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

Combined In Silico and In Vitro Study to Reveal the Structural Insights and Nucleotide-Binding Ability of the Transcriptional Regulator PehR from the Phytopathogen Ralstonia solanacearum

The transcriptional regulator PehR regulates the synthesis of the extracellular plant cell wall-degrading enzyme polygalacturonase, which is essential in the bacterial wilt of plants caused by one of the most devastating plant phytopathogens, Ralstonia solanacearum. The bacterium has a wide global distribution infecting many different plant species, resulting in massive agricultural and economic losses. Because the PehR molecular structure has not yet been determined and the structural consequences of PehR on ligand binding have not been thoroughly investigated, we have used an in silico approach combined with in vitro experiments for the first time to characterize the PehR regulator from a local isolate (Tezpur, Assam, India) of the phytopathogenic bacterium R. solanacearum F1C1. In this study, an in silico approach was employed to model the 3D structure of the PehR regulator, followed by the binding analysis of different ligands against this regulatory protein. Molecular docking studies suggest that ATP has the highest binding affinity for the PehR regulator. By using molecular dynamics (MD) simulation analysis, involving root-mean-square deviation, root-mean-square fluctuations, hydrogen bonding, radius of gyration, solvent-accessible surface area, and principal component analysis, it was possible to confirm the sudden conformational changes of the PehR regulator caused by the presence of ATP. We used an in vitro approach to further validate the formation of the PehR-ATP complex. In this approach, recombinant DNA technology was used to clone, express, and purify the gene encoding the PehR regulator from R. solanacearum F1C1. Purified PehR was used in ATP-binding experiments using fluorescence spectroscopy and Fourier transform infrared spectroscopy, the outcomes of which showed a potent binding to ATP. The putative PehR-ATP-binding analysis revealed the importance of the amino acids Lys190, Glu191, Arg192, Arg375, and Asp378 for the ATP-binding process, but further study is required to confirm this. It will be simpler to comprehend the catalytic mechanisms of a crucial PehR regulator process in R. solanacearum with the aid of the ATP-binding process hints provided by these structural biology applications.

Identification of histidine kinase inhibitors through screening of natural compounds to combat mastitis caused by Streptococcus agalactiae in dairy cattle

BACKGROUND

Mastitis poses a major threat to dairy farms globally; it results in reduced milk production, increased treatment costs, untimely compromised genetic potential, animal deaths, and economic losses. Streptococcus agalactiae is a highly virulent bacteria that cause mastitis. The administration of antibiotics for the treatment of this infection is not advised due to concerns about the emergence of antibiotic resistance and potential adverse effects on human health. Thus, there is a critical need to identify new therapeutic approaches to combat mastitis. One promising target for the development of antibacterial therapies is the transmembrane histidine kinase of bacteria, which plays a key role in signal transduction pathways, secretion systems, virulence, and antibiotic resistance.

RESULTS

In this study, we aimed to identify novel natural compounds that can inhibit transmembrane histidine kinase. To achieve this goal, we conducted a virtual screening of 224,205 natural compounds, selecting the top ten based on their lowest binding energy and favorable protein-ligand interactions. Furthermore, molecular docking of eight selected antibiotics and five histidine kinase inhibitors with transmembrane histidine kinase was performed to evaluate the binding energy with respect to top-screened natural compounds. We also analyzed the ADMET properties of these compounds to assess their drug-likeness. The top two compounds (ZINC000085569031 and ZINC000257435291) and top-screened antibiotics (Tetracycline) that demonstrated a strong binding affinity were subjected to molecular dynamics simulations (100 ns), free energy landscape, and binding free energy calculations using the MM-PBSA method.

CONCLUSION

Our results suggest that the selected natural compounds have the potential to serve as effective inhibitors of transmembrane histidine kinase and can be utilized for the development of novel antibacterial veterinary medicine for mastitis after further validation through clinical studies.

Prediction of Protein Ion-Ligand Binding Sites with ELECTRA

Interactions between proteins and ions are essential for various biological functions like structural stability, metabolism, and signal transport. Given that more than half of all proteins bind to ions, it is becoming crucial to identify ion-binding sites. The accurate identification of protein-ion binding sites helps us to understand proteins' biological functions and plays a significant role in drug discovery. While several computational approaches have been proposed, this remains a challenging problem due to the small size and high versatility of metals and acid radicals. In this study, we propose IonPred, a sequence-based approach that employs ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately) to predict ion-binding sites using only raw protein sequences. We successfully fine-tuned our pretrained model to predict the binding sites for nine metal ions (Zn2+, Cu2+, Fe2+, Fe3+, Ca2+, Mg2+, Mn2+, Na+, and K+) and four acid radical ion ligands (CO32-, SO42-, PO43-, NO2-). IonPred surpassed six current state-of-the-art tools by over 44.65% and 28.46%, respectively, in the F1 score and MCC when compared on an independent test dataset. Our method is more computationally efficient than existing tools, producing prediction results for a hundred sequences for a specific ion in under ten minutes.

In-silico prediction, characterization, molecular docking and dynamic simulation studies for screening potential fungicides against leaf rust of Triticum aestivum

Triticum aestivum is an important crop worldwide, which is a large source of food grain. T.aestivum demands on developed countries will grow every year, this increase in the demand is profoundly serious especially in the light climate change which would lead to a 29% reduction in final productivity. Rust fungus attacks the T.aestivum, specifically newly planted T.aestivum plants, which block the vascular system, stun, and finally damage grain and tillers. In present study we predict the 3D structure then find the binding pocket and conserved domains for MAPkinase-1 of Puccinia triticina. After that, screen the FungiPAD, PubChem, NPAtlas databases by physicochemical properties, docking, clustering, ADME (Absorption, distribution, metabolism, and excretion) and PAINS (pan assay interference compounds) filter analysis. Through this screening process screen the nine compounds, which are benzovindiflupyr, furametpyr, isopyrazam, fenaminstrobin, and flumorph from Fungicide database: zoxamide, vinclozolin, pentachloronitrobenzene, and dithianon from PubChem database, based on the binding energy, clustering, ADME and PAINS analysis. All these nine compounds bind in the same pocket and show the same pattern of interaction. Among these nine compounds, select the two compounds (PubChem:122087 (-6.96 kcal/mol) and FDBD02904 (-8.62 kcal/mol)) based on binding energy for 100 ns MD simulation and free energy calculation. MD simulation shows stability throughout the simulation, and it shows the sable interaction when compounds bind to the MAPKinase 1 protein which may help to protein kinase pathways in plant defense response. This result helps to design alternative fungicide against the wheat rust disease.Communicated by Ramaswamy H. Sarma.

Structure-based prediction of nucleic acid binding residues by merging deep learning- and template-based approaches

Accurate prediction of nucleic binding residues is essential for the understanding of transcription and translation processes. Integration of feature- and template-based strategies could improve the prediction of these key residues in proteins. Nevertheless, traditional hybrid algorithms have been surpassed by recently developed deep learning-based methods, and the possibility of integrating deep learning- and template-based approaches to improve performance remains to be explored. To address these issues, we developed a novel structure-based integrative algorithm called NABind that can accurately predict DNA- and RNA-binding residues. A deep learning module was built based on the diversified sequence and structural descriptors and edge aggregated graph attention networks, while a template module was constructed by transforming the alignments between the query and its multiple templates into features for supervised learning. Furthermore, the stacking strategy was adopted to integrate the above two modules for improving prediction performance. Finally, a post-processing module dependent on the random walk algorithm was proposed to further correct the integrative predictions. Extensive evaluations indicated that our approach could not only achieve excellent performance on both native and predicted structures but also outperformed existing hybrid algorithms and recent deep learning methods. The NABind server is available at http://liulab.hzau.edu.cn/NABind/.

Protein-DNA Binding Residues Prediction Using a Deep Learning Model With Hierarchical Feature Extraction

Biologically important effects occur when proteins bind to other substances, of which binding to DNA is a crucial one. Therefore, accurate identification of protein-DNA binding residues is important for further understanding of the protein-DNA interaction mechanism. Although wet-lab methods can accurately obtain the location of bound residues, it requires significant human, financial and time costs. There is thus an urgent need to develop efficient computational-based methods. Most current state-of-the-art methods are two-step approaches: the first step uses a sliding window technique to extract residue features; the second step uses each residue as an input to the model for prediction. This has a negative impact on the efficiency of prediction and ease of use. In this study, we propose a sequence-to-sequence (seq2seq) model that can input the entire protein sequence of variable length and use two modules, Transformer Encoder Block and Feature Extracting Block, for hierarchical feature extraction, where Transformer Encoder Block is used to extract global features, and then Feature Extracting Block is used to extract local features to further improve the recognition capability of the model. The comparison results on two benchmark datasets, namely PDNA-543 and PDNA-41, prove the effectiveness of our method in identifying protein-DNA binding residues.

PocketAnchor: Learning structure-based pocket representations for protein-ligand interaction prediction

Protein-ligand interactions are essential for cellular activities and drug discovery processes. Appropriately and effectively representing protein features is of vital importance for developing computational approaches, especially data-driven methods, for predicting protein-ligand interactions. However, existing approaches may not fully investigate the features of the ligand-occupying regions in the protein pockets. Here, we design a structure-based protein representation method, named PocketAnchor, for capturing the local environmental and spatial features of protein pockets to facilitate protein-ligand interaction-related learning tasks. We define "anchors" as probe points reaching into the cavities and those located near the surface of proteins, and we design a specific message passing strategy for gathering local information from the atoms and surface neighboring these anchors. Comprehensive evaluation of our method demonstrated its successful applications in pocket detection and binding affinity prediction, which indicated that our anchor-based approach can provide effective protein feature representations for improving the prediction of protein-ligand interactions.

Structure-Based Multi-Targeted Molecular Docking and Dynamic Simulation of Soybean-Derived Isoflavone Genistin as a Potential Breast Cancer Signaling Proteins Inhibitor

Globally, breast cancer (BC), the second-biggest cause of cancer death, occurs due to unregulated cell proliferation leading to metastasis to other parts of the human organ. Recently, the exploration of naturally derived anticancer agents has become popular due to their fewer adverse effects. Among the natural products, soybean is a very well-known legume that contains important bioactive compounds such as diadazine, glycetin, genistein, and genistin. Therefore, keeping its therapeutic potential in mind, multi-targeted molecular docking and simulation studies were conducted to explore the potential role of soybean-derived isoflavone genistin against several breast cancer-signaling proteins (ER-alpha, ER-Beta, collapsin response mediator protein 2, CA 15-3, human epidermal growth factor receptor 2). A comparative study of the genistin-protein docked complex was explored to investigate its potential role in BC. The molecular binding energy (∆G) of the docked complex was calculated along with ADMET properties. The molecular docking score of genistin with ubiquitin-like protein activation complex-a type of Cancer Antigen (CA) 15.3 (PDB ID-2NVU, 5T6P, and 1YX8) showed the highest binding energy, ranging from -9.5 to -7.0 Kcal/mol, respectively. Furthermore, the highest docking scores of the complex were additionally put through molecular dynamics (MD) simulation analysis. MD simulations of the selected complex were performed at 100 ns to study the stability of the genistin-ubiquitin-like protein CA 15.3 complex, which appeared to be quite stable. Additionally, the ADMET study demonstrated that genistin complies with all drug-likeness standards, including Lipinski, Egan, Veber, Ghose, and Muegge. Therefore, based on the results, genistin can be considered as one of the potential drugs for the management and treatment of BC. In addition, the obtained results suggest that genistin could pave the way for new drug discovery to manage breast cancer and has potential in the development of nutraceuticals.

Caftaric Acid Ameliorates Oxidative Stress, Inflammation, and Bladder Overactivity in Rats Having Interstitial Cystitis: An In Silico Study

Interstitial cystitis (IC) is the principal unwanted effect associated with the use of cyclophosphamide (CYP). It results in increased oxidative stress, overexpression of proinflammatory cytokines, and bladder overactivity. Patients receiving CYP treatment had severely depreciated quality of life, as the treatment available is not safe and effective. The goal of this study was to assess the protective effect of caftaric acid in CYP-induced IC. IC was induced in female Sprague Dawley by injecting CYP (150 mg/kg, i.p.). In the present study, oral administration of caftaric acid (20, 40, and 60 mg/kg) significantly decreased inflammation. Caftaric acid significantly increased SOD (93%), CAT (92%), and GSH (90%) while decreased iNOS (97%), IL-6 (90%), TGF 1-β (83%), and TNF-α (96%) compared to the diseased. DPPH assay showed the antioxidant capacity comparable to ascorbic acid. Molecular docking of caftaric acid with selected protein targets further confirmed its antioxidant and anti-inflammatory activities. The cyclophosphamide-induced bladder overactivity had been decreased possibly through the inhibition of M3 receptors, ATP-sensitive potassium channels, calcium channels, and COX enzyme by caftaric acid. Therefore, our findings demonstrate that caftaric acid has a considerable protective role against CYP-induced IC by decreasing the oxidative stress, inflammation, and bladder smooth muscle hyperexcitability. Thus, caftaric acid signifies a likely adjuvant agent in CYP-based chemotherapy treatments.

DIPS-Plus: The enhanced database of interacting protein structures for interface prediction

In this work, we expand on a dataset recently introduced for protein interface prediction (PIP), the Database of Interacting Protein Structures (DIPS), to present DIPS-Plus, an enhanced, feature-rich dataset of 42,112 complexes for machine learning of protein interfaces. While the original DIPS dataset contains only the Cartesian coordinates for atoms contained in the protein complex along with their types, DIPS-Plus contains multiple residue-level features including surface proximities, half-sphere amino acid compositions, and new profile hidden Markov model (HMM)-based sequence features for each amino acid, providing researchers a curated feature bank for training protein interface prediction methods. We demonstrate through rigorous benchmarks that training an existing state-of-the-art (SOTA) model for PIP on DIPS-Plus yields new SOTA results, surpassing the performance of some of the latest models trained on residue-level and atom-level encodings of protein complexes to date.

Structural Insights into the Giardia lamblia Target of Rapamycin Homolog: A Bioinformatics Approach

TOR proteins, also known as targets of rapamycin, are serine/threonine kinases involved in various signaling pathways that regulate cell growth. The protozoan parasite Giardia lamblia is the causative agent of giardiasis, a neglected infectious disease in humans. In this study, we used a bioinformatics approach to examine the structural features of GTOR, a G. lamblia TOR-like protein, and predict functional associations. Our findings confirmed that it shares significant similarities with functional TOR kinases, including a binding domain for the FKBP-rapamycin complex and a kinase domain resembling that of phosphatidylinositol 3-kinase-related kinases. In addition, it can form multiprotein complexes such as TORC1 and TORC2. These results provide valuable insights into the structure-function relationship of GTOR, highlighting its potential as a molecular target for controlling G. lamblia cell proliferation. Furthermore, our study represents a step toward rational drug design for specific anti-giardiasis therapeutic agents.

Discovering Deleterious Single Nucleotide Polymorphisms of Human AKT1 Oncogene: An In Silico Study

BACKGROUND

AKT1 is a serine/threonine kinase necessary for the mediation of apoptosis, angiogenesis, metabolism, and cell proliferation in both normal and cancerous cells. The mutations in the AKT1 gene have been associated with different types of cancer. Further, the AKT1 gene mutations are also reported to be associated with other diseases such as Proteus syndrome and Cowden syndromes. Hence, this study aims to identify the deleterious AKT1 missense SNPs and predict their effect on the function and structure of the AKT1 protein using various computational tools.

METHODS

Extensive in silico approaches were applied to identify deleterious SNPs of the human AKT1 gene and assessment of their impact on the function and structure of the AKT1 protein. The association of these highly deleterious missense SNPs with different forms of cancers was also analyzed. The in silico approach can help in reducing the cost and time required to identify SNPs associated with diseases.

RESULTS

In this study, 12 highly deleterious SNPs were identified which could affect the structure and function of the AKT1 protein. Out of the 12, four SNPs-namely, G157R, G159V, G336D, and H265Y-were predicted to be located at highly conserved residues. G157R could affect the ligand binding to the AKT1 protein. Another highly deleterious SNP, R273Q, was predicted to be associated with liver cancer.

CONCLUSIONS

This study can be useful for pharmacogenomics, molecular diagnosis of diseases, and developing inhibitors of the AKT1 oncogene.

Identification of inhibitors for the transmembrane Trypanosoma cruzi eIF2α kinase relevant for parasite proliferation

The TcK2 protein kinase of Trypanosoma cruzi, the causative agent of Chagas disease, is structurally similar to the human kinase PERK, which phosphorylates the initiation factor eIF2α and, in turn, inhibits translation initiation. We have previously shown that absence of TcK2 kinase impairs parasite proliferation within mammalian cells, positioning it as a potential target for treatment of Chagas disease. To better understand its role in the parasite, here we initially confirmed the importance of TcK2 in parasite proliferation by generating CRISPR/Cas9 TcK2-null cells, albeit they more efficiently differentiate into infective forms. Proteomics indicates that the TcK2 knockout of proliferative forms expresses proteins including trans-sialidases, normally restricted to infective and nonproliferative trypomastigotes explaining decreased proliferation and better differentiation. TcK2 knockout cells lost phosphorylation of eukaryotic initiation factor 3 and cyclic AMP responsive-like element, recognized to promote growth, likely explaining both decreased proliferation and augmented differentiation. To identify specific inhibitors, a library of 379 kinase inhibitors was screened by differential scanning fluorimetry using a recombinant TcK2 encompassing the kinase domain and selected molecules were tested for kinase inhibition. Only Dasatinib and PF-477736, inhibitors of Src/Abl and ChK1 kinases, showed inhibitory activity with IC50 of 0.2 ± 0.02 mM and 0.8 ± 0.1, respectively. In infected cells Dasatinib inhibited growth of parental amastigotes (IC50 = 0.6 ± 0.2 mM) but not TcK2 of depleted parasites (IC50 > 34 mM) identifying Dasatinib as a potential lead for development of therapeutics for Chagas disease targeting TcK2.

20-hydroxyecdysone reprograms amino acid metabolism to support the metamorphic development of Helicoverpa armigera

Amino acid metabolism is regulated according to nutrient conditions; however, the mechanism is not fully understood. Using the holometabolous insect cotton bollworm (Helicoverpa armigera) as a model, we report that hemolymph metabolites are greatly changed from the feeding larvae to the wandering larvae and to pupae. Arginine, alpha-ketoglutarate (α-KG), and glutamate (Glu) are identified as marker metabolites of feeding larvae, wandering larvae, and pupae, respectively. Arginine level is decreased by 20-hydroxyecdysone (20E) regulation via repression of argininosuccinate synthetase (Ass) expression and upregulation of arginase (Arg) expression during metamorphosis. α-KG is transformed from Glu by glutamate dehydrogenase (GDH) in larval midgut, which is repressed by 20E. The α-KG is then transformed to Glu by GDH-like in pupal fat body, which is upregulated by 20E. Thus, 20E reprogrammed amino acid metabolism during metamorphosis by regulating gene expression in a stage- and tissue-specific manner to support insect metamorphic development.

Evolutionary origin of B family DNA-dependent DNA polymerases from retrotranscriptases

The evolution of DNA and DNA polymerases was a crucial step in the evolution of life on Earth. In the present work, we reconstruct the ancestral sequence and structure for the B family polymerases. Using comparative analyses, we infer the transient state between the ancestor retrotranscriptase and the contemporary B family DNA polymerases. Exonuclease motif was detected in the primary ancestral sequence, as well as an elongation-functioning motif. It is remarkable that the ancestral molecule is more comparable to the retrotranscriptases in terms of structural domains, even though we discovered similarities in the primary sequence with proteins from the B family of DNA polymerases. The present B family proteins differ structurally from retrotranscriptases the most, although the reconstruction of the ancestor protein was able to capture the transitional steps between these two families of polymerases.

An Outstandingly Rare Occurrence of Mycoviruses in Soil Strains of the Plant-Beneficial Fungi from the Genus Trichoderma and a Novel Polymycoviridae Isolate

In fungi, viral infections frequently remain cryptic causing little or no phenotypic changes. It can indicate either a long history of coevolution or a strong immune system of the host. Some fungi are outstandingly ubiquitous and can be recovered from a great diversity of habitats. However, the role of viral infection in the emergence of environmental opportunistic species is not known. The genus of filamentous and mycoparasitic fungi Trichoderma (Hypocreales, Ascomycota) consists of more than 400 species, which mainly occur on dead wood, other fungi, or as endo- and epiphytes. However, some species are environmental opportunists because they are cosmopolitan, can establish in a diversity of habitats, and can also become pests on mushroom farms and infect immunocompromised humans. In this study, we investigated the library of 163 Trichoderma strains isolated from grassland soils in Inner Mongolia, China, and found only four strains with signs of the mycoviral nucleic acids, including a strain of T. barbatum infected with a novel strain of the Polymycoviridae and named and characterized here as Trichoderma barbatum polymycovirus 1 (TbPMV1). Phylogenetic analysis suggested that TbPMV1 was evolutionarily distinct from the Polymycoviridae isolated either from Eurotialean fungi or from the order Magnaportales. Although the Polymycoviridae viruses were also known from Hypocrealean Beauveria bassiana, the phylogeny of TbPMV1 did not reflect the phylogeny of the host. Our analysis lays the groundwork for further in-depth characterization of TbPMV1 and the role of mycoviruses in the emergence of environmental opportunism in Trichoderma. IMPORTANCE Although viruses infect all organisms, our knowledge of some groups of eukaryotes remains limited. For instance, the diversity of viruses infecting fungi-mycoviruses-is largely unknown. However, the knowledge of viruses associated with industrially relevant and plant-beneficial fungi, such as Trichoderma spp. (Hypocreales, Ascomycota), may shed light on the stability of their phenotypes and the expression of beneficial traits. In this study, we screened the library of soilborne Trichoderma strains because these isolates may be developed into bioeffectors for plant protection and sustainable agriculture. Notably, the diversity of endophytic viruses in soil Trichoderma was outstandingly low. Only 2% of 163 strains contained traces of dsRNA viruses, including the new Trichoderma barbatum polymycovirus 1 (TbPMV1) characterized in this study. TbPMV1 is the first mycovirus found in Trichoderma. Our results indicate that the limited data prevent the in-depth study of the evolutionary relationship between soilborne fungi and is worth further investigation.

In-silico assessment of high-risk non-synonymous SNPs in ADAMTS3 gene associated with Hennekam syndrome and their impact on protein stability and function

Hennekam Lymphangiectasia-Lymphedema Syndrome 3 (HKLLS3) is a rare genetical disorder caused by mutations in a few genes including ADAMTS3. It is characterized by lymphatic dysplasia, intestinal lymphangiectasia, severe lymphedema and distinctive facial appearance. Up till now, no extensive studies have been conducted to elucidate the mechanism of the disease caused by various mutations. As a preliminary investigation of HKLLS3, we sorted out the most deleterious nonsynonymous single nucleotide polymorphisms (nsSNPs) that might affect the structure and function of ADAMTS3 protein by using a variety of in silico tools. A total of 919 nsSNPs in the ADAMTS3 gene were identified. 50 nsSNPs were predicted to be deleterious by multiple computational tools. 5 nsSNPs (G298R, C567Y, A370T, C567R and G374S) were found to be the most dangerous and can be associated with the disease as predicted by different bioinformatics tools. Modelling of the protein shows it can be divided into segments 1, 2 and 3, which are connected by short loops. Segment 3 mainly consists of loops without substantial secondary structures. With prediction tools and molecular dynamics simulation, some SNPs were found to significantly destabilize the protein structure and disrupt the secondary structures, especially in segment 2. The deleterious effects of mutations in segment 1 are possibly not from destabilization but from other factors such as the change in phosphorylation as suggested by post-translational modification (PTM) studies. This is the first-ever study of ADAMTS3 gene polymorphism, and the predicted nsSNPs in ADAMST3, some of which have not been reported yet in patients, will serve for diagnostic purposes and further therapeutic implications in Hennekam syndrome, contributing to better diagnosis and treatment.

Lactate oxidation is linked to energy conservation and to oxygen detoxification via a putative terminal cytochrome oxidase in Methanosarcina acetivorans

The marine archaeon Methanosarcina acetivorans contains a putative NAD ⁺ -independent d-lactate dehydrogenase (D-iLDH/glycolate oxidase) encoded by the MA4631 gene, belonging to the FAD-oxidase C superfamily. Nucleotide sequences similar to MA4631 gene, were identified in other methanogens and Firmicutes with >90 and 35-40% identity, respectively. Therefore, the lactate metabolism in M. acetivorans is reported here. Cells subjected to intermittent pulses of oxygen (air-adapted; AA-Ma cells) consumed lactate only in combination with acetate, increasing methane production and biomass yield. In AA-Ma cells incubated with d-lactate plus [¹⁴C]-l-lactate, the radioactive label was found in methane, CO₂ and glycogen, indicating that lactate metabolism fed both methanogenesis and gluconeogenesis. Moreover, d-lactate oxidation was coupled to O₂-consumption which was sensitive to HQNO; also, AA-Ma cells showed high transcript levels of gene dld and those encoding subunits A (MA1006) and B (MA1007) of a putative cytochrome bd quinol oxidase, compared to anaerobic control cells. An E. coli mutant deficient in dld complemented with the MA4631 gene, grew with d-lactate as carbon source and showed membrane-bound d-lactate:quinone oxidoreductase activity. The product of the MA4631 gene is a FAD-containing monomer showing activity of iLDH with preference to d-lactate. The results suggested that air adapted M. acetivorans is able to co-metabolize lactate and acetate with associated oxygen consumption by triggering the transcription and synthesis of the D-iLDH and a putative cytochrome bd: methanophenazine (quinol) oxidoreductase. Biomass generation and O₂ consumption, suggest a potentially new oxygen detoxification mechanism coupled to energy conservation in this methanogen.

GeoBind: segmentation of nucleic acid binding interface on protein surface with geometric deep learning

Unveiling the nucleic acid binding sites of a protein helps reveal its regulatory functions in vivo. Current methods encode protein sites from the handcrafted features of their local neighbors and recognize them via a classification, which are limited in expressive ability. Here, we present GeoBind, a geometric deep learning method for predicting nucleic binding sites on protein surface in a segmentation manner. GeoBind takes the whole point clouds of protein surface as input and learns the high-level representation based on the aggregation of their neighbors in local reference frames. Testing GeoBind on benchmark datasets, we demonstrate GeoBind is superior to state-of-the-art predictors. Specific case studies are performed to show the powerful ability of GeoBind to explore molecular surfaces when deciphering proteins with multimer formation. To show the versatility of GeoBind, we further extend GeoBind to five other types of ligand binding sites prediction tasks and achieve competitive performances.

Molecular docking in organic, inorganic, and hybrid systems: a tutorial review

Molecular docking simulation is a very popular and well-established computational approach and has been extensively used to understand molecular interactions between a natural organic molecule (ideally taken as a receptor) such as an enzyme, protein, DNA, RNA and a natural or synthetic organic/inorganic molecule (considered as a ligand). But the implementation of docking ideas to synthetic organic, inorganic, or hybrid systems is very limited with respect to their use as a receptor despite their huge popularity in different experimental systems. In this context, molecular docking can be an efficient computational tool for understanding the role of intermolecular interactions in hybrid systems that can help in designing materials on mesoscale for different applications. The current review focuses on the implementation of the docking method in organic, inorganic, and hybrid systems along with examples from different case studies. We describe different resources, including databases and tools required in the docking study and applications. The concept of docking techniques, types of docking models, and the role of different intermolecular interactions involved in the docking process to understand the binding mechanisms are explained. Finally, the challenges and limitations of dockings are also discussed in this review.

Graphical abstract

Spasmolytic and Uroprotective Effects of Apigenin by Downregulation of TGF-β and iNOS Pathways and Upregulation of Antioxidant Mechanisms: In Vitro and In Silico Analysis

Apigenin is a phytochemical obtained from Chamomilla recutita. Its role in interstitial cystitis is not yet known. The present study is aimed at understanding the uroprotective and spasmolytic effects of apigenin in cyclophosphamide-induced interstitial cystitis. The uroprotective role of apigenin was analyzed by qRT-PCR, macroscopic analysis, Evans blue dye leakage, histological evaluation, and molecular docking. The spasmolytic response was measured by adding cumulative concentrations of apigenin to isolated bladder tissue pre-contracted with KCl (80 mM) and carbachol (10^-9-10^-4) on non-incubated and pre-incubated tissues with atropine, 4DAMP, methoctramine, glibenclamide, barium chloride, nifedipine, indomethacin, and propranolol. Apigenin inhibited pro-inflammatory cytokines (IL-6, TNF-α and TGF 1-β) and oxidant enzymes (iNOS) while increasing antioxidant enzymes (SOD, CAT, and GSH) in CYP-treated groups compared to the control. Apigenin restored normal tissue of the bladder by decreasing pain, edema, and hemorrhage. Molecular docking further confirmed the antioxidant and anti-inflammatory properties of apigenin. Apigenin produced relaxation against carbachol-mediated contractions, probably via blockade of M₃ receptors, K_ATP channels, L-type calcium channels, and prostaglandin inhibition. While the blockade of M₂ receptors, K_IR channels, and β-adrenergic receptors did not contribute to an apigenin-induced spasmolytic effect, apigenin presented as a possible spasmolytic and uroprotective agent with anti-inflammatory, antioxidant effects by attenuating TGF-β/iNOS-related tissue damage and bladder muscle overactivity. Thus, it is a potential agent likely to be used in treatment of interstitial cystitis.

Dihydroergotamine ameliorates liver fibrosis by targeting transforming growth factor β type II receptor

BACKGROUND

The transforming growth factor β (TGFβ) signaling pathway plays a crucial role in the development of liver fibrosis by activating TGFβ type II receptor (TGFβR2), followed by the recruitment of TGFβR1 finally triggering downstream signaling pathway.

AIM

To find drugs targeting TGFβR2 that inhibit TGFβR1/TGFβR2 complex formation, theoretically inhibit TGFβ signaling pathway, and thereby ameliorate liver fibrosis.

METHODS

Food and Drug Administration-approved drugs were screened for binding affinity with TGFβR2 by virtual molecular docking. We identified 6 candidates and further explored their potential by Cell Counting Kit-8 (CCK-8) cell cytotoxic experiment to validate toxicity and titrated the best cellular working concentrations. Next, we further demonstrated the detailed molecular working mechanisms using mutagenesis analysis. Finally, we used a mouse model to investigate its potential anti-liver fibrosis effect.

RESULTS

We identified 6 drug candidates. Among these 6 drugs, dihydroergotamine (DHE) shows great ability in reducing fibrotic gene expressions such as collagen, p-SMAD3, and α-SMA in TGFβ induced cellular model of liver fibrosis in LX-2 cells. Furthermore, we demonstrated that DHE binds to TGFβR2. Moreover, mutation of Leu27, Phe30, Thr51, Ser52, Ile53, and Glu55 of TGFβR2 disrupted the binding of TGFβR2 with DHE. In addition, DHE significantly improved liver fibrosis, as evidenced by Masson’s trichrome staining of liver sections. This is further supported by the width and the velocity of the portal vein, and serum markers of liver function. In line with those observations, DHE also decreased macrophages infiltration and extracellular matrix deposition in the liver.

CONCLUSION

DHE alleviates liver fibrosis by binding to TGFβR2 thereby suppressing TGFβ signaling pathway. We show here that as far as drug repurposing, DHE has great potential to treat liver fibrosis.

Genome-Wide Identification and Expression Profiling of Pathogenesis-Related Protein 1 (PR-1) Genes in Durum Wheat (Triticum durum Desf.)

Pathogen-related proteins (PRs) are diversified proteins with a low molecular weight implicated in plant response to biotic and abiotic stress as well in regulating different functions in plant maturation. Interestingly, no systematical study has been conducted in durum wheat (Triticum turgidum subsp. durum). In the present study, 12 PR-1 genes encoding a CAP superfamily domain were identified in the genome of Triticum turgidum subsp. durum, which is an important cereal, using in silico approaches. Additionally, phylogenetic analysis showed that the PR-1 genes were classified into three groups based on their isoelectric point and the conserved motif domain. Moreover, our analysis showed that most of the TdPR-1 proteins presented an N-terminal signal peptide. Expression patterns analysis showed that the PR-1 gene family presented temporal and spatial specificity and was induced by different abiotic stresses. This is the first report describing the genome-scale analysis of the durum wheat PR-1 gene family, and these data will help further study the roles of PR-1 genes during stress responses, leading to crop improvement.

The interaction of an effector protein Hap secreted by Aeromonas salmonicida and myofibrillar protein of meat: Possible mechanisms from structural changes to sites of molecular docking

Microbial spoilage of meat products is a significant problem in the food industry. Aeromonas salmonicida is a significant microorganism responsible for spoilage in chilled meat. Its effector protein, hemagglutinin protease (Hap), has been identified as an effective substance for degrading meat proteins. The ability of Hap to hydrolyze myofibrillar proteins (MPs) in vitro demonstrated that Hap has obvious proteolytic activity, which could alter MPs' tertiary structure, secondary structure, and sulfhydryl groups. Moreover, Hap could significantly degrade MPs, focusing primarily on myosin heavy chain (MHC) and actin. Active site analysis and molecular docking revealed that the active center of Hap was bound to MPs via hydrophobic interaction and hydrogen bonding. It may preferentially cleave peptide bonds between Gly44-Val45 in actin, and Ala825-Phe826 in MHC. These findings suggest that Hap may be involved in the spoilage mechanism of microorganisms and provide crucial insights into the mechanisms of meat spoilage induced by bacteria.

Characterization and Engineering Studies of a New Endolysin from the Propionibacterium acnes Bacteriophage PAC1 for the Development of a Broad-Spectrum Artilysin with Altered Specificity

The emergence of multidrug-resistant (MDR) bacteria has risen rapidly, leading to a great threat to global public health. A promising solution to this problem is the exploitation of phage endolysins. In the present study, a putative N-acetylmuramoyl-L-alanine type-2 amidase (NALAA-2, EC 3.5.1.28) from Propionibacterium bacteriophage PAC1 was characterized. The enzyme (PaAmi1) was cloned into a T7 expression vector and expressed in E. coli BL21 cells. Kinetics analysis using turbidity reduction assays allowed the determination of the optimal conditions for lytic activity against a range of Gram-positive and negative human pathogens. The peptidoglycan degradation activity of PaAmi1 was confirmed using isolated peptidoglycan from P. acnes. The antibacterial activity of PaAmi1 was investigated using live P. acnes cells growing on agar plates. Two engineered variants of PaAmi1 were designed by fusion to its N-terminus two short antimicrobial peptides (AMPs). One AMP was selected by searching the genomes of Propionibacterium bacteriophages using bioinformatics tools, whereas the other AMP sequence was selected from the antimicrobial peptide databases. Both engineered variants exhibited improved lytic activity towards P. acnes and the enterococci species Enterococcus faecalis and Enterococcus faecium. The results of the present study suggest that PaAmi1 is a new antimicrobial agent and provide proof of concept that bacteriophage genomes are a rich source of AMP sequences that can be further exploited for designing novel or improved endolysins.

New robust subtilisins from halotolerant and halophilic Bacillaceae

The aim of the present study was the characterisation of three true subtilisins and one phylogenetically intermediate subtilisin from halotolerant and halophilic microorganisms. Considering the currently growing enzyme market for efficient and novel biocatalysts, data mining is a promising source for novel, as yet uncharacterised enzymes, especially from halophilic or halotolerant Bacillaceae, which offer great potential to meet industrial needs. Both halophilic bacteria Pontibacillus marinus DSM 16465^T and Alkalibacillus haloalkaliphilus DSM 5271^T and both halotolerant bacteria Metabacillus indicus DSM 16189 and Litchfieldia alkalitelluris DSM 16976^T served as a source for the four new subtilisins SPPM, SPAH, SPMI and SPLA. The protease genes were cloned and expressed in Bacillus subtilis DB104. Purification to apparent homogeneity was achieved by ethanol precipitation, desalting and ion-exchange chromatography. Enzyme activity could be observed between pH 5.0-12.0 with an optimum for SPPM, SPMI and SPLA around pH 9.0 and for SPAH at pH 10.0. The optimal temperature for SPMI and SPLA was 70 °C and for SPPM and SPAH 55 °C and 50 °C, respectively. All proteases showed high stability towards 5% (w/v) SDS and were active even at NaCl concentrations of 5 M. The four proteases demonstrate potential for future biotechnological applications. KEY POINTS: • Halophilic and halotolerant Bacillaceae are a valuable source of new subtilisins. • Four new subtilisins were biochemically characterised in detail. • The four proteases show potential for future biotechnological applications.

Structural and functional features of a broad-spectrum prophage-encoded enzybiotic from Enterococcus faecium

Multidrug-resistant (MDR) bacteria have become a growing threat to public health. The gram-positive Enterococcus faecium is classified by WHO as a high-priority pathogen among the global priority list of antibiotic-resistant bacteria. Peptidoglycan-degrading enzymes (PDEs), also known as enzybiotics, are useful bactericidal agents in the fight against resistant bacteria. In this work, a genome-based screening approach of the genome of E. faecium allowed the identification of a putative PDE gene with predictive amidase activity (EfAmi1; EC 3.5.1.28) in a prophage-integrated sequence. EfAmi1 is composed by two domains: a N-terminal Zn²⁺-dependent N-acetylmuramoyl-L-alanine amidase-2 (NALAA-2) domain and a C-terminal domain with unknown structure and function. The full-length gene of EfAmi1 was cloned and expressed as a 6xHis-tagged protein in E. coli. EfAmi1 was produced as a soluble protein, purified, and its lytic and antimicrobial activities were investigated using turbidity reduction and Kirby-Bauer disk-diffusion assays against clinically isolated bacterial pathogens. The crystal structure of the N-terminal amidase-2 domain was determined using X-ray crystallography at 1.97 Å resolution. It adopts a globular fold with several α-helices surrounding a central five-stranded β-sheet. Sequence comparison revealed a cluster of conserved amino acids that defines a putative binding site for a buried zinc ion. The results of the present study suggest that EfAmi1 displays high lytic and antimicrobial activity and may represent a promising new antimicrobial in the post-antibiotic era.

Molecular Structural Analysis of Porcine CMAH-Native Ligand Complex and High Throughput Virtual Screening to Identify Novel Inhibitors

Porcine meat is the most consumed red meat worldwide. Pigs are also vital tools in biological and medical research. However, xenoreactivity between porcine's N-glycolylneuraminic acid (Neu5Gc) and human anti-Neu5Gc antibodies poses a significant challenge. On the one hand, dietary Neu5Gc intake has been connected to particular human disorders. On the other hand, some pathogens connected to pig diseases have a preference for Neu5Gc. The Cytidine monophospho-N-acetylneuraminic acid hydroxylase (CMAH) catalyses the conversion of N-acetylneuraminic acid (Neu5Ac) to Neu5Gc. In this study, we predicted the tertiary structure of CMAH, performed molecular docking, and analysed the protein-native ligand complex. We performed a virtual screening from a drug library of 5M compounds and selected the two top inhibitors with Vina scores of -9.9 kcal/mol for inhibitor 1 and -9.4 kcal/mol for inhibitor 2. We further analysed their pharmacokinetic and pharmacophoric properties. We conducted stability analyses of the complexes with molecular dynamic simulations of 200 ns and binding free energy calculations. The overall analyses revealed the inhibitors' stable binding, which was further validated by the MMGBSA studies. In conclusion, this result may pave the way for future studies to determine how to inhibit CMAH activities. Further in vitro studies can provide in-depth insight into these compounds' therapeutic potential.

Binding Site Detection Remastered: Enabling Fast, Robust, and Reliable Binding Site Detection and Descriptor Calculation with DoGSite3

Binding site prediction on protein structures is a crucial step in early phase drug discovery whenever experimental or predicted structure models are involved. DoGSite belongs to the widely used tools for this task. It is a grid-based method that uses a Difference-of-Gaussian filter to detect cavities on the protein surface. We recently reimplemented the first version of this method, released in 2010, focusing on improved binding site detection in the presence of ligands and optimized parameters for more robust, reliable, and fast predictions and binding site descriptor calculations. Here, we introduce the new version, DoGSite3, compare it to its predecessor, and re-evaluate DoGSite on published data sets for a large-scale comparative performance evaluation.

Domain-agnostic predictions of nanoscale interactions in proteins and nanoparticles

Although challenging, the accurate and rapid prediction of nanoscale interactions has broad applications for numerous biological processes and material properties. While several models have been developed to predict the interaction of specific biological components, they use system-specific information that hinders their application to more general materials. Here we present NeCLAS, a general and efficient machine learning pipeline that predicts the location of nanoscale interactions, providing human-intelligible predictions. NeCLAS outperforms current nanoscale prediction models for generic nanoparticles up to 10-20 nm, reproducing interactions for biological and non-biological systems. Two aspects contribute to these results: a low-dimensional representation of nanoparticles and molecules (to reduce the effect of data uncertainty), and environmental features (to encode the physicochemical neighborhood at multiple scales). This framework has several applications, from basic research to rapid prototyping and design in nanobiotechnology.

Functional characterization of hypothetical proteins from Monkeypox virus

BACKGROUND

Monkeypox virus is a small, double-stranded DNA virus that causes a zoonotic disease called Monkeypox. The disease has spread from Central and West Africa to Europe and North America and created havoc in some countries all around the world. The complete genome of the Monkeypox virus Zaire-96-I-16 has been sequenced. The viral strain contains 191 protein-coding genes with 30 hypothetical proteins whose structure and function are still unknown. Hence, it is imperative to functionally and structurally annotate the hypothetical proteins to get a clear understanding of novel drug and vaccine targets. The purpose of the study was to characterize the 30 hypothetical proteins through the determination of physicochemical properties, subcellular characterization, function prediction, functional domain prediction, structure prediction, structure validation, structural analysis, and ligand binding sites using Bioinformatics tools.

RESULTS

The structural and functional analysis of 30 hypothetical proteins was carried out in this research. Out of these, 3 hypothetical functions (Q8V547, Q8V4S4, Q8V4Q4) could be assigned a structure and function confidently. Q8V547 protein in Monkeypox virus Zaire-96-I-16 is predicted as an apoptosis regulator which promotes viral replication in the infected host cell. Q8V4S4 is predicted as a nuclease responsible for viral evasion in the host. The function of Q8V4Q4 is to prevent host NF-kappa-B activation in response to pro-inflammatory cytokines like TNF alpha or interleukin 1 beta.

CONCLUSIONS

Out of the 30 hypothetical proteins of Monkeypox virus Zaire-96-I-16, 3 were annotated using various bioinformatics tools. These proteins function as apoptosis regulators, nuclease, and inhibitors of NF-Kappa-B activator. The functional and structural annotation of the proteins can be used to perform a docking with potential leads to discover novel drugs and vaccines against the Monkeypox. In vivo research can be carried out to identify the complete potential of the annotated proteins.

Effective holistic characterization of small molecule effects using heterogeneous biological networks

The two most common reasons for attrition in therapeutic clinical trials are efficacy and safety. We integrated heterogeneous data to create a human interactome network to comprehensively describe drug behavior in biological systems, with the goal of accurate therapeutic candidate generation. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multiscale therapeutic discovery, repurposing, and design was enhanced by integrating drug side effects, protein pathways, protein-protein interactions, protein-disease associations, and the Gene Ontology, and complemented with its existing drug/compound, protein, and indication libraries. These integrated networks were reduced to a "multiscale interactomic signature" for each compound that describe its functional behavior as vectors of real values. These signatures are then used for relating compounds to each other with the hypothesis that similar signatures yield similar behavior. Our results indicated that there is significant biological information captured within our networks (particularly via side effects) which enhance the performance of our platform, as evaluated by performing all-against-all leave-one-out drug-indication association benchmarking as well as generating novel drug candidates for colon cancer and migraine disorders corroborated via literature search. Further, drug impacts on pathways derived from computed compound-protein interaction scores served as the features for a random forest machine learning model trained to predict drug-indication associations, with applications to mental disorders and cancer metastasis highlighted. This interactomic pipeline highlights the ability of Computational Analysis of Novel Drug Opportunities to accurately relate drugs in a multitarget and multiscale context, particularly for generating putative drug candidates using the information gleaned from indirect data such as side effect profiles and protein pathway information.

LigBind: identifying binding residues for over 1000 ligands with relation-aware graph neural networks

Identifying the interactions between proteins and ligands is significant for drug discovery and design. Considering the diverse binding patterns of ligands, the ligand-specific methods are trained per ligand to predict binding residues. However, most of the existing ligand-specific methods ignore shared binding preferences among various ligands and generally only cover a limited number of ligands with a sufficient number of known binding proteins. In this study, we propose a relation-aware framework LigBind with graph-level pre-training to enhance the ligand-specific binding residue predictions for 1159 ligands, which can effectively cover the ligands with a few known binding proteins. LigBind first pre-trains a graph neural network-based feature extractor for ligand-residue pairs and relation-aware classifiers for similar ligands. Then, LigBind is fine-tuned with ligand-specific binding data, where a domain adaptive neural network is designed to automatically leverage the diversity and similarity of various ligand-binding patterns for accurate binding residue prediction. We construct ligand-specific benchmark datasets of 1159 ligands and 16 unseen ligands, which are used to evaluate the effectiveness of LigBind. The results demonstrate the LigBind's efficacy on the large-scale ligand-specific benchmark datasets, and generalizes well to unseen ligands. LigBind also enables accurate identification of the ligand-binding residues in the main protease, papain-like protease and the RNA-dependent RNA polymerase of SARS-CoV-2. The webserver and source codes of LigBind are available at http://www.csbio.sjtu.edu.cn/bioinf/LigBind/ and https://github.com/YYingXia/LigBind/ for academic use.

Ensemble-based, high-throughput virtual screening of potential inhibitor targeting putative farnesol dehydrogenase of Metisa plana (Lepidoptera: Psychidae)

Metisa plana (Lepidoptera: Psychidae) bagworm is a leaf-eater caterpillar ubiquitously found as a damaging pest in oil palm plantations, specifically in Malaysia. Various strategies have been implemented, including the usage of chemical insecticides. However, the main challenges include the development of insecticide resistance and its detrimental effects on the environment and non-target organisms. Therefore, a biorational insecticide is introduced by targeting the juvenile hormone (JH) biosynthetic pathway, which is mainly present in the insect and vital for the insect's growth, diapause, metamorphosis, and adult reproduction. This study aimed to investigate the potential inhibitor for the rate-limiting enzyme involved in the JH pathway known as farnesol dehydrogenase. A 255 amino acids sequence encoded for the putative M. plana farnesol dehydrogenase (MpFolDH) open reading frame had been identified and isolated. The three-dimensional structure of MpFolDH was predicted to have seven β- sheets with α-helices at both sides, showing typical characteristics for classical short-chain dehydrogenase and associated with oxidoreductase activity. Then, the ensemble-based virtual screening was conducted based on the ZINC20 database, in which 43 768 compounds that fulfilled pesticide-likeness criteria were screened by site-specific molecular docking. After a short molecular dynamics simulation (5 ns) was conducted towards 102 compounds, only the top 10 compounds based on their most favourable binding energy were selected for a more extended simulation (100 ns). Based on the protein-ligand stability, protein compactness, residues rigidity, binding interaction, binding energy throughout the 100 ns simulation, and physicochemical analysis, ZINC000408743205 was selected as a potential inhibitor for this enzyme. Amino acids decomposition analysis indicates Ile18, Ala95, Val198 and Val202 were the critical contributor residues for MpFolDH-inhibitors(s) complex.

High-Risk Pedigree Study Identifies LRBA (rs62346982) as a Likely Predisposition Variant for Prostate Cancer

There is evidence for contribution of inherited factors to prostate cancer, and more specifically to lethal prostate cancer, but few responsible genes/variants have been identified. We examined genetic sequence data for 51 affected cousin pairs who each died from prostate cancer and who were members of high-risk prostate cancer pedigrees in order to identify rare variants shared by the cousins as candidate predisposition variants. Candidate variants were tested for association with prostate cancer risk in UK Biobank data. Candidate variants were also assayed in 1195 additional sampled Utah prostate cancer cases. We used 3D protein structure prediction methods to analyze structural changes and provide insights into mechanisms of pathogenicity. Almost 4000 rare (<0.005) variants were identified as shared in the 51 affected cousin pairs. One candidate variant was also significantly associated with prostate cancer risk among the 840 variants with data in UK Biobank, in the gene LRBA (p = 3.2 × 10-5; OR = 2.09). The rare risk variant in LRBA was observed to segregate in five pedigrees. The overall predicted structures of the mutant protein do not show any significant overall changes upon mutation, but the mutated structure loses a helical structure for the two residues after the mutation. This unique analysis of closely related individuals with lethal prostate cancer, who were members of high-risk prostate cancer pedigrees, has identified a strong set of candidate predisposition variants which should be pursued in independent studies. Validation data for a subset of the candidates identified are presented, with strong evidence for a rare variant in LRBA.

Integrated computational and experimental approach for novel anti-leishmanial molecules by targeting Dephospho-coenzyme A kinase

Coenzyme A acts as a necessary cofactor for many enzymes and is a part of many biochemical processes. One of the critical enzymes involved in Coenzyme A synthesis is Dephospho-coenzyme A-kinase (DPCK). In this study, we have used integrated computational and experimental approaches for promising inhibitors of DPCK using the natural products available in the ZINC database for anti-leishmanial drug development. The top hit compounds chosen after molecular docking were Veratramine, Azulene, Hupehenine, and Hederagenin. The free binding energy of Veratramine, Azulene, Hupehenine, and Hederagenin was estimated. Besides the favourable binding point, the ligands also showed good hydrogen bonding and other interactions with key residues of the enzyme's active site. The natural compounds were also experimentally investigated for their effect on the L. donovani promastigotes and murine macrophage (J774A.1). A good antileishmanial activity by the compounds on the promastigotes was observed as estimated by the MTT assay. The in-vitro experiments revealed that Hupehenine (IC₅₀ = 7.34 ± 0.37 μM) and Veratramine (IC₅₀ = 12.46 ± 2.28 μM) exhibited better inhibition than Hederagenin (IC₅₀ = 23.36 ± 0.54 μM) and Azulene (IC₅₀ = 24.42 ± 3.28 μM). This work has identified novel anti-leishmanial molecules possibly acting through the inhibition of DPCK.

Target-specificity of different amyrin subunits in impeding HCV influx mechanism inside the human cells considering the quantum tunnel profiles and molecular strings of the CD81 receptor: a combined in silico and in vivo study

HCV is a hepatotropic RNA virus recognized for its frequent virulence and fatality worldwide. Despite many vaccine development programs underway, researchers are on a quest for natural bioactive compounds due to their multivalent efficiencies against viral infections, considering which the current research aimed to figure out the target-specificity and therapeutic potentiality of α, β, and δ subunits of amyrin, as novel bioactive components against the HCV influx mechanism. Initially, the novelty of amyrin subunits was conducted from 203 pharmacophores, comparing their in-silico pharmacokinetic and pharmacodynamic profiles. Besides, the best active site of CD81 was determined following the quantum tunneling algorithm. The molecular dynamic simulation was conducted (100 ns) following the molecular docking steps to reveal the parameters- RMSD (Å); Cα; RMSF (Å); MolSA (Ų); Rg (nm); PSA (Å); SASA (Ų), and the MM-GBSA dG binding scores. Besides, molecular strings of CD81, along with the co-expressed genes, were classified, as responsible for encoding CD81-mediated protein clusters during HCV infection, resulting in the potentiality of amyrins as targeted prophylactics in HCV infection. Finally, in vivo profiling of the oxidative stress marker, liver-specific enzymes, and antioxidant markers was conducted in the DMN-induced mice model, where β-amyrin scored the most significant values in all aspects.

Role of calcium sensor protein module CBL-CIPK in abiotic stress and light signaling responses in green algae

Ca²⁺ signaling is an important biological process that enable to perceive and communicate information in the cell. Our current understanding of the signaling system suggests that plants and animals have certain differences in signal-sensing mechanisms. The Ca²⁺-mediated CBL-CIPK module has emerged as a major sensor responder network for Ca²⁺ signaling and has been speculated to be involved in plant terrestrial life adaptation. This module has previously been reported in Archaeplastids, Chromalveolates, and Excavates. In our experimental analysis of Chlamydomonas reinhardtii CBLs, we proved that the CrCBL1 protein interacts with Phototropin and Channelrhodopsin, and the expression of CrCBLs is modulated by light. Further analysis using chlorophyte and streptophyte algal sequences allowed us to identify the differences that have evolved in CBL and CIPK proteins since plants have progressed from aquatic to terrestrial habitats. Moreover, an investigation of Klebsormidium CBL and CIPK genes led us to know that they are abiotic stress stimuli-responsive, indicating that their role was defined very early during terrestrial adaptations. Structure-based prediction and Ca²⁺-binding assays indicated that the KnCBL1 protein in Klebsormidium showed a typical Ca²⁺-binding pocket. In summary, the results of this study suggest that these stress-responsive proteins enable crosstalk between Ca²⁺ and light signaling pathways very early during plant adaptation from aquatic to terrestrial habitats.

Identification of potential therapeutic intervening targets by in-silico analysis of nsSNPs in preterm birth-related genes

Prematurity is the foremost cause of death in children under 5 years of age. Genetics contributes to 25-40% of all preterm births (PTB) yet we still need to identify specific targets for intervention based on genetic pathways. This study involved the effect of region-specific non-synonymous variations and their transcript level mutational impact on protein functioning and stability by various in-silico tools. This investigation identifies potential therapeutic targets to manage the challenge of PTB, corresponding protein cavities and explores their binding interactions with intervening compounds. We searched 20 genes coding 55 PTB proteins from NCBI. Single Nucleotide Polymorphisms (SNPs) of concerned genes were extracted from ENSEMBL, and filtration of exonic variants (non-synonymous) was performed. Several in-silico downstream protein functional effect prediction tools were used to identify damaging variants. Rare coding variants were selected with an allele frequency of ≤1% in 1KGD, further supported by South Asian ALFA frequencies and GTEx gene/tissue expression database. CNN1, COL24A1, IQGAP2 and SLIT2 were identified with 7 rare pathogenic variants found in 17 transcript sequences. The functional impact analyses of rs532147352 (R>H) of CNN1 computed through PhD-SNP, PROVEAN, SNP&GO, PMut and MutPred2 algorithms showed impending deleterious effects, and the presence of this pathogenic mutation in CNN1 resulted in large decrease in protein structural stability (ΔΔG (kcal/mol). After structural protein identification, homology modelling of CNN1, which has been previously reported as a biomarker for the prediction of PTB, was performed, followed by the stereochemical quality checks of the 3D model. Blind docking approach were used to search the binding cavities and molecular interactions with progesterone, ranked with energetic estimations. Molecular interactions of CNN1 with progesterone were investigated through LigPlot 2D. Further, molecular docking experimentation of CNN1 showed the significant interactions at S102, L105, A106, K123, Y124 with five selected PTB-drugs, Allylestrenol (-7.56 kcal/mol), Hydroxyprogesterone caproate (-8.19 kcal/mol), Retosiban (-9.43 kcal/mol), Ritodrine (-7.39 kcal/mol) and Terbutaline (-6.87 kcal/mol). Calponin-1 gene and its molecular interaction analysis could serve as an intervention target for the prevention of PTB.

Structural Complementarity of Bruton’s Tyrosine Kinase and Its Inhibitors for Implication in B-Cell Malignancies and Autoimmune Diseases

Bruton’s tyrosine kinase (BTK) is a critical component in B-cell receptor (BCR) signaling and is also expressed in haematogenic and innate immune cells. Inhibition of BTK hyperactivity is implicated in B-cell malignancies and autoimmune diseases. This review derives the structural complementarity of the BTK-kinase domain and its inhibitors from recent three-dimensional structures of inhibitor-bound BTK in the protein data bank (PDB). Additionally, this review analyzes BTK-mediated effector responses of B-cell development and antibody production. Covalent inhibitors contain an α, β-unsaturated carbonyl moiety that forms a covalent bond with Cys481, stabilizing αC-helix in inactive-out conformation which inhibits Tyr551 autophosphorylation. Asn484, located two carbons far from Cys481, influences the stability of the BTK-transition complex. Non-covalent inhibitors engage the BTK-kinase domain through an induced-fit mechanism independent of Cys481 interaction and bind to Tyr551 in the activation kink resulting in H3 cleft, determining BTK selectivity. Covalent and non-covalent binding to the kinase domain of BTK shall induce conformational changes in other domains; therefore, investigating the whole-length BTK conformation is necessary to comprehend BTK’s autophosphorylation inhibition. Knowledge about the structural complementarity of BTK and its inhibitors supports the optimization of existing drugs and the discovery of drugs for implication in B-cell malignancies and autoimmune diseases.

Structural Characterization of the Chlorophyllide a Oxygenase (CAO) Enzyme Through an In Silico Approach

Chlorophyllide a oxygenase (CAO) is responsible for converting chlorophyll a to chlorophyll b in a two-step oxygenation reaction. CAO belongs to the family of Rieske-mononuclear iron oxygenases. Although the structure and reaction mechanism of other Rieske monooxygenases have been described, a member of plant Rieske non-heme iron-dependent monooxygenase has not been structurally characterized. The enzymes in this family usually form a trimeric structure and electrons are transferred between the non-heme iron site and the Rieske center of the adjoining subunits. CAO is supposed to form a similar structural arrangement. However, in Mamiellales such as Micromonas and Ostreococcus, CAO is encoded by two genes where non-heme iron site and Rieske cluster localize on the distinct polypeptides. It is not clear if they can form a similar structural organization to achieve the enzymatic activity. In this study, the tertiary structures of CAO from the model plant Arabidopsis thaliana and the Prasinophyte Micromonas pusilla were predicted by deep learning-based methods, followed by energy minimization and subsequent stereochemical quality assessment of the predicted models. Furthermore, the chlorophyll a binding cavity and the interaction of ferredoxin, which is the electron donor, on the surface of Micromonas CAO were predicted. The electron transfer pathway was predicted in Micromonas CAO and the overall structure of the CAO active site was conserved even though it forms a heterodimeric complex. The structures presented in this study will serve as a basis for understanding the reaction mechanism and regulation of the plant monooxygenase family to which CAO belongs.

Potential of Onchocerca ochengi inosine-5'-monophosphate dehydrogenase (IMPDH) and guanosine-5'-monophosphate oxidoreductase (GMPR) as druggable and vaccine candidates: immunoinformatics screening

Onchocerciasis is a vector-borne disease caused by the filarial nematode Onchocerca volvulus, which is responsible for most of the visual impairments recorded in Africa, Asia and the Americas. It is known that O. volvulus has similar molecular and biological characteristics as Onchocerca ochengi in cattle. This study was designed to screen for immunogenic epitopes and binding pockets of O. ochengi IMPDH and GMPR ligands using immunoinformatic approaches. In this study, a total of 23 B cell epitopes for IMPDH and 7 B cell epitopes for GMPR were predicted using ABCpred tool, Bepipred 2.0 and Kolaskar and Tongaonkar methods. The CD4+ Th computational results showed 16 antigenic epitopes from IMPDH with strong binding affinity for DRB1_0301, DRB3_0101, DRB1_0103 and DRB1_1501 MHC II alleles while 8 antigenic epitopes from GMPR were predicted to bind DRB1_0101 and DRB1_0401 MHC II alleles, respectively. For the CD8+ CTLs analysis, 8 antigenic epitopes from IMPDH showed strong binding affinity to human leukocyte antigen HLA-A*26:01, HLA-A*03:01, HLA-A*24:02 and HLA-A*01:01 MHC I alleles while 2 antigenic epitopes from GMPR showed strong binding affinity to HLA-A*01:01 allele, respectively. The immunogenic B cell and T cell epitopes were further evaluated for antigenicity, non-alllergernicity, toxicity, IFN-gamma, IL4 and IL10. The docking score revealed favorable binding free energy with IMP and MYD scoring the highest binding affinity at -6.6 kcal/mol with IMPDH and -8.3 kcal/mol with GMPR. This study provides valuable insight on IMPDH and GMPR as potential drug targets and for the development of multiple epitope vaccine candidates.Communicated by Ramaswamy H. Sarma.

Application of Molecular Simulation Methods in Food Science: Status and Prospects

Molecular simulation methods, such as molecular docking, molecular dynamic (MD) simulation, and quantum chemical (QC) calculation, have become popular as characterization and/or virtual screening tools because they can visually display interaction details that in vitro experiments can not capture and quickly screen bioactive compounds from large databases with millions of molecules. Currently, interdisciplinary research has expanded molecular simulation technology from computer aided drug design (CADD) to food science. More food scientists are supporting their hypotheses/results with this technology. To understand better the use of molecular simulation methods, it is necessary to systematically summarize the latest applications and usage trends of molecular simulation methods in the research field of food science. However, this type of review article is rare. To bridge this gap, we have comprehensively summarized the principle, combination usage, and application of molecular simulation methods in food science. We also analyzed the limitations and future trends and offered valuable strategies with the latest technologies to help food scientists use molecular simulation methods.

Insights into the role of the cobalt(III)-thiosemicarbazone complex as a potential inhibitor of the Chikungunya virus nsP4

Chikungunya virus (CHIKV) is the causative agent of chikungunya fever, a disease that can result in disability. Until now, there is no antiviral treatment against CHIKV, demonstrating that there is a need for development of new drugs. Studies have shown that thiosemicarbazones and their metal complexes possess biological activities, and their synthesis is simple, clean, versatile, and results in high yields. Here, we evaluated the mechanism of action (MOA) of a cobalt(III) thiosemicarbazone complex named [CoIII(L1)2]Cl based on its in vitro potent antiviral activity against CHIKV previously evaluated (80% of inhibition on replication). Furthermore, the complex has no toxicity in healthy cells, as confirmed by infecting BHK-21 cells with CHIKV-nanoluciferase in the presence of the compound, showing that [CoIII(L1)2]Cl inhibited CHIKV infection with the selective index of 3.26. [CoIII(L1)2]Cl presented a post-entry effect on viral replication, emphasized by the strong interaction of [CoIII(L1)2]Cl with CHIKV non-structural protein 4 (nsP4) in the microscale thermophoresis assay, suggesting a potential mode of action of this compound against CHIKV. Moreover, in silico analyses by molecular docking demonstrated potential interaction of [CoIII(L1)2]Cl with nsP4 through hydrogen bonds, hydrophobic and electrostatic interactions. The evaluation of ADME-Tox properties showed that [CoIII(L1)2]Cl presents appropriate lipophilicity, good human intestinal absorption, and has no toxicological effect as irritant, mutagenic, reproductive, and tumorigenic side effects.

Soybean (Glycine max) isoflavone conjugate hydrolysing β-glucosidase (GmICHG): a promising candidate for soy isoflavone bioavailability enhancement

Isoflavones are a sub-class of phenylpropanoids having health benefits and a role in plant defence and plant-rhizobium interaction. Isoflavone conjugate hydrolysis is crucial in determining the bioactivity and bioavailability of these isoflavones inside the human body. This study examined the different characteristics of soy isoflavone conjugate hydrolysing β-glucosidase (GmICHG) to explore its potential for isoflavone bioavailability enhancement. We cloned the full-length GmICHG cDNA from the soybean seedling roots from the DS2706 variety of 1545 bp. The bioinformatics analysis revealed secretion and glycosylation of this protein. The evolutionary relatedness of this gene to the other glucosidases interestingly had related sequences outside the Papilionaceae family. The protein had a pI above neutral of 7.62 and optimum pH of 6.0, indicating its activity in the extracellular acidic environment. The GmICHG gene expression at three stages of seedling roots gradually rose to 1.84 ± 0.54 fold and a concomitant increase in the β-glucosidase activity. The enzyme kinetics of GmICHG showed a K m of 6.38 mM and V max of 2.82 U/ml and an optimum temperature of 40 °C. These hint that soy ICHG can be a potent candidate for the isoflavone bioavailability enhancement by hydrolysing their β-glycosidic bonds.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03427-5.

Comparative study, homology modelling and molecular docking with cancer associated glycans of two non-fetuin-binding Tepary bean lectins

We present the purification and characterization of the two most abundant isoforms of lectins isolated from Tepary bean (Phaseolus acutifolius) seeds, which have been shown to differentially affect the survival of different cancer cells. They were separated by concanavalin A-affinity chromatography. After purification, to release the N-glycans, they were digested with the endoglycosidases PNGase and Glycanase A. Fractions resulted from the hydrolysis products were analyzed to determine their carbohydrate composition. Mass spectrometry data indicated that both isoforms contained high mannose glycans being mannose 6 the most abundant form. Furthermore, based on sequence Ans-X-Ser/Thr, where X is any amino acid except proline, a glycosylation site was determined on asparagine 36. When their metal requirement to preserve their biological activity was determined, the lectins showed differences. While lectin A (LA) agglutination activity was best in the presence of magnesium, lectin B (LB) was best with calcium. Additionally, only LA exhibited affinity to human type-A erythrocytes. Although both lectins showed small differences in their properties, an identical structure-model for both lectins was generated by the homology modelling process. Also, the analysis of ligand binding sites and in silico glycosylation were achieved. Molecular docking with colon adenocarcinoma associated-N-glycans revealed some highly possible interactions and, on the other hand, that N-glycan interaction zones of Tepary bean lectins is not restricted to the carbohydrate binding domain but to an extended part of their surface, which could lead new strategies to explain their biological activity.

Application of machine learning on understanding biomolecule interactions in cellular machinery

Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecular mechanisms involved in protein binding and interactions with other components in the experimental setups or the human body. Researchers are working on several wet-lab techniques and generating data for a better understanding of concepts and mechanics involved. The information like biomolecular structure, binding affinities, structure fluctuations and movements are enormous which can be handled and analyzed by ML. Therefore, this review highlights the significance of ML in understanding the biomolecular interactions while assisting in various fields of research such as drug discovery, nanomedicine, nanotoxicity and material science. Hence, the way ahead would be to force hand-in hand of laboratory work and computational techniques.

Jewel Beetle Opsin Duplication and Divergence Is the Mechanism for Diverse Spectral Sensitivities

The evolutionary history of visual genes in Coleoptera differs from other well-studied insect orders, such as Lepidoptera and Diptera, as beetles have lost the widely conserved short-wavelength (SW) insect opsin gene that typically underpins sensitivity to blue light (∼440 nm). Duplications of the ancestral ultraviolet (UV) and long-wavelength (LW) opsins have occurred in many beetle lineages and have been proposed as an evolutionary route for expanded spectral sensitivity. The jewel beetles (Buprestidae) are a highly ecologically diverse and colorful family of beetles that use color cues for mate and host detection. In addition, there is evidence that buprestids have complex spectral sensitivity with up to five photoreceptor classes. Previous work suggested that opsin duplication and subfunctionalization of the two ancestral buprestid opsins, UV and LW, has expanded sensitivity to different regions of the light spectrum, but this has not yet been tested. We show that both duplications are likely unique to Buprestidae or the wider superfamily of Buprestoidea. To directly test photopigment sensitivity, we expressed buprestid opsins from two Chrysochroa species in Drosophila melanogaster and functionally characterized each photopigment type as UV- (356-357 nm), blue- (431-442 nm), green- (507-509 nm), and orange-sensitive (572-584 nm). As these novel opsin duplicates result in significantly shifted spectral sensitivities from the ancestral copies, we explored spectral tuning at four candidate sites using site-directed mutagenesis. This is the first study to directly test opsin spectral tuning mechanisms in the diverse and specious beetles.

Interaction of Ibuprofen with Partially Unfolded Bovine Serum Albumin in the Presence of Ionic Micelles and Oligosaccharides at Different λex and pH: A Spectroscopic Analysis

The interaction between the plasma protein bovine serum albumin (BSA) and the drug ibuprofen (IBU) has been investigated at three different pH values (7.4, 6.5, and 8.0) in the presence of oligosaccharides and surfactants. The interaction analysis of BSA with oligosaccharides and surfactants has also been studied in the absence of the drug ibuprofen. The results obtained give convenient and efficient access to understand the mechanism of binding of ibuprofen to BSA, and the major forces involved are found to be hydrophobic forces, hydrogen bonding and ionic interactions. In addition to that, the formation of inclusion complexes of ibuprofen with oligosaccharides (β-CD and 2-HP-β-CD) has been observed, which has depicted that due to the hydrophobic nature of ibuprofen, it becomes more soluble in the presence of oligosaccharides, but due to the larger size of the inclusion complexes, these could not be able to access the hydrophobic pocket of BSA where tryptophan-212 (Trp-212) resides. The binding interaction between BSA and ibuprofen is observed in the presence of surfactants (SDS and CTAB), which partially unfold the protein. Non-radiative fluorescence resonance energy transfer (FRET) from Trp and Tyr residues of BSA in the presence of an anionic surfactant SDS to ibuprofen has depicted that there is a possibility of drug binding even in the partially unfolded state of BSA protein. Furthermore, the distance between the protein and the drug has been calculated from the FRET efficiency, which gives a comprehensive overview of ibuprofen binding to BSA even in its partially denatured state. The hydrophobic drug binding to the partially unfolded serum albumin protein (BSA) supports the "necklace and bead structures" model and opens up a new direction of drug loading and delivery system, which will have critical therapeutic applications in the efficient delivery of pharmacologically prominent drugs.

In Silico Structural and Functional Analyses of NLRP3 Inflammasomes to Provide Insights for Treating Neurodegenerative Diseases

Inflammasomes are cytoplasmic intracellular multiprotein complexes that control the innate immune system's activation of inflammation in response to derived chemicals. Recent advancements increased our molecular knowledge of activation of NLRP3 inflammasomes. Although several studies have been done to investigate the role of inflammasomes in innate immunity and other diseases, structural, functional, and evolutionary investigations are needed to further understand the clinical consequences of NLRP3 gene. The purpose of this study is to investigate the structural and functional impact of the NLRP3 protein by using a computational analysis to uncover putative protein sites involved in the stabilization of the protein-ligand complexes with inhibitors. This will allow for a deeper understanding of the molecular mechanism underlying these interactions. It was found that human NLRP3 gene coexpresses with PYCARD, NLRC4, CASP1, MAVS, and CTSB based on observed coexpression of homologs in other species. The NACHT, LRR, and PYD domain-containing protein 3 is a key player in innate immunity and inflammation as the sensor subunit of the NLRP3 inflammasome. The inflammasome polymeric complex, consisting of NLRP3, PYCARD, and CASP1, is formed in response to pathogens and other damage-associated signals (and possibly CASP4 and CASP5). Comprehensive structural and functional analyses of NLRP3 inflammasome components offer a fresh approach to the development of new treatments for a wide variety of human disorders.