Graphical representation methods: How well do they discriminate between homologous gene sequences?

Graphical representation methods constitute a class of alignment-free techniques for comparative study of biomolecular sequences. In this brief commentary, we study how well some of these methods can discriminate among closely related genes.

A Brief Review of Computer-Assisted Approaches to Rational Design of Peptide Vaccines.

The growing incidences of new viral diseases and increasingly frequent viral epidemics have strained therapeutic and preventive measures; the high mutability of viral genes puts additional strains on developmental efforts. Given the high cost and time requirements for new drugs development, vaccines remain as a viable alternative, but there too traditional techniques of live-attenuated or inactivated vaccines have the danger of allergenic reactions and others. Peptide vaccines have, over the last several years, begun to be looked on as more appropriate alternatives, which are economically affordable, require less time for development and hold the promise of multi-valent dosages. The developments in bioinformatics, proteomics, immunogenomics, structural biology and other sciences have spurred the growth of vaccinomics where computer assisted approaches serve to identify suitable peptide targets for eventual development of vaccines. In this mini-review we give a brief overview of some of the recent trends in computer assisted vaccine development with emphasis on the primary selection procedures of probable peptide candidates for vaccine development.

COVID-19 and the Importance of Being Prepared: A Multidisciplinary Strategy for the Discovery of Antivirals to Combat Pandemics.

During an emergency, such as a pandemic in which time and resources are extremely scarce, it is important to find effective and rapid solutions when searching for possible treatments. One possibility in this regard is the repurposing of available "on the market" drugs. This is a proof of the concept study showing the potential of a collaboration between two research groups, engaged in computer-aided drug design and control of viral infections, for the development of early strategies to combat future pandemics. We describe a QSAR (quantitative structure activity relationship) based repurposing study on molecular topology and molecular docking for identifying inhibitors of the main protease (Mpro) of SARS-CoV-2, the causative agent of COVID-19. The aim of this computational strategy was to create an agile, rapid, and efficient way to enable the selection of molecules capable of inhibiting SARS-CoV-2 protease. Molecules selected through in silico method were tested in vitro using human coronavirus 229E as a surrogate for SARS-CoV-2. Three strategies were used to screen the antiviral activity of these molecules against human coronavirus 229E in cell cultures, e.g., pre-treatment, co-treatment, and post-treatment. We found >99% of virus inhibition during pre-treatment and co-treatment and 90−99% inhibition when the molecules were applied post-treatment (after infection with the virus). From all tested compounds, Molport-046-067-769 and Molport-046-568-802 are here reported for the first time as potential anti-SARS-CoV-2 compounds.

Characterization of dihydrofolate reductases from multiple strains of Plasmodium falciparum using mathematical descriptors of their inhibitors.

Two classes of graph-theoretic molecular descriptors, viz., topological indices (TIs) and atom pairs (APs), have been used to derive high-quality quantitative structure-activity relationships (QSARs) for inhibitors of dihydrofolate reductases (DHFRs) isolated from the wild and four mutant strains of Plasmodium falciparum. Of the three methods used for QSAR formulation, viz., principal-components regression (PCR), partial least squares (PLS), and ridge regression (RR), the RR method outperformed the other two. Cohen's kappa values, based on the overlap of significant and insignificant structural descriptors calculated for the QSAR development, show that DHFR from the wild strain is substantially different from the four mutant strains. The differential QSAR approach reported in this study can be used in protocols for the development of drugs to combat drug-resistant pathogens arising continuously in nature due to mutations. The pairwise kappa values in conjunction with appropriate drug targets and their corresponding set of ligands may be a useful tool in gauging the evolving mutual similarities and dissimilarities of pathogenic organisms from purely computed mathematical descriptors of the ligands.

New Computational Approach for Peptide Vaccine Design Against SARS-COV-2.

The design for vaccines using in silico analysis of genomic data of different viruses has taken many different paths, but lack of any precise computational approach has constrained them to alignment methods and some alignment-free techniques. In this work, a precise computational approach has been established wherein two new mathematical parameters have been suggested to identify the highly conserved and surface-exposed regions which are spread over a large region of the surface protein of the virus so that one can determine possible peptide vaccine candidates from those regions. The first parameter, w, is the sum of the normalized values of the measure of surface accessibility and the normalized measure of conservativeness, and the second parameter is the area of a triangle formed by a mathematical model named 2D Polygon Representation. This method has been, therefore, used to determine possible vaccine targets against SARS-CoV-2 by considering its surface-situated spike glycoprotein. The results of this model have been verified by a parallel analysis using the older approach of manually estimating the graphs describing the variation of conservativeness and surface-exposure across the protein sequence. Furthermore, the working of the method has been tested by applying it to find out peptide vaccine candidates for Zika and Hendra viruses respectively. A satisfactory consistency of the model results with pre-established results for both the test cases shows that this in silico alignment-free analysis proposed by the model is suitable not only to determine vaccine targets against SARS-CoV-2 but also ready to extend against other viruses.

Clustering of Zika Viruses Originating from Different Geographical Regions using Computational Sequence Descriptors.

BACKGROUND: In this report, we consider a data set, which consists of 310 Zika virus genome sequences taken from different continents, Africa, Asia and South America. The sequences, which were compiled from GenBank, were derived from the host cells of different mammalian species (Simiiformes, Aedes opok, Aedes africanus, Aedes luteocephalus, Aedes dalzieli, Aedes aegypti, and Homo sapiens). METHODS: For chemometrical treatment, the sequences have been represented by sequence descriptors derived from their graphs or neighborhood matrices. The set was analyzed with three chemometrical methods: Mahalanobis distances, principal component analysis (PCA) and self organizing maps (SOM). A good separation of samples with respect to the region of origin was observed using these three methods. RESULTS: Study of 310 Zika virus genome sequences from different continents. To characterize and compare Zika virus sequences from around the world using alignment-free sequence comparison and chemometrical methods. CONCLUSION: Mahalanobis distance analysis, self organizing maps, principal components were used to carry out the chemometrical analyses of the Zika sequence data. Genome sequences are clustered with respect to the region of origin (continent, country). Africa samples are well separated from Asian and South American ones.

Cluster Analysis of Coronavirus Sequences using Computational Sequence Descriptors: With Applications to SARS, MERS and SARS-CoV-2 (CoVID-19).

INTRODUCTION: Coronaviruses comprise a group of enveloped, positive-sense single-stranded RNA viruses that infect humans as well as a wide range of animals. The study was performed on a set of 573 sequences belonging to SARS, MERS and SARS-CoV-2 (CoVID-19) viruses. The sequences were represented with alignment-free sequence descriptors and analyzed with different chemometric methods: Euclidean/Mahalanobis distances, principal component analysis and self-organizing maps (Kohonen networks). We report the cluster structures of the data. The sequences are well-clustered regarding the type of virus; however, some of them show the tendency to belong to more than one virus type. BACKGROUND: This is a study of 573 genome sequences belonging to SARS, MERS and SARS-- CoV-2 (CoVID-19) coronaviruses. OBJECTIVES: The aim was to compare the virus sequences, which originate from different places around the world. METHODS: The study used alignment free sequence descriptors for the representation of sequences and chemometric methods for analyzing clusters. RESULTS: Majority of genome sequences are clustered with respect to the virus type, but some of them are outliers. CONCLUSION: We indicate 71 sequences, which tend to belong to more than one cluster.

Prediction of Anticancer Activity of 2-phenylindoles: Comparative Molecular Field Analysis Versus Ridge Regression using Mathematical Molecular Descriptors.

Topological indices (TIs) and atom pairs (APs) were used to develop quantitative structure-activity relationships (QSARs) for anticancer activity for a set of 43 derivatives of 2-phenylindole. Results show that QSARs formulated using TI+AP outperform those using either TI or AP alone. The q2 of the ridge regression model using TI+AP was 0.867 as compared to 0.705 reported in the literature using the comparative molecular field analysis (CoMFA) method.

Computational Approaches for the Design of Mosquito Repellent Chemicals.

BACKGROUND: In view of many current mosquito-borne diseases there is a need for the design of novel repellents. OBJECTIVE: The objective of this article is to review the results of the researches carried out by the authors in the computer-assisted design of novel mosquito repellents. METHODS: Two methods in the computational design of repellents have been discussed: a) Quantitative Structure Activity Relationship (QSAR) studies from a set of repellents structurally related to DEET using computed mathematical descriptors, and b) Pharmacophore based modeling for design and discovery of novel repellent compounds including virtual screening of compound databases and synthesis of novel analogues. RESULTS: Effective QSARs could be developed using mathematical structural descriptors. The pharmacophore based method is an effective tool for the discovery of new repellent molecules. CONCLUSION: Results reviewed in this article show that both QSAR and pharmacophore based methods can be used to design novel repellent molecules.

Computational Methodology for Peptide Vaccine Design for Zika Virus: A Bioinformatics Approach.

With the increasing frequency of viral epidemics, vaccines to augment the human immune response system have been the medium of choice to combat viral infections. The tragic consequences of the Zika virus pandemic in South and Central America a few years ago brought the issues into sharper focus. While traditional vaccine development is time-consuming and expensive, recent advances in information technology, immunoinformatics, genetics, bioinformatics, and related sciences have opened the doors to new paradigms in vaccine design and applications.Peptide vaccines are one group of the new approaches to vaccine formulation. In this chapter, we discuss the various issues involved in the design of peptide vaccines and their advantages and shortcomings, with special reference to the Zika virus for which no drugs or vaccines are as yet available. In the process, we outline our work in this field giving a detailed step-by-step description of the protocol we follow for such vaccine design so that interested researchers can easily follow them and do their own designing. Several flowcharts and figures are included to provide a background of the software to be used and results to be anticipated.

Computational study of dispersion and extent of mutated and duplicated sequences of the H5N1 influenza neuraminidase over the period 1997-2008.

Study of mutational changes in neuraminidase (NA) gene sequences is important to track the effectiveness of the inhibitors to the H5N1 avian flu virus that targets this component of the viral apparatus. Our analysis based on numerical characterization studies of 682 complete neuraminidase gene and protein sequences available in the database, updated to March 2009, and which extends our previous work based on a sample of 173 sequences has revealed several interesting features. We have noticed that identical sequences have appeared over significant distances in space and time, raising the need for a deeper understanding of the longevity of such viral strains in the environment. Structural sections like transmembrane, stalk, body, and C-terminal tail regions have shown independent recombinations between strains from various species including human and avian hosts highlighting influenza's flexibility in host selection and recombination. Our analysis confirmed a biased nature in mutational accumulation in structural segments: a highly conserved 50-base C-terminal tail section identified in our earlier paper seems to accumulate mutational changes at a rate of about a fifth to an eighth of transmembrane and stalk regions, although the length is about half of these. Parallel study of the equivalent section to the C-terminal region in protein sequences reveals only 13 separate varieties, and all the other 669 sequences are duplicates to three of these varieties showing the highly conserved nature of this segment. Our analysis of active site related bases and amino acids showed highly conserved characteristic of those constructs, whereas the rest of the segments demonstrated rather large mutational changes. These kinds of high level of mutation in major part of the H5N1 NA sequences and recombinations within structural segments coupled with strong conservation of a few select segments show that the potential of rapid mutations to more virulent forms of this variety of avian flu continue to remain of concern, especially with the possibility of long duration dormancy of some of these viral strains, whereas islands of highly conserved segments could signify potential regions for inhibitor designs.

Quantitative structure-activity relationship (QSAR) modeling of human blood: air partitioning with proper statistical methods and validation.

Blood: air partition coefficient (BApc) is important in assessing toxicokinetics of chemicals. Since very few experimental data are available, quantitative structure-activity relationship (QSAR) models based on calculated molecular descriptors can be useful in estimating BApc. Since all descriptors used in the analysis are computed strictly from structure, they can be applied to any chemical, real or hypothetical. In this article, we report models for BApc estimation using four methods, including stepwise ordinary least-squares regression, which is commonly used in QSAR studies but often results in an inflated 'naïve' q2, over-representing the predictive ability of the model. The models developed using proper statistical techniques had q2 values of 0.825 and 0.841, and may be used to reliably predict BApc values for new compounds that are structurally similar to those upon which the models are based. The models developed using improper techniques had associated q2 values, as computed using naïve methods, of 0.920 and 0.934, severely overstating their actual quality.

Base Distribution in Dengue Nucleotide Sequences Differs Significantly from Other Mosquito-Borne Human-Infecting Flavivirus Members.

INTRODUCTION: Among the mosquito-borne human-infecting flavivirus species that include Zika, West Nile, yellow fever, Japanese encephalitis and Dengue viruses, the Zika virus is found to be closest to Dengue virus, sharing the same clade in the Flavivirus phylogenetic tree. We consider these five flaviviruses and on closer examination in our analyses, the nucleotide sequences of the Dengue viral genes (envelope and NS5) and genomes are seen to be quite widely different from the other four flaviviruses. We consider the extent of this distinction and determine the advantage and/or disadvantage such differences may confer upon the Dengue viral pathogenesis. METHODS: We have primarily used a 2D graphical representation technique to show the differences in base distributions in these five flaviviruses and subsequently, obtained quantitative estimates of the differences. Similarity/dissimilarity between the viruses based on the genes were also determined which showed that the differences with the Dengue genes are more pronounced. RESULTS: We found that the Dengue viruses compared to the other four flaviviruses spread rapidly worldwide and became endemic in various regions with small alterations in sequence composition relative to the host populations as revealed by codon usage biases and phylogenetic examination. CONCLUSION: We conclude that the Dengue genes are indeed more widely separated from the other aforementioned mosquito-borne human-infecting flaviviruses due to excess adenine component, a feature that is sparse in the literature. Such excesses have a bearing on drug and vaccine, especially peptide vaccine, development and should be considered appropriately.

Finding Needles in a Haystack: Determining Key Molecular Descriptors Associated with the Blood-brain Barrier Entry of Chemical Compounds Using Machine Learning.

In this paper we used two sets of calculated molecular descriptors to predict blood-brain barrier (BBB) entry of a collection of 415 chemicals. The set of 579 descriptors were calculated by Schrodinger and TopoCluj software. Polly and Triplet software were used to calculate the second set of 198 descriptors. Following this, modelling and a two-deep, repeated external validation method was used for QSAR formulation. Results show that both sets of descriptors individually and their combination give models of reasonable prediction accuracy. We also uncover the effectiveness of a variable selection approach, by showing that for one of our descriptor sets, the top 5 % predictors in terms of random forest variable importance are able to provide a better performing model than the model with all predictors. The top influential descriptors indicate important aspects of molecular structural features that govern BBB entry of chemicals.

Computer-Assisted and Data Driven Approaches for Surveillance, Drug Discovery, and Vaccine Design for the Zika Virus.

Human life has been at the edge of catastrophe for millennia due diseases which emerge and reemerge at random. The recent outbreak of the Zika virus (ZIKV) is one such menace that shook the global public health community abruptly. Modern technologies, including computational tools as well as experimental approaches, need to be harnessed fast and effectively in a coordinated manner in order to properly address such challenges. In this paper, based on our earlier research, we have proposed a four-pronged approach to tackle the emerging pathogens like ZIKV: (a) Epidemiological modelling of spread mechanisms of ZIKV; (b) assessment of the public health risk of newly emerging strains of the pathogens by comparing them with existing strains/pathogens using fast computational sequence comparison methods; (c) implementation of vaccine design methods in order to produce a set of probable peptide vaccine candidates for quick synthesis/production and testing in the laboratory; and (d) designing of novel therapeutic molecules and their laboratory testing as well as validation of new drugs or repurposing of drugs for use against ZIKV. For each of these stages, we provide an extensive review of the technical challenges and current state-of-the-art. Further, we outline the future areas of research and discuss how they can work together to proactively combat ZIKV or future emerging pathogens.

Mathematical biodescriptors of proteomics maps: background and applications.

This article reviews recent developments in the formulation and application of biodescriptors to characterize proteomics maps. Such biodescriptors can be derived by applying techniques from discrete mathematics (graph theory, linear algebra and information theory). This review focuses on the development of biodescriptors for proteomics maps derived from 2D gel electrophoresis. Preliminary results demonstrated that such descriptors have a reasonable ability to differentiate between proteomics patterns that result from exposure to closely related individual chemicals and complex mixtures, such as the jet fuel JP-8. Further research is required to evaluate the utility of these proteomics-based biodescriptors for drug discovery and predictive toxicology.

Beware of External Validation! - A Comparative Study of Several Validation Techniques used in QSAR Modelling.

BACKGROUND: Proper validation is an important aspect of QSAR modelling. External validation is one of the widely used validation methods in QSAR where the model is built on a subset of the data and validated on the rest of the samples. However, its effectiveness for datasets with a small number of samples but a large number of predictors remains suspect. OBJECTIVE: Calculating hundreds or thousands of molecular descriptors using currently available software has become the norm in QSAR research, owing to computational advances in the past few decades. Thus, for n chemical compounds and p descriptors calculated for each molecule, the typical chemometric dataset today has a high value of p but small n (i.e. n << p). Motivated by the evidence of inadequacies of external validation in estimating the true predictive capability of a statistical model in recent literature, this paper performs an extensive and comparative study of this method with several other validation techniques. METHODOLOGY: We compared four validation methods: Leave-one-out, K-fold, external and multi-split validation, using statistical models built using the LASSO regression, which simultaneously performs variable selection and modelling. We used 300 simulated datasets and one real dataset of 95 congeneric amine mutagens for this evaluation. RESULTS: External validation metrics have high variation among different random splits of the data, hence are not recommended for predictive QSAR models. LOO has the overall best performance among all validation methods applied in our scenario. CONCLUSION: Results from external validation are too unstable for the datasets we analyzed. Based on our findings, we recommend using the LOO procedure for validating QSAR predictive models built on high-dimensional small-sample data.

Epidemics and Peptide Vaccine Response: A Brief Review.

We briefly review the situations arising out of epidemics that erupt rather suddenly, threatening life and livelihoods of humans. Ebola, Zika and the Nipah virus outbreaks are recent examples where the viral epidemics have led to considerably high degree of fatalities or debilitating consequences. The problems are accentuated by a lack of drugs or vaccines effective against the new and emergent viruses, and the inordinate amount of temporal and financial resources that are required to combat the novel pathogens. Progress in computational, biological and informational sciences have made it possible to consider design of synthetic vaccines that can be rapidly developed and deployed to help stem the damages. In this review, we consider the pros and cons of this new paradigm and suggest a new system where the manufacturing process can be decentralized to provide more targeted vaccines to meet the urgent needs of protection in case of a rampaging epidemic.

A Bioinformatics approach to designing a Zika virus vaccine.

The Zika virus infections have reached epidemic proportions in the Latin American countries causing severe birth defects and neurological disorders. While several organizations have begun research into design of prophylactic vaccines and therapeutic drugs, computer assisted methods with adequate data resources can be expected to assist in these measures to reduce lead times through bioinformatics approaches. Using 60 sequences of the Zika virus envelope protein available in the GenBank database, our analysis with numerical characterization techniques and several web based bioinformatics servers identified four peptide stretches on the Zika virus envelope protein that are well conserved and surface exposed and are predicted to have reasonable epitope binding efficiency. These peptides can be expected to form the basis for a nascent peptide vaccine which, enhanced by incorporation of suitable adjuvants, can elicit immune response against the Zika virus infections.