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

In silico Structure Prediction, Molecular Docking, and Dynamic Simulation of Plasmodium falciparum AP2-I Transcription Factor

Plasmodium falciparum Apicomplexan Apetala 2 Invasion (PfAP2-I) transcription factor (TF) is a protein that regulates the expression of a subset of gene families involved in P. falciparum red blood cell (RBC) invasion. Inhibiting PfAP2-I TF with small molecules represents a potential new antimalarial therapeutic target to combat drug resistance, which this study aims to achieve. The 3D model structure of PfAP2-I was predicted ab initio using ROBETTA prediction tool and was validated using Save server 6.0 and MolProbity. Computed Atlas of Surface Topography of proteins (CASTp) 3.0 was used to predict the active sites of the PfAP2-I modeled structure. Pharmacophore modeling of the control ligand and PfAP2-I modeled structure was carried out using the Pharmit server to obtain several compounds used for molecular docking analysis. Molecular docking and postdocking studies were conducted using AutoDock vina and Discovery studio. The designed ligands' toxicity predictions and in silico drug-likeness were performed using the SwissADME predictor and OSIRIS Property Explorer. The modeled protein structure from the ROBETTA showed a validation result of 96.827 for ERRAT, 90.2% of the amino acid residues in the most favored region for the Ramachandran plot, and MolProbity score of 1.30 in the 98th percentile. Five (5) best hit compounds from molecular docking analysis were selected based on their binding affinity (between -8.9 and -11.7 Kcal/mol) to the active site of PfAP2-I and were considered for postdocking studies. For the absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties, compound MCULE-7146940834 had the highest drug score (0.63) and drug-likeness (6.76). MCULE-7146940834 maintained a stable conformation within the flexible protein's active site during simulation. The good, estimated binding energies, drug-likeness, drug score, and molecular dynamics simulation interaction observed for MCULE-7146940834 against PfAP2-I show that MCULE-7146940834 can be considered a lead candidate for PfAP2-I inhibition. Experimental validations should be carried out to ascertain the efficacy of these predicted best hit compounds.

Molecular Interactions of the Copper Chaperone Atx1 of Paracoccidioides brasiliensis with Fungal Proteins Suggest a Crosstalk between Iron and Copper Homeostasis

Paracoccidioides spp. are endemic fungi from Latin America that cause Paracoccidioidomycosis, a systemic disease. These fungi present systems for high-affinity metal uptake, storage, and mobilization, which counteract host nutritional immunity and mitigate the toxic effects of metals. Regarding Cu mobilization, the metallochaperone Atx1 is regulated according to Cu bioavailability in Paracoccidioides spp., contributing to metal homeostasis. However, additional information in the literature on PbAtx1 is scarce. Therefore, in the present work, we aimed to study the PbAtx1 protein-protein interaction networks. Heterologous expressed PbAtx1 was used in a pull-down assay with Paracoccidioides brasiliensis cytoplasmic extract. Nineteen proteins that interacted with PbAtx1 were identified by HPLC-MS^E. Among them, a relevant finding was a Cytochrome b₅ (PbCyb5), regulated by Fe bioavailability in Aspergillus fumigatus and highly secreted by P. brasiliensis in Fe deprivation. We validated the interaction between PbAtx1-PbCyb5 through molecular modeling and far-Western analyses. It is known that there is a relationship between Fe homeostasis and Cu homeostasis in organisms. In this sense, would PbAtx1-PbCyb5 interaction be a new metal-sensor system? Would it be supported by the presence/absence of metals? We intend to answer those questions in future works to contribute to the understanding of the strategies employed by Paracoccidioides spp. to overcome host defenses.

Imidazonaphthyridine effects on Chikungunya virus replication: Antiviral activity by dependent and independent of interferon type 1 pathways

The Chikungunya virus (CHIKV) causes Chikungunya fever, a disease characterized by symptoms such as arthralgia/polyarthralgia. Currently, there are no antivirals approved against CHIKV, emphasizing the need to develop novel therapies. The imidazonaphthyridine compound (RO8191), an interferon-α (IFN-α) agonist, was reported as a potent inhibitor of HCV. Here RO8191 was investigated for its potential to inhibit CHIKV replication in vitro. RO8191 inhibited CHIKV infection in BHK-21 and Vero-E6 cells with a selectivity index (SI) of 12.3 and 37.3, respectively. Additionally, RO8191 was capable to protect cells against CHIKV infection, inhibit entry by virucidal activity, and strongly impair post-entry steps of viral replication. An effect of RO8191 on CHIKV replication was demonstrated in BHK-21 through type-1 IFN production mechanism and in Vero-E6 cells which has a defective type-1 IFN production, also suggesting a type-1 IFN independent mode of action. Molecular docking calculations demonstrated interactions of RO8191 with the CHIKV E proteins, corroborated by the ATR-FTIR assay, and with non-structural proteins, supported by the CHIKV-subgenomic replicon cells assay.

Deciphering the mechanism of Tinospora cordifolia extract on Th17 cells through in-depth transcriptomic profiling and in silico analysis

Naive CD4⁺ T cells differentiate into effector (Th1, Th2, Th17) cells and immunosuppressive (Treg) cells upon antigenic stimulation in the presence of a specific cytokine milieu. The T cell in vitro culture system provides a very efficient model to study compounds' therapeutic activity and mechanism of action. Tinospora cordifolia (Willd.) Hook.f. & Thomson (Family. Menispermaceae) is one of the widely used drugs in Ayurveda (ancient Indian system of medicine) for various ailments such as inflammatory conditions, autoimmune disorders, and cancer as well as for promoting general health. In vitro and in vivo studies on immune cells comprising dendritic cells, macrophages, and B cells suggest its immune-modulating abilities. However, to date, the effect of T. cordifolia on individual purified and polarized T cell subsets has not been studied. Studying drug effects on T cell subsets is needed to understand their immunomodulatory mechanism and to develop treatments for diseases linked with T cell abnormalities. In this study, we examined the immunomodulatory activity of T. cordifolia on primary CD4⁺ T cells, i.e., Th1, Th17, and iTreg cells. An aqueous extract of T. cordifolia was non-cytotoxic at concentrations below 1500 µg/ml and moderately inhibited the proliferation of naive CD4⁺ T cells stimulated with anti-CD3ε and anti-CD28 for 96 h. T. cordifolia treatment of naive CD4⁺ T cells differentiated under Th17-polarizing conditions exhibited reduced frequency of IL-17 producing cells with inhibition of differentiation and proliferation. For the first time, in-depth genome-wide expression profiling of T. cordifolia treated naive CD4⁺ T cells, polarized to Th17 cells, suggests the broad-spectrum activity of T. cordifolia. It shows inhibition of the cytokine-receptor signaling pathway, majorly via the JAK-STAT signaling pathway, subsequently causing inhibition of Th17 cell differentiation, proliferation, and effector function. Additionally, the molecular docking studies of the 69 metabolites of T. cordifolia further substantiate the inhibitory activity of T. cordifolia via the cytokine-receptor signaling pathway. Furthermore, in vitro polarized Th1 and iTreg cells treated with T. cordifolia extract also showed reduced IFN-γ production and FoxP3 expression, respectively. This study provides insight into the plausible mechanism/s of anti-inflammatory activity of T. cordifolia involving T cells, mainly effective in Th17-associated autoimmune and inflammatory diseases.

New nickel(ii) Schiff base complexes as potential tools against SARS-CoV-2 Omicron target proteins: an in silico approach

Herein, we report the in silico design and synthesis of two new nickel(ii) coordination complexes, based on Schiff bases derived from the 2-hydroxy-1-naphthaldehyde moiety.

In silico analysis of a putative dehalogenase from the genome of halophilic bacterium Halomonas smyrnensis AAD6T

Microbial-assisted removal of natural or synthetic pollutants is the prevailing green, low-cost technology to treat polluted environments. However, the challenge with enzyme-assisted bioremediation is the laborious nature of dehalogenase-producing microorganisms' bioprospecting. This bottleneck could be circumvented by in-silico analysis of certain microorganisms' whole-genome sequences to predict their protein functions and enzyme versatility for improved biotechnological applications. Herein, this study performed structural analysis on a dehalogenase (DehHsAAD6) from the genome of Halomonas smyrnensis AAD6 by molecular docking and molecular dynamic (MD) simulations. Other bioinformatics tools were also employed to identify substrate preference (haloacids and haloacetates) of the DehHsAAD6. The DehHsAAD6 preferentially degraded haloacids and haloacetates (-3.2-4.8 kcal/mol) and which formed three hydrogen bonds with Tyr12, Lys46, and Asp182. MD simulations data revealed the higher stability of DehHsAAD6-haloacid- (RMSD 0.22-0.3 nm) and DehHsAAD6-haloacetates (RMSF 0.05-0.14 nm) complexes, with the DehHsAAD6-L-2CP complex being the most stable. The detail of molecular docking calculations ranked complexes with the lowest binding free energies as: DehHsAAD6-L-2CP complex (-4.8 kcal/mol) = DehHsAAD6-MCA (-4.8 kcal/mol) < DehHsAAD6-TCA (-4.5 kcal/mol) < DehHsAAD6-2,3-DCP (-4.1 kcal/mol) < DehHsAAD6-D-2CP (-3.9 kcal/mol) < DehHsAAD6-2,2-DCP (-3.5 kcal/mol) < DehHsAAD6-3CP (-3.2 kcal/mol). In a nutshell, the study findings offer valuable perceptions into the elucidation of possible reaction mechanisms of dehalogenases for extended substrate specificity and higher catalytic activity.Communicated by Ramaswamy H. Sarma.

Unveiling the potential of Lichtheimia ramosa AJP11 for myco-transformation of polystyrene sulfonate and its driving molecular mechanism

Plastic pollution is a major environmental concern due to its deleterious effects on various ecosystems. The limitations and shortcomings of waste management strategies has led to the over-accumulation of plastic waste, mainly comprised of single-use plastics, such as polystyrene (PS). Considering the advantages of biotransformation over the other plastic disposal methods, it has become a major focus of the modern research. Biotransformation of plastics involves its microbial hydrolysis into short chain oligomers and monomers that are eventually assimilated as carbon source by the microbes leading to the release of CO₂. As fungi are known to possess multifarious and highly regulated enzyme system capable of utilizing diverse nutrient sources, the present study explored the potential of Lichtheimia ramosa AJP11 towards myco-transformation of polystyrene sulfonate (PSS), a structural analogue of polystyrene (PS). During the 30-day incubation period of L. ramosa AJP11 in minimal salt medium (MSM)+1% PSS, the fungus showed 41.6% increment in its fresh weight biomass, indicating the utilization of PSS as sole carbon source. Further analysis revealed the generation of various reaction intermediates such as alkanes and fatty acids, crucial for the continuum of fungal metabolic pathways. Moreover, detection of PS oligomers such as cyclohexane and 2,4-DTBP confirmed the myco-transformation of PSS. The extracellular fungal protein profile showed considerable overexpression of a 14.4 kDa protein, characterized to be a hydrophobic surface binding (Hsb) protein, which is hypothesized to adsorb onto the PSS to facilitate its transformation. Further, in silico analysis of Hsb protein indicated it to be an amphiphilic α-helical protein with ability to bind styrene sulfonate unit via both hydrogen and hydrophobic interactions, with a binding energy of -5.02 kcal mol^-1. These findings open new avenues for over expression of Hsb under controlled reactor conditions to accelerate the PS waste disposal.

RNAi-mediated silencing of an egg-specific gene Nllet1 results in hatch failure in the brown planthopper

BACKGROUND

The brown planthopper (BPH) is one of the most destructive agricultural pests in Asia. RNA interference (RNAi)-mediated pest management has been under development for years, and the selection of appropriate target genes is important for pest-targeted RNAi. C-type lectins (CTLs) are a class of genes that perform a variety of functions, such as the regulation of growth and development.

RESULTS

A CTL-S protein named Nllet1, containing a single calcium ion (Ca²⁺ )-dependent carbohydrate-binding domain (CRD) with a conserved triplet motif QPD was identified and functionally characterized in BPH. Expression profiles at both the transcriptional and translational levels show that Nllet1 accumulates during the serosal cuticle (SC) formation period. Immunofluorescence and immunogold labeling further demonstrated that Nllet1 is located in the serosal endocuticle (en-SC). Maternal RNAi-mediated silencing of Nllet1 disrupted the SC structure, accompanied by a loss of the outward barrier and 100% embryo mortality. Injection of 10 ng dsNllet1 or dsNllet1' per female adult BPH resulted in a total failure of egg hatching.

CONCLUSION

Nllet1 is essential for SC formation and embryonic development in BPH, which helps us understand the important roles of CTL-Ss. Additionally, BPH eggs show high sensitivity to the depletion of Nllet1. This study indicates that Nllet1 is a promising candidate gene that can be used to develop RNAi-based control strategies at the BPH egg stage, and it can also be used as a target for developing novel ovicides. © 2022 Society of Chemical Industry.

Bioprospection of the trichomonacidal activity of lipid extracts derived from marine macroalgae Gigartina skottsbergii

Algal extracts are sources of bioactive substances with applications in the development of novel alternative drugs against several diseases, including trichomoniasis sexually transmitted infection caused by Trichomonas vaginalis. Factors such as clinical failures and resistant strains limit the success of the existing drugs available for treating this disease. Therefore, searching for viable alternatives to these drugs is essential for the treatment of this disease. The present study was conducted for, in vitro and in silico characterization of extracts obtained from marine macroalgae Gigartina skottsbergii at stages gametophidic, cystocarpic, and tetrasporophidic. In addition, antiparasitic activity of these extracts against the ATCC 30236 isolate of T. vaginalis, their cytotoxicity, and gene expression of trophozoites after treatment were evaluated. The minimum inhibitory concentration and 50% inhibition concentration were determined for each extract. Results: In vitro analysis of the extracts' anti-T. vaginalis activity revealed an inhibitory effect of 100%, 89.61%, and 86.95% for Gigartina skottsbergii at stages gametophidic, cystocarpic, and tetrasporophidic, respectively, at 100 μg/mL. In silico analysis revealed the interactions between constituents of the extracts and enzymes from T. vaginalis, with significant free energy values obtained for the binding. None of the extract concentrations exhibited cytotoxic effects on VERO cell line compared to control, while cytotoxicity on HMVII vaginal epithelial cells line was observed at 100 μg/mL (30% inhibition). Gene expression analysis revealed differences in the expression profile of T. vaginalis enzymes between the extract-treated and control groups. According to these results, Gigartina skottsbergii extracts exhibited satisfactory antiparasitic activity.

Fragmented blind docking: a novel protein-ligand binding prediction protocol

In the present paper we propose a novel blind docking protocol based on Autodock-Vina. The developed docking protocol can provide binding site identification and binding pose prediction at the same time, by a systematical exploration of the protein volume performed with several preliminary docking calculations. In our opinion, this protocol can be successfully applied during the first steps of the virtual screening pipeline, because it provides binding site identification and binding pose prediction at the same time without visual evaluation of the binding site. After the binding pose prediction, MM/GBSA re-scoring rescoring procedures has been applied to improve the accuracy of the protein-ligand bound state. The FRAD protocol has been tested on 116 protein-ligand complexes of the Heat Shock Protein 90 - alpha, on 176 of Human Immunodeficiency virus protease 1, and on more than 100 protein-ligand system taken from the PDBbind dataset. Overall, the FRAD approach combined to MM/GBSA re-scoring can be considered as a powerful tool to increase the accuracy and efficiency with respect to other standard docking approaches when the ligand-binding site is unknown.Communicated by Ramaswamy H. Sarma.

Computational investigation of phytoalexins as potential antiviral RAP-1 and RAP-2 (Replication Associated Proteins) inhibitor for the management of cucumber mosaic virus (CMV): a molecular modeling, in silico docking and MM-GBSA study

The Replication Associated Proteins (RAP-1 and RAP-2) encoded by CMV ORF 1a and ORF 2a are required for the different stages of the viral replication cycle; being multi-functional, they are good inhibitory targets for anti-CMV compounds. As a new perspective for sustainable crop improvement, we investigated the natural plant-based antimicrobial phytoalexins for their anti-CMV potential. Here, we modeled and predicted the functional domains of RAP-1 and RAP-2, docked with a ligand library comprising 128 phytoalexins reported with broad-spectrum activity, determined their binding energies (BEs), molecular interactions, and inhibition constant (Ki), and compared with the reference plant antiviral compounds ribavirin, ningnanmycin, and benzothiadiazole (BTH). Further, the change in Gibb's free energy of binding (ΔG) and the per residue contribution of the selected top-scored ligand molecules was assessed by the prime MM-GBSA approach. Our results revealed RAP-1 as a discontinuous two-domain and RAP-2 as a multi-domain protein. The compounds glyceollidin (9.8 kcal/mol) and moracin D (7.8 kcal/mol) topped the list for RAP-1 and RAP-2 protein targets respectively and also, the lead molecules had energetically more favorable and comparative ΔG values than the top-scored plant antiviral agent ningnanmycin. The evaluation of in vitro toxicity and agrochemical-like properties showed the least toxicity of these anti-CMV compounds. Taken together, our results provide new insights in understanding the inhibitory effects of phytoalexins towards the RAP proteins and could be employed as new promising anti-CMV candidate compounds for their application in agriculture as biopesticides to combat the CMV disease incidence.Communicated by Ramaswamy H. Sarma.

In silico screening of FDA approved drugs against ACE2 receptor: potential therapeutics to inhibit the entry of SARS-CoV-2 to human cells

An unknown coronavirus that emerged sometime at the end of 2019 in China, the novel SARS-CoV-2, now called COVID-19, has spread all over the world. Several efforts have been made to prevent or treat this disease, though not with success. The initiation of COVID-19 viral infection involves specific binding of SARS-CoV-2 to the host surface of the receptor, ACE2. The ACE2- SARS-CoV-2 complex then gets transferred into the endosomes where the endosomal acidic proteases cleave the S protein present in SARS-CoV-2, activating its fusion and release of the viral genome. We have carried out detailed and thorough in silico studies to repurpose FDA approved compounds to inhibit human ACE2 receptor so as to prevent the viral entry. Our study reveals that five compounds show good binding to the ACE2 receptor and hence are potential candidates to interact with ACE2 and prevent it's recognition by the virus, SARS-CoV-2. Communicated by Ramaswamy H. Sarma.

Finding potential inhibitors against RNA-dependent RNA polymerase (RdRp) of bovine ephemeral fever virus (BEFV): an in-silico study

The bovine ephemeral fever virus (BEFV) is an enzootic agent that affects millions of bovines and causes major economic losses. Though the virus is seasonally reported with a very high morbidity rate (80-100%) from African, Australian, and Asiatic continents, it remains a neglected pathogen in many of its endemic areas, with no proper therapeutic drugs or vaccines presently available for treatment. The RNA-dependent RNA polymerase (RdRp) catalyzes the viral RNA synthesis and is an appropriate candidate for antiviral drug developments. We utilized integrated computational tools to build the 3D model of BEFV-RdRp and then predicted its probable active binding sites. The virtual screening and optimization against these active sites, using several small-molecule inhibitors from a different category of Life Chemical database and FDA-approved drugs from the ZINC database, was performed. We found nine molecules that have docking scores varying between -6.84 to -10.43 kcal/mol. Furthermore, these complexes were analyzed for their conformational dynamics and thermodynamic stability using molecular dynamics simulations in conjunction with the molecular mechanics generalized Born surface area (MM-GBSA) scheme. The binding free energy calculations depict that the electrostatic interactions play a dominant role in the RdRp-inhibitor binding. The hot spot residues, such as Arg565, Asp631, Glu633, Asp740, and Glu707, were found to control the RdRp-inhibitor interaction. The ADMET analysis strongly suggests favorable pharmacokinetics of these compounds that may prove useful for treating the BEFV ailment. Overall, we anticipate that these findings would help explore and develop a wide range of anti-BEFV therapy.Communicated by Ramaswamy H. Sarma.

In vitro and in silico study of an exclusive insertion in the nicotinamide/nicotinate mononucleotide adenylyltransferase from Leishmania braziliensis

The intracellular parasite Leishmania braziliensis is the causal agent of cutaneous and mucocutaneous leishmaniasis, a group of endemic diseases in tropical regions, including Latin America. New therapeutic targets are required to inhibit the pathogen without affecting the host. The enzyme nicotinamide/nicotinate mononucleotide adenylyltransferase (NMNAT; EC: 2.7.7.1/18) is a potential target, since it catalyzes the final step in the biosynthesis of nicotinamide adenine dinucleotide (NAD⁺), which is an essential metabolite in multiple cellular processes. In this work, we produced and evaluated the catalytic activity of the recombinant protein 6HisΔ_241-249LbNMNAT to study the functional relevance of the exclusive insertion present in the enzyme of L. braziliensis (LbNMNAT), but absent in the primary structure of human NMNATs. Our results indicate that the 241-249 insertion constitutes a structural element that connects the protein structure Rossmann topology with the carboxyl-terminal domain of the enzyme. The removal of this region drastically decreases the solubility, and enzymatic activity of the recombinant, causing its inactivation. Molecular dynamics simulations were carried out with the wild-type and truncated enzymes to verify additional changes in their stability, which indicated a better stability in the wild-type protein. These findings constitute an initial step to identify a new inhibition mechanism for the development of focused pharmacological strategies on exclusive insertions from the LbNMNAT protein.

BindWeb: A web server for ligand binding residue and pocket prediction from protein structures

Knowledge of protein-ligand interactions is beneficial for biological process analysis and drug design. Given the complexity of the interactions and the inadequacy of experimental data, accurate ligand binding residue and pocket prediction remains challenging. In this study, we introduce an easy-to-use web server BindWeb for ligand-specific and ligand-general binding residue and pocket prediction from protein structures. BindWeb integrates a graph neural network GraphBind with a hybrid convolutional neural network and bidirectional long short-term memory network DELIA to identify binding residues. Furthermore, BindWeb clusters the predicted binding residues to binding pockets with mean shift clustering. The experimental results and case study demonstrate that BindWeb benefits from the complementarity of two base methods. BindWeb is freely available for academic use at http://www.csbio.sjtu.edu.cn/bioinf/BindWeb/.

Structural Evaluation and Conformational Dynamics of ZNF141T474I Mutation Provoking Postaxial Polydactyly Type A

Postaxial Polydactyly (PAP) is a congenital disorder of limb abnormalities characterized by posterior extra digits. Mutations in the N-terminal region of the Zinc finger protein 141 (ZNF141) gene were recently linked with PAP type A. Zinc finger proteins exhibit similarity at their N-terminal regions due to C2-H2 type Zinc finger domains, but their functional preferences vary significantly by the binding patterns of DNA. Methods: This study delineates the pathogenic association, miss-fold aggregation, and conformational paradigm of a missense variant (c.1420C > T; p.T474I) in ZNF141 gene segregating PAP through a molecular dynamics simulations approach. Results: In ZNF141 protein, helices play a crucial role by attaching three specific target DNA base pairs. In ZNF141T474I protein, H1, H3, and H6 helices attain more flexibility by acquiring loop conformation. The outward disposition of the proximal portion of H9-helix in mutant protein occurs due to the loss of prior beta-hairpins at the C terminal region of the C2-H2 domain. The loss of hydrogen bonds and exposure of hydrophobic residues to solvent and helices turning to loops cause dysfunction of ZNF141 protein. These significant changes in the stability and conformation of the mutant protein were validated using essential dynamics and cross-correlation maps, which revealed that upon point mutation, the overall motion of the proteins and the correlation between them were completely different, resulting in Postaxial polydactyly type A. Conclusions: This study provides molecular insights into the structural association of ZNF141 protein with PAP type A. Identification of active site residues and legends offers new therapeutic targets for ZNF141 protein. Further, it reiterates the functional importance of the last residue of a protein.

A fibrinogen-related Lectin from Echinometra lucunter represents a new FReP family in Echinodermata phylum

Fibrinogen-related proteins (FREPs) have been identified in several animals. They are involved in the body's defense, acting as mediators of phagocytosis. Ficolins and intelectins are some of the most studied Fibrinogen-related Domain (FReD)-containing lectins. In this work, we have isolated a singular FReD-containing lectin, which cannot be classified as ficolin or intelectin. ELL (Echinometra lucunter lectin) was isolated from coelomic plasma by affinity chromatography on xanthan gum. Primary structure was determined by tandem mass spectrometry. Moreover, antimicrobial activity of ELL was evaluated against planktonic cells and biofilm of Escherichia coli, Staphylococcus aureus and S. epidermidis. ELL showed hemagglutinating activity in Ca²⁺ presence, which was inhibited by glycoprotein mucin and thyroglobulin. Complete amino acid sequence consisted of 229 residues, including a FReD in the N-terminal. Searches for similarity found that ELL was very close to putative proteins from Strongylocentrotus purpuratus. ELL showed moderate similarity with uncharacterized sea stars proteins and protochordate intelectins. ELL was able to inhibit the planktonic growth of the Gram-positive bacteria and significantly reduce the biofilm formation of all bacteria tested. In conclusion, we identified a new type of FReP-containing lectin with some structural and functional conservation towards intelectins.

Identification of FDA-approved drugs against SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) through computational virtual screening

The SARS-CoV-2 coronavirus is responsible for the COVID-19 outbreak, which overwhelmed millions of people worldwide; hence, there is an urgency to identify appropriate antiviral drugs. This study focuses on screening compounds that inhibit RNA-dependent RNA-polymerase (RdRp) essential for RNA synthesis required for replication of positive-strand RNA viruses. Computational screening against RdRp using Food and Drug Administration (FDA)-approved drugs identified ten prominent compounds with binding energies of more than - 10.00 kcal/mol, each a potential inhibitor of RdRp. These compounds' binding energy is comparable to known RdRp inhibitors remdesivir (IC50 = 10.09 μM, SI = 4.96) and molnupiravir (EC50 = 0.67 - 2.66 µM) and 0.32-2.03 µM). Remdesivir and molnupiravir have been tested in clinical trial and remain authorized for emergency use in the treatment of COVID-19. In docking simulations, selected compounds are bound to the substrate-binding pocket of RdRp and showed hydrophobic and hydrogen bond interaction. For molecular dynamics simulation, capmatinib, pralsetinib, ponatinib, and tedizolid phosphate were selected from the initial ten candidate compounds. MD simulation indicated that these compounds are stable at 50-ns MD simulation when bound to RdRp protein. The screen hit compounds, remdesivir, molnupiravir, and GS-441524, are bound in the substrate binding pocket with good binding-free energy. As a consequence, capmatinib, pralsetinib, ponatinib, and tedizolid phosphate are potential new inhibitors of RdRp protein with potential of limiting COVID-19 infection by blocking RNA synthesis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11224-022-02072-1.

Alignment-free metal ion-binding site prediction from protein sequence through pretrained language model and multi-task learning

More than one-third of the proteins contain metal ions in the Protein Data Bank. Correct identification of metal ion-binding residues is important for understanding protein functions and designing novel drugs. Due to the small size and high versatility of metal ions, it remains challenging to computationally predict their binding sites from protein sequence. Existing sequence-based methods are of low accuracy due to the lack of structural information, and time-consuming owing to the usage of multi-sequence alignment. Here, we propose LMetalSite, an alignment-free sequence-based predictor for binding sites of the four most frequently seen metal ions in BioLiP (Zn2+, Ca2+, Mg2+ and Mn2+). LMetalSite leverages the pretrained language model to rapidly generate informative sequence representations and employs transformer to capture long-range dependencies. Multi-task learning is adopted to compensate for the scarcity of training data and capture the intrinsic similarities between different metal ions. LMetalSite was shown to surpass state-of-the-art structure-based methods by more than 19.7, 14.4, 36.8 and 12.6% in area under the precision recall on the four independent tests, respectively. Further analyses indicated that the self-attention modules are effective to learn the structural contexts of residues from protein sequence. We provide the data sets, source codes and trained models of LMetalSite at https://github.com/biomed-AI/LMetalSite.

Vetinformatics from functional genomics to drug discovery: Insights into decoding complex molecular mechanisms of livestock systems in veterinary science

Having played important roles in human growth and development, livestock animals are regarded as integral parts of society. However, industrialization has depleted natural resources and exacerbated climate change worldwide, spurring the emergence of various diseases that reduce livestock productivity. Meanwhile, a growing human population demands sufficient food to meet their needs, necessitating innovations in veterinary sciences that increase productivity both quantitatively and qualitatively. We have been able to address various challenges facing veterinary and farm systems with new scientific and technological advances, which might open new opportunities for research. Recent breakthroughs in multi-omics platforms have produced a wealth of genetic and genomic data for livestock that must be converted into knowledge for breeding, disease prevention and management, productivity, and sustainability. Vetinformatics is regarded as a new bioinformatics research concept or approach that is revolutionizing the field of veterinary science. It employs an interdisciplinary approach to understand the complex molecular mechanisms of animal systems in order to expedite veterinary research, ensuring food and nutritional security. This review article highlights the background, recent advances, challenges, opportunities, and application of vetinformatics for quality veterinary services.

Structure-based virtual screening and molecular dynamics of potential inhibitors targeting sodium-bile acid co-transporter of carcinogenic liver fluke Clonorchis sinensis

Background

Clonorchis sinensis requires bile acid transporters as this fluke inhabits bile juice-filled biliary ducts, which provide an extreme environment. Clonorchis sinensis sodium-bile acid co-transporter (CsSBAT) is indispensable for the fluke’s survival in the final host, as it circulates taurocholate and prevents bile toxicity in the fluke; hence, it is recognized as a useful drug target.

Methodology and principal findings

In the present study, using structure-based virtual screening approach, we presented inhibitor candidates targeting a bile acid-binding pocket of CsSBAT. CsSBAT models were built using tertiary structure modeling based on a bile acid transporter template (PDB ID: 3zuy and 4n7x) and were applied into AutoDock Vina for competitive docking simulation. First, potential compounds were identified from PubChem (holding more than 100,000 compounds) by applying three criteria: i) interacting more favorably with CsSBAT than with a human homolog, ii) intimate interaction to the inward- and outward-facing conformational states, iii) binding with CsSBAT preferably to natural bile acids. Second, two compounds were identified following the Lipinski’s rule of five. Third, other two compounds of molecular weight higher than 500 Da (Mr > 500 Da) were presumed to efficiently block the transporter via a feasible rational screening strategy. Of these candidates, compound 9806452 exhibited the least hepatotoxicity that may enhance drug-likeness properties.

Conclusions

It is proposed that compound 9806452 act as a potential inhibitor toward CsSBAT and further studies are warranted for drug development process against clonorchiasis.

Application of Computational Biology and Artificial Intelligence in Drug Design

Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.

A multilayer dynamic perturbation analysis method for predicting ligand-protein interactions

BACKGROUND

Ligand-protein interactions play a key role in defining protein function, and detecting natural ligands for a given protein is thus a very important bioengineering task. In particular, with the rapid development of AI-based structure prediction algorithms, batch structural models with high reliability and accuracy can be obtained at low cost, giving rise to the urgent requirement for the prediction of natural ligands based on protein structures. In recent years, although several structure-based methods have been developed to predict ligand-binding pockets and ligand-binding sites, accurate and rapid methods are still lacking, especially for the prediction of ligand-binding regions and the spatial extension of ligands in the pockets.

RESULTS

In this paper, we proposed a multilayer dynamics perturbation analysis (MDPA) method for predicting ligand-binding regions based solely on protein structure, which is an extended version of our previously developed fast dynamic perturbation analysis (FDPA) method. In MDPA/FDPA, ligand binding tends to occur in regions that cause large changes in protein conformational dynamics. MDPA, examined using a standard validation dataset of ligand-protein complexes, yielded an averaged ligand-binding site prediction Matthews coefficient of 0.40, with a prediction precision of at least 50% for 71% of the cases. In particular, for 80% of the cases, the predicted ligand-binding region overlaps the natural ligand by at least 50%. The method was also compared with other state-of-the-art structure-based methods.

CONCLUSIONS

MDPA is a structure-based method to detect ligand-binding regions on protein surface. Our calculations suggested that a range of spaces inside the protein pockets has subtle interactions with the protein, which can significantly impact on the overall dynamics of the protein. This work provides a valuable tool as a starting point upon which further docking and analysis methods can be used for natural ligand detection in protein functional annotation. The source code of MDPA method is freely available at: https://github.com/mingdengming/mdpa .

Protein-DNA Binding Residue Prediction via Bagging Strategy and Sequence-Based Cube-Format Feature

Protein-DNA interactions play an important role in diverse biological processes. Accurately identifying protein-DNA binding residues is a critical but challenging task for protein function annotations and drug design. Although wet-lab experimental methods are the most accurate way to identify protein-DNA binding residues, they are time consuming and labor intensive. There is an urgent need to develop computational methods to rapidly and accurately predict protein-DNA binding residues. In this study, we propose a novel sequence-based method, named PredDBR, for predicting DNA-binding residues. In PredDBR, for each query protein, its position-specific frequency matrix (PSFM), predicted secondary structure (PSS), and predicted probabilities of ligand-binding residues (PPLBR) are first generated as three feature sources. Secondly, for each feature source, the sliding window technique is employed to extract the matrix-format feature of each residue. Then, we design two strategies, i.e., square root (SR) and average (AVE), to separately transform PSFM-based and two predicted feature source-based, i.e., PSS-based and PPLBR-based, matrix-format features of each residue into three corresponding cube-format features. Finally, after serially combining the three cube-format features, the ensemble classifier is generated via applying bagging strategy to multiple base classifiers built by the framework of 2D convolutional neural network. The computational experimental results demonstrate that the proposed PredDBR achieves an average overall accuracy of 93.7% and a Mathew's correlation coefficient of 0.405 on two independent validation datasets and outperforms several state-of-the-art sequenced-based protein-DNA binding residue predictors. The PredDBR web-server is available at https://jun-csbio.github.io/PredDBR/.

Amino acid utilization allows intestinal dominance of Lactobacillus amylovorus

The mammalian intestine harbors heterogeneous distribution of microbes among which specific taxa (e.g. Lactobacillus) dominate across mammals. Deterministic factors such as nutrient availability and utilization may affect microbial distributions. Due to physiological complexity, mechanisms linking nutrient utilization and the dominance of key taxa remain unclear. Lactobacillus amylovorus is a predominant species in the small intestine of pigs. Employing a pig model, we found that the small intestine was dominated by Lactobacillus and particularly L. amylovorus, and enriched with peptide-bound amino acids (PBAAs), all of which were further boosted after a peptide-rich diet. To investigate the bacterial growth dominance mechanism, a representative strain L. amylovorus S1 was isolated from the small intestine and anaerobically cultured in media with free amino acids or peptides as sole nitrogen sources. L. amylovorus S1 grew preferentially with peptide-rich rather than amino acid-rich substrates, as reflected by enhanced growth and PBAA utilization, and peptide transporter upregulations. Utilization of free amino acids (e.g. methionine, valine, lysine) and expressions of transporters and metabolic enzymes were enhanced simultaneously in peptide-rich substrate. Additionally, lactate was elevated in peptide-rich substrates while acetate in amino acid-rich substrates, indicating distinct metabolic patterns depending on substrate forms. These results suggest that an increased capability of utilizing PBAAs contributes to the dominance of L. amylovorus, indicating amino acid utilization as a deterministic factor affecting intestinal microbial distribution. These findings may provide new insights into the microbe-gut nutrition interplay and guidelines for dietary manipulations toward gut health especially small intestine health.

Computational analysis and functional characterisation of Tor putitora toll-like receptor 4 with the elucidation of its binding sites for microbial mimicking ligands

In the current study, full-length Toll-like receptor 4 (TLR4) cDNA was cloned and characterised in Tor putitora, an important fish inhibiting Himalayan rivers. The complete coding sequence of TpTLR4 is 2457 bp with nine key structural domains, including six leucine-rich repeats (LRRs). The phylogenetic tree revealed that TpTLR4 showed the closest relationship with TLR4 of Cyprinus carpio (96%), Labeo rohita (91%) and Megalobrama amblycephala (88%), all belonging to the Cyprinidae family. CELLO2GO tool revealed that TpTLR4 protein is highly localised in the plasma (67.7%), and the protein has a strong association with myeloid differentiation primary response 88 (MYD88) followed by Tumor necrosis factor receptor-associated factor (TRAF) family. In the toll-interleukin-1 receptor (TIR) domain of TpTLR4, the proline is replaced by the alanine amino acid, thus may give plasticity to the receptor to recognise both bacterial and viral ligands. Molecular docking has revealed that TpTLR4 showed the strongest affinity towards poly (I:C) with the binding energy of -6.1 kcal/mol and five hydrogen bonds among all ligands. Based on our molecular docking results, it can be presumed that TpTLR4 can sense bacterial, fungal and viral molecular patterns with binding sites mainly present in the TpTLR4 LRR9 motif, which spans between 515 and 602 amino acids. Tor putiora TLR4 transcript was ubiquitously expressed in all the tested fish tissues. Although, transcript level was found to be highest in blood and spleen followed by the kidney. The TpTLR4 transcripts showed peak expression in spleen and kidney at 12 h post-injection (hpi) (p < 0.05) of poly (I:C). The constitutive expression of TpTLR4 in various tissues, up-regulation in different tissues and strong binding affinities with poly (I:C) indicate that TpTLR4 may play an essential role in sensing pathogen-associated molecular patterns (PAMPs), particularly of viral origin.

Broad Antiviral Effects of Echinacea purpurea against SARS-CoV-2 Variants of Concern and Potential Mechanism of Action

SARS-CoV-2 variants of concern (VOCs) represent an alarming threat as they show altered biological behavior and may escape vaccination effectiveness. Broad-spectrum antivirals could play an important role to control infections. The activity of Echinacea purpurea (Echinaforce® extract, EF) against (i) VOCs B1.1.7 (alpha), B.1.351.1 (beta), P.1 (gamma), B1.617.2 (delta), AV.1 (Scottish), B1.525 (eta), and B.1.1.529.BA1 (omicron); (ii) SARS-CoV-2 spike (S) protein-pseudotyped viral particles and reference strain OC43 as well as (iii) wild type SARS-CoV-2 (Hu-1) was analyzed. Molecular dynamics (MD) were applied to study the interaction of Echinacea's phytochemical markers with known pharmacological viral and host cell targets. EF extract broadly inhibited the propagation of all investigated SARS-CoV-2 VOCs as well as the entry of SARS-CoV-2 pseudoparticles at EC50's ranging from 3.62 to 12.03 µg/mL. The preventive addition of 25 µg/mL EF to epithelial cells significantly reduced sequential infection with SARS-CoV-2 (Hu-1) and OC43. MD analyses showed constant binding affinities to VOC-typical S protein variants for alkylamides, caftaric acid, and feruloyl-tartaric acid in EF extract and interactions with serine protease TMPRSS-2. EF extract demonstrated stable virucidal activity across seven tested VOCs, likely due to the constant affinity of the contained phytochemical substances to all spike variants. A possible interaction of EF with TMPRSS-2 partially would explain the cell protective benefits of the extract by the inhibition of membrane fusion and cell entry. EF may therefore offer a supportive addition to vaccination endeavors in the control of existing and future SARS-CoV-2 virus mutations.

In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets

Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.

A Single Nucleotide Polymorphism Translates into a Radical Amino Acid Substitution at the Ligand-Binding Site in Fasciola hepatica Carboxylesterase B

Fasciola hepatica anthelmintic resistance may be associated with the catalytic activity of xenobiotic metabolizing enzymes. The gene expression of one of these enzymes, identified as carboxylesterase B (CestB), was previously described as inducible in adult parasites under anthelmintic treatment and exhibited a single nucleotide polymorphism at position 643 that translates into a radical amino acid substitution at position 215 from Glutamic acid to Lysine. Alphafold 3D models of both allelic sequences exhibited a significant affinity pocket rearrangement and different ligand-docking modeling results. Further bioinformatics analysis confirmed that the radical amino acid substitution is located at the ligand affinity site of the enzyme, affecting its affinity to serine hydrolase inhibitors and preferences for ester ligands. A field genotyping survey from parasite samples obtained from two developmental stages isolated from different host species from Argentina and Mexico exhibited a 37% allele distribution for 215E and a 29% allele distribution for 215K as well as a 34% E/K heterozygous distribution. No linkage to host species or geographic origin was found in any of the allele variants.

HProteome-BSite: predicted binding sites and ligands in human 3D proteome

Atomic-level knowledge of protein-ligand interactions allows a detailed understanding of protein functions and provides critical clues to discovering molecules regulating the functions. While recent innovative deep learning methods for protein structure prediction dramatically increased the structural coverage of the human proteome, molecular interactions remain largely unknown. A new database, HProteome-BSite, provides predictions of binding sites and ligands in the enlarged 3D human proteome. The model structures for human proteins from the AlphaFold Protein Structure Database were processed to structural domains of high confidence to maximize the coverage and reliability of interaction prediction. For ligand binding site prediction, an updated version of a template-based method GalaxySite was used. A high-level performance of the updated GalaxySite was confirmed. HProteome-BSite covers 80.74% of the UniProt entries in the AlphaFold human 3D proteome. Predicted binding sites and binding poses of potential ligands are provided for effective applications to further functional studies and drug discovery. The HProteome-BSite database is available at https://galaxy.seoklab.org/hproteome-bsite/database and is free and open to all users.

In Silico Structural Analysis of Serine Carboxypeptidase Nf314, a Potential Drug Target in Naegleria fowleri Infections

Naegleria fowleri, also known as the “brain-eating” amoeba, is a free-living protozoan that resides in freshwater bodies. This pathogenic amoeba infects humans as a casual event when swimming in contaminated water. Upon inhalation, N. fowleri invades the central nervous system and causes primary amoebic meningoencephalitis (PAM), a rapidly progressive and often fatal disease. Although PAM is considered rare, reducing its case fatality rate compels the search for pathogen-specific proteins with a structure–function relationship that favors their application as targets for discovering new or improved drugs against N. fowleri infections. Herein, we report a computational approach to study the structural features of Nf314 (a serine carboxypeptidase that is a virulence-related protein in N. fowleri infections) and assess its potential as a drug target, using bioinformatics tools and in silico molecular docking experiments. Our findings suggest that Nf314 has a ligand binding site suitable for the structure-based design of specific inhibitors. This study represents a further step toward postulating a reliable therapeutic target to treat PAM with drugs specifically aimed at blocking the pathogen proliferation by inhibiting protein function.

DrugRep: an automatic virtual screening server for drug repurposing

Computationally identifying new targets for existing drugs has drawn much attention in drug repurposing due to its advantages over de novo drugs, including low risk, low costs, and rapid pace. To facilitate the drug repurposing computation, we constructed an automated and parameter-free virtual screening server, namely DrugRep, which performed molecular 3D structure construction, binding pocket prediction, docking, similarity comparison and binding affinity screening in a fully automatic manner. DrugRep repurposed drugs not only by receptor-based screening but also by ligand-based screening. The former automatically detected possible binding pockets of the receptor with our cavity detection approach, and then performed batch docking over drugs with a widespread docking program, AutoDock Vina. The latter explored drugs using seven well-established similarity measuring tools, including our recently developed ligand-similarity-based methods LigMate and FitDock. DrugRep utilized easy-to-use graphic interfaces for the user operation, and offered interactive predictions with state-of-the-art accuracy. We expect that this freely available online drug repurposing tool could be beneficial to the drug discovery community. The web site is http://cao.labshare.cn/drugrep/.

Identification of resistance gene analogs of the NBS-LRR family through transcriptome probing and in silico prediction of the expressome of Dalbergia sissoo under dieback disease stress

Dalbergia sissoo is an important timber tree, and dieback disease poses a dire threat to it toward extinction. The genomic record of D. sissoo is not available yet on any database; that is why it is challenging to probe the genetic elements involved in stress resistance. Hence, we attempted to unlock the genetics involved in dieback resistance through probing the NBS-LRR family, linked with mostly disease resistance in plants. We analyzed the transcriptome of D. sissoo under dieback challenge through DOP-rtPCR analysis using degenerate primers from conserved regions of NBS domain-encoded gene sequences. The differentially expressed gene sequences were sequenced and in silico characterized for predicting the expressome that contributes resistance to D. sissoo against dieback. The molecular and bioinformatic analyses predicted the presence of motifs including ATP/GTP-binding site motif A (P-loop NTPase domain), GLPL domain, casein kinase II phosphorylation site, and N-myristoylation site that are the attributes of proteins encoded by disease resistance genes. The physicochemical characteristics of identified resistance gene analogs, subcellular localization, predicted protein fingerprints, in silico functional annotation, and predicted protein structure proved their role in disease and stress resistance.

A chronotherapeutics-applicable multi-target therapeutics based on AI: Example of therapeutic hypothermia

Nowadays, the complexity of disease mechanisms and the inadequacy of single-target therapies in restoring the biological system have inevitably instigated the strategy of multi-target therapeutics with the analysis of each target individually. However, it is not suitable for dealing with the conflicts between targets or between drugs. With the release of high-precision protein structure prediction artificial intelligence, large-scale high-precision protein structure prediction and docking have become possible. In this article, we propose a multi-target drug discovery method by the example of therapeutic hypothermia (TH). First, we performed protein structure prediction for all protein targets of each group by AlphaFold2 and RoseTTAFold. Then, QuickVina 2 is used for molecular docking between the proteins and drugs. After docking, we use PageRank to rank single drugs and drug combinations of each group. The ePharmaLib was used for predicting the side effect targets. Given the differences in the weights of different targets, the method can effectively avoid inhibiting beneficial proteins while inhibiting harmful proteins. So it could minimize the conflicts between different doses and be friendly to chronotherapeutics. Besides, this method also has potential in precision medicine for its high compatibility with bioinformatics and promotes the development of pharmacogenomics and bioinfo-pharmacology.

Genotyping and In Silico Analysis of Delmarva (DMV/1639) Infectious Bronchitis Virus (IBV) Spike 1 (S1) Glycoprotein

Genetic diversity and evolution of infectious bronchitis virus (IBV) are mainly impacted by mutations in the spike 1 (S1) gene. This study focused on whole genome sequencing of an IBV isolate (IBV/Ck/Can/2558004), which represents strains highly prevalent in Canadian commercial poultry, especially concerning features related to its S1 gene and protein sequences. Based on the phylogeny of the S1 gene, IBV/Ck/Can/2558004 belongs to the GI-17 lineage. According to S1 gene and protein pairwise alignment, IBV/Ck/Can/2558004 had 99.44–99.63% and 98.88–99.25% nucleotide (nt) and deduced amino acid (aa) identities, respectively, with five Canadian Delmarva (DMV/1639) IBVs isolated in 2019, and it also shared 96.63–97.69% and 94.78–97.20% nt and aa similarities with US DMV/1639 IBVs isolated in 2011 and 2019, respectively. Further homology analysis of aa sequences showed the existence of some aa substitutions in the hypervariable regions (HVRs) of the S1 protein of IBV/Ck/Can/2558004 compared to US DMV/1639 isolates; most of these variant aa residues have been subjected to positive selection pressure. Predictive analysis of potential N-glycosylation and phosphorylation motifs showed either loss or acquisition in the S1 glycoprotein of IBV/Ck/Can/2558004 compared to S1 of US DMV/1639 IBV. Furthermore, bioinformatic analysis showed some of the aa changes within the S1 protein of IBV/Ck/Can/2558004 have been predicted to impact the function and structure of the S1 protein, potentially leading to a lower binding affinity of the S1 protein to its relevant ligand (sialic acid). In conclusion, these findings revealed that the DMV/1639 IBV isolates are under continuous evolution among Canadian poultry.

An Effective Swarm Intelligence Optimization Algorithm for Flexible Ligand Docking

In general, flexible ligand docking is used for docking simulations under the premise that the position of the binding site is already known, and meanwhile it can also be used without prior knowledge of the binding site. However, most of the optimization search algorithms used in popular docking software are far from being ideal in the first case, and they can hardly be directly utilized for the latter case due to the relatively large search area. In order to design an algorithm that can flexibly adapt to different sizes of the search area, we propose an effective swarm intelligence optimization algorithm in this paper, called diversity-controlled Lamarckian quantum particle swarm optimization (DCL-QPSO). The highlights of the algorithm are a diversity-controlled strategy and a modified local search method. Integrated with the docking environment of Autodock, the DCL-QPSO is compared with Autodock Vina, Glide and other two Autodock-based search algorithms for flexible ligand docking. Experimental results revealed that the proposed algorithm has a performance comparable to those of Autodock Vina and Glide for dockings within a certain area around the binding sites, and is a more effective solver than all the compared methods for dockings without prior knowledge of the binding sites.

Suppressing crucial oncogenes of leukemia initiator cells by major royal jelly protein 2 for mediating apoptosis in myeloid and lymphoid leukemia cells

Relapse of leukemia and drug resistance are still the major obstacles to therapy due to leukemia-initiating stem/progenitor cells (LICs); thus, targeting them using safe compounds is crucial. Here, we evaluated the anti-leukemic effect of royal jelly (RJ) components, which had a higher safe concentration (EC₁₀₀ values) than the chemotherapeutic drug doxorubicin (DOX). The RJ-protein fraction 50 (PF₅₀, precipitated at 40-50% ammonium sulfate saturation) and its constituents, major RJ protein (MRJP) 2 and its isoform X1, exhibited the highest growth inhibitory effect against myeloid NFS-60 and lymphoid Jurkat cell lines. MRJP2 has a nanosize, which may be the reason for its higher anti-leukemic activity than its isoform. These RJ proteins, particularly MRJP2, suppressed LIC-associated oncogenes (GATA2 and Evi-1) and eliminated CD34⁺ LICs, in contrast to the low anti-LIC efficacy of DOX. MRJP2 demonstrated higher apoptotic activity than its isoform by upregulating p53 and p21-mediated cell cycle arrest. This study also reported the potent inhibitory effect of RJ-proteins on matrix metallopeptidase 10 (metastatic marker) and histone deacetylase 8 (mediates LIC survival) activities. Thus, MRJP2 can be considered a promising novel therapeutic agent for both myeloid and lymphoid leukemia.

Enhancing the Catalytic Activity of Type II L-Asparaginase from Bacillus licheniformis through Semi-Rational Design

Low catalytic activity is a key factor limiting the widespread application of type II L-asparaginase (ASNase) in the food and pharmaceutical industries. In this study, smart libraries were constructed by semi-rational design to improve the catalytic activity of type II ASNase from Bacillus licheniformis. Mutants with greatly enhanced catalytic efficiency were screened by saturation mutations and combinatorial mutations. A quintuple mutant ILRAC was ultimately obtained with specific activity of 841.62 IU/mg and kcat/Km of 537.15 min−1·mM−1, which were 4.24-fold and 6.32-fold more than those of wild-type ASNase. The highest specific activity and kcat/Km were firstly reported in type II ASNase from Bacillus licheniformis. Additionally, enhanced pH stability and superior thermostability were both achieved in mutant ILRAC. Meanwhile, structural alignment and molecular dynamic simulation demonstrated that high structure stability and strong substrate binding were beneficial for the improved thermal stability and enzymatic activity of mutant ILRAC. This is the first time that enzymatic activity of type II ASNase from Bacillus licheniformis has been enhanced by the semi-rational approach, and results provide new insights into enzymatic modification of L-asparaginase for industrial applications.

Optimal COVID-19 therapeutic candidate discovery using the CANDO platform

The worldwide outbreak of SARS-CoV-2 in early 2020 caused numerous deaths and unprecedented measures to control its spread. We employed our Computational Analysis of Novel Drug Opportunities (CANDO) multiscale therapeutic discovery, repurposing, and design platform to identify small molecule inhibitors of the virus to treat its resulting indication, COVID-19. Initially, few experimental studies existed on SARS-CoV-2, so we optimized our drug candidate prediction pipelines using results from two independent high-throughput screens against prevalent human coronaviruses. Ranked lists of candidate drugs were generated using our open source cando.py software based on viral protein inhibition and proteomic interaction similarity. For the former viral protein inhibition pipeline, we computed interaction scores between all compounds in the corresponding candidate library and eighteen SARS-CoV proteins using an interaction scoring protocol with extensive parameter optimization which was then applied to the SARS-CoV-2 proteome for prediction. For the latter similarity based pipeline, we computed interaction scores between all compounds and human protein structures in our libraries then used a consensus scoring approach to identify candidates with highly similar proteomic interaction signatures to multiple known anti-coronavirus actives. We published our ranked candidate lists at the very beginning of the COVID-19 pandemic. Since then, 51 of our 276 predictions have demonstrated anti-SARS-CoV-2 activity in published clinical and experimental studies. These results illustrate the ability of our platform to rapidly respond to emergent pathogens and provide greater evidence that treating compounds in a multitarget context more accurately describes their behavior in biological systems.

Leptolysin, a Leptospira secreted metalloprotease of the pappalysin family with broad-spectrum activity

Extracellular proteolytic enzymes are produced by a variety of pathogenic microorganisms, and contribute to host colonization by modulating virulence. Here, we present a first characterization of leptolysin, a Leptospira metalloprotease of the pappalysin family identified in a previous exoproteomic study. Comparative molecular analysis of leptolysin with two other pappalysins from prokaryotes, ulilysin and mirolysin, reveals similarities regarding calcium, zinc, and arginine -binding sites conservation within the catalytic domain, but also discloses peculiarities. Variations observed in the primary and tertiary structures may reflect differences in primary specificities. Purified recombinant leptolysin of L. interrogans was obtained as a ~50 kDa protein. The protease exhibited maximal activity at pH 8.0 and 37°C, and hydrolytic activity was observed in the presence of different salts with maximum efficiency in NaCl. Substrate specificity was assessed using a small number of FRET peptides, and showed a marked preference for arginine residues at the P1 position. L. interrogans leptolysin proteolytic activity on proteinaceous substrates such as proteoglycans and plasma fibronectin was also evaluated. All proteins tested were efficiently degraded over time, confirming the protease´s broad-spectrum activity in vitro. In addition, leptolysin induced morphological alterations on HK-2 cells, which may be partially attributed to extracellular matrix (ECM) degradation. Hemorrhagic foci were observed in the dorsal skin of mice intradermally injected with leptolysin, as a plausible consequence of ECM disarray and vascular endothelium glycocalyx damage. Assuming that leptospiral proteases play an important role in all stages of the infectious process, characterizing their functional properties, substrates and mechanisms of action is of great importance for therapeutic purposes.

Ab initio modelling of an essential mammalian protein: Transcription Termination Factor 1 (TTF1)

Transcription Termination Factor 1 (TTF1) is an essential mammalian protein that regulates transcription, replication fork arrest, DNA damage repair, chromatin remodelling etc. TTF1 interacts with numerous cellular proteins to regulate various cellular phenomena which play a crucial role in maintaining normal cellular physiology, and dysregulation of this protein has been reported to induce oncogenic transformation of the cells. However, despite its key role in many cellular processes, the complete structure of human TTF1 has not been elucidated to date, neither experimentally nor computationally. Therefore, understanding the structure of human TTF1 is crucial for studying its functions and interactions with other cellular factors. The aim of this study was to construct the complete structure of human TTF1 protein, using molecular modelling approaches. Owing to the lack of suitable homologues in the Protein Data Bank (PDB), the complete structure of human TTF1 was constructed by ab initio modelling. The structural stability was determined with molecular dynamics (MD) simulations in explicit solvent, and trajectory analyses. The frequently occurring conformation of human TTF1 was selected by trajectory clustering, and the central residues of this conformation were determined by centrality analyses of the Residue Interaction Network (RIN) of TTF1. Two residue clusters, one in the oligomerization domain and other in the C-terminal domain, were found to be central to the structural stability of human TTF1. To the best of our knowledge, this study is the first to report the complete structure of this essential mammalian protein, and the results obtained herein will provide structural insights for future research including that in cancer biology and related studies.Communicated by Ramaswamy H. Sarma.

Predicting compound-protein interaction using hierarchical graph convolutional networks

Motivation

Convolutional neural networks have enabled unprecedented breakthroughs in a variety of computer vision tasks. They have also drawn much attention from other domains, including drug discovery and drug development. In this study, we develop a computational method based on convolutional neural networks to tackle a fundamental question in drug discovery and development, i.e. the prediction of compound-protein interactions based on compound structure and protein sequence. We propose a hierarchical graph convolutional network (HGCN) to encode small molecules. The HGCN aggregates a molecule embedding from substructure embeddings, which are synthesized from atom embeddings. As small molecules usually share substructures, computing a molecule embedding from those common substructures allows us to learn better generic models. We then combined the HGCN with a one-dimensional convolutional network to construct a complete model for predicting compound-protein interactions. Furthermore we apply an explanation technique, Grad-CAM, to visualize the contribution of each amino acid into the prediction.

Results

Experiments using different datasets show the improvement of our model compared to other GCN-based methods and a sequence based method, DeepDTA, in predicting compound-protein interactions. Each prediction made by the model is also explainable and can be used to identify critical residues mediating the interaction.

Open-Source Browser-Based Tools for Structure-Based Computer-Aided Drug Discovery

We here outline the importance of open-source, accessible tools for computer-aided drug discovery (CADD). We begin with a discussion of drug discovery in general to provide context for a subsequent discussion of structure-based CADD applied to small-molecule ligand discovery. Next, we identify usability challenges common to many open-source CADD tools. To address these challenges, we propose a browser-based approach to CADD tool deployment in which CADD calculations run in modern web browsers on users' local computers. The browser app approach eliminates the need for user-initiated download and installation, ensures broad operating system compatibility, enables easy updates, and provides a user-friendly graphical user interface. Unlike server apps-which run calculations "in the cloud" rather than on users' local computers-browser apps do not require users to upload proprietary information to a third-party (remote) server. They also eliminate the need for the difficult-to-maintain computer infrastructure required to run user-initiated calculations remotely. We conclude by describing some CADD browser apps developed in our lab, which illustrate the utility of this approach. Aside from introducing readers to these specific tools, we are hopeful that this review highlights the need for additional browser-compatible, user-friendly CADD software.

Structural Bioinformatics and Deep Learning of Metalloproteins: Recent Advances and Applications

All living organisms require metal ions for their energy production and metabolic and biosynthetic processes. Within cells, the metal ions involved in the formation of adducts interact with metabolites and macromolecules (proteins and nucleic acids). The proteins that require binding to one or more metal ions in order to be able to carry out their physiological function are called metalloproteins. About one third of all protein structures in the Protein Data Bank involve metalloproteins. Over the past few years there has been tremendous progress in the number of computational tools and techniques making use of 3D structural information to support the investigation of metalloproteins. This trend has been boosted by the successful applications of neural networks and machine/deep learning approaches in molecular and structural biology at large. In this review, we discuss recent advances in the development and availability of resources dealing with metalloproteins from a structure-based perspective. We start by addressing tools for the prediction of metal-binding sites (MBSs) using structural information on apo-proteins. Then, we provide an overview of the methods for and lessons learned from the structural comparison of MBSs in a fold-independent manner. We then move to describing databases of metalloprotein/MBS structures. Finally, we summarizing recent ML/DL applications enhancing the functional interpretation of metalloprotein structures.

VS, Gorityala N, Sagurthi SR, Mungapati S, Manikanta KN, Allam US. In Silico Modeling and Docking Analysis of CTX-M-5, Cefotaxime-Hydrolyzing β-Lactamase from Human-Associated Salmonella Typhimurium

Background:

CTX-M-type enzymes represent a novel and rapidly evolving group of extended-spectrum β-lactamases, which confer resistance to advance generation cephalosporins. Despite the interaction of CTX-M-5 with drugs and inhibitors, its structure is not reported till date. The present study aimed to computationally model the CTX-M-5 β-lactamase and establish its structure, which is exclusively present in human-associated Salmonella.

Methods:

The CTX-M-5 aminoacid sequence (Uniprot ID:O65975) of Salmonella enterica subsp. enterica serovar typhimurium was retrieved from UniProt database and subjected to homology modeling using MODELLER 9v7. The homology models were duly validated using RAMPAGE tool by generating Ramachandran plots, ERRAT graphs, and ProSA score. DoGSiteScorer server and ConSurf server were used to detect the cavities, pockets, and clefts to identify conserved amino acid sites in the predicted model. Subsequently, the modeled structure was docked using CLC Drug Discovery Workbench against proven drugs and known inhibitors.

Results:

Obtained high-quality homology model with 91.7% of the residues in favorable regions in Ramachandran plot and qualified in other quality parameters. Docking studies resulted in a higher dock score for PNK (D-benzylpenicilloic acid) molecule when compared to other reported inhibitors.

Conclusion:

This in silico study suggests that the compound PNK could be an efficient ligand for CTX-M-5 β-lactamase and serve as a potent inhibitor of CTX-M-5.