-binding residues in proteins. This model applies transfer learning by using two recently developed pretrain protein language models, Ankh and ProstT5, to extract residue-level embeddings that capture protein functionality. ATP-Pred also integrates a CNN-BiLSTM network and a Kolmogorov-Arnold network to build the prediction model. To handle data imbalance, we introduced a weighted focal loss function. Experimental results on three independent test data sets showed that ATP-Pred outperforms most existing methods. Its generalizability was further validated on four protein-mononucleotide binding residue data sets, where it delivered promising results. These findings suggest that ATP-Pred is a robust and reliable predictor.

Genomic and phenotypic evidence support visual and olfactory shifts in primate evolution

Sensory trade-offs between vision and olfaction in the evolution and radiation of primates have long been debated. However, insights have been limited by a lack of sensory gene sequences and accompanying functional predictions. Here we conduct large-scale functional analyses of visual and olfactory receptors and related brain regions across extant primates. Our results reveal a visual shift from ultraviolet to violet colour sensitivity in early haplorrhine primates, followed by acceleration in the rhodopsin retinal release rates at the origin of anthropoids, both of which are expected to greatly enhance visual acuity under brighter light conditions. Additionally, we find that the sensitivity of olfactory receptors shifted from narrowly to broadly tuned early in anthropoid evolution. In contrast, strepsirrhines appear to have retained sensitive dim-light vision and underwent functional enhancement of narrowly tuned olfactory receptors. Our models indicate that this would have enhanced odorant discrimination and facilitated olfaction-mediated physiology and behaviour. These differences in tuning patterns of olfactory receptors between major primate lineages mirror well-established morphological differences in external anatomy and brain structures, revealing new mechanisms of olfactory adaptation and evolutionary plasticity. Our multisystem analyses reveal patterns of co-evolution in genomic, molecular and neuroanatomical traits that are consistent with a sensory 'reallocation' rather than strict trade-offs.

Decoding deception: the binding affinity of cuttlefish ink on shark smell receptors

Chemical signaling can play a crucial role in predator-prey dynamics. Here, we present evidence that ink from the common cuttlefish (Sepia officinalis) targets olfactory receptor proteins in sharks, potentially acting as a predator deterrent. We apply in silico 3D docking analysis to investigate the binding affinity of various odorant molecules to shark olfactory receptors of 2 shark species: cloudy catshark (Scyliorhinus torazame) and white shark (Carcharodon carcharias). Pavoninin-4 (a known shark repellent compound) displayed selectivity in binding to receptors in the white shark. In contrast, the primary component of cuttlefish ink, melanin, displayed the highest binding affinities to all shark olfactory receptor proteins in both species. Taurine, another important ink component, exhibited standard to strong bindings for both species. Trans-4,5-epoxy-(E)-2-decenal ("blood decenal"), an odorant associated with the smell of blood, displayed strong binding affinities to all shark olfactory receptors, similar to that of melanin. These findings provide new insights into the molecular interplay between cephalopod inking behavior and their shark predators, with cuttlefish ink likely exploiting the narrow band of the shark olfactory repertoire.

Biochemical characterization of a bilfunctional endoglucanase/glucomannanase derived from mountain soil

Metagenomics is increasingly recognized as a vital technique for exploring uncultured microorganisms, with one key application being the discovery of novel enzymes for industrial use. This study identified an endoglucanase gene from soil metagenome, termed ZFEG1801, which was expressed in E. coli BL21, purified, and characterized for its biochemical properties. The 72.8 kDa recombinant protein exhibited hydrolytic activity against sodium carboxymethyl cellulose (CMC) and konjac glucomannan (KG), with activities of 12.1 U/mg and 42.1 U/mg, respectively. The enzyme displayed optimal activity at pH 5 for CMC and pH 6 for KG, with broad pH stability ranging from 5 to 9. The optimal temperature was 40 °C, and it remained thermally stable between 20 and 40 °C, retaining over 60% of its activity. The enzyme activity remained stable in the presence of most metal ions; however, CMCase activity was inhibited by Cu2+, while glucomannanase activity was inhibited by Mn2+, Fe3+, and Ca2+. The catalytic efficiency towards both substrates was reduced by addition of SDS, DMSO, ethanol, isopropanol and acetonitrile. The Vmax and Km of the purified recombinant enzyme were 106.4 μmol/L/min and 4.9 mg/mL for CMC, and 833.3 μmol/L/min and 11.1 mg/mL for KG, respectively. The dual catalytic properties of ZFEG1801, broad pH stability and resistance to additives, demonstrate its potential for use in various biomass degradation processes.

Coevolution of Lentiviral Vif with Host A3F and A3G: Insights from Computational Modelling and Ancestral Sequence Reconstruction

The evolutionary arms race between host restriction factors and viral antagonists provides crucial insights into immune system evolution and viral adaptation. This study investigates the structural and evolutionary dynamics of the double-domain restriction factors A3F and A3G and their viral inhibitor, Vif, across diverse primate species. By constructing 3D structural homology models and integrating ancestral sequence reconstruction (ASR), we identified patterns of sequence diversity, structural conservation, and functional adaptation. Inactive CD1 (Catalytic Domain 1) domains displayed greater sequence diversity and more positive surface charges than active CD2 domains, aiding nucleotide chain binding and intersegmental transfer. Despite variability, the CD2 DNA-binding grooves remained structurally consistent with conserved residues maintaining critical functions. A3F and A3G diverged in loop 7' interaction strategies, utilising distinct molecular interactions to facilitate their roles. Vif exhibited charge variation linked to host species, reflecting its coevolution with A3 proteins. These findings illuminate how structural adaptations and charge dynamics enable both restriction factors and their viral antagonists to adapt to selective pressures. Our results emphasize the importance of studying structural evolution in host-virus interactions, with implications for understanding immune defense mechanisms, zoonotic risks, and viral evolution. This work establishes a foundation for further exploration of restriction factor diversity and coevolution across species.

DockEM: an enhanced method for atomic-scale protein-ligand docking refinement leveraging low-to-medium resolution cryo-EM density maps

Protein-ligand docking plays a pivotal role in virtual drug screening, and recent advancements in cryo-electron microscopy (cryo-EM) technology have significantly accelerated the progress of structure-based drug discovery. However, the majority of cryo-EM density maps are of medium to low resolution (3-10 Å), which presents challenges in effectively integrating cryo-EM data into molecular docking workflows. In this study, we present an updated protein-ligand docking method, DockEM, which leverages local cryo-EM density maps and physical energy refinement to precisely dock ligands into specific protein binding sites. Tested on a dataset of 121 protein-ligand compound, our results demonstrate that DockEM outperforms other advanced docking methods. The strength of DockEM lies in its ability to incorporate cryo-EM density map information, effectively leveraging the structural information of ligands embedded within these maps. This advancement enhances the use of cryo-EM density maps in virtual drug screening, offering a more reliable framework for drug discovery.

Brussonol and komaroviquinone as inhibitors of the SARS-CoV-2 Omicron BA.2 variant spike protein: A molecular docking, molecular dynamics, and quantum biochemistry approach

Since late 2019, humanity has faced the challenges posed by the COVID-19 pandemic, caused by the SARS-CoV-2 virus. The continuous evolution of SARS-CoV-2 has led to the emergence of multiple Variants of Concern (VOCs) and Variants of Interest (VOIs), posing significant risks to global health. SARS-CoV-2 infects host cells via the angiotensin-converting enzyme 2 (ACE2) receptors, facilitated by the spike (S) protein. Icetexane diterpenes, including brussonol and komaroviquinone, exhibit notable anti-inflammatory, antibacterial, antiviral, antiproliferative, and anticancer properties. Recent research has explored their potential as inhibitors of the SARS-CoV-2 3Clpro protease, showing promising efficacy comparable to Nirmatrelvir. This study investigates brussonol and komaroviquinone as potential inhibitors of the SARS-CoV-2 Omicron BA.2 variant spike protein using molecular docking, molecular dynamics simulations, and quantum biochemistry approaches. The stability and interaction energies of brussonol, komaroviquinone, and mefloquine with the SARS-CoV-2 Omicron BA.2 variant spike protein were evaluated. RMSD analysis demonstrated that komaroviquinone and mefloquine maintain more stable binding poses with the spike protein compared to various NAGs and glycans. Electrostatic potential maps revealed significant interactions with ASN603, a critical residue for ligand binding efficacy. Furthermore, this study addresses a gap in current research, as no studies were found that simulate the trimer of the SARS-CoV-2 BA.2 variant spike protein. Most existing studies focus on the monomer and often exclude the NAGs and glycans. This research underscores the importance of maintaining the NAGs and glycans in the trimer simulations, providing a more accurate representation of the protein's structure and its interactions with ligands. The findings indicate that both komaroviquinone and brussonol exhibit higher binding affinities compared to mefloquine. This study provides valuable insights into the molecular interactions of these compounds, highlighting their potential for further development as antiviral agents against SARS-CoV-2.

Yaravirus brasiliense genomic structure analysis and its possible influence on the metabolism

Here we analyze the Yaravirus brasiliense, an amoeba-infecting 80-nm-sized virus with a 45-kbp dsDNA, using structural molecular modeling. Almost all of its 74 genes were previously identified as ORFans. Considering its unprecedented genetic content, we analyzed Yaravirus genome to understand its genetic organization, its proteome, and how it interacts with its host. We reported possible functions for all Yaravirus proteins. Our results suggest the first ever report of a fragment proteome, in which the proteins are separated in modules and joined together at a protein level. Given the structural resemblance between some Yaravirus proteins and proteins related to tricarboxylic acid cycle (TCA), glyoxylate cycle, and the respiratory complexes, our work also allows us to hypothesize that these viral proteins could be modulating cell metabolism by upregulation. The presence of these TCA cycle-related enzymes specifically could be trying to overcome the cycle's control points, since they are strategic proteins that maintain malate and oxaloacetate levels. Therefore, we propose that Yaravirus proteins are redirecting energy and resources towards viral production, and avoiding TCA cycle control points, "unlocking" the cycle. Altogether, our data helped understand a previously almost completely unknown virus, and a little bit more of the incredible diversity of viruses.

Multiple combinatorial interactions among natural structural variants of Brassica SOC1 promoters and SVP: conservation of binding affinity despite diversity in bimolecular interactions

BACKGROUND

Analysis of binding patterns of biomolecules underpin new paradigms for trait engineering. One way of designing early flowering crops is to manipulate genes controlling flowering time. SOC1, a central integrator of flowering, is downregulated by SVP. In amphidiploid Brassica juncea, flowering is plausibly mediated by combinatorial interactions involving natural variants of SOC1 promoter and SVP protein homologs. Although fluctuating temperatures influence energetics of molecular interactions and phenotypes, mechanistic insights on these remain unknown. Herein, we report diversity in 50 homologs of SVP proteins from 25 Brassicaceae species.

MATERIALS AND METHODS AND RESULTS

Sequence and phylogenetic analysis of 9 natural variants of B. juncea SVP revealed differences in MIKC domains and sub-genome of origin. Generation and refinement of 15 SVP protein models (natural and hypothetical) using I-TASSER and ALPHAFOLD, and 3 SOC1 promoter fragments using 3D-DART, revealed structural diversity. Notwithstanding, binding affinity of 48 docked complexes analysed using HADDOCK and PreDBA were similar. Analysis of 27 docked complexes for distribution of shared or unique binding patterns and type of molecular contacts (π-π stacking, hydrophobic interactions, Van-der-Waals forces, H-bonds) using PyMOL, CCP4i, DNAproDB, PremPDI and DIMPLOT revealed extensive variation implicating compensatory mutations in preserving binding affinity. Yeast one-hybrid assays validated binding potential predicted in docked complexes. Conserved amino-acid and nucleotide residues involved in non-covalent interactions were identified. Computational alanine substitution established cruciality of amino-acid hotspots conferring stability to docked complexes.

CONCLUSIONS

Our study is important as identification of crucial amino-acid hotspots is essential for rational protein design. Targeted mutagenesis resulting in modified binding spectrum of regulatory proteins suggests a way forward for trait engineering.

Improving Identification of Drug-Target Binding Sites Based on Structures of Targets Using Residual Graph Transformer Network

Improving identification of drug-target binding sites can significantly aid in drug screening and design, thereby accelerating the drug development process. However, due to challenges such as insufficient fusion of multimodal information from targets and imbalanced datasets, enhancing the performance of drug-target binding sites prediction models remains exceptionally difficult. Leveraging structures of targets, we proposed a novel deep learning framework, RGTsite, which employed a Residual Graph Transformer Network to improve the identification of drug-target binding sites. First, a residual 1D convolutional neural network (1D-CNN) and the pre-trained model ProtT5 were employed to extract the local and global sequence features from the target, respectively. These features were then combined with the physicochemical properties of amino acid residues to serve as the vertex features in graph. Next, the edge features were incorporated, and the residual graph transformer network (GTN) was applied to extract the more comprehensive vertex features. Finally, a fully connected network was used to classify whether the vertex was a binding site. Experimental results showed that RGTsite outperformed the existing state-of-the-art methods in key evaluation metrics, such as F1-score (F1) and Matthews Correlation Coefficient (MCC), across multiple benchmark datasets. Additionally, we conducted interpretability analysis for RGTsite through the real-world cases, and the results confirmed that RGTsite can effectively identify drug-target binding sites in practical applications.

Machine learning approaches for predicting protein-ligand binding sites from sequence data

Proteins, composed of amino acids, are crucial for a wide range of biological functions. Proteins have various interaction sites, one of which is the protein-ligand binding site, essential for molecular interactions and biochemical reactions. These sites enable proteins to bind with other molecules, facilitating key biological functions. Accurate prediction of these binding sites is pivotal in computational drug discovery, helping to identify therapeutic targets and facilitate treatment development. Machine learning has made significant contributions to this field by improving the prediction of protein-ligand interactions. This paper reviews studies that use machine learning to predict protein-ligand binding sites from sequence data, focusing on recent advancements. The review examines various embedding methods and machine learning architectures, addressing current challenges and the ongoing debates in the field. Additionally, research gaps in the existing literature are highlighted, and potential future directions for advancing the field are discussed. This study provides a thorough overview of sequence-based approaches for predicting protein-ligand binding sites, offering insights into the current state of research and future possibilities.

ATP_mCNN: Predicting ATP binding sites through pretrained language models and multi-window neural networks

Adenosine triphosphate plays a vital role in providing energy and enabling key cellular processes through interactions with binding proteins. The increasing amount of protein sequence data necessitates computational methods for identifying binding sites. However, experimental identification of adenosine triphosphate-binding residues remains challenging. To address the challenge, we developed a multi-window convolutional neural network architecture taking pre-trained protein language model embeddings as input features. In particular, multiple parallel convolutional layers scan for motifs localized to different window sizes. Max pooling extracts salient features concatenated across windows into a final multi-scale representation for residue-level classification. On benchmark datasets, our model achieves an area under the ROC curve of 0.95, significantly improving on prior sequence-based models and outperforming convolutional neural network baselines. This demonstrates the utility of pre-trained language models and multi-window convolutional neural networks for advanced sequence-based prediction of adenosine triphosphate-binding residues. Our approach provides a promising new direction for elucidating binding mechanisms and interactions from primary structure.

Characterization and kinetics of a cathepsin B-inhibiting protein from Musa acuminata Colla peel

Hyperexpression of cathepsin B caused by an imbalance of endogenous inhibitors is involved in multiple pathologies, hence making it a key therapeutic target. Protease inhibitors are effective biomolecules that regulate protease activities and are considered potential therapeutic agents in various diseases. Plant protease inhibitors have been reported as an effective complementary alternative drug. A proteinaceous cathepsin B inhibitor (CBI-BP) has been isolated from Musa acuminata Colla (banana) peel with a molecular weight of 27.9 kDa on SDS-PAGE. The purity of the CBI-BP was confirmed on the native- PAGE. The isolated CBI-BP showed an IC50 value of 8.14 μg and a Ki value of 10.59 μg (0.19 μM). Cathepsin B inhibition kinetics indicated that CBI-BP follows a mixed-type of cathepsin B inhibition. Its inhibition activity was also confirmed by reverse zymography. The inhibitor was stable from pH 2.6-10.0 with maximum activity at pH 7.2, temperature 25-100 °C and exhibited thermostability for 60 min at 70 °C. MALDI/TOF/MS analysis of CBI-BP showed 40 % similarity to the GH18 domain-containing protein (A0A4S8JRM9) from Musa balbisiana. Although in-silico docking studies showed binding of A0A4S8JRM9 to cathepsin B affects the binding energy of the substrate to cathepsin B but is not reported for any anti-cathepsin B activity. This suggests that isolated CBI-BP might be a novel protein with anti-cathepsin B activity. Thus the isolated CBI-BP may be further explored as possible anti-cathepsin B drug.

Compromising the immunogenicity of diphtheria toxin-based immunotoxins through epitope engineering: An in silico approach

Immunotoxins are genetically engineered recombinant proteins consisting of a targeting moiety, such as an antibody, and a cytotoxic toxin moiety of microbial origin. Pseudomonas exotoxin A and diphtheria toxin (DT) have been abundantly used in immunotoxins, with the latter applied as the toxin moiety of the FDA-approved drug Denileukin diftitox (ONTAK®). However, the use of immunotoxins provokes an adverse immune response in the host body against the toxin moiety, limiting their efficacy. In silico approaches have received increasing attention in protein engineering. In this study, the epitopes responsible for immunogenicity were identified through multiple platforms. By subtracting conserved and ligand-binding residues, K33, T111, and E112 were identified as common epitopes across all platforms. Substitution analysis evaluated alternative residues regarding their impact on protein stability, considering 19 different amino acid substitutions. Among the mutants explored, the T111A-E112G mutant exhibited the most destabilizing substitution for DT, thereby reducing immunogenicity. Finally, a 3D model of the mutant was generated and verified. The model was then docked with its native ligand NADH, and the complex's molecular behavior was simulated using molecular dynamics.

In-silico screening and analysis of missense SNPs in human CYP3A4/5 affecting drug-enzyme interactions of FDA-approved COVID-19 antiviral drugs

Single nucleotide polymorphisms (SNPs) represent the prevailing form of genetic variations observed in the human population. Such variations could alter the encoded enzymes' activities. CYP3A4/5 enzymes are involved in metabolizing drugs, notably antivirals against SARS-CoV-2. In this work, we computationally investigated antiviral-enzyme interactions of CYP3A4/5 genetic variants. We also examined the deleterious impact of 751 missense single nucleotide polymorphisms (SNPs) within the CYP3A4/5 genes. An ensemble of bioinformatics tools, [SIFT, PolyPhen-2, cadd, revel, metaLr, mutation assessor, Panther, SNP&GO, PhD-SNP, SNAP, Meta-SNP, FATHMM, I-Mutant, MuPro, INPS, CONSURF, GPS 5.0, MusiteDeep and NetPhos], identified a total of 94 variants (47 SNPs in CYP3A4, 47 SNPs in CYP3A5) to potentially impact the structural integrity as well as the activity of the CYP3A4/5 enzymes. Molecular docking was done to recognize the structural stability and binding properties of the CYP3A4/5 protein isoforms with 3 FDA-approved antiviral drugs. Our findings indicated that the CYP3A4 gene variants; R418T, I335T and R130P and the CYP3A5 gene variants; I335T, L133P and R130Q are considered the most deleterious missense SNPs. These mutants potentially affect drug-enzyme binding and hence may alter therapeutic response. Cataloguing deleterious SNPs is essential for personalized gene-based pharmacotherapy.

Residue-Level Multiview Deep Learning for ATP Binding Site Prediction and Applications in Kinase Inhibitors

Accurate identification of adenosine triphosphate (ATP) binding sites is crucial for understanding cellular functions and advancing drug discovery, particularly in targeting kinases for cancer treatment. Existing methods face significant challenges due to their reliance on time-consuming precomputed features and the heavily imbalanced nature of binding site data without further investigations on their utility in drug discovery. To address these limitations, we introduced Multiview-ATPBind and ResiBoost. Multiview-ATPBind is an end-to-end deep learning model that integrates one-dimensional (1D) sequence and three-dimensional (3D) structural information for rapid and precise residue-level pocket-ligand interaction predictions. Additionally, ResiBoost is a novel residue-level boosting algorithm designed to mitigate data imbalance by enhancing the prediction of rare positive binding residues. Our approach outperforms state-of-the-art models on benchmark data sets, showing significant improvements in balanced metrics with both experimental and AI-predicted structures. Furthermore, our model seamlessly transfers to predicting binding sites and enhancing docking simulations for kinase inhibitors, including imatinib and dasatinib, underscoring the potential of our method in drug discovery applications.

Predicting the location of coordinated metal ion-ligand binding sites using geometry-aware graph neural networks

More than 50 % of proteins bind to metal ions. Interactions between metal ions and proteins, especially coordinated interactions, are essential for biological functions, such as maintaining protein structure and signal transport. Physiological metal-ion binding prediction is pivotal for both elucidating the biological functions of proteins and for the design of new drugs. However, accurately predicting these interactions remains challenging. In this study, we proposed GPred, a novel structure-based method that transforms the 3-dimensional structure of a protein into a point cloud representation and then designs a geometry-aware graph neural network to learn the local structural properties of each amino acid residue under specific ligand-binding supervision. We trained our model to predict the location of coordinated binding sites for five essential metal ions: Zn2+, Ca2+, Mg2+, Mn2+, and Fe2+. We further demonstrated the versatility of GPred by applying transfer learning to predict the binding sites of 2 heavy metal ions, that is, cadmium (Cd2+) and mercury (Hg2+). We achieved greater than 19.62 %, 14.32 %, 36.62 %, and 40.69 % improvement in the area under the precision-recall curve (AUPR) of Zn2+, Ca2+, Mg2+, Mn2+, and Fe2+, respectively, when compared with 6 current accessible state-of-the-art sequence-based or structure-based tools. We also validated the proposed approach on protein structures predicted by AlphaFold2, and its performance was similar to experimental protein structures. In both cases, achieving a low false discovery rate for proteins without annotated ion-binding sites was demonstrated. © 2017 Elsevier Inc. All rights reserved.

Biophysical characterization and structural insights of leptospiral complement regulator-acquiring protein A

Many pathogens establish a successful infection by evading the host complement system, an essential arm of innate immunity. Pathogenic Leptospira is reported to escape complement-mediated killing by recruiting the host complement regulators by lipoproteins or outer surface proteins. One of the outer surface proteins, Leptospiral complement regulator-acquiring protein A (LcpA), is known to recruit complement regulators, C4b-binding protein (C4BP), and Factor H (FH) on the bacterial surface. Mapping of interacting domains from C4BP and FH with the LcpA has already been reported. However, the region or structural part of the LcpA mediating the interaction is not known yet. Here, we report cloning, expression, refolding and purification of recombinant LcpA from an inclusion body of E. coli heterologous expression system. We also demonstrate the biophysical characterization of recombinant LcpA and reveal its secondary structure contents. Moreover, the protein displays a moderate thermostability. The change of intrinsic fluorescence and CD spectra demonstrate a change in the secondary structure of protein due to binding with Zn2+ ions. Molecular docking of LcpA with the complement regulators displays important interface residues from both the individual counterparts. Molecular dynamic simulation analysis demonstrates the stability of interactions between LcpA and C4BP. In our understanding, this is the first report on the large-scale purification of LcpA through refolding experiments and biophysical characterization of LcpA. This study may provide additional information on the structural basis of binding with the complement regulators.

Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms

Benchmarking is an important step in the improvement, assessment, and comparison of the performance of drug discovery platforms and technologies. We revised the existing benchmarking protocols in our Computational Analysis of Novel Drug Opportunities (CANDO) multiscale therapeutic discovery platform to improve utility and performance. We optimized multiple parameters used in drug candidate prediction and assessment with these updated benchmarking protocols. CANDO ranked 7.4% of known drugs in the top 10 compounds for their respective diseases/indications based on drug-indication associations/mappings obtained from the Comparative Toxicogenomics Database (CTD) using these optimized parameters. This increased to 12.1% when drug-indication mappings were obtained from the Therapeutic Targets Database. Performance on an indication was weakly correlated (Spearman correlation coefficient >0.3) with indication size (number of drugs associated with an indication) and moderately correlated (correlation coefficient >0.5) with compound chemical similarity. There was also moderate correlation between our new and original benchmarking protocols when assessing performance per indication using each protocol. Benchmarking results were also dependent on the source of the drug-indication mapping used: a higher proportion of indication-associated drugs were recalled in the top 100 compounds when using the Therapeutic Targets Database (TTD), which only includes FDA-approved drug-indication associations (in contrast to the CTD, which includes associations drawn from the literature). We also created compbench, a publicly available head-to-head benchmarking protocol that allows consistent assessment and comparison of different drug discovery platforms. Using this protocol, we compared two pipelines for drug repurposing within CANDO; our primary pipeline outperformed another similarity-based pipeline still in development that clusters signatures based on their associated Gene Ontology terms. Our study sets a precedent for the complete, comprehensive, and comparable benchmarking of drug discovery platforms, resulting in more accurate drug candidate predictions.

Delving into human α1,4-galactosyltransferase acceptor specificity: The role of enzyme dimerization

Human α1,4-galactosyltransferase (A4galt), a Golgi apparatus-resident GT, synthesizes Gb3 glycosphingolipid (GSL) and P1 glycotope on glycoproteins (GPs), which are receptors for Shiga toxin types 1 and 2. Despite the significant role of A4galt in glycosylation processes, the molecular mechanisms underlying its varied acceptor specificities remain poorly understood. Here, we attempted to elucidate A4galt specificity towards GSLs and GPs by exploring its interaction with GTs with various acceptor specificities, GP-specific β1,4-galactosyltransferase 1 (B4galt1) and GSL-specific β1,4-galactosyltransferase isoenzymes 5 and 6 (B4galt5 and B4galt6). Using a novel NanoBiT assay, we found that A4galt can form homodimers and heterodimers with B4galt1 and B4galt5 in two cell lines, human embryonic kidney cells (HEK293T) and Chinese hamster ovary cells (CHO-Lec2). We found that A4galt-B4galts heterodimers preferred N-terminally tagged interactions, while in A4galt homodimers, the favored localization of the fused tag depended on the cell line used. Furthermore, by employing AlphaFold for state-of-the-art structural prediction, we analyzed the interactions and structures of these enzyme complexes. Our analysis highlighted that the A4galt-B4galt5 heterodimer exhibited the highest prediction confidence, indicating a significant role of A4galt heterodimerization in determining enzyme specificity toward GSLs and GPs. These findings enhance our knowledge of A4galt acceptor specificity and may contribute to a better comprehension of pathomechanisms of the Shiga toxin-related diseases.

Databases of ligand-binding pockets and protein-ligand interactions

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

Structure-based drug design and characterization of novel pyrazine hydrazinylidene derivatives with a benzenesulfonate scaffold as noncovalent inhibitors of DprE1 tor tuberculosis treatment

In this study, we present a novel series of (E)-4-((2-(pyrazine-2-carbonyl) hydrazineylidene)methyl)phenyl benzenesulfonate (T1-T8) and 4-((E)-(((Z)-amino(pyrazin-2-yl)methylene)hydrazineylidene)methyl)phenyl benzenesulfonate (T9-T16) derivatives which exert their inhibitory effects on decaprenylphosphoryl-β-D-ribose 2'-epimerase (DprE1) through the formation of hydrogen bonds with the pivotal active site Cys387 residue. Their effectiveness against the M. tuberculosis H37Rv strain was examined and notably, three compounds (namely T4, T7, and T12) exhibited promising antitubercular activity, with a minimum inhibitory concentration (MIC) of 1.56 µg/mL. The target compounds were screened for their antibacterial activity against a range of bacterial strains, encompassing S. aureus, B. subtilis, S. mutans, E. coli, S. typhi, and K. pneumoniae. Additionally, their antifungal efficacy against A. fumigatus and A. niger also was scrutinized. Compounds T6 and T12 demonstrated significant antibacterial activity, while compound T6 exhibited substantial antifungal activity. Importantly, all of these active compounds demonstrated exceedingly low toxicity without any adverse effects on normal cells. To deepen our understanding of these compounds, we have undertaken an in silico analysis encompassing Absorption, Distribution, Metabolism, and Excretion (ADME) considerations. Furthermore, molecular docking analyses against the DprE1 enzyme was conducted and Density-Functional Theory (DFT) studies were employed to elucidate the electronic properties of the compounds, thereby enhancing our understanding of their pharmacological potential.

RNA-protein interaction prediction without high-throughput data: An overview and benchmark of in silico tools

RNA-protein interactions (RPIs) are crucial for accurately operating various processes in and between organisms across kingdoms of life. Mutual detection of RPI partner molecules depends on distinct sequential, structural, or thermodynamic features, which can be determined via experimental and bioinformatic methods. Still, the underlying molecular mechanisms of many RPIs are poorly understood. It is further hypothesized that many RPIs are not even described yet. Computational RPI prediction is continuously challenged by the lack of data and detailed research of very specific examples. With the discovery of novel RPI complexes in all kingdoms of life, adaptations of existing RPI prediction methods are necessary. Continuously improving computational RPI prediction is key in advancing the understanding of RPIs in detail and supplementing experimental RPI determination. The growing amount of data covering more species and detailed mechanisms support the accuracy of prediction tools, which in turn support specific experimental research on RPIs. Here, we give an overview of RPI prediction tools that do not use high-throughput data as the user's input. We review the tools according to their input, usability, and output. We then apply the tools to known RPI examples across different kingdoms of life. Our comparison shows that the investigated prediction tools do not favor a certain species and equip the user with results varying in degree of information, from an overall RPI score to detailed interacting residues. Furthermore, we provide a guide tree to assist users which RPI prediction tool is appropriate for their available input data and desired output.

Language models can identify enzymatic binding sites in protein sequences

Recent advances in language modeling have had a tremendous impact on how we handle sequential data in science. Language architectures have emerged as a hotbed of innovation and creativity in natural language processing over the last decade, and have since gained prominence in modeling proteins and chemical processes, elucidating structural relationships from textual/sequential data. Surprisingly, some of these relationships refer to three-dimensional structural features, raising important questions on the dimensionality of the information encoded within sequential data. Here, we demonstrate that the unsupervised use of a language model architecture to a language representation of bio-catalyzed chemical reactions can capture the signal at the base of the substrate-binding site atomic interactions. This allows us to identify the three-dimensional binding site position in unknown protein sequences. The language representation comprises a reaction-simplified molecular-input line-entry system (SMILES) for substrate and products, and amino acid sequence information for the enzyme. This approach can recover, with no supervision, 52.13% of the binding site when considering co-crystallized substrate-enzyme structures as ground truth, vastly outperforming other attention-based models.

Genome-wide identification and data mining reveals major-latex protein (MLP) from the PR-10 protein family played defense-related roles against phytopathogenic challenges in cassava (Manihot esculenta Crantz)

Despite being identified in previous articles, the pathogenesis-related 10 (PR-10) protein remains relatively overlooked and has yet to be fully characterized in numerous plant species. This research employs a comprehensive data mining approach to in silico characterize PR-10 proteins in cassava, a vital crop plant globally. In this study, the focus was on in silico identified 53 cassava PR-10 proteins, which can be categorized into two main subgroups: 34 major latex proteins (MLPs) and 13 major allergen proteins, Pru ar 1, based on their phylogenetic relationship. The genome collinearity analysis with the rubber tree showed a possible evolutionary relationship of the PR-10 gene between these two Euphorbiaceae species, specifically on their chromosome 15. Notably, MLP423 and other MLP proteins were identified in various previously published cassava transcriptome datasets in response to biotic treatments from diverse phytopathogens, including anthracnose fungus, viruses, and bacterial blight. Ligand prediction and molecular docking of three MLP423 proteins have revealed potential interaction with cytokinin and abscisic acid hormones. Their expressions and predicted binding affinities are discussed here, highlighting their role as contributors to cassava's defense network against key diseases.

The Historical Evolution and Significance of Multiple Sequence Alignment in Molecular Structure and Function Prediction

Multiple sequence alignment (MSA) has evolved into a fundamental tool in the biological sciences, playing a pivotal role in predicting molecular structures and functions. With broad applications in protein and nucleic acid modeling, MSAs continue to underpin advancements across a range of disciplines. MSAs are not only foundational for traditional sequence comparison techniques but also increasingly important in the context of artificial intelligence (AI)-driven advancements. Recent breakthroughs in AI, particularly in protein and nucleic acid structure prediction, rely heavily on the accuracy and efficiency of MSAs to enhance remote homology detection and guide spatial restraints. This review traces the historical evolution of MSA, highlighting its significance in molecular structure and function prediction. We cover the methodologies used for protein monomers, protein complexes, and RNA, while also exploring emerging AI-based alternatives, such as protein language models, as complementary or replacement approaches to traditional MSAs in application tasks. By discussing the strengths, limitations, and applications of these methods, this review aims to provide researchers with valuable insights into MSA's evolving role, equipping them to make informed decisions in structural prediction research.

Noncovalent inhibitors of DprE1 for tuberculosis treatment: design, synthesis, characterization, in vitro and in silico studies of 4-oxo-1,4-dihydroquinazolinylpyrazine-2-carboxamides

In this study, we present a novel series of 4-oxo-1,4-dihydroquinazolinylpyrazine-2-carboxamide derivatives, which exert their inhibitory effect on decaprenylphosphoryl-β-D-ribose 2'-epimerase (DprE1) via the establishment of non-covalent interactions with the pivotal Cys387 residue located within the enzyme's active site. These compounds underwent scrutiny for their efficacy in combatting the Mycobacterium tuberculosis H37Rv strain, and compounds T8 and T13 exhibited promising antitubercular activity, boasting a minimal inhibitory concentration (MIC) of 7.99 and 8.27 µM respectively. Additionally, three compounds, T2, T3 and T12, showcased substantial antibacterial activity whereas compounds T12 and T13 exhibited pronounced antifungal efficacy. Remarkably, all active compounds demonstrated negligible cytotoxicity, and none posed harm to normal cells. To attain a more profound comprehension of the attributes of these compounds, we conducted in silico investigations to evaluate their Absorption, Distribution, Metabolism and Excretion properties. Additionally, molecular docking analyses were executed to elucidate their interactions with the DprE1 enzyme. Finally, Density Functional Theory studies were leveraged to explore the electronic characteristics of these compounds, thereby providing insights into their potential utility in the realm of pharmaceuticals.

Comparative evaluation of methods for the prediction of protein-ligand binding sites

The accurate identification of protein-ligand binding sites is of critical importance in understanding and modulating protein function. Accordingly, ligand binding site prediction has remained a research focus for over three decades with over 50 methods developed and a change of paradigm from geometry-based to machine learning. In this work, we collate 13 ligand binding site predictors, spanning 30 years, focusing on the latest machine learning-based methods such as VN-EGNN, IF-SitePred, GrASP, PUResNet, and DeepPocket and compare them to the established P2Rank, PRANK and fpocket and earlier methods like PocketFinder, Ligsite and Surfnet. We benchmark the methods against the human subset of our new curated reference dataset, LIGYSIS. LIGYSIS is a comprehensive protein-ligand complex dataset comprising 30,000 proteins with bound ligands which aggregates biologically relevant unique protein-ligand interfaces across biological units of multiple structures from the same protein. LIGYSIS is an improvement for testing methods over earlier datasets like sc-PDB, PDBbind, binding MOAD, COACH420 and HOLO4K which either include 1:1 protein-ligand complexes or consider asymmetric units. Re-scoring of fpocket predictions by PRANK and DeepPocket display the highest recall (60%) whilst IF-SitePred presents the lowest recall (39%). We demonstrate the detrimental effect that redundant prediction of binding sites has on performance as well as the beneficial impact of stronger pocket scoring schemes, with improvements up to 14% in recall (IF-SitePred) and 30% in precision (Surfnet). Finally, we propose top-N+2 recall as the universal benchmark metric for ligand binding site prediction and urge authors to share not only the source code of their methods, but also of their benchmark.Scientific contributionsThis study conducts the largest benchmark of ligand binding site prediction methods to date, comparing 13 original methods and 15 variants using 10 informative metrics. The LIGYSIS dataset is introduced, which aggregates biologically relevant protein-ligand interfaces across multiple structures of the same protein. The study highlights the detrimental effect of redundant binding site prediction and demonstrates significant improvement in recall and precision through stronger scoring schemes. Finally, top-N+2 recall is proposed as a universal benchmark metric for ligand binding site prediction, with a recommendation for open-source sharing of both methods and benchmarks.

Protein-small molecule binding site prediction based on a pre-trained protein language model with contrastive learning

Predicting protein-small molecule binding sites, the initial step in structure-guided drug design, remains challenging for proteins lacking experimentally derived ligand-bound structures. Here, we propose CLAPE-SMB, which integrates a pre-trained protein language model with contrastive learning to provide high accuracy predictions of small molecule binding sites that can accommodate proteins without a published crystal structure. We trained and tested CLAPE-SMB on the SJC dataset, a non-redundant dataset based on sc-PDB, JOINED, and COACH420, and achieved an MCC of 0.529. We also compiled the UniProtSMB dataset, which merges sites from similar proteins based on raw data from UniProtKB database, and achieved an MCC of 0.699 on the test set. In addition, CLAPE-SMB achieved an MCC of 0.815 on our intrinsically disordered protein (IDP) dataset that contains 336 non-redundant sequences. Case studies of DAPK1, RebH, and Nep1 support the potential of this binding site prediction tool to aid in drug design. The code and datasets are freely available at https://github.com/JueWangTHU/CLAPE-SMB . SCIENTIFIC CONTRIBUTION: CLAPE-SMB combines a pre-trained protein language model with contrastive learning to accurately predict protein-small molecule binding sites, especially for proteins without experimental structures, such as IDPs. Trained across various datasets, this model shows strong adaptability, making it a valuable tool for advancing drug design and understanding protein-small molecule interactions.

Investigating the potential of natural compounds as novel inhibitors of SARS-CoV-2 RdRP using computational approaches

COVID-19 is a highly contagious disease caused by SARS-CoV-2. Currently, no vaccines or antiviral treatments are available to combat this deadly virus; however, precautions and some repurposed medicines are available to contain COVID-19. RNA-dependent RNA polymerase (RdRP) plays an important role in the replication or transcription of viral mechanisms. Approved antiviral drug such as Remdesivir has shown inhibitory activity against SARS-CoV-2 RdRP. The purpose of this study was to carry out a rational screening of natural products against SARS-CoV-2 RdRP, which may serve as a basis to develop a treatment option against COVID-19. For this purpose, a protein and structure conservation analysis of SARS-CoV-2 RdRP was performed to check mutations. A library of 15,000 phytochemicals was developed from literature review, ZINC database, PubChem and MPD3 database; and was used to performed molecular docking and molecular dynamics simulation (MD) analysis. The top-ranked compounds were subjected to pharmacokinetic and pharmacological studies. Among them, top 7 compounds (Spinasaponin A, Monotropane, Neohesperidoe, Posin, Docetaxel, Psychosaponin B2, Daphnodrine M, and Target Remedesvir) were noticed to interact with the active site residues. MD simulation in aqueous solution suggested conformational flexibility of loop regions in the complex to stabilize the docked inhibitors. Our study revealed that the studied compounds have potential to bind to the active site residues of SARS-CoV-2 RdRP. Although, this computational work is not experimentally determined but the structural information and selected compounds might help in the design of antiviral drugs targeting SAR-CoV-2 by inhibiting the activity of SARS-CoV-2 RdRP.

Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots

Identifying druggable binding sites on proteins is an important and challenging problem, particularly for cryptic, allosteric binding sites that may not be obvious from X-ray, cryo-EM, or predicted structures. The Site-Identification by Ligand Competitive Saturation (SILCS) method accounts for the flexibility of the target protein using all-atom molecular simulations that include various small molecule solutes in aqueous solution. During the simulations, the combination of protein flexibility and comprehensive sampling of the water and solute spatial distributions can identify buried binding pockets absent in experimentally determined structures. Previously, we reported a method for leveraging the information in the SILCS sampling to identify binding sites (termed Hotspots) of small mono- or bicyclic compounds, a subset of which coincide with known binding sites of drug-like molecules. Here, we build on that physics-based approach and present a ML model for ranking the Hotspots according to the likelihood they can accommodate drug-like molecules (e.g., molecular weight >200 Da). In the independent validation set, which includes various enzymes and receptors, our model recalls 67% and 89% of experimentally validated ligand binding sites in the top 10 and 20 ranked Hotspots, respectively. Furthermore, we show that the model's output Decision Function is a useful metric to predict binding sites and their potential druggability in new targets. Given the utility the SILCS method for ligand discovery and optimization, the tools presented represent an important advancement in the identification of orthosteric and allosteric binding sites and the discovery of drug-like molecules targeting those sites.

Exploring the antiviral inhibitory activity of Niloticin against the NS2B/NS3 protease of Dengue virus (DENV2)

Dengue virus (DENV2) is the cause of dengue disease and a worldwide health problem. DENV2 replicates in the host cell using polyproteins such as NS3 protease in conjugation with NS2B cofactor, making NS3 protease a promising antiviral drug-target. This study investigated the efficacy of 'Niloticin' against NS2B/NS3-protease. In silico and in vitro analyses were performed which included interaction of niloticin with NS2B/NS3-protease, protein stability and flexibility, mutation effect, betweenness centrality of residues and analysis of cytotoxicity, protein expression and WNV NS3-protease activity. Similar like acyclovir, niloticin forms strong H-bonds and hydrophobic interactions with residues LEU149, ASN152, LYS74, GLY148 and ALA164. The stability of the niloticin-NS2B/NS3-protease complex was found to be stable compared to the apo NS2B/NS3-protease in structural deviation, PCA, compactness and FEL analysis. The IC50 value of niloticin was 0.14 μM in BHK cells based on in vitro cytotoxicity analysis and showed significant activity at 2.5 μM in a concentration-dependent manner. Western blotting and qRT-PCR analyses showed that niloticin reduced DENV2 protein transcription in a dose-dependent manner. Besides, niloticin confirmed the inhibition of NS3-protease by the SensoLyte 440 WNV protease detection kit. These promising results suggest that niloticin could be an effective antiviral drug against DENV2 and other flaviviruses.

Evaluation of the novel endophytic fungus Chaetomium ascotrichoides 1-24-2 from Pinus massoniana as a biocontrol agent against pine wilt disease caused by Bursaphelenchus xylophilus

BACKGROUND

Bursaphelenchus xylophilus, the causative agent of pine wilt disease (PWD), is an ever-increasing threat to Pinus forests worldwide. This study aimed to develop biological control of PWD by the application of endophytic fungi isolated from healthy pine trees.

RESULTS

We successfully isolated a novel endophytic fungal strain 1-24-2 from branches of healthy Pinus massoniana. The culture filtrates (CFs) of strain 1-24-2 exhibited strong nematicidal activity against Bursaphelenchus xylophilus, with a corrected mortality rate of 99.00%. Based on the morphological and molecular characteristics, the isolated strain 1-24-2 was identified as Chaetomium ascotrichoides. In the in-planta assay, pine seedlings (2-years-old) treated with 1-24-2 CFs + pine wood nematode (T2) showed a significant control effect of 80%. A total of 24 toxic compounds were first identified from 1-24-2 CFs through gas chromatography-mass spectrometry (GC-MS) analysis, from which O-methylisourea, 2-chlorobenzothiazole, and 4,5,6-trihydroxy-7-methylphthalide showed robust binding sites at Tyr119 against phosphoethanolamine methyltransferase (PMT) protein of Bursaphelenchus xylophilus by molecular docking approach and could be used as potential compounds for developing effective nematicides. Interestingly, strain 1-24-2 produces toxic volatile organic compounds (VOCs), which disturb the natural development process of B. xylophilus, whose total number decreased by up to 83.32% in the treatment group as compared to control and also reduced Botrytis cinerea growth by up to 71.01%.

CONCLUSION

Our results highlight the potential of C. ascotrichoides 1-24-2 as a promising biocontrol agent with solid nematicidal activity against B. xylophilus. This is the first report of C. ascotrichoides isolated from P. massoniana exhibiting strong biocontrol potential against B. xylophilus in the world. © 2024 Society of Chemical Industry.

Effect of cysteine mutation at Ca2+ coordinating residues to the autolysis, folding and hydrophobicity of full length and mature Rand protease: molecular dynamics simulation and essential dynamics

Rand protease is a serine protease that shared common characteristics with members of the MEROPS S8 subtilisin family. It is thermostable, highly stable in organic solvent and broad in specificity. Many structures of homologous protein solved by X-ray crystallography and NMR have been deposited to Protein Data Bank (PDB) which allowed this study to rely on structure prediction by deep learning to build three-dimensional (3D) structure of full length and mature Rand protease (flRP and mRP). In silico cysteine mutation to 7 predicted high affinity Ca2+ coordinating residues were introduced, and the mutants were subjected to molecular dynamics simulation to study its effect on flRP and mRP. MD simulation showed a marked increase in flexibility of the pro-peptide segment indicating the impact of single cysteine substitution at high affinity Ca2+ coordinating residues to autolysis of flRP. MD simulation for mRP reaffirmed the role of Ca2+ coordinating sites in providing stability to Rand protease. In addition, these residues also affect the autolysis, folding and hydrophobicity of RP. Essential dynamics observed large contribution of the first few eigenvectors of flRP, mRP and their high affinity Ca2+ coordinating residues mutants to the TMSF values which indicates that these values account for a large portion of the overall atomic fluctuations. These results have given a more comprehensive understanding on the role of cysteine substituted Ca2+ coordinating surface loop to the structure of flRP and mRP which are important in contributing to the structural stability of subtilisin.Communicated by Ramaswamy H. Sarma.

Enterobacter cloacae-mediated polymer biodegradation: in-silico analysis predicts broad spectrum degradation potential by Alkane monooxygenase

Plastic pollution poses a significant environmental challenge. In this study, the strain Enterobacter cloacae O5-E, a bacterium displaying polyethylene-degrading capabilities was isolated. Over a span of 30 days, analytical techniques including x-ray diffractometry, scanning electron microscopy, optical profilometry, hardness testing and mass spectrometric analysis were employed to examine alterations in the polymer. Results revealed an 11.48% reduction in crystallinity, a 50% decrease in hardness, and a substantial 25-fold increase in surface roughness resulting from the pits and cracks introduced in the polymer by the isolate. Additionally, the presence of degradational by-products revealed via gas chromatography ascertains the steady progression of degradation. Further, recognizing the pivotal role of alkane monooxygenase in plastic degradation, the study expanded to detect this enzyme in the isolate molecularly. Molecular docking studies were conducted to assess the enzyme's affinity with various polymers, demonstrating notable binding capability with most polymers, especially with polyurethane (- 5.47 kcal/mol). These findings highlight the biodegradation potential of Enterobacter cloacae O5-E and the crucial involvement of alkane monooxygenase in the initial steps of the degradation process, offering a promising avenue to address the global plastic pollution crisis.

Turbocharging protein binding site prediction with geometric attention, inter-resolution transfer learning, and homology-based augmentation

BACKGROUND

Locating small molecule binding sites in target proteins, in the resolution of either pocket or residue, is critical in many drug-discovery scenarios. Since it is not always easy to find such binding sites using conventional methods, different deep learning methods to predict binding sites out of protein structures have been developed in recent years. The existing deep learning based methods have several limitations, including (1) the inefficiency of the CNN-only architecture, (2) loss of information due to excessive post-processing, and (3) the under-utilization of available data sources.

METHODS

We present a new model architecture and training method that resolves the aforementioned problems. First, by layering geometric self-attention units on top of residue-level 3D CNN outputs, our model overcomes the problems of CNN-only architectures. Second, by configuring the fundamental units of computation as residues and pockets instead of voxels, our method reduced the information loss from post-processing. Lastly, by employing inter-resolution transfer learning and homology-based augmentation, our method maximizes the utilization of available data sources to a significant extent.

RESULTS

The proposed method significantly outperformed all state-of-the-art baselines regarding both resolutions-pocket and residue. An ablation study demonstrated the indispensability of our proposed architecture, as well as transfer learning and homology-based augmentation, for achieving optimal performance. We further scrutinized our model's performance through a case study involving human serum albumin, which demonstrated our model's superior capability in identifying multiple binding sites of the protein, outperforming the existing methods.

CONCLUSIONS

We believe that our contribution to the literature is twofold. Firstly, we introduce a novel computational method for binding site prediction with practical applications, substantiated by its strong performance across diverse benchmarks and case studies. Secondly, the innovative aspects in our method- specifically, the design of the model architecture, inter-resolution transfer learning, and homology-based augmentation-would serve as useful components for future work.

Functional characterization of prenyltransferases involved in de novo synthesis of isoprenoids in the leaf beetle Monolepta hieroglyphica

Prenyltransferases play a pivotal role in the isoprenoid biosynthesis and transfer in insects. In the current study, two classes of prenyltransferases (MhieFPPS1 and MhieFPPS2, MhiePFT-β and MhiePF/GGT-α) were identified in the leaf beetle, Monolepta hieroglyphica. Phylogenetic analysis revealed that MhieFPPS1, MhieFPPS2, MhiePFT-β and MhiePF/GGT-α were clustered in one clade with homologous in insects. Moreover, MhieFPPS2 lacked one aspartate-rich motif SARM. Molecular docking and kinetic analysis indicated that the (E)-GPP displayed higher affinity with MhieFPPS1 compared to DMAPP within the binding pocket containing metal binding sites (MG). The other class of prenyltransferases (MhiePFT-β and MhiePF/GGT-α) lack the aspartate-rich motif. Docking results indicated that binding site of MhiePFT-β involved divalent metal ions (Zn) and bound farnesyl or geranylgeranyl. In vitro, only recombiant MhieFPPS1 could catalyze the formation of (E)-farnesol against different combination of substrates, including IPP/DMAPP and IPP/(E)-GPP, highlighting the importance of SARM for enzyme activities. Kinetic analysis further indicated that MhiePFT-β operated via Zn2+-dependent substrate binding, while MhiePF/GGT-α stabilized the β-subunit during catalytic reaction. These findings contribute to a valuable insight in to understanding of the mechanisms involved in the biosynthesis and delivery of isoprenoid products in beetles.

Structure-based drug designing against Leishmania donovani using docking and molecular dynamics simulation studies: exploring glutathione synthetase as a drug target

In the pursuit of developing novel anti-leishmanial agents, we conducted an extensive computational study to screen inhibitors from the FDA-approved ZINC database against Leishmania donovani glutathione synthetase. The three-dimensional structure of Leishmania donovani glutathione synthetase was constructed by homology modeling, using the crystallographic structure of Trypanosoma brucei glutathione synthetase as a template. Subsequently, molecular docking studies were carried out for a large number of compounds using AutoDock Vina. Among the screened compounds, we selected the top five with strong binding affinity to Leishmania donovani glutathione synthetase but having a very low affinity to its human homolog. Further investigations on protein-ligand complexes were done by conducting molecular dynamics (MD) simulation and MM/PBSA analysis. The results revealed that Olysio (Simeprevir) exhibited the lowest binding energy (-89.21 kcal/mol), followed by Telithromycin (-45.34 kcal/mol). These findings showed that these compounds have the potential to act as inhibitors of glutathione synthetase. Hence, our study provides valuable insights for the development of a novel therapeutic strategy against Leishmania donovani by targeting the glutathione synthetase enzyme.Communicated by Ramaswamy H. Sarma.

A Point Cloud Graph Neural Network for Protein-Ligand Binding Site Prediction

Predicting protein-ligand binding sites is an integral part of structural biology and drug design. A comprehensive understanding of these binding sites is essential for advancing drug innovation, elucidating mechanisms of biological function, and exploring the nature of disease. However, accurately identifying protein-ligand binding sites remains a challenging task. To address this, we propose PGpocket, a geometric deep learning-based framework to improve protein-ligand binding site prediction. Initially, the protein surface is converted into a point cloud, and then the geometric and chemical properties of each point are calculated. Subsequently, the point cloud graph is constructed based on the inter-point distances, and the point cloud graph neural network (GNN) is applied to extract and analyze the protein surface information to predict potential binding sites. PGpocket is trained on the scPDB dataset, and its performance is verified on two independent test sets, Coach420 and HOLO4K. The results show that PGpocket achieves a 58% success rate on the Coach420 dataset and a 56% success rate on the HOLO4K dataset. These results surpass competing algorithms, demonstrating PGpocket's advancement and practicality for protein-ligand binding site prediction.

Decoding the TAome and computational insights into parDE toxin-antitoxin systems in Pseudomonas aeruginosa

Toxin-antitoxin (TA) modules are widely found in the genomes of pathogenic bacteria. They regulate vital cellular functions like transcription, translation, and DNA replication, and are therefore essential to the survival of bacteria under stress. With a focus on the type II parDE modules, this study thoroughly examines TAome in Pseudomonas aeruginosa, a bacterium well-known for its adaptability and antibiotic resistance. We explored the TAome in three P. aeruginosa strains: ATCC 27,853, PAO1, and PA14, and found 15 type II TAs in ATCC 27,853, 12 in PAO1, and 13 in PA14, with significant variation in the associated mobile genetic elements. Five different parDE homologs were found by further TAome analysis in ATCC 27,853, and their relationships were confirmed by sequence alignments and precise genomic positions. After comparing these ParDE modules' sequences to those of other pathogenic bacteria, it was discovered that they were conserved throughout many taxa, especially Proteobacteria. Nucleic acids were predicted as potential ligands for ParD antitoxins, whereas ParE toxins interacted with a wide range of small molecules, indicating a diverse functional repertoire. The interaction interfaces between ParDE TAs were clarified by protein-protein interaction networks and docking studies, which also highlighted important residues involved in binding. This thorough examination improves our understanding of the diversity, evolutionary dynamics, and functional significance of TA systems in P. aeruginosa, providing insights into their roles in bacterial physiology and pathogenicity.