Homology modeling: an important tool for the drug discovery

An evaluation of the precision of computational methods used in drug development initiatives

Computational approaches are commonly employed to expedite and provide decision-making for the drug development process. Drug development programs that involve targets without known crystal structures can be quite challenging. In many cases, a viable approach is to generate reliable homology models using the amino acid sequence of the target. This is followed by a series of validation steps, druggable pocket detection, and then moving forward with lead identification and validation. This study commenced by conducting an initial benchmark exercise using a series of computationally designed sequences for steroid-binding proteins. By conducting an unbiased comparison with the released X-ray crystal structures, the homology models that were generated demonstrated reliable outcomes. The aligned homology models showed a root mean square deviation (RMSD) of less than 0.6 Å when compared to the corresponding X-ray structures. Three different methods were used to detect the druggable cavities for comparison, and the identified pockets closely resembled those of the crystal structures. The achievement of near-native pose prediction was made possible by utilizing the comprehensive binding energy function that characterizes the interaction between each pose and the neighboring residues. In order to address the issue of limited correlation between entropy and internal energy in docking, an alternative was devised by incorporating entropy as a post-docking optimization step to enhance the accuracy of ligand binding affinity predictions and improve the overall quality of the results.

Quercetin increases the antidepressant-like effects of sclareol and antagonizes diazepam in thiopental sodium-induced sleeping mice: A possible GABAergic transmission intervention

Quercetin is the most common polyphenolic flavonoid present in fruits and vegetables demonstrating versatile health-promoting effects. This study aimed to examine the effects of quercetin (QR) and sclareol (SCL) on the thiopental sodium (TS)-induced sleeping and forced swimming test (FST) mouse models. SCL (1, 5, and 10 mg/kg, p.o.) or QR (50 mg/kg, p.o.) and/or diazepam (DZP) (3 mg/kg, i.p.) were employed. After 30 min of TS induction, individual or combined effects on the animals were checked. In the FST test, the animals were subjected to forced swimming after 30 min of administration of the test and/or controls for 5 min. In this case, immobility time was measured. In silico studies were conducted to evaluate the involvement of GABA receptors. SCL (5 and 10 mg/kg) significantly increased the latency and decreased sleeping time compared to the control in the TS-induced sleeping time study. DZP (3 mg/kg) showed a sedative-like effect in animals in both sleeping and FST studies. QR (50 mg/kg) exhibited a similar pattern of activity as SCL. However, its effects were more prominent than those of SCL groups. SCL (10 mg/kg) altered the DZP-3-mediated effects. SCL-10 co-treated with QR-50 significantly (p < 0.05) increased the latency and decreased sleep time and immobility time, suggesting possible synergistic antidepressant-like effects. In silico studies revealed that SCL and QR demonstrated better binding affinities with GABAA receptor, especially α2, α3, and α5 subunits. Both compounds also exhibited good ADMET and drug-like properties. In animal studies, the both compounds worked synergistically to provide antidepressant-like effects in a slightly different fashion. As a conclusion, the combined administration of SCL and QR may be used in upcoming neurological clinical trials, according to in vivo and in silico findings. However, additional investigation is necessary to verify this behavior and clarify the potential mechanism of action.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

Roles of Virtual Screening and Molecular Dynamics Simulations in Discovering and Understanding Antimalarial Drugs

Malaria continues to be a global health threat, with approximately 247 million cases worldwide. Despite therapeutic interventions being available, patient compliance is a problem due to the length of treatment. Moreover, drug-resistant strains have emerged over the years, necessitating urgent identification of novel and more potent treatments. Given that traditional drug discovery often requires a great deal of time and resources, most drug discovery efforts now use computational methods. In silico techniques such as quantitative structure-activity relationship (QSAR), docking, and molecular dynamics (MD) can be used to study protein-ligand interactions and determine the potency and safety profile of a set of candidate compounds to help prioritize those tested using assays and animal models. This paper provides an overview of antimalarial drug discovery and the application of computational methods in identifying candidate inhibitors and elucidating their potential mechanisms of action. We conclude with the continued challenges and future perspectives in the field of antimalarial drug discovery.

Ligand-based virtual screening, molecular dynamics, and biological evaluation of repurposed drugs as inhibitors of Trypanosoma cruzi proteasome

Chagas disease is a well-known Neglected Tropical Disease, mostly endemic in continental Latin America, but that has spread to North America and Europe. Unfortunately, current treatments against such disease are ineffective and produce known and undesirable side effects. To find novel effective drug candidates to treat Chagas disease, we uniquely explore the Trypanosoma cruzi proteasome as a recent biological target and, also, apply drug repurposing through different computational methodologies. For this, we initially applied protein homology modeling to build a robust model of proteasome β4/β5 subunits, since there is no crystallographic structure of this target. Then, we used it on a drug repurposing via a virtual screening campaign starting with more than 8,000 drugs and including the methodologies: ligand-based similarity, toxicity predictions, and molecular docking. Three drugs were selected concerning their favorable interactions at the protein binding site and subsequently submitted to molecular dynamics simulations, which allowed us to elucidate their behavior and compare such theoretical results with experimental ones, obtained in biological assays also described in this paper.Communicated by Ramaswamy H. Sarma.

Protein Homology Modeling for Effective Drug Design

The effective drug design, especially for combating the multi-drug-resistant bacterial pathogens, requires more and more sophisticated procedures to obtain novel lead-like compounds. New classes of enzymes should be explored, especially those that help bacteria overcome existing treatments. The homology modeling is useful in obtaining the models of new enzymes; however, the active sites of them are sometimes present in closed conformations in the crystal structures, not suitable for drug design purposes. In such difficult cases, the combination of homology modeling, molecular dynamics simulations, and fragment screening can give satisfactory results.

Modeling of Protein Complexes

The recent advances in structural biology, combined with continuously increasing computational capabilities and development of advanced softwares, have drastically simplified the workflow for protein homology modeling. Modeling of individual proteins is nowadays quick and straightforward for a large variety of protein targets, thanks to guided pipelines relying on advanced computational tools and user-friendly interfaces, which have extended and promoted the use of modeling also to scientists not focusing on molecular structures of proteins. Nevertheless, construction of models of multi-protein complexes remains quite challenging for the non-experts, often due to the usage of specific procedures depending on the system under investigation and the need for experimental validation approaches to strengthen the generated output.In this chapter, we provide a brief overview of the approaches enabling generation of multi-protein complex models starting from homology models of individual protein components. Using real-life examples, we include two examples to guide the reader in the generation of homomeric and heteromeric protein models.

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.

Conformational Stability and Denaturation Processes of Proteins Investigated by Electrophoresis under Extreme Conditions

The functional structure of proteins results from marginally stable folded conformations. Reversible unfolding, irreversible denaturation, and deterioration can be caused by chemical and physical agents due to changes in the physicochemical conditions of pH, ionic strength, temperature, pressure, and electric field or due to the presence of a cosolvent that perturbs the delicate balance between stabilizing and destabilizing interactions and eventually induces chemical modifications. For most proteins, denaturation is a complex process involving transient intermediates in several reversible and eventually irreversible steps. Knowledge of protein stability and denaturation processes is mandatory for the development of enzymes as industrial catalysts, biopharmaceuticals, analytical and medical bioreagents, and safe industrial food. Electrophoresis techniques operating under extreme conditions are convenient tools for analyzing unfolding transitions, trapping transient intermediates, and gaining insight into the mechanisms of denaturation processes. Moreover, quantitative analysis of electrophoretic mobility transition curves allows the estimation of the conformational stability of proteins. These approaches include polyacrylamide gel electrophoresis and capillary zone electrophoresis under cold, heat, and hydrostatic pressure and in the presence of non-ionic denaturing agents or stabilizers such as polyols and heavy water. Lastly, after exposure to extremes of physical conditions, electrophoresis under standard conditions provides information on irreversible processes, slow conformational drifts, and slow renaturation processes. The impressive developments of enzyme technology with multiple applications in fine chemistry, biopharmaceutics, and nanomedicine prompted us to revisit the potentialities of these electrophoretic approaches. This feature review is illustrated with published and unpublished results obtained by the authors on cholinesterases and paraoxonase, two physiologically and toxicologically important enzymes.

Molecular Modeling Targeting Transmembrane Serine Protease 2 (TMPRSS2) as an Alternative Drug Target Against Coronaviruses

Since November 2019, the new Coronavirus disease (COVID-19) caused by the etiological agent SARS-CoV-2 has been responsible for several cases worldwide, becoming pandemic in March 2020. Pharmaceutical industries and academics have joined their efforts to discover new therapies to control the disease, since there are no specific drugs to combat this emerging virus. Thus, several targets have been explored, among them the transmembrane protease serine 2 (TMPRSS2) has gained greater interest in the scientific community. In this context, this review will describe the importance of TMPRSS2 protease and the significant advances in virtual screening focused on discovering new inhibitors. In this review, it was observed that molecular modeling methods could be powerful tools in identifying new molecules against SARS-CoV-2. Thus, this review could be used to guide researchers worldwide to explore the biological and clinical potential of compounds that could be promising drug candidates against SARS-CoV-2, acting by inhibition of TMPRSS2 protein.

Conserved lysine residues in decorin binding proteins of Borrelia garinii are critical in adhesion to human brain microvascular endothelial cells

Lyme borreliosis is a tick-borne disease caused by Borrelia burgdorferi sensu lato spirochetes (Lyme borreliae). When the disease affects the central nervous system, it is referred to as neuroborreliosis. In Europe, neuroborreliosis is most often caused by Borrelia garinii. Although it is known that in the host Lyme borreliae spread from the tick bite site to distant tissues via the blood vasculature, the adherence of Lyme borreliae to human brain microvascular endothelial cells has not been studied before. Decorin binding proteins are adhesins expressed on Lyme borreliae. They mediate the adhesion of Lyme borreliae to decorin and biglycan, and the lysine residues located in the binding site of decorin binding proteins are important to the binding activity. In this study, we show that lysine residues located in the canonical binding site can also be found in decorin binding proteins of Borrelia garinii, and that these lysines contribute to biglycan and decorin binding. Most importantly, we show that the lysine residues are crucial for the binding of Lyme borreliae to decorin and biglycan expressing human brain microvascular endothelial cells, which in turn suggests that they are involved in the pathogenesis of neuroborreliosis.

KLF7: a new candidate biomarker and therapeutic target for high-grade serous ovarian cancer

BACKGROUND

In spite of great progress in the surgical and clinical management, until now no significant improvement in overall survival of High-Grade Serous Ovarian Cancer (HGSOC) patients has been achieved. Important aspects for disease control remain unresolved, including unclear pathogenesis, high heterogeneity and relapse resistance after chemotherapy. Therefore, further research on molecular mechanisms involved in cancer progression are needed to find new targets for disease management. The Krüppel-like factors (KLFs) are a family of transcriptional regulators controlling several basic cellular processes, including proliferation, differentiation and migration. They have been shown to play a role in various cancer-relevant processes, in a context-dependent way.

METHODS

To investigate a possible role of KLF family members as prognostic biomarkers, we carried out a bioinformatic meta-analysis of ovarian transcriptome datasets in different cohorts of late-stage HGSOC patients. In vitro cellular models of HGSOC were used for functional studies exploring the role of KLF7 in disease development and progression. Finally, molecular modelling and virtual screening were performed to identify putative KLF7 inhibitors.

RESULTS

Bioinformatic analysis highlighted KLF7 as the most significant prognostic gene, among the 17 family members. Univariate and multivariate analyses identified KLF7 as an unfavourable prognostic marker for overall survival in late-stage TCGA-OV and GSE26712 HGSOC cohorts. Functional in vitro studies demonstrated that KLF7 can play a role as oncogene, driving tumour growth and dissemination. Mechanistic targets of KLF7 included genes involved in epithelial to mesenchymal transition, and in maintaining pluripotency and self-renewal characteristics of cancer stem cells. Finally, in silico analysis provided reliable information for drug-target interaction prediction.

CONCLUSIONS

Results from the present study provide the first evidence for an oncogenic role of KLF7 in HGSOC, suggesting it as a promising prognostic marker and therapeutic target.

G protein-coupled estrogen receptor-1: homology modeling approaches and application in screening new GPER-1 modulators

G protein-coupled receptors (GPCRs) belong to the largest family of protein targets comprising over 800 members in which at least 500 members are the therapeutic targets. Among the GPCRs, G protein-coupled estrogen receptor-1 (GPER-1) has shown to have the ability in estrogen signaling. As GPER-1 plays a critical role in several physiological responses, GPER-1 has been considered as a potential therapeutic target to treat estrogen-based cancers and other non-communicable diseases. However, the progress in the understanding of GPER-1 structure and function is relatively slow due to the availability of a only a few selective GPER-1 modulators. As with many GPCRs, the X-ray crystal structure of GPER-1 is yet to be resolved and thus has led the researchers to search for new GPER-1 modulators using homology models of GPER-1. In this review, we aim to summarize various approaches used in the generation of GPER-1 homology model and their applications that have resulted in new GPER-1 ligands.

Klebsiella pneumoniae type VI secretion system-mediated microbial competition is PhoPQ controlled and reactive oxygen species dependent

Klebsiella pneumoniae is recognized as an urgent threat to human health due to the increasing isolation of multidrug resistant strains. Hypervirulent strains are a major concern due to their ability to cause life-threating infections in healthy hosts. The type VI secretion system (T6SS) is widely implicated in microbial antagonism, and it mediates interactions with host eukaryotic cells in some cases. In silico search for genes orthologous to T6SS component genes and T6SS effector genes across 700 K. pneumoniae genomes shows extensive diversity in T6SS genes across the K. pneumoniae species. Temperature, oxygen tension, pH, osmolarity, iron levels, and NaCl regulate the expression of the T6SS encoded by a hypervirulent K. pneumoniae strain. Polymyxins and human defensin 3 also increase the activity of the T6SS. A screen for regulators governing T6SS uncover the correlation between the transcription of the T6SS and the ability to kill E. coli prey. Whereas H-NS represses the T6SS, PhoPQ, PmrAB, Hfq, Fur, RpoS and RpoN positively regulate the T6SS. K. pneumoniae T6SS mediates intra and inter species bacterial competition. This antagonism is only evident when the prey possesses an active T6SS. The PhoPQ two component system governs the activation of K. pneumoniae T6SS in bacterial competitions. Mechanistically, PhoQ periplasmic domain, and the acid patch within, is essential to activate K. pneumoniae T6SS. Klebsiella T6SS also mediates anti-fungal competition. We have delineated the contribution of each of the individual VgrGs in microbial competition and identified VgrG4 as a T6SS effector. The DUF2345 domain of VgrG4 is sufficient to intoxicate bacteria and yeast. ROS generation mediates the antibacterial effects of VgrG4, and the antitoxin Sel1E protects against the toxic activity of VgrG4. Our findings provide a better understanding of the regulation of the T6SS in bacterial competitions, and place ROS as an early event in microbial competition.

Klebsiella pneumoniae has been singled out as an “urgent threat to human health” due to extremely drug resistant strains. Numerous studies investigate the molecular mechanisms underlying antibiotic resistance in K. pneumoniae, while others dissect the virulence strategies of this pathogen. However, there is still limited knowledge on the fitness of Klebsiella in the environment, and, particularly, the competition of Klebsiella with other species. Here, we demonstrate that Klebsiella exploits the type VI secretion system (T6SS) nanoweapon to kill bacterial competitors and fungi. K. pneumoniae perceives T6SS attacks from bacterial competitors, resulting in retaliation against the aggressive cell. The perception of the attack involved the sensor PhoPQ and led to the up-regulation of the T6SS. We identified one of the toxins deployed by the T6SS to antagonize other microbes and revealed how Klebsiella protects itself from this toxin. Our findings provide a better understanding of the T6SS role in microbial competition and uncover new aspects on how bacteria regulate T6SS-mediated microbial antagonism.

Atranorin, an antimicrobial metabolite from lichen Parmotrema rampoddense exhibited in vitro anti-breast cancer activity through interaction with Akt activity

Atranorin (ATR), lichenized secondary metabolite and depside molecule with several biological potentials such as antimicrobial, anticancer, anti-inflammatory, antinociceptive, wound healing and photoprotective activities. Cytotoxic reports of ATR are documented in several cancer cells and in vivo models but its molecular interaction studies are poorly understood. Therefore, in this present investigation, we have used the in silico studies with biological validation of the molecular targets for the anti-breast cancer mechanism of ATR. The molecular docking studies with the breast cancer oncoproteins such as Bcl-2, Bax, Akt, Bcl-w and Bcl-xL revealed the highest interaction was observed with the Akt followed by Bax, Bcl-xL and Bcl-2 & least with the Bcl-w proteins. The cytotoxicity studies showed ATR selectively inhibited MDA MB-231 and MCF-7 breast cancer cells in differential and dose-dependent manner with the IC₅₀ concentration of 5.36 ± 0.85 μM and 7.55 ± 1.2 μM respectively. Further mechanistic investigations revealed that ATR significantly inhibited ROS production and significantly down-regulated the anti apoptotic Akt than Bcl-2, Bcl-xL and Bcl-w proteins with a significant increase in the Bax level and caspases-3 activity in the breast cancer cells when comparison with Akt inhibitor, ipatasertib. In vitro biological activities well correlated with the molecular interaction data suggesting that atranorin had higher interaction with Akt than Bax and Bcl-2 but weak interaction with Bcl-w and Bcl-xL. In this present study, the first time we report the interactions of atranorin with molecular targets for anti-breast cancer potential. Hence, ATR represents the nature-inspired molecule for pharmacophore moiety for design in targeted therapy.Communicated by Ramaswamy H. Sarma.

In Silico Strategies in Tuberculosis Drug Discovery

Tuberculosis (TB) remains a serious threat to global public health, responsible for an estimated 1.5 million mortalities in 2018. While there are available therapeutics for this infection, slow-acting drugs, poor patient compliance, drug toxicity, and drug resistance require the discovery of novel TB drugs. Discovering new and more potent antibiotics that target novel TB protein targets is an attractive strategy towards controlling the global TB epidemic. In silico strategies can be applied at multiple stages of the drug discovery paradigm to expedite the identification of novel anti-TB therapeutics. In this paper, we discuss the current TB treatment, emergence of drug resistance, and the effective application of computational tools to the different stages of TB drug discovery when combined with traditional biochemical methods. We will also highlight the strengths and points of improvement in in silico TB drug discovery research, as well as possible future perspectives in this field.

From biomedicinal to in silico models and back to therapeutics: a review on the advancement of peptidic modeling

Bioactive peptides participate in numerous metabolic functions of living organisms and have emerged as potential therapeutics on a diverse range of diseases. Albeit peptide design does not go without challenges, overwhelming advancements on in silico methodologies have increased the scope of peptide-based drug design and discovery to an unprecedented amount. Within an in silico model versus an experimental validation scenario, this review aims to summarize and discuss how different in silico techniques contribute at present to the design of peptide-based molecules. Published in silico results from 2014 to 2018 were selected and discriminated in major methodological groups, allowing a transversal analysis, promoting a landscape vision and asserting its increasing value in drug design.

Molecular characterization and interactome analysis of aerolysin (aer) gene from fish pathogen Aeromonas veronii: The pathogenicity inferred from sequence divergence and linked to histidine kinase (cheA)

Aerolysin (aer) is one of the most important and abundant virulence factors in the infection of fish by Aeromonas veronii. A comprehensive study on the molecular characterization and pathogenicity of the aer gene from 34 A. veronii isolates from diseased carp and catfish was carried out and its interactome was analysed to observe the functional correlations between aer and other proteins within the A. veronii network. The PCR-based amplification of aer from the 34 isolates of A. veronii showed more aer-positive isolates from catfish with a high pathogenic potential in the in vivo challenge test than the carp fish. The analysis of aer gene sequence from challenged fish revealed significant sequence divergence according to the types and geographical distribution of the fish. The networking analysis of aer from the model A. veronii B565 revealed histidine kinase (cheA) as the most functional interacting partner. The study of the interaction between aer from the experimental A. veronii and cheA demonstrated that the A chain of cheA plays a more important role than the corresponding B chain during contact, and a linker sequence of 15 residues controlled the entire interaction process. Therefore, cheA could be an excellent drug target for controlling A. veronii infection of fish.

In Silico Analysis of Homologous Heterodimers of Cruzipain-Chagasin from Structural Models Built by Homology

The present study gives an overview of the binding energetics of the homologous heterodimers of cruzipain−chagasin based on the binding energy (ΔG_b) prediction obtained with FoldX. This analysis involves a total of 70 homologous models of the cruzipain−chagasin complex which were constructed by homology from the combinatory variation of nine papain-like cysteine peptidase structures and seven cysteine protease inhibitor structures (as chagasin-like and cystatin-like inhibitors). Only 32 systems have been evaluated experimentally, ΔG_b^experimental values previously reported. Therefore, the result of the multiple analysis in terms of the thermodynamic parameters, are shown as relative energy |ΔΔG| = |ΔG_b^fromFoldX − ΔG_b^experimental|. Nine models were identified that recorded |ΔΔG| < 1.3, five models to 2.8 > |ΔΔG| > 1.3 and the other 18 models, values of |ΔΔG| > 2.8. The energetic analysis of the contribution of ΔH and ΔS to ΔG_b to the 14-molecular model presents a ΔG_b mostly ΔH-driven at neutral pH and at an ionic strength (I) of 0.15 M. The dependence of ΔG_b(I,pH) at 298 K to the cruzipain−chagasin complex predicts a linear dependence of ΔG_b(I). The computational protocol allowed the identification and prediction of thermodynamics binding energy parameters for cruzipain−chagasin-like heterodimers.

A model to predict the function of hypothetical proteins through a nine-point classification scoring schema

BACKGROUND

Hypothetical proteins [HP] are those that are predicted to be expressed in an organism, but no evidence of their existence is known. In the recent past, annotation and curation efforts have helped overcome the challenge in understanding their diverse functions. Techniques to decipher sequence-structure-function relationship, especially in terms of functional modelling of the HPs have been developed by researchers, but using the features as classifiers for HPs has not been attempted. With the rise in number of annotation strategies, next-generation sequencing methods have provided further understanding the functions of HPs.

RESULTS

In our previous work, we developed a six-point classification scoring schema with annotation pertaining to protein family scores, orthology, protein interaction/association studies, bidirectional best BLAST hits, sorting signals, known databases and visualizers which were used to validate protein interactions. In this study, we introduced three more classifiers to our annotation system, viz. pseudogenes linked to HPs, homology modelling and non-coding RNAs associated to HPs. We discuss the challenges and performance of these classifiers using machine learning heuristics with an improved accuracy from Perceptron (81.08 to 97.67), Naive Bayes (54.05 to 96.67), Decision tree J48 (67.57 to 97.00), and SMO_npolyk (59.46 to 96.67).

CONCLUSION

With the introduction of three new classification features, the performance of the nine-point classification scoring schema has an improved accuracy to functionally annotate the HPs.

Supersecondary Structures and Fragment Libraries

The use of smotifs and fragment libraries has proven useful to both simplify and increase the quality of protein models. Here, we present Profrager, a tool that automatically generates putative structural fragments to reproduce local motifs of proteins given a target sequence. Profrager is highly customizable, allowing the user to select the number of fragments per library, the ranking method is able to generate fragments of all sizes, and it was recently modified to include the possibility of output exclusively smotifs.

Predicting the ligand-binding properties of Borrelia burgdorferi s.s. Bmp proteins in light of the conserved features of related Borrelia proteins

Bacteria of the genus Borrelia cause vector-borne infections like the most important hard tick-borne disease in the northern hemisphere, Lyme borreliosis (LB), and soft tick or louse transmitted relapsing fevers (RF), prevalent in temperate and tropical areas. Borrelia burgdorferi sensu lato (s.l.) includes several genospecies and causes LB in humans. In infected patients, Borrelia burgdorferi sensu stricto (s.s.) expresses the BmpA, BmpB, BmpC and BmpD proteins. The role of these proteins in the pathogenesis of LB remains incompletely characterized, but they are, however, closely related to Treponema pallidum PnrA (Purine nucleoside receptor A), a substrate-binding lipoprotein of the ATP-binding cassette (ABC) transporter family preferentially binding purine nucleosides. Based on 3D homology modeling, the Bmp proteins share the typical fold of the substrate-binding protein family and the ligand-binding properties of BmpA, BmpB and BmpD are highly similar, whereas those of BmpC differ markedly. Nevertheless, these residues are highly conserved within the genus Borrelia and the inferred phylogenetic tree also reveals that the RF Borrelia lack BmpB proteins but has an additional Bmp protein (BmpA2) missing in LB-causing Borrelia burgdorferi s.l. Our results indicate that the Bmp proteins could bind nucleosides, although BmpC might have a different ligand-binding specificity and, therefore, a distinct function. Furthermore, the work provides a means for classifying the Bmp proteins and supports further elucidation of the roles of these proteins.

Research Models and Tools for the Identification of Antivirals and Therapeutics against Zika Virus Infection

Zika virus recently re-emerged and caused global outbreaks mainly in Central Africa, Southeast Asia, the Pacific Islands and in Central and South America. Even though there is a declining trend, the virus continues to spread throughout different geographical regions of the world. Since its re-emergence in 2015, massive advances have been made regarding our understanding of clinical manifestations, epidemiology, genetic diversity, genomic structure and potential therapeutic intervention strategies. Nevertheless, treatment remains a challenge as there is no licensed effective therapy available. This review focuses on the recent advances regarding research models, as well as available experimental tools that can be used for the identification and characterization of potential antiviral targets and therapeutic intervention strategies.

Molecular modeling and inhibitor docking analysis of the Na+/H+ exchanger isoform one 1

Na⁺/H⁺ exchanger isoform one (NHE1) is a mammalian plasma membrane protein that removes intracellular protons, thereby elevating intracellular pH (pH_i). NHE1 uses the energy of allowing an extracellular sodium down its gradient into cells to remove one intracellular proton. The ubiquitous protein has several important physiological and pathological influences on mammalian cells as a result of its activity. The three-dimensional structure of human NHE1 (hNHE1) is not known. Here, we modeled NHE1 based on the structure of MjNhaP1 of Methanocaldoccocus jannaschii in combination with biochemical surface accessibility data. hNHE1 contained 12 transmembrane segments including a characteristic Na⁺/H⁺ antiporter fold of two transmembrane segments with a helix - extended region - helix conformation crossing each other within the membrane. Amino acids 363-410 mapped principally to the extracellular surface as an extracellular loop (EL5). A large preponderance of amino acids shown to be surface accessible by biochemical experiments mapped near to, or on, the extracellular surface. Docking of Na⁺/H⁺ exchanger inhibitors to the extracellular surface suggested that inhibitor binding on an extracellular site is made up from several amino acids of different regions of the protein. The results present a novel testable, three-dimensional model illustrating NHE1 structure and accounting for experimental biochemical data.

Venom allergen-like protein 28 in Clonorchis sinensis: four epitopes on its surface and the potential role of Cys124 for its conformational stability

Venom allergen-like (VAL) proteins are important to host-parasite interactions. We previously demonstrated that a Clonorchis sinensis VAL (CsVAL) protein-derived synthetic peptide suppresses allergic and inflammatory responses. However, little is known regarding the physicochemical and antigenic properties of CsVAL proteins. Here, we identified a novel 194 amino acid VAL protein, named C. sinensis VAL 28 (CsVAL28), and characterized its functional motifs and structural details as a new member of the CAP superfamily. Unlike members of the Schistosoma mansoni VAL (SmVAL) family, CsVAL28 has a single CAP1 motif and six highly conserved disulfide bond-forming cysteines. Tertiary models of wild-type CsVAL28 and mutants were built using SmVAL4 as template via homology modeling. Normal mode analysis predicted that disulfide bond breaking by mutation of cysteine 124 to serine would greatly affect protein mobility. Four major immunoreactive linear epitopes were identified in the surface-exposed region or its vicinity via epitope mapping, using sera from clonorchiasis patients and healthy controls. Our findings provide in-depth knowledge on the structure-function properties of VAL proteins and may help determine highly antigenic regions for developing new diagnostic approaches.

Exploring 3D structure of human gonadotropin hormone receptor at antagonist state using homology modeling, molecular dynamic simulation, and cross-docking studies

Human gonadotropin hormone receptor, a G-protein coupled receptor, is the target of many medications used in fertility disorders. Obtaining more structural information about the receptor could be useful in many studies related to drug design. In this study, the structure of human gonadotropin receptor was subjected to homology modeling studies and molecular dynamic simulation within a DPPC lipid bilayer for 100 ns. Several frames were thereafter extracted from simulation trajectories representing the receptor at different states. In order to find a proper model of the receptor at the antagonist state, all frames were subjected to cross-docking studies of some antagonists with known experimental values (Ki). Frame 194 revealed a reasonable correlation between docking calculated energy scores and experimental activity values (|r| = 0.91). The obtained correlation was validated by means of SSLR and showed the presence of no chance correlation for the obtained model. Different structural features reported for the receptor, such as two disulfide bridges and ionic lock between GLU90 and LYS 121 were also investigated in the final model.

A computational study on role of 6-(hydroxymethyl)-3-[3,4,5-trihydroxy-6-[(3,4,5-trihydroxyoxan-2-yl)oxymethyl]oxan-2-yl]oxyoxane-2,4,5-triol in the regulation of blood glucose level

6-(hydroxymethyl)-3-[3,4,5-trihydroxy-6-[(3,4,5-trihydroxyoxan-2-yl)oxymethyl]oxan-2-yl]oxyoxane-2,4,5-triol (SID 242078875) was isolated from the fruits of Syzygium densiflorum Wall. ex Wight & Arn (Myrtaceae), which has been traditionally used in the treatment of diabetes by the tribes of The Nilgiris, Tamil Nadu, India. In this study, reverse pharmacophore mapping approach and text-based database search identified the dipeptidyl peptidase-IV, protein-tyrosine phosphatase 1B, phosphoenolpyruvate carboxykinase, glycogen synthase kinase-3β and glucokinase as potential targets of SID 242078875 in diabetes management. Further, molecular docking was performed to predict the binding pose of SID 242078875 in the active site region of the target protein. In addition, dynamic behaviour and stability of protein-ligand complexes were observed for a period of 50 ns through molecular dynamics simulation.