Machine learning approaches to study the structure-activity relationships of LpxC inhibitors.

Antimicrobial resistance (AMR) has emerged as one of the global threats to human health in the 21st century. Drug discovery of inhibitors against novel targets rather than conventional bacterial targets has been considered an inevitable strategy for the growing threat of AMR infections. In this study, we applied quantitative structure-activity relationship (QSAR) modeling to the LpxC inhibitors to predict the inhibitory activity. In addition, we performed various cheminformatics analysis consisting of the exploration of the chemical space, identification of chemotypes, performing structure-activity landscape and activity cliffs as well as construction of the Structure-Activity Similarity (SAS) map. We built a total of 24 QSAR classification models using PubChem and MACCS fingerprint with 12 various machine learning algorithms. The best model with PubChem fingerprint is the Extremely Gradient Boost model (accuracy on the training set: 0.937; accuracy on the 10-fold cross-validation set: 0.795; accuracy on the test set: 0.799). Furthermore, it was found that the best model using the MACCS fingerprint was the Random Forest model (accuracy on the training set: 0.955; accuracy on the 10-fold cross-validation set: 0.803; accuracy on the test set: 0.785). In addition, we have identified eight consensus activity cliff generators that are highly informative for further SAR investigations. It is hoped that findings presented herein can provide guidance for further lead optimization of LpxC inhibitors.

Viroid-like RNA-dependent RNA polymerase-encoding ambiviruses are abundant in complex fungi.

Ambiviruses are hybrid infectious elements encoding the hallmark gene of RNA viruses, the RNA-dependent RNA polymerase, and self-cleaving RNA ribozymes found in many viroids. Ambiviruses are thought to be pathogens of fungi, although the majority of reported genomes have been identified in metatranscriptomes. Here, we present a comprehensive screen for ambiviruses in more than 46,500 fungal transcriptomes from the Sequence Read Archive (SRA). Our data-driven virus discovery approach identified more than 2,500 ambiviral sequences across the kingdom Fungi with a striking expansion in members of the phylum Basidiomycota representing the most complex fungal organisms. Our study unveils a large diversity of unknown ambiviruses with as little as 27% protein sequence identity to known members and sheds new light on the evolution of this distinct class of infectious agents with RNA genomes. No evidence for the presence of ambiviruses in human microbiomes was obtained from a comprehensive screen of respective metatranscriptomes available in the SRA.

Genomic analysis and biological characterization of a novel Schitoviridae phage infecting Vibrio alginolyticus.

Vibrio alginolyticus is a Gram-negative bacterium commonly associated with mackerel poisoning. A bacteriophage that specifically targets and lyses this bacterium could be employed as a biocontrol agent for treating the bacterial infection or improving the shelf-life of mackerel products. However, only a few well-characterized V. alginolyticus phages have been reported in the literature. In this study, a novel lytic phage, named ΦImVa-1, specifically infecting V. alginolyticus strain ATCC 17749, was isolated from Indian mackerel. The phage has a short latent period of 15 min and a burst size of approximately 66 particles per infected bacterium. ΦImVa-1 remained stable for 2 h at a wide temperature (27-75 °C) and within a pH range of 5 to 10. Transmission electron microscopy revealed that ΦImVa-1 has an icosahedral head of approximately 60 nm in diameter with a short tail, resembling those in the Schitoviridae family. High throughput sequencing and bioinformatics analysis elucidated that ΦImVa-1 has a linear dsDNA genome of 77,479 base pairs (bp), with a G + C content of ~ 38.72% and 110 predicted gene coding regions (106 open reading frames and four tRNAs). The genome contains an extremely large virion-associated RNA polymerase gene and two smaller non-virion-associated RNA polymerase genes, which are hallmarks of schitoviruses. No antibiotic genes were found in the ΦImVa-1 genome. This is the first paper describing the biological properties, morphology, and the complete genome of a V. alginolyticus-infecting schitovirus. When raw mackerel fish flesh slices were treated with ΦImVa-1, the pathogen loads reduced significantly, demonstrating the potential of the phage as a biocontrol agent for V. alginolyticus strain ATCC 17749 in the food. KEY POINTS: • A novel schitovirus infecting Vibrio alginolyticus ATCC 17749 was isolated from Indian mackerel. • The complete genome of the phage was determined, analyzed, and compared with other phages. • The phage is heat stable making it a potential biocontrol agent in extreme environments.

The third international hackathon for applying insights into large-scale genomic composition to use cases in a wide range of organisms.

In October 2021, 59 scientists from 14 countries and 13 U.S. states collaborated virtually in the Third Annual Baylor College of Medicine & DNANexus Structural Variation hackathon. The goal of the hackathon was to advance research on structural variants (SVs) by prototyping and iterating on open-source software. This led to nine hackathon projects focused on diverse genomics research interests, including various SV discovery and genotyping methods, SV sequence reconstruction, and clinically relevant structural variation, including SARS-CoV-2 variants. Repositories for the projects that participated in the hackathon are available at https://github.com/collaborativebioinformatics.

iCn3D: From Web-Based 3D Viewer to Structural Analysis Tool in Batch Mode.

iCn3D was initially developed as a web-based 3D molecular viewer. It then evolved from visualization into a full-featured interactive structural analysis software. It became a collaborative research instrument through the sharing of permanent, shortened URLs that encapsulate not only annotated visual molecular scenes, but also all underlying data and analysis scripts in a FAIR manner. More recently, with the growth of structural databases, the need to analyze large structural datasets systematically led us to use Python scripts and convert the code to be used in Node. js scripts. We showed a few examples of Python scripts at https://github.com/ncbi/icn3d/tree/master/icn3dpython to export secondary structures or PNG images from iCn3D. Users just need to replace the URL in the Python scripts to export other annotations from iCn3D. Furthermore, any interactive iCn3D feature can be converted into a Node. js script to be run in batch mode, enabling an interactive analysis performed on one or a handful of protein complexes to be scaled up to analysis features of large ensembles of structures. Currently available Node. js analysis scripts examples are available at https://github.com/ncbi/icn3d/tree/master/icn3dnode. This development will enable ensemble analyses on growing structural databases such as AlphaFold or RoseTTAFold on one hand and Electron Microscopy on the other. In this paper, we also review new features such as DelPhi electrostatic potential, 3D view of mutations, alignment of multiple chains, assembly of multiple structures by realignment, dynamic symmetry calculation, 2D cartoons at different levels, interactive contact maps, and use of iCn3D in Jupyter Notebook as described at https://pypi.org/project/icn3dpy.

Role of Probiotics in the Management of COVID-19: A Computational Perspective.

Coronavirus disease 2019 (COVID-19) was declared a pandemic at the beginning of 2020, causing millions of deaths worldwide. Millions of vaccine doses have been administered worldwide; however, outbreaks continue. Probiotics are known to restore a stable gut microbiota by regulating innate and adaptive immunity within the gut, demonstrating the possibility that they may be used to combat COVID-19 because of several pieces of evidence suggesting that COVID-19 has an adverse impact on gut microbiota dysbiosis. Thus, probiotics and their metabolites with known antiviral properties may be used as an adjunctive treatment to combat COVID-19. Several clinical trials have revealed the efficacy of probiotics and their metabolites in treating patients with SARS-CoV-2. However, its molecular mechanism has not been unraveled. The availability of abundant data resources and computational methods has significantly changed research finding molecular insights between probiotics and COVID-19. This review highlights computational approaches involving microbiome-based approaches and ensemble-driven docking approaches, as well as a case study proving the effects of probiotic metabolites on SARS-CoV-2.

An Alignment-Independent Approach for the Study of Viral Sequence Diversity at Any Given Rank of Taxonomy Lineage.

The study of viral diversity is imperative in understanding sequence change and its implications for intervention strategies. The widely used alignment-dependent approaches to study viral diversity are limited in their utility as sequence dissimilarity increases, particularly when expanded to the genus or higher ranks of viral species lineage. Herein, we present an alignment-independent algorithm, implemented as a tool, UNIQmin, to determine the effective viral sequence diversity at any rank of the viral taxonomy lineage. This is done by performing an exhaustive search to generate the minimal set of sequences for a given viral non-redundant sequence dataset. The minimal set is comprised of the smallest possible number of unique sequences required to capture the diversity inherent in the complete set of overlapping k-mers encoded by all the unique sequences in the given dataset. Such dataset compression is possible through the removal of unique sequences, whose entire repertoire of overlapping k-mers can be represented by other sequences, thus rendering them redundant to the collective pool of sequence diversity. A significant reduction, namely ~44%, ~45%, and ~53%, was observed for all reported unique sequences of species Dengue virus, genus Flavivirus, and family Flaviviridae, respectively, while still capturing the entire repertoire of nonamer (9-mer) viral peptidome diversity present in the initial input dataset. The algorithm is scalable for big data as it was applied to ~2.2 million non-redundant sequences of all reported viruses. UNIQmin is open source and publicly available on GitHub. The concept of a minimal set is generic and, thus, potentially applicable to other pathogenic microorganisms of non-viral origin, such as bacteria.

Drug Discovery of Spinal Muscular Atrophy (SMA) from the Computational Perspective: A Comprehensive Review.

Spinal muscular atrophy (SMA), one of the leading inherited causes of child mortality, is a rare neuromuscular disease arising from loss-of-function mutations of the survival motor neuron 1 (SMN1) gene, which encodes the SMN protein. When lacking the SMN protein in neurons, patients suffer from muscle weakness and atrophy, and in the severe cases, respiratory failure and death. Several therapeutic approaches show promise with human testing and three medications have been approved by the U.S. Food and Drug Administration (FDA) to date. Despite the shown promise of these approved therapies, there are some crucial limitations, one of the most important being the cost. The FDA-approved drugs are high-priced and are shortlisted among the most expensive treatments in the world. The price is still far beyond affordable and may serve as a burden for patients. The blooming of the biomedical data and advancement of computational approaches have opened new possibilities for SMA therapeutic development. This article highlights the present status of computationally aided approaches, including in silico drug repurposing, network driven drug discovery as well as artificial intelligence (AI)-assisted drug discovery, and discusses the future prospects.

Avian Influenza H7N9 Virus Adaptation to Human Hosts.

Avian influenza virus A (H7N9), after circulating in avian hosts for decades, was identified as a human pathogen in 2013. Herein, amino acid substitutions possibly essential for human adaptation were identified by comparing the 4706 aligned overlapping nonamer position sequences (1-9, 2-10, etc.) of the reported 2014 and 2017 avian and human H7N9 datasets. The initial set of virus sequences (as of year 2014) exhibited a total of 109 avian-to-human (A2H) signature amino acid substitutions. Each represented the most prevalent substitution at a given avian virus nonamer position that was selectively adapted as the corresponding index (most prevalent sequence) of the human viruses. The majority of these avian substitutions were long-standing in the evolution of H7N9, and only 17 were first detected in 2013 as possibly essential for the initial human adaptation. Strikingly, continued evolution of the avian H7N9 virus has resulted in avian and human protein sequences that are almost identical. This rapid and continued adaptation of the avian H7N9 virus to the human host, with near identity of the avian and human viruses, is associated with increased human infection and a predicted greater risk of human-to-human transmission.

Identification of highly conserved, serotype-specific dengue virus sequences: implications for vaccine design.

BACKGROUND: The sequence diversity of dengue virus (DENV) is one of the challenges in developing an effective vaccine against the virus. Highly conserved, serotype-specific (HCSS), immune-relevant DENV sequences are attractive candidates for vaccine design, and represent an alternative to the approach of selecting pan-DENV conserved sequences. The former aims to limit the number of possible cross-reactive epitope variants in the population, while the latter aims to limit the cross-reactivity between the serotypes to favour a serotype-specific response. Herein, we performed a large-scale systematic study to map and characterise HCSS sequences in the DENV proteome. METHODS: All reported DENV protein sequence data for each serotype was retrieved from the NCBI Entrez Protein (nr) Database (txid: 12637). The downloaded sequences were then separated according to the individual serotype proteins by use of BLASTp search, and subsequently removed for duplicates and co-aligned across the serotypes. Shannon's entropy and mutual information (MI) analyses, by use of AVANA, were performed to measure the diversity within and between the serotype proteins to identify HCSS nonamers. The sequences were evaluated for the presence of promiscuous T-cell epitopes by use of NetCTLpan 1.1 and NetMHCIIpan 3.2 server for human leukocyte antigen (HLA) class I and class II supertypes, respectively. The predicted epitopes were matched to reported epitopes in the Immune Epitope Database. RESULTS: A total of 2321 nonamers met the HCSS selection criteria of entropy < 0.25 and MI > 0.8. Concatenating these resulted in a total of 337 HCSS sequences. DENV4 had the most number of HCSS nonamers; NS5, NS3 and E proteins had among the highest, with none in the C and only one in prM. The HCSS sequences were immune-relevant; 87 HCSS sequences were both reported T-cell epitopes/ligands in human and predicted epitopes, supporting the accuracy of the predictions. A number of the HCSS clustered as immunological hotspots and exhibited putative promiscuity beyond a single HLA supertype. The HCSS sequences represented, on average, ~ 40% of the proteome length for each serotype; more than double of pan-DENV sequences (conserved across the four serotypes), and thus offer a larger choice of sequences for vaccine target selection. HCSS sequences of a given serotype showed significant amino acid difference to all the variants of the other serotypes, supporting the notion of serotype-specificity. CONCLUSION: This work provides a catalogue of HCSS sequences in the DENV proteome, as candidates for vaccine target selection. The methodology described herein provides a framework for similar application to other pathogens.

Expression, purification and characterization of the dimeric protruding domain of Macrobrachium rosenbergii nodavirus capsid protein expressed in Escherichia coli.

Macrobrachium rosenbergii nodavirus (MrNV) is the causative agent of white tail disease (WTD) which seriously impedes the production of the giant freshwater prawn and has a major economic impact. MrNV contains two segmented RNA molecules, which encode the RNA dependent RNA polymerase (RdRp) and the capsid protein (MrNV-CP) containing 371 amino acid residues. MrNV-CP comprises of the Shell (S) and the Protruding (P) domains, ranging from amino acid residues 1-252 and 253-371, respectively. The P-domain assembles into dimeric protruding spikes, and it is believed to be involved in host cell attachment and internalization. In this study, the recombinant P-domain of MrNV-CP was successfully cloned and expressed in Escherichia coli, purified with an immobilized metal affinity chromatography (IMAC) and size exclusion chromatography (SEC) up to ~90% purity. Characterization of the purified recombinant P-domain with SEC revealed that it formed dimers, and dynamic light scattering (DLS) analysis demonstrated that the hydrodynamic diameter of the dimers was ~6 nm. Circular dichroism (CD) analysis showed that the P-domain contained 67.9% of beta-sheets, but without alpha-helical structures. This is in good agreement with the cryo-electron microscopic analysis of MrNV which demonstrated that the P-domain contains only beta-stranded structures. Our findings of this study provide essential information for the production of the P-domain of MrNV-CP that will aid future studies particularly studies that will shed light on anti-viral drug discovery and provide an understanding of virus-host interactions and the viral pathogenicity.