PRIMO: An Interactive Homology Modeling Pipeline

Allosteric modulation of Plasmodium falciparum Isoleucyl tRNA synthetase by South African natural compounds

Targeting Plasmodium falciparum (Pf) aminoacyl tRNA synthetases is a viable strategy to overcome malaria parasite multi-drug resistance. Here, we focused on Pf Isoleucyl tRNA synthetase (PfIleRS) to identify potential allosteric inhibitors from 1019 South African Natural Compounds (SANC). Eleven potential hits, which passed ADMET and PAINS, were selected based on their docking binding affinity which was higher for PfIleRS than for human IleRS. Molecular dynamics simulations revealed that the compounds, particularly SANC456, commonly induced considerable changes in the global conformation and dynamics of PfIleRS, suggesting potential allosteric modulatory effects. Importantly, all 11 SANC hits reduced the binding affinity of the nucleotide AMP molecule by at least 25%. Some SANC ligand-bound systems (SANC456, SANC1095, and SANC1104) significantly increased the distance between the AMP and Ile ligands. Possible explanations for these changes were explored using three dynamic residue network centrality metrics. Betweenness centrality identified a major allosteric pathway in holo PfIleRS spanning the entire protein length. In contrast, SANC382, SANC456, SANC522, SANC806 and SANC1095 ligand-bound systems exhibited delta BC pathways (SANC-protein minus holo-protein), induced by the ligands, extending from their respective pockets into the active site. Additionally, eigenvector centrality revealed two important residue clusters either side of the holo active site which became altered in the ligand-bound systems, indicating possible allosteric activity. Lastly, many SANC systems showed decreased closeness centrality of zinc finger and active site residues, including the HYGH and KMSKR motifs. We believe that the compounds identified in this study as potential allosteric inhibitors have strong translational potential and warrant further investigation through in vitro and in vivo experiments. Overall, they hold promise as starting points for the development of new and effective antimalarial therapies, particularly against multidrug-resistant Plasmodium parasites.

Identification of potent TMPRSS4 inhibitors through structural modeling and molecular dynamics simulations

TMPRSS4, a transmembrane serine protease type II, is associated with various pathological illnesses. It has been found to activate SARS-CoV-2, enhance viral infection of human small-intestinal enterocytes and is overexpressed in different types of cancers. Therefore, this study aims to disover potential TMPRSS4 inhibitors that have better binding affinity than the approved inhibitors: 2-hydroxydiarylamide and tyroserleutide. Since no 3D-structure is known for TMPRSS4, structural models for the TMPRSS4 serine protease domain were developed. The modeled structures were validated and subjected to molecular dynamics simulations. FDA-approved, clinical/preclinical drugs and natural products were docked to the pocket of TMPRSS4. Moreover, through a systematic analysis, MD simulations and MM-GBSA binding free energy calculations revealed that the best candidates Ergotamine, S55746, NPC478048, Lifirafenib, and NPC77101 are highly stable drug candidates in complex with TMPRSS4, displaying low RMSD and RMSF values with strong binding stability. Among these compounds, Ergotamine showed the most favorable binding energy (-33.73 kcal/mol). Overall, our in silico results revealed that these compounds could act as potent TMPRSS4 inhibitors and need to be validated by future experimental studies.

Bioinformatics analysis of the Microsporidia sp. MB genome: a malaria transmission-blocking symbiont of the Anopheles arabiensis mosquito

BACKGROUND

The use of microsporidia as a disease-transmission-blocking tool has garnered significant attention. Microsporidia sp. MB, known for its ability to block malaria development in mosquitoes, is an optimal candidate for supplementing malaria vector control methods. This symbiont, found in Anopheles mosquitoes, can be transmitted both vertically and horizontally with minimal effects on its mosquito host. Its genome, recently sequenced from An. arabiensis, comprises a compact 5.9 Mbp.

RESULTS

Here, we analyze the Microsporidia sp. MB genome, highlighting its major genomic features, gene content, and protein function. The genome contains 2247 genes, predominantly encoding enzymes. Unlike other members of the Enterocytozoonida group, Microsporidia sp. MB has retained most of the genes in the glycolytic pathway. Genes involved in RNA interference (RNAi) were also identified, suggesting a mechanism for host immune suppression. Importantly, meiosis-related genes (MRG) were detected, indicating potential for sexual reproduction in this organism. Comparative analyses revealed similarities with its closest relative, Vittaforma corneae, despite key differences in host interactions.

CONCLUSION

This study provides an in-depth analysis of the newly sequenced Microsporidia sp. MB genome, uncovering its unique adaptations for intracellular parasitism, including retention of essential metabolic pathways and RNAi machinery. The identification of MRGs suggests the possibility of sexual reproduction, offering insights into the symbiont's evolutionary strategies. Establishing a reference genome for Microsporidia sp. MB sets the foundation for future studies on its role in malaria transmission dynamics and host-parasite interactions.

Structural and Functional Annotation of Hypothetical Proteins from the Microsporidia Species Vittaforma corneae ATCC 50505 Using in silico Approaches

Microsporidia are spore-forming eukaryotes that are related to fungi but have unique traits that set them apart. They have compact genomes as a result of evolutionary gene loss associated with their complete dependency on hosts for survival. Despite having a relatively small number of genes, a disproportionately high percentage of the genes in microsporidia genomes code for proteins whose functions remain unknown (hypothetical proteins-HPs). Computational annotation of HPs has become a more efficient and cost-effective alternative to experimental investigation. This research developed a robust bioinformatics annotation pipeline of HPs from Vittaforma corneae, a clinically important microsporidian that causes ocular infections in immunocompromised individuals. Here, we describe various steps to retrieve sequences and homologs and to carry out physicochemical characterization, protein family classification, identification of motifs and domains, protein-protein interaction network analysis, and homology modelling using a variety of online resources. Classification of protein families produced consistent findings across platforms, demonstrating the accuracy of annotation utilizing in silico methods. A total of 162 out of 2034 HPs were fully annotated, with the bulk of them categorized as binding proteins, enzymes, or regulatory proteins. The protein functions of several HPs from Vittaforma corneae were accurately inferred. This improved our understanding of microsporidian HPs despite challenges related to the obligate nature of microsporidia, the absence of fully characterized genes, and the lack of homologous genes in other systems.

Global Analysis of Plasmodium falciparum Dihydropteroate Synthase Variants Associated with Sulfadoxine Resistance Reveals Variant Distribution and Mechanisms of Resistance: A Computational-Based Study

The continual rise in sulfadoxine (SDX) resistance affects the therapeutic efficacy of sulfadoxine-pyrimethamine; therefore, careful monitoring will help guide its prolonged usage. Mutations in Plasmodium falciparum dihydropteroate synthase (Pfdhps) are being surveilled, based on their link with SDX resistance. However, there is a lack of continuous analyses and data on the potential effect of molecular markers on the Pfdhps structure and function. This study explored single-nucleotide polymorphisms (SNPs) in Pfdhps that were isolated in Africa and other countries, highlighting the regional distribution and its link with structure. In total, 6336 genomic sequences from 13 countries were subjected to SNPs, haplotypes, and structure-based analyses. The SNP analysis revealed that the key SDX resistance marker, A437G, was nearing fixation in all countries, peaking in Malawi. The mutation A613S was rare except in isolates from the Democratic Republic of Congo and Malawi. Molecular docking revealed a general loss of interactions when comparing mutant proteins to the wild-type protein. During MD simulations, SDX was released from the active site in mutants A581G and A613S before the end of run-time, whereas an unstable binding of SDX to mutant A613S and haplotype A437A/A581G/A613S was observed. Conformational changes in mutant A581G and the haplotypes A581G/A613S, A437G/A581G, and A437G/A581G/A613S were seen. The radius of gyration revealed an unfolding behavior for the A613S, K540E/A581G, and A437G/A581G systems. Overall, tracking such mutations by the continuous analysis of Pfdhps SNPs is encouraged. SNPs on the Pfdhps structure may cause protein-drug function loss, which could affect the applicability of SDX in preventing malaria in pregnant women and children.

Sibling rivalry among the ZBTB transcription factor family: homodimers versus heterodimers

BTB domains potentially can form homo- or heterodimers. The study examines the dimerization choice of several BTB domains and finds only one heterodimer, while all tested pairs can homodimerize.

The BTB domain is an oligomerization domain found in over 300 proteins encoded in the human genome. In the family of BTB domain and zinc finger–containing (ZBTB) transcription factors, 49 members share the same protein architecture. The N-terminal BTB domain is structurally conserved among the family members and serves as the dimerization site, whereas the C-terminal zinc finger motifs mediate DNA binding. The available BTB domain structures from this family reveal a natural inclination for homodimerization. In this study, we investigated the potential for heterodimer formation in the cellular environment. We selected five BTB homodimers and four heterodimer structures. We performed cell-based binding assays with fluorescent protein–BTB domain fusions to assess dimer formation. We tested the binding of several BTB pairs, and we were able to confirm the heterodimeric physical interaction between the BTB domains of PATZ1 and PATZ2, previously reported only in an interactome mapping experiment. We also found this pair to be co-expressed in several immune system cell types. Finally, we used the available structures of BTB domain dimers and newly constructed models in extended molecular dynamics simulations (500 ns) to understand the energetic determinants of homo- and heterodimer formation. We conclude that heterodimer formation, although frequently described as less preferred than homodimers, is a possible mechanism to increase the combinatorial specificity of this transcription factor family.

Differential performance of RoseTTAFold in antibody modeling

Antibodies are essential to life, and knowing their structures can facilitate the understanding of antibody-antigen recognition mechanisms. Precise antibody structure prediction has been a core challenge for a prolonged period, especially the accuracy of H3 loop prediction. Despite recent progress, existing methods cannot achieve atomic accuracy, especially when the homologous structures required for these methods are not available. Recently, RoseTTAFold, a deep learning-based algorithm, has shown remarkable breakthroughs in predicting the 3D structures of proteins. To assess the antibody modeling ability of RoseTTAFold, we first retrieved the sequences of 30 antibodies as the test set and used RoseTTAFold to model their 3D structures. We then compared the models constructed by RoseTTAFold with those of SWISS-MODEL in a different way, in which we stratified Global Model Quality Estimate (GMQE) into three different ranges. The results indicated that RoseTTAFold could achieve results similar to SWISS-MODEL in modeling most CDR loops, especially the templates with a GMQE score under 0.8. In addition, we also compared the structures modeled by RoseTTAFold, SWISS-MODEL and ABodyBuilder. In brief, RoseTTAFold could accurately predict 3D structures of antibodies, but its accuracy was not as good as the other two methods. However, RoseTTAFold exhibited better accuracy for modeling H3 loop than ABodyBuilder and was comparable to SWISS-MODEL. Finally, we discussed the limitations and potential improvements of the current RoseTTAFold, which may help to further the accuracy of RoseTTAFold's antibody modeling.

Structural analysis of M1AP variants associated with severely impaired spermatogenesis causing male infertility

Background

Impaired meiosis can result in absence of sperm in the seminal fluid. This condition, namely non-obstructive azoospermia (NOA), is one of the reasons of male infertility. Despite the low number of studies on meiosis 1-associated protein (M1AP) in the literature, M1AP is known to be crucial for spermatogenesis. Recently, seven variants (five missense, one frameshift, one splice-site) have been reported in the M1AP gene as associated with NOA, cryptozoospermia and oligozoospermia in two separate studies. However, all missense variants were evaluated as variant of uncertain significance by these studies. Therefore, we aimed to analyze their structural impacts on the M1AP protein that could lead to NOA.

Methods

We firstly performed an evolutionary conservation analysis for the variant positions. Afterwards, a comprehensive molecular modelling study was performed for the M1AP structure. By utilizing this model, protein dynamics were sampled for the wild-type and variants by performing molecular dynamics (MD) simulations.

Results

All variant positions are highly conserved, indicating that they are potentially important for function. In MD simulations, none of the variants led to a general misfolding or loss of stability in the protein structure, but they did cause severe modifications in the conformational dynamics of M1AP, particularly through changes in local interactions affecting flexibility, hinge and secondary structure.

Conclusions

Due to critical perturbations in protein dynamics, we propose that these variants may cause NOA by affecting important interactions regulating meiosis, particularly in wild-type M1AP deficiency since the variants are reported to be homozygous or bi-allelic in the infertile individuals. Our results provided reasonable insights about the M1AP structure and the effects of the variants to the structure and dynamics, which should be further investigated by experimental studies to validate.

Computational models for studying physical instabilities in high concentration biotherapeutic formulations

Computational prediction of the behavior of concentrated protein solutions is particularly advantageous in early development stages of biotherapeutics when material availability is limited and a large set of formulation conditions needs to be explored. This review provides an overview of the different computational paradigms that have been successfully used in modeling undesirable physical behaviors of protein solutions with a particular emphasis on high-concentration drug formulations. This includes models ranging from all-atom simulations, coarse-grained representations to macro-scale mathematical descriptions used to study physical instability phenomena of protein solutions such as aggregation, elevated viscosity, and phase separation. These models are compared and summarized in the context of the physical processes and their underlying assumptions and limitations. A detailed analysis is also given for identifying protein interaction processes that are explicitly or implicitly considered in the different modeling approaches and particularly their relations to various formulation parameters. Lastly, many of the shortcomings of existing computational models are discussed, providing perspectives and possible directions toward an efficient computational framework for designing effective protein formulations.

Homology Modelling, Docking-based Virtual Screening, ADME Properties, and Molecular Dynamics Simulation for Identification of Probable Type II Inhibitors of AXL Kinase

Background:

AXL kinase is an important member of the TAM family for kinases which is involved in most cancers. Considering its role in different cancers due to its pro-tumorigenic effects and its involvement in the resistance, it has gained importance recently. Majority of research carried out is on Type I inhibitors and limited studies have been carried out for Type II inhibitors. Taking this into consideration, we have attempted to build Homology models to identify the Type II inhibitors for the AXL kinase.

Methods:

Homology Models for DFG-out C-helix-in/out state were developed using SWISS Model, PRIMO, and Prime. These models were validated by different methods and further evaluated for stability by molecular dynamics simulation using Desmond software. Selected models PED1-EB and PEDI1-EB were used for the docking-based virtual screening of four compound libraries using Glide software. The hits identified were subjected to interaction analysis and shortlisted compounds were subjected to Prime MM-GBSA studies for energy calculation. These compounds were also docked in the DFG-in state to check for binding and elimination of any compounds that may not be Type II inhibitors. The Prime energies were calculated for these complexes as well and some compounds were eliminated. ADMET studies were carried out using Qikprop. Some selected compounds were subjected to molecular dynamics simulation using Desmond for evaluating the stability of the complexes.

Results:

Out of 78 models inclusive of both DFG-out C-helix-in and DFG-out C-helix-out, 5 models were identified after different types of evaluation as well as validation studies. 1 model representing each type (PED1-EB and PEDI1-EB) was selected for the screening studies. The screening studies resulted in the identification of 29 compounds from the screen on PED1-EB and 10 compounds from the screen on PEDI1-EB. Hydrogen bonding interactions with Pro621, Met623, and Asp690 were observed for these compounds primarily. In some compounds, hydrogen bonding with Leu542, Glu544, Lys567, and Asn677 as well as pi-pi stacking interactions with either Phe622 or Phe691 were also seen. 4 compounds identified from PED1-EB screen were subjected to molecular dynamics simulation and their interactions were found to be consistent during the simulation. 2 compounds identified from PEDI1-EB screen were also subjected to the simulation studies, however, their interactions with Asp690 were not observed for a significant time and in both cases differed from the docked pose.

Conclusion:

Multiple models of DFG-out conformations of AXL kinase were built, validated and used for virtual screening. Different compounds were identified in the virtual screening, which may possibly act as Type II inhibitors for AXL kinase. Some more experimental studies can be done to validate these findings in future. This study will play a guiding role in the further development of the newer Type II inhibitors of the AXL kinase for the probable treatment of cancer.

Virtual screening and in vitro validation identifies the first reported inhibitors of Salmonella enterica HPPK

HPPK, which directly precedes DHPS in the folate biosynthetic pathway, is a promising but chronically under-exploited anti-microbial target. Here we report the identification of new S. enterica HPPK inhibitors, offering potential for new resistance circumventing S. enterica therapies as well as avenues for diversifying the current HPPK inhibitor space.

Structural and catalytic characterization of Blastochloris viridis and Pseudomonas aeruginosa homospermidine synthases supports the essential role of cation-π interaction

Polyamines influence medically relevant processes in the opportunistic pathogen Pseudomonas aeruginosa, including virulence, biofilm formation and susceptibility to antibiotics. Although homospermidine synthase (HSS) is part of the polyamine metabolism in various strains of P. aeruginosa, neither its role nor its structure has been examined so far. The reaction mechanism of the nicotinamide adenine dinucleotide (NAD+)-dependent bacterial HSS has previously been characterized based on crystal structures of Blastochloris viridis HSS (BvHSS). This study presents the crystal structure of P. aeruginosa HSS (PaHSS) in complex with its substrate putrescine. A high structural similarity between PaHSS and BvHSS with conservation of the catalytically relevant residues is demonstrated, qualifying BvHSS as a model for mechanistic studies of PaHSS. Following this strategy, crystal structures of single-residue variants of BvHSS are presented together with activity assays of PaHSS, BvHSS and BvHSS variants. For efficient homospermidine production, acidic residues are required at the entrance to the binding pocket (`ionic slide') and near the active site (`inner amino site') to attract and bind the substrate putrescine via salt bridges. The tryptophan residue at the active site stabilizes cationic reaction components by cation-π interaction, as inferred from the interaction geometry between putrescine and the indole ring plane. Exchange of this tryptophan for other amino acids suggests a distinct catalytic requirement for an aromatic interaction partner with a highly negative electrostatic potential. These findings substantiate the structural and mechanistic knowledge on bacterial HSS, a potential target for antibiotic design.

Gamma Carbonic Anhydrases from Hydrothermal Vent Bacteria: Cases of Alternating Active Site Due to a Long Loop with Proton Shuttle Residue

Accelerated CO2 sequestration uses carbonic anhydrases (CAs) as catalysts; thus, there is much research on these enzymes. The γ-CA from Escherichia coli (EcoCA-γ) was the first γ-CA to display an active site that switches between “open” and “closed” states through Zn2+ coordination by the proton-shuttling His residue. Here, we explored this occurrence in γ-CAs from hydrothermal vent bacteria and also the γ-CA from Methanosarcina thermophila (Cam) using molecular dynamics. Ten sequences were analyzed through multiple sequence alignment and motif analysis, along with three others from a previous study. Conservation of residues and motifs was high, and phylogeny indicated a close relationship amongst the sequences. All structures, like EcoCA-γ, had a long loop harboring the proton-shuttling residue. Trimeric structures were modeled and simulated for 100 ns at 423 K, with all the structures displaying thermostability. A shift between “open” and “closed” active sites was observed in the 10 models simulated through monitoring the behavior of the His proton-shuttling residue. Cam, which has two Glu proton shuttling residues on long loops (Glu62 and Glu84), also showed an active site switch affected by the first Glu proton shuttle, Glu62. This switch was thus concluded to be common amongst γ-CAs and not an isolated occurrence.

Differences in Gluco and Galacto Substrate-Binding Interactions in a Dual 6Pβ-Glucosidase/6Pβ-Galactosidase Glycoside Hydrolase 1 Enzyme from Bacillus licheniformis

Bacterial glycoside hydrolase 1 (GH1) enzymes with 6-phospho-β-galactosidase and 6-phospho-β-glucosidase activities have the important task of releasing phosphorylated and nonphosphorylated monosaccharides into the cytoplasm. Curiously, dual 6-phospho-β-galactosidase/6-phospho-β-glucosidase (dual-phospho) enzymes have broad specificity and are able to hydrolyze galacto- and gluco-derived substrates. This study investigates the structure and substrate specificity of a GH family 1 enzyme from Bacillus licheniformis, hereafter known as BlBglC. The enzyme structure has been solved, and sequence analysis, molecular dynamics simulations, and binding free energy calculations offered evidence of dual-phospho activity. Both test ligands p-nitrophenyl-β-d-galactoside-6-phosphate (PNP6Pgal) and p-nitrophenyl-β-d-glucoside-6-phosphate (PNP6Pglc) demonstrated strong binding to BlBglC although the pose and interactions of the PNP6Pglc triplicates were slightly more consistent. Interestingly, known specificity-inducing residues, Gln23 and Trp433, bind strongly to the ligand O3 hydroxyl group in the PNP6Pgal-BlBglC complex and to the ligand O4 hydroxyl group in the PNP6Pglc-BlBglC complex. Additionally, the BlBglC-His124 residue is a major contributor of hydrogen bonds to the PNP6Pgal O3 hydroxyl group but does not form any hydrogen bonds with PNP6Pglc. On the other hand, BlBglC residues Tyr173, Tyr301, Gln302, and Thr321 form hydrogen bonds with PNP6Pglc but not PNP6Pgal. These findings provide important details of the broad specificity of dual-phospho activity GH1 enzymes.

Structural insights and inhibition mechanism of TMPRSS2 by experimentally known inhibitors Camostat mesylate, Nafamostat and Bromhexine hydrochloride to control SARS-coronavirus-2: A molecular modeling approach

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) has been responsible for the cause of global pandemic Covid-19 and to date, there is no effective treatment available. The spike ‘S’ protein of SARS-CoV-2 and ACE2 of the host cell are being targeted to design new drugs to control Covid-19. Similarly, a transmembrane serine protease, TMPRSS2 of the host cell plays a significant role in the proteolytic cleavage of viral ‘S’ protein helpful for the priming of ACE2 receptors and viral entry into human cells. However, three-dimensional structural information and the inhibition mechanism of TMPRSS2 is yet to be explored experimentally. Hence, we have used a molecular dynamics (MD) simulated homology model of TMPRSS2 to study the inhibition mechanism of experimentally known inhibitors Camostat mesylate, Nafamostat and Bromhexine hydrochloride (BHH) using molecular modeling techniques. Prior to docking, all three inhibitors were geometry optimized by semi-empirical quantum chemical RM1 method. Molecular docking analysis revealed that Camostat mesylate and its structural analogue Nafamostat interact strongly with residues His296 and Ser441 present in the catalytic triad of TMPRSS2, whereas BHH binds with Ala386 along with other residues. Comparative molecular dynamics simulations revealed the stable behavior of all the docked complexes. MM-PBSA calculations also revealed the stronger binding of Camostat mesylate to TMPRSS2 active site residues as compared to Nafamostat and BHH. Thus, this structural information could be useful to understand the mechanistic approach of TMPRSS2 inhibition, which may be helpful to design new lead compounds to prevent the entry of SARS-Coronavirus 2 in human cells.

Force Field Parameters for Fe2+4S2-4 Clusters of Dihydropyrimidine Dehydrogenase, the 5-Fluorouracil Cancer Drug Deactivation Protein: A Step towards In Silico Pharmacogenomics Studies

The dimeric dihydropyrimidine dehydrogenase (DPD), metalloenzyme, an adjunct anti-cancer drug target, contains highly specialized 4 × Fe²⁺₄S²⁻₄ clusters per chain. These clusters facilitate the catalysis of the rate-limiting step in the pyrimidine degradation pathway through a harmonized electron transfer cascade that triggers a redox catabolic reaction. In the process, the bulk of the administered 5-fluorouracil (5-FU) cancer drug is inactivated, while a small proportion is activated to nucleic acid antimetabolites. The occurrence of missense mutations in DPD protein within the general population, including those of African descent, has adverse toxicity effects due to altered 5-FU metabolism. Thus, deciphering mutation effects on protein structure and function is vital, especially for precision medicine purposes. We previously proposed combining molecular dynamics (MD) and dynamic residue network (DRN) analysis to decipher the molecular mechanisms of missense mutations in other proteins. However, the presence of Fe²⁺₄S²⁻₄ clusters in DPD poses a challenge for such in silico studies. The existing AMBER force field parameters cannot accurately describe the Fe²⁺ center coordination exhibited by this enzyme. Therefore, this study aimed to derive AMBER force field parameters for DPD enzyme Fe²⁺ centers, using the original Seminario method and the collation features Visual Force Field Derivation Toolkit as a supportive approach. All-atom MD simulations were performed to validate the results. Both approaches generated similar force field parameters, which accurately described the human DPD protein Fe²⁺₄S²⁻₄ cluster architecture. This information is crucial and opens new avenues for in silico cancer pharmacogenomics and drug discovery related research on 5-FU drug efficacy and toxicity issues.

Compound heterozygous p. Arg949Trp and p. Gly970Ala mutations deteriorated the function of PEX1p: A study on PEX1 in a patient with Zellweger syndrome

The peroxisome is responsible for a variety of vital pathways in primary metabolism, including the very long-chain fatty-acid oxidation and plasmalogen lipid biosynthesis. Autosomal recessive disorder of the Zellweger spectrum (ZSD) is a major subset of peroxisome biogenesis disorders (PBDs) that can be caused by mutations in any of the 14 PEX genes. Zellweger syndrome (ZS) is the foremost common and severe phenotype within the heterogeneous ZSD. However, missense mutations encode proteins with residual functions, which are associated with phenotypes that are milder than ZS. Mutations in the PEX1 gene are among the most prevalent. PEX1 and PEX6 proteins, belonging to the AAA family of ATPases, form a hexameric complex, which is associated with peroxisome membranes and essential for peroxisome biology. In this study, a two-month-old Iranian boy with hypotonia, poor feeding, and difficulty in breathing was diagnosed with Zellweger syndrome. The parents of the patient were second cousins and healthy and no similar cases were observed in the parents' family. The PEX1 gene was sequenced in the patient and his parents. The compound heterozygous mutations, p. Arg949Trp and p. Gly970Ala, were identified in the patient, while the parents were heterozygous for these alleles. Sequence analysis of the mutant PEX1 D2 domain revealed that mutation p. Arg949Trp precisely occurred in a conserved arginine residue (P4 Arg), which hinders the substrate processing of the complex. Several database records have reported mutation p. Arg949Trp(R949W) but its clinical significance is given as uncertain. We report here a novel mutation, p. Gly970Ala, which is not recorded before and may prevent proper interaction of PEX1 and PEX6 proteins. In summary, the clinical findings and peroxisome profile of the patient suggested that compound heterozygosity for these two missense mutations resulted in a nonfunctional PEX1/PEX6 complex causing the severe ZS phenotype.

ResiRole: residue-level functional site predictions to gauge the accuracies of protein structure prediction techniques

Motivation

Methods to assess the quality of protein structure models are needed for user applications. To aid with the selection of structure models and further inform the development of structure prediction techniques, we describe the ResiRole method for the assessment of the quality of structure models.

Results

Structure prediction techniques are ranked according to the results of round-robin, head-to-head comparisons using difference scores. Each difference score was defined as the absolute value of the cumulative probability for a functional site prediction made with the FEATURE program for the reference structure minus that for the structure model. Overall, the difference scores correlate well with other model quality metrics; and based on benchmarking studies with NaïveBLAST, they are found to detect additional local structural similarities between the structure models and reference structures.

Availabilityand implementation

Automated analyses of models addressed in CAMEO are available via the ResiRole server, URL http://protein.som.geisinger.edu/ResiRole/. Interactive analyses with user-provided models and reference structures are also enabled. Code is available at github.com/wamclaughlin/ResiRole.

Supplementary information

Supplementary data are available at Bioinformatics online.

Human OMICs and Computational Biology Research in Africa: Current Challenges and Prospects

Following the publication of the first human genome, OMICs research, including genomics, transcriptomics, proteomics, and metagenomics, has been on the rise. OMICs studies revealed the complex genetic diversity among human populations and challenged our understandings of genotype-phenotype correlations. Africa, being the cradle of the first modern humans, is distinguished by a large genetic diversity within its populations and rich ethnolinguistic history. However, the available human OMICs tools and databases are not representative of this diversity, therefore creating significant gaps in biomedical research. African scientists, students, and publics are among the key contributors to OMICs systems science. This expert review examines the pressing issues in human OMICs research, education, and development in Africa, as seen through a lens of computational biology, public health relevant technology innovation, critically-informed science governance, and how best to harness OMICs data to benefit health and societies in Africa and beyond. We underscore the disparities between North and Sub-Saharan Africa at different levels. A harmonized African ethnolinguistic classification would help address annotation challenges associated with population diversity. Finally, building on the existing strategic research initiatives, such as the H3Africa and H3ABioNet Consortia, we highly recommend addressing large-scale multidisciplinary research challenges, strengthening research collaborations and knowledge transfer, and enhancing the ability of African researchers to influence and shape national and international research, policy, and funding agendas. This article and analysis contribute to a deeper understanding of past and current challenges in the African OMICs innovation ecosystem, while also offering foresight on future innovation trajectories.

Decoding the Molecular Effects of Atovaquone Linked Resistant Mutations on Plasmodium falciparum Cytb-ISP Complex in the Phospholipid Bilayer Membrane

Atovaquone (ATQ) is a drug used to prevent and treat malaria that functions by targeting the Plasmodium falciparum cytochrome b (PfCytb) protein. PfCytb catalyzes the transmembrane electron transfer (ET) pathway which maintains the mitochondrial membrane potential. The ubiquinol substrate binding site of the protein has heme bL, heme bH and iron-sulphur [2FE-2S] cluster cofactors that act as redox centers to aid in ET. Recent studies investigating ATQ resistance mechanisms have shown that point mutations of PfCytb confer resistance. Thus, understanding the resistance mechanisms at the molecular level via computational approaches incorporating phospholipid bilayer would help in the design of new efficacious drugs that are also capable of bypassing parasite resistance. With this knowledge gap, this article seeks to explore the effect of three drug resistant mutations Y268C, Y268N and Y268S on the PfCytb structure and function in the presence and absence of ATQ. To draw reliable conclusions, 350 ns all-atom membrane (POPC:POPE phospholipid bilayer) molecular dynamics (MD) simulations with derived metal parameters for the holo and ATQ-bound -proteins were performed. Thereafter, simulation outputs were analyzed using dynamic residue network (DRN) analysis. Across the triplicate MD runs, hydrophobic interactions, reported to be crucial in protein function were assessed. In both, the presence and absence of ATQ and a loss of key active site residue interactions were observed as a result of mutations. These active site residues included: Met 133, Trp136, Val140, Thr142, Ile258, Val259, Pro260 and Phe264. These changes to residue interactions are likely to destabilize the overall intra-protein residue communication network where the proteins' function could be implicated. Protein dynamics of the ATQ-bound mutant complexes showed that they assumed a different pose to the wild-type, resulting in diminished residue interactions in the mutant proteins. In summary, this study presents insights on the possible effect of the mutations on ATQ drug activity causing resistance and describes accurate MD simulations in the presence of the lipid bilayer prior to conducting inhibitory drug discovery for the PfCytb-iron sulphur protein (Cytb-ISP) complex.

Characterisation of plasmodial transketolases and identification of potential inhibitors: an in silico study

BACKGROUND

Plasmodial transketolase (PTKT) enzyme is one of the novel pharmacological targets being explored as potential anti-malarial drug target due to its functional role and low sequence identity to the human enzyme. Despite this, features contributing to such have not been exploited for anti-malarial drug design. Additionally, there are no anti-malarial drugs targeting PTKTs whereas the broad activity of these inhibitors against PTKTs from other Plasmodium spp. is yet to be reported. This study characterises different PTKTs [Plasmodium falciparum (PfTKT), Plasmodium vivax (PvTKT), Plasmodium ovale (PoTKT), Plasmodium malariae (PmTKT) and Plasmodium knowlesi (PkTKT) and the human homolog (HsTKT)] to identify key sequence and structural based differences as well as the identification of selective potential inhibitors against PTKTs.

METHODS

A sequence-based study was carried out using multiple sequence alignment, phylogenetic tree calculations and motif discovery analysis. Additionally, TKT models of PfTKT, PmTKT, PoTKT, PmTKT and PkTKT were modelled using the Saccharomyces cerevisiae TKT structure as template. Based on the modelled structures, molecular docking using 623 South African natural compounds was done. The stability, conformational changes and detailed interactions of selected compounds were accessed viz all-atom molecular dynamics (MD) simulations and binding free energy (BFE) calculations.

RESULTS

Sequence alignment, evolutionary and motif analyses revealed key differences between plasmodial and the human TKTs. High quality homodimeric three-dimensional PTKTs structures were constructed. Molecular docking results identified three compounds (SANC00107, SANC00411 and SANC00620) which selectively bind in the active site of all PTKTs with the lowest (better) binding affinity ≤ - 8.5 kcal/mol. MD simulations of ligand-bound systems showed stable fluctuations upon ligand binding. In all systems, ligands bind stably throughout the simulation and form crucial interactions with key active site residues. Simulations of selected compounds in complex with human TKT showed that ligands exited their binding sites at different time steps. BFE of protein-ligand complexes showed key residues involved in binding.

CONCLUSIONS

This study highlights significant differences between plasmodial and human TKTs and may provide valuable information for the development of novel anti-malarial inhibitors. Identified compounds may provide a starting point in the rational design of PTKT inhibitors and analogues based on these scaffolds.

X-ray Structure, Bioinformatics Analysis, and Substrate Specificity of a 6-Phospho-β-glucosidase Glycoside Hydrolase 1 Enzyme from Bacillus licheniformis

In bacteria, mono- and disaccharides are phosphorylated during the uptake processes through the vastly spread transport system phosphoenolpyruvate-dependent phosphotransferase. As an initial step in the phosphorylated disaccharide metabolism pathway, 6-phospho-β-glucosidases and 6-phospho-β-galactosidases play a crucial role by releasing phosphorylated and nonphosphorylated monosaccharides. However, structural determinants for the specificity of these enzymes still need to be clarified. Here, an X-ray structure of a glycoside hydrolase family 1 enzyme from Bacillus licheniformis, hereafter known as BlBglH, was determined at 2.2 Å resolution, and its substrate specificity was investigated. The sequence of BlBglH was compared to the sequences of 58 other GH1 enzymes using sequence alignments, sequence identity calculations, phylogenetic analysis, and motif discovery. Through these various analyses, BlBglH was found to have sequence features characteristic of the 6-phospho-β-glucosidase activity enzymes. Motif and structural observations highlighted the importance of loop L8 in 6-phospho-β-glucosidase activity enzymes. To further affirm enzyme specificity, molecular docking and molecular dynamics simulations were performed using the crystallographic structure of BlBglH. Docking was carried out with a 6-phospho-β-glucosidase enzyme activity positive and negative control ligand, followed by 400 ns of MD simulations. The positive and negative control ligands were PNP6Pglc and PNP6Pgal, respectively. PNP6Pglc maintained favorable interactions within the active site until the end of the MD simulation, while PNP6Pgal exhibited instability. The favorable binding of substrate stabilized the loops that surround the active site. Binding free energy calculations showed that the PNP6Pglc complex had a substantially lower binding energy compared to the PNP6Pgal complex. Altogether, the findings of this study suggest that BlBglH possesses 6-phospho-β-glucosidase enzymatic activity and revealed sequence and structural differences between bacterial GH1 enzymes of various activities.

Structural analysis of the PATZ1 BTB domain homodimer

PATZ1 is a ubiquitously expressed transcriptional repressor belonging to the ZBTB family that is functionally expressed in T lymphocytes. PATZ1 targets the CD8 gene in lymphocyte development and interacts with the p53 protein to control genes that are important in proliferation and in the DNA-damage response. PATZ1 exerts its activity through an N-terminal BTB domain that mediates dimerization and co-repressor interactions and a C-terminal zinc-finger motif-containing domain that mediates DNA binding. Here, the crystal structures of the murine and zebrafish PATZ1 BTB domains are reported at 2.3 and 1.8 Å resolution, respectively. The structures revealed that the PATZ1 BTB domain forms a stable homodimer with a lateral surface groove, as in other ZBTB structures. Analysis of the lateral groove revealed a large acidic patch in this region, which contrasts with the previously resolved basic co-repressor binding interface of BCL6. A large 30-amino-acid glycine- and alanine-rich central loop, which is unique to mammalian PATZ1 amongst all ZBTB proteins, could not be resolved, probably owing to its flexibility. Molecular-dynamics simulations suggest a contribution of this loop to modulation of the mammalian BTB dimerization interface.

Identification of Selective Novel Hits against Plasmodium falciparum Prolyl tRNA Synthetase Active Site and a Predicted Allosteric Site Using in silico Approaches

Recently, there has been increased interest in aminoacyl tRNA synthetases (aaRSs) as potential malarial drug targets. These enzymes play a key role in protein translation by the addition of amino acids to their cognate tRNA. The aaRSs are present in all Plasmodium life cycle stages, and thus present an attractive malarial drug target. Prolyl tRNA synthetase is a class II aaRS that functions in charging tRNA with proline. Various inhibitors against Plasmodium falciparum ProRS (PfProRS) active site have been designed. However, none have gone through clinical trials as they have been found to be highly toxic to human cells. Recently, a possible allosteric site was reported in PfProRS with two possible allosteric modulators: glyburide and TCMDC-124506. In this study, we sought to identify novel selective inhibitors targeting PfProRS active site and possible novel allosteric modulators of this enzyme. To achieve this, virtual screening of South African natural compounds against PfProRS and the human homologue was carried out using AutoDock Vina. The modulation of protein motions by ligand binding was studied by molecular dynamics (MD) using the GROningen MAchine for Chemical Simulations (GROMACS) tool. To further analyse the protein global motions and energetic changes upon ligand binding, principal component analysis (PCA), and free energy landscape (FEL) calculations were performed. Further, to understand the effect of ligand binding on the protein communication, dynamic residue network (DRN) analysis of the MD trajectories was carried out using the MD-TASK tool. A total of ten potential natural hit compounds were identified with strong binding energy scores. Binding of ligands to the protein caused observable global and residue level changes. Dynamic residue network calculations showed increase in betweenness centrality (BC) metric of residues at the allosteric site implying these residues are important in protein communication. A loop region at the catalytic domain between residues 300 and 350 and the anticodon binding domain showed significant contributions to both PC1 and PC2. Large motions were observed at a loop in the Z-domain between residues 697 and 710 which was also in agreement with RMSF calculations that showed increase in flexibility of residues in this region. Residues in this loop region are implicated in ATP binding and thus a change in dynamics may affect ATP binding affinity. Free energy landscape (FEL) calculations showed that the holo protein (protein-ADN complex) and PfProRS-SANC184 complexes were stable, as shown by the low energy with very few intermediates and hardly distinguishable low energy barriers. In addition, FEL results agreed with backbone RMSD distribution plots where stable complexes showed a normal RMSD distribution while unstable complexes had multimodal RMSD distribution. The betweenness centrality metric showed a loss of functional importance of key ATP binding site residues upon allosteric ligand binding. The deep basins in average L observed at the allosteric region imply that there is high accessibility of residues at this region. To further analyse BC and average L metrics data, we calculated the ΔBC and ΔL values by taking each value in the holo protein BC or L matrix less the corresponding value in the ligand-bound complex BC or L matrix. Interestingly, in allosteric complexes, residues located in a loop region implicated in ATP binding had negative ΔL values while in orthosteric complexes these residues had positive ΔL values. An increase in contact frequency between residues Ser263, Thr267, Tyr285, and Leu707 at the allosteric site and residues Thr397, Pro398, Thr402, and Gln395 at the ATP binding TXE loop was observed. In summary, this study identified five potential orthosteric inhibitors and five allosteric modulators against PfProRS. Allosteric modulators changed ATP binding site dynamics, as shown by RMSF, PCA, and DRN calculations. Changes in dynamics of the ATP binding site and increased contact frequency between residues at the proposed allosteric site and the ATP binding site may explain how allosteric modulators distort the ATP binding site and thus might inhibit PfProRS. The scaffolds of the identified hits in the study can be used as a starting point for antimalarial inhibitor development with low human cytotoxicity.

Protein-protein interaction of Rv0148 with Htdy and its predicted role towards drug resistance in Mycobacterium tuberculosis

BACKGROUND

Mycobacterium tuberculosis resides inside host macrophages during infection and adapts to resilient stresses generated by the host immune system. As a response, M. tuberculosis codes for short-chain dehydrogenases/reductases (SDRs). These SDRs are nicotinamide adenine dinucleotide-reliant oxidoreductases involved in cell homeostasis. The precise function of oxidoreductases in bacteria especially M. tuberculosis were not fully explored. This study aimed to know the detail functional role of one of the oxidoreductase Rv0148 in M. tuberculosis.

RESULTS

In silico analysis revealed that Rv0148 interacts with Htdy (Rv3389) and the protein interactions were confirmed using far western blot. Gene knockout mutant of Rv0148 in M. tuberculosis was constructed by specialized transduction. Macrophage cell line infection with this knockout mutant showed increased expression of pro-inflammatory cytokines. This knockout mutant is sensitive to oxidative, nitrogen, redox and electron transport inhibitor stress agents. Drug susceptibility testing of the deletion mutant showed resistance to first-line drugs such as streptomycin and ethambutol and second-line aminoglycosides such as amikacin and kanamycin. Based on interactorme analysis for Rv0148 using STRING database, we identified 220 most probable interacting partners for Htdy protein. In the Rv0148 knockout mutants, high expression of htdy was observed and we hypothesize that this would have perturbed the interactome thus resulting in drug resistance. Finally, we propose that Rv0148 and Htdy are functionally interconnected and involved in drug resistance and cell homeostasis of M. tuberculosis.

CONCLUSIONS

Our study suggests that Rv0148 plays a significant role in various functional aspects such as intermediatory metabolism, stress, homeostasis and also in drug resistance.

In silico prediction of the epitopes for the immunogenic proteins present in Mycobacterium avium subsp. paratuberculosis

Johne’s disease caused by Mycobacterium avium subsp. paratuberculosis is a widespread problem in ruminants worldwide. Diagnosis of the disease during the early stages of infection is difficult. In search of newer proteins with antigenic and immunogenic characters, in silico epitope analysis of the immunogenic proteins was performed which identifies the proteins expressed during the early stages of infection and which could stimulate cell mediated immune response. T cell epitopes were predicted for the six immunogenic proteins and the epitopes were sorted based on the percentile ranking and repetition among MHC Class I alleles. 3D modeling and protein-protein interaction studies revealed that ELPLPQTYVD, DVVGYDRTQD, PDLQSVLGATPGAG, DGLRAQDD, DGLRAQDD and PGHVTDD epitopes interact with the MHC Class I molecule through hydrogen bonding. These epitopes are identified as potent candidates for the immunodiagnostic studies and could be further evaluated using in vitro studies.

Understanding the Pyrimethamine Drug Resistance Mechanism via Combined Molecular Dynamics and Dynamic Residue Network Analysis

In this era of precision medicine, insights into the resistance mechanism of drugs are integral for the development of potent therapeutics. Here, we sought to understand the contribution of four point mutations (N51I, C59R, S108N, and I164L) within the active site of the malaria parasite enzyme dihydrofolate reductase (DHFR) towards the resistance of the antimalarial drug pyrimethamine. Homology modeling was used to obtain full-length models of wild type (WT) and mutant DHFR. Molecular docking was employed to dock pyrimethamine onto the generated structures. Subsequent all-atom molecular dynamics (MD) simulations and binding free-energy computations highlighted that pyrimethamine's stability and affinity inversely relates to the number of mutations within its binding site and, hence, resistance severity. Generally, mutations led to reduced binding affinity to pyrimethamine and increased conformational plasticity of DHFR. Next, dynamic residue network analysis (DRN) was applied to determine the impact of mutations and pyrimethamine binding on communication dispositions of DHFR residues. DRN revealed residues with distinctive communication profiles, distinguishing WT from drug-resistant mutants as well as pyrimethamine-bound from pyrimethamine-free models. Our results provide a new perspective on the understanding of mutation-induced drug resistance.

Integrated Computational Approaches and Tools forAllosteric Drug Discovery

Understanding molecular mechanisms underlying the complexity of allosteric regulationin proteins has attracted considerable attention in drug discovery due to the benefits and versatilityof allosteric modulators in providing desirable selectivity against protein targets while minimizingtoxicity and other side effects. The proliferation of novel computational approaches for predictingligand-protein interactions and binding using dynamic and network-centric perspectives has ledto new insights into allosteric mechanisms and facilitated computer-based discovery of allostericdrugs. Although no absolute method of experimental and in silico allosteric drug/site discoveryexists, current methods are still being improved. As such, the critical analysis and integration ofestablished approaches into robust, reproducible, and customizable computational pipelines withexperimental feedback could make allosteric drug discovery more efficient and reliable. In this article,we review computational approaches for allosteric drug discovery and discuss how these tools can beutilized to develop consensus workflows for in silico identification of allosteric sites and modulatorswith some applications to pathogen resistance and precision medicine. The emerging realization thatallosteric modulators can exploit distinct regulatory mechanisms and can provide access to targetedmodulation of protein activities could open opportunities for probing biological processes and insilico design of drug combinations with improved therapeutic indices and a broad range of activities.

Establishing Computational Approaches Towards Identifying Malarial Allosteric Modulators: A Case Study of Plasmodium falciparum Hsp70s

Combating malaria is almost a never-ending battle, as Plasmodium parasites develop resistance to the drugs used against them, as observed recently in artemisinin-based combination therapies. The main concern now is if the resistant parasite strains spread from Southeast Asia to Africa, the continent hosting most malaria cases. To prevent catastrophic results, we need to find non-conventional approaches. Allosteric drug targeting sites and modulators might be a new hope for malarial treatments. Heat shock proteins (HSPs) are potential malarial drug targets and have complex allosteric control mechanisms. Yet, studies on designing allosteric modulators against them are limited. Here, we identified allosteric modulators (SANC190 and SANC651) against P. falciparum Hsp70-1 and Hsp70-x, affecting the conformational dynamics of the proteins, delicately balanced by the endogenous ligands. Previously, we established a pipeline to identify allosteric sites and modulators. This study also further investigated alternative approaches to speed up the process by comparing all atom molecular dynamics simulations and dynamic residue network analysis with the coarse-grained (CG) versions of the calculations. Betweenness centrality (BC) profiles for PfHsp70-1 and PfHsp70-x derived from CG simulations not only revealed similar trends but also pointed to the same functional regions and specific residues corresponding to BC profile peaks.

Introducing "best single template" models as reference baseline for the Continuous Automated Model Evaluation (CAMEO)

Critical blind assessment of structure prediction techniques is crucial for the scientific community to establish the state of the art, identify bottlenecks, and guide future developments. In Critical Assessment of Techniques in Structure Prediction (CASP), human experts assess the performance of participating methods in relation to the difficulty of the prediction task in a biennial experiment on approximately 100 targets. Yet, the development of automated computational modeling methods requires more frequent evaluation cycles and larger sets of data. The "Continuous Automated Model EvaluatiOn (CAMEO)" platform complements CASP by conducting fully automated blind prediction evaluations based on the weekly pre-release of sequences of those structures, which are going to be published in the next release of the Protein Data Bank (PDB). Each week, CAMEO publishes benchmarking results for predictions corresponding to a set of about 20 targets collected during a 4-day prediction window. CAMEO benchmarking data are generated consistently for all methods at the same point in time, enabling developers to cross-validate their method's performance, and referring to their results in publications. Many successful participants of CASP have used CAMEO-either by directly benchmarking their methods within the system or by comparing their own performance to CAMEO reference data. CAMEO offers a variety of scores reflecting different aspects of structure modeling, for example, binding site accuracy, homo-oligomer interface quality, or accuracy of local model confidence estimates. By introducing the "bestSingleTemplate" method based on structure superpositions as a reference for the accuracy of 3D modeling predictions, CAMEO facilitates objective comparison of techniques and fosters the development of advanced methods.

Proteome-wide subtractive approach to prioritize a hypothetical protein of XDR-Mycobacterium tuberculosis as potential drug target

BACKGROUND

Among the resistant isolates of MTB, multidrug resistant tuberculosis (MDR-TB) and extensively drug resistant tuberculosis (XDR-TB) have been the areas of growing concern. The genomic analysis showed that the respective genomic pool of the XDR-MTB proteome contains more than 30% of the hypothetical proteins for which no functions have been annotated yet. This class of proteins presumably have their own importance to complete genome and proteome information. The bioinformatics advancements have helped to annotate those hypothetical proteins by using various computational tools and have potential to classify them functionally.

OBJECTIVE

The objective of this study was to propose a new and unique drug target against the deadly Mycobacterium tuberculosis using Bioinformatics approaches to characterize the hypothetical proteins.

RESULTS

We stepwise reduced the hypothetical proteins (total number: 1256) out of the complete proteome to only 26 essential hypothetical proteins. Out of those 26 proteins, the protein WP_003401246.1 was computationally characterized as the druggable target.

CONCLUSION

The study proposed a hypothetical protein from complete proteome of the XDR-MTB as a new drug target against which new drug candidates can be proposed. Hence, the study opens up the new avenues in the areas of drug discovery against deadly M. tuberculosis.

Bioinformatic discovery of a toxin family in Chryseobacterium piperi with sequence similarity to botulinum neurotoxins

Clostridial neurotoxins (CNTs), which include botulinum neurotoxins (BoNTs) and tetanus neurotoxin (TeNT), are the most potent toxins known to science and are the causative agents of botulism and tetanus, respectively. The evolutionary origins of CNTs and their relationships to other proteins remains an intriguing question. Here we present a large-scale bioinformatic screen for putative toxin genes in all currently available genomes. We detect a total of 311 protein sequences displaying at least partial homology to BoNTs, including 161 predicted toxin sequences that have never been characterized. We focus on a novel toxin family from Chryseobacterium piperi with homology to BoNTs. We resequenced the genome of C. piperi to confirm and further analyze the genomic context of these toxins, and also examined their potential toxicity by expression of the protease domain of one C. piperi toxin in human cells. Our analysis suggests that these C. piperi sequences encode a novel family of metalloprotease toxins that are distantly related to BoNTs with similar domain architecture. These toxins target a yet unknown class of substrates, potentially reflecting divergence in substrate specificity between the metalloprotease domains of these toxins and the related metalloprotease domain of clostridial neurotoxins.

Aminoacyl tRNA synthetases as malarial drug targets: a comparative bioinformatics study

BACKGROUND

Treatment of parasitic diseases has been challenging due to evolution of drug resistant parasites, and thus there is need to identify new class of drugs and drug targets. Protein translation is important for survival of malarial parasite, Plasmodium, and the pathway is present in all of its life cycle stages. Aminoacyl tRNA synthetases are primary enzymes in protein translation as they catalyse amino acid addition to the cognate tRNA. This study sought to understand differences between Plasmodium and human aminoacyl tRNA synthetases through bioinformatics analysis.

METHODS

Plasmodium berghei, Plasmodium falciparum, Plasmodium fragile, Plasmodium knowlesi, Plasmodium malariae, Plasmodium ovale, Plasmodium vivax, Plasmodium yoelii and human aminoacyl tRNA synthetase sequences were retrieved from UniProt database and grouped into 20 families based on amino acid specificity. These families were further divided into two classes. Both families and classes were analysed. Motif discovery was carried out using the MEME software, sequence identity calculation was done using an in-house Python script, multiple sequence alignments were performed using PROMALS3D and TCOFFEE tools, and phylogenetic tree calculations were performed using MEGA vs 7.0 tool. Possible alternative binding sites were predicted using FTMap webserver and SiteMap tool.

RESULTS

Motif discovery revealed Plasmodium-specific motifs while phylogenetic tree calculations showed that Plasmodium proteins have different evolutionary history to the human homologues. Human aaRSs sequences showed low sequence identity (below 40%) compared to Plasmodium sequences. Prediction of alternative binding sites revealed potential druggable sites in PfArgRS, PfMetRS and PfProRS at regions that are weakly conserved when compared to the human homologues. Multiple sequence analysis, motif discovery, pairwise sequence identity calculations and phylogenetic tree analysis showed significant differences between parasite and human aaRSs proteins despite functional and structural conservation. These differences may provide a basis for further exploration of Plasmodium aminoacyl tRNA synthetases as potential drug targets.

CONCLUSION

This study showed that, despite, functional and structural conservation, Plasmodium aaRSs have key differences from the human homologues. These differences in Plasmodium aaRSs can be targeted to develop anti-malarial drugs with less toxicity to the host.

Modeling of Disordered Protein Structures Using Monte Carlo Simulations and Knowledge-Based Statistical Force Fields

The description of protein disordered states is important for understanding protein folding mechanisms and their functions. In this short review, we briefly describe a simulation approach to modeling protein interactions, which involve disordered peptide partners or intrinsically disordered protein regions, and unfolded states of globular proteins. It is based on the CABS coarse-grained protein model that uses a Monte Carlo (MC) sampling scheme and a knowledge-based statistical force field. We review several case studies showing that description of protein disordered states resulting from CABS simulations is consistent with experimental data. The case studies comprise investigations of protein⁻peptide binding and protein folding processes. The CABS model has been recently made available as the simulation engine of multiscale modeling tools enabling studies of protein⁻peptide docking and protein flexibility. Those tools offer customization of the modeling process, driving the conformational search using distance restraints, reconstruction of selected models to all-atom resolution, and simulation of large protein systems in a reasonable computational time. Therefore, CABS can be combined in integrative modeling pipelines incorporating experimental data and other modeling tools of various resolution.

Discorhabdin N, a South African Natural Compound, for Hsp72 and Hsc70 Allosteric Modulation: Combined Study of Molecular Modeling and Dynamic Residue Network Analysis

The human heat shock proteins (Hsps), predominantly Hsp72 and Hsp90, have been strongly implicated in various critical stages of oncogenesis and progression of human cancers. While drug development has extensively focused on Hsp90 as a potential anticancer target, much less effort has been put against Hsp72. This work investigated the therapeutic potential of Hsp72 and its constitutive isoform, Hsc70, via in silico-based screening against the South African Natural Compounds Database (SANCDB). A comparative modeling approach was used to obtain nearly full-length 3D structures of the closed conformation of Hsp72 and Hsc70 proteins. Molecular docking of SANCDB compounds identified one potential allosteric modulator, Discorhabdin N, binding to the allosteric β substrate binding domain (SBDβ) back pocket, with good binding affinities in both cases. This allosteric region was identified in one of our previous studies. Subsequent all-atom molecular dynamics simulations and free energy calculations exhibited promising protein⁻ligand association characteristics, indicative of strong binding qualities. Further, we utilised dynamic residue network analysis (DRN) to highlight protein regions actively involved in cross-domain communication. Most residues identified agreed with known allosteric signal regulators from literature, and were further investigated for the purpose of deducing meaningful insights into the allosteric modulation properties of Discorhabdin N.

HUMA: A platform for the analysis of genetic variation in humans

The completion of the human genome project at the beginning of the 21st century, along with the rapid advancement of sequencing technologies thereafter, has resulted in exponential growth of biological data. In genetics, this has given rise to numerous variation databases, created to store and annotate the ever-expanding dataset of known mutations. Usually, these databases focus on variation at the sequence level. Few databases focus on the analysis of variation at the 3D level, that is, mapping, visualizing, and determining the effects of variation in protein structures. Additionally, these Web servers seldom incorporate tools to help analyze these data. Here, we present the Human Mutation Analysis (HUMA) Web server and database. HUMA integrates sequence, structure, variation, and disease data into a single, connected database. A user-friendly interface provides click-based data access and visualization, whereas a RESTful Web API provides programmatic access to the data. Tools have been integrated into HUMA to allow initial analyses to be carried out on the server. Furthermore, users can upload their private variation datasets, which are automatically mapped to public data and can be analyzed using the integrated tools. HUMA is freely accessible at https://huma.rubi.ru.ac.za.

Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12

Every second year, the community experiment "Critical Assessment of Techniques for Structure Prediction" (CASP) is conducting an independent blind assessment of structure prediction methods, providing a framework for comparing the performance of different approaches and discussing the latest developments in the field. Yet, developers of automated computational modeling methods clearly benefit from more frequent evaluations based on larger sets of data. The "Continuous Automated Model EvaluatiOn (CAMEO)" platform complements the CASP experiment by conducting fully automated blind prediction assessments based on the weekly pre-release of sequences of those structures, which are going to be published in the next release of the PDB Protein Data Bank. CAMEO publishes weekly benchmarking results based on models collected during a 4-day prediction window, on average assessing ca. 100 targets during a time frame of 5 weeks. CAMEO benchmarking data is generated consistently for all participating methods at the same point in time, enabling developers to benchmark and cross-validate their method's performance, and directly refer to the benchmarking results in publications. In order to facilitate server development and promote shorter release cycles, CAMEO sends weekly email with submission statistics and low performance warnings. Many participants of CASP have successfully employed CAMEO when preparing their methods for upcoming community experiments. CAMEO offers a variety of scores to allow benchmarking diverse aspects of structure prediction methods. By introducing new scoring schemes, CAMEO facilitates new development in areas of active research, for example, modeling quaternary structure, complexes, or ligand binding sites.

Development of Bioinformatics Infrastructure for Genomics Research

BACKGROUND

Although pockets of bioinformatics excellence have developed in Africa, generally, large-scale genomic data analysis has been limited by the availability of expertise and infrastructure. H3ABioNet, a pan-African bioinformatics network, was established to build capacity specifically to enable H3Africa (Human Heredity and Health in Africa) researchers to analyze their data in Africa. Since the inception of the H3Africa initiative, H3ABioNet's role has evolved in response to changing needs from the consortium and the African bioinformatics community.

OBJECTIVES

H3ABioNet set out to develop core bioinformatics infrastructure and capacity for genomics research in various aspects of data collection, transfer, storage, and analysis.

METHODS AND RESULTS

Various resources have been developed to address genomic data management and analysis needs of H3Africa researchers and other scientific communities on the continent. NetMap was developed and used to build an accurate picture of network performance within Africa and between Africa and the rest of the world, and Globus Online has been rolled out to facilitate data transfer. A participant recruitment database was developed to monitor participant enrollment, and data is being harmonized through the use of ontologies and controlled vocabularies. The standardized metadata will be integrated to provide a search facility for H3Africa data and biospecimens. Because H3Africa projects are generating large-scale genomic data, facilities for analysis and interpretation are critical. H3ABioNet is implementing several data analysis platforms that provide a large range of bioinformatics tools or workflows, such as Galaxy, the Job Management System, and eBiokits. A set of reproducible, portable, and cloud-scalable pipelines to support the multiple H3Africa data types are also being developed and dockerized to enable execution on multiple computing infrastructures. In addition, new tools have been developed for analysis of the uniquely divergent African data and for downstream interpretation of prioritized variants. To provide support for these and other bioinformatics queries, an online bioinformatics helpdesk backed by broad consortium expertise has been established. Further support is provided by means of various modes of bioinformatics training.

CONCLUSIONS

For the past 4 years, the development of infrastructure support and human capacity through H3ABioNet, have significantly contributed to the establishment of African scientific networks, data analysis facilities, and training programs. Here, we describe the infrastructure and how it has affected genomics and bioinformatics research in Africa.

Role of Structural Bioinformatics in Drug Discovery by Computational SNP Analysis: Analyzing Variation at the Protein Level

With the completion of the human genome project at the beginning of the 21st century, the biological sciences entered an unprecedented age of data generation, and made its first steps toward an era of personalized medicine. This abundance of sequence data has led to the proliferation of numerous sequence-based techniques for associating variation with disease, such as genome-wide association studies and candidate gene association studies. However, these statistical methods do not provide an understanding of the functional effects of variation. Structure-based drug discovery and design is increasingly incorporating structural bioinformatics techniques to model and analyze protein targets, perform large scale virtual screening to identify hit to lead compounds, and simulate molecular interactions. These techniques are fast, cost-effective, and complement existing experimental techniques such as high throughput sequencing. In this paper, we discuss the contributions of structural bioinformatics to drug discovery, focusing particularly on the analysis of nonsynonymous single nucleotide polymorphisms. We conclude by suggesting a protocol for future analyses of the structural effects of nonsynonymous single nucleotide polymorphisms on proteins and protein complexes.