International Journal of Computational Biology and Drug Design (14 papers in press)
Importance of safety maintenance of the survived with recent former infection experience during a pandemic syndrome episode: A Study by Difference Equation Approach
by Subhasis Bhattacharya, Suman Paul, Sudip Mukherjee
Abstract: During the outbreak of a highly infectious disease conceded by a virus, handling of healthcare catastrophe is the most momentous part. Any type of known or unknown relaxation may generate enormous loss in terms of population. Present study consider the concern that survived one who has some fresh former infection history can be fingered with appropriate care throughout the syndrome period otherwise a huge harm can be advent by the state. The study follow difference equation modelling considering two aspects where the survived with former infection history handled with care and not reckoned as a part of sustained population and the other is they encompassed with the general population category. The study considers an example of a hypothetical state with some give infection rate, death rate and quarantine rate. By using R- programme language the study observes that proper care for such group of population is very significant to reduce the situation like human loss.
Keywords: Infectious disease; SARS-CoV-2; 2019-nCov; Difference Equation; Survived from the infected; Quarantine rate; Death Rate.
A New Edge Effect Correction for Sequence Alignment
by Amirhossein Karami, Afshin Fayyaz Movaghar
Abstract: The edge effect correction is an appropriate way to improve the sequences alignment. In this paper, the edge effect correction is applied to the h−tuple method. Then, the results of the corrected h−tuple methodrnare compared with the ones based on the extreme value theory in a real database. The Receiver Operating Characteristic (ROC) curve reveals that the corrected h−tuple method has an advantage over another one.
Keywords: Edge Effect Correction; h−tuple method; Extreme Value Theory; Sequence Alignment; Local Score.
Identification and optimization of novel selective inhibitors against human Regulator of G protein signaling 2 (RGS2) protein for type 2 diabetes mellitus: An in silico approach
by Goverdhan Lanka, Revanth Bathula, Mahendar Dasari, Manan Bhargavi, Sarita Rajender Potlapally
Abstract: Regulator G protein Signaling 2 (RGS2) protein negatively modulates the GPCR signaling pathway thereby causing type 2 diabetes and is considered as potential drug target against type 2 diabetes mellitus for the identification of potential inhibitors as drug candidates. In this present work, the 3D homology model of RGS2 protein was constructed using MODELLER9.9 software to understand its structural and functional features. The energy optimization of RGS2 was carried out by NAMD-VMD interface followed by validation of the 3D model for the reliability assessment with standard validation protocols. The potential binding site of RGS2 protein was identified by literature studies, manual correlation technique (Ligplot), and SiteMap tools. The structure-based virtual screening was carried out using the Asinex-signature library of 21859 minimized small molecules at the RGS2 active site by GLIDE module of Schrodinger suite for the hit identification. A total of 14 molecules were identified as final hits and are prioritized using glide score, glide energy, and intermolecular interactions. The potential leads were identified by optimization of 14 hit molecules using AutoDock (binding energies), Prime-MM/GBSA, percent human oral absorption, and ligand-residue interactions of RGS2-ligand complexes. The drug-likeness of newly identified lead inhibitors was verified through ADME assessment by QikProp module and all ligands molecules were showed permissible ranges of pharmacokinetic properties. The comparative binding affinity study of current drugs of diabetes to that of newly identified leads was carried out to justify the potency of new leads against RGS2 protein. Thus, the results of the present study may provide the potential strategy in the identification of new selective chemical entities as potential type 2 diabetes mellitus drug candidates.
Keywords: RGS2; NAMD-VMD; Virtual Screening; Prime MM/GBSA; AutoDock; Type 2 diabetes mellitus.
Genes to Drug: An in-silico approach to design a drug for Huntington disease (HD) in Homo sapiens
by Sachin Kumar, Shivalika Panwar, Manoj Kumar Sharma, Manoj Kumar Sharma
Abstract: The current study is carried out using in silico approach to find some potent drug molecule against Huntington's disease. Mutant Huntingtin protein (HTT), HTT-interacting protein 1 (HIP-1) were used as the target protein for drug designing. Swiss PDB Viewer (SPDBW) was used for active site determination. Ligands were prepared through MolinspirationCheminformatics following Lipinski rule of five (RO5). Bioinformatics software Molegro Virtual Docker (MVD) and HEX were used for docking between the ligands and target proteins.
Eleven ligands were prepared and compared with drugs based on MolDock score and hydrogen bond score. Four ligands - named [1-benzyl-6bromo-8-((4-ethyl-2hydroxy-2H- pyran-6y1)methyl)-4-hydroxy-7,8dihydro-1,8-naphthydrin-2(1H)-one], [4-((3-amino- 5bromocyclohexyl)methoxy)-5-butyl-6hydroxydechydronaphthalene-2-carboxylic acid], [3- amino-2-chloro-13-(2-hydroxybutyl)-5-nitro-2,3-dihydro-7H-benzo[c][1,4]dioxino[2,3- h]xanthen-7-one] and [3-(3-bromo-[1,1-bi(cyclohexan)]-3-y1)-6-(4-methoxy-4- oxobutanamido)hexanoic acid] respectively with most appropriate values as -731.31 for MolDock score and -33.51 for H-Bond score are found to be potential drug candidate for Huntington's Disease.
Keywords: Huntington's Disease; HIP-1; HTT; ligands; neurodegenerative disorder; Haloperidol; GSK356278.
Bottomhat Kernel Analysis based on Different Shape and Size using Colonies Extraction for Counting of Bacterial Colonies in Petri Dish
by Fitri Utaminingrum, Abdurrohman Hidayat
Abstract: Research involving microorganisms such as bacteria mostly were done in many areas such as health, medicine, biology, chemistry, and another particle science. The counting process to calculate the number of bacteria in the petri dish is manually using the colony counter. It is possible to obtain error calculation because of human fatigue, inaccuracy, and neglect. Counting the number of bacterial colonies is one of the crucial steps in microbiology testing. The manual process is time-consuming, labor-intensive, and imprecise. An automatic system counted off the number of microbial colonies by using digital image processing is proposed. The proposed method aims to calculate the number of bacterial colonies more quickly, precisely, and effectively. Comparisons show that the proposed method can provide excellent F-measure with the mean value of F-measure is 93.02%. An embed-ready program is implemented in the embedded system to do the automatic bacterial colony counting. This system could give contribution value to the world of microbiology.
Keywords: colony extraction; colony counting; petri dish; bacterial colonies.
A novel formulation with potential for improving osteoarthritis mediated through COX2 and MMP9 receptors
by Mohit M. Jain, Geeta Rai
Abstract: Osteoarthritis is the most common form of arthritis, affecting the synovial joints and specifically characterized by loss of cartilage within the joint. The present line of treatment mostly includes non-steroidal anti-inflammatory drugs which have limited advantage and high associated cytotoxicity. In this study, we evaluated a formulation joint joy containing ingredients from natural sources like Withania somnifera, Curcumin-C3-complex along with Glucosamine and Chondroitin Sulfate, in-silico for mechanism involved in improvement of osteoarthritis. We conducted molecular docking and ADMET analysis of these compounds on COX-2R and MMP-9 receptors, which are implicated in inflammation and joint degradation and are also key therapeutic targets for drugs against joint degradation. Binding free energy was calculated and information on hydrogen bonds, interacting residues and drug-likeness properties were generated. Molecular docking and pharmacokinetics analysis showed docking scores of joint joy ingredients comparable to that of reference drug celecoxib, suggesting that the formulation has a potential to improve the joint pain and inflammation of osteoarthritis patients.
Keywords: Joint Joy; COX-2R; MMP-9R; Molecular docking; ADMET analysis.
IN SILICO characterization and phylogenetic analysis of Arabidopsis homologues of the human antiretroviral SERINC proteins
by Siarhei Dabravolski, Yury Kavalionak
Abstract: Serine incorporator (SERINC) family proteins are well-known for their antiretroviral properties strong inhibition of the HIV-1 replication in human T-cells. SERINC proteins are highly conserved in all eukaryotes, representing 10-12 transmembrane domains. In the present study, for the first time, five SERINC homologues from the model plant Arabidopsis thaliana were analysed. We primarily focused on the comprehensive computational, structural and functional characterization, phylogeny reconstruction. Overall, our analyses revealed an evolutionally conserved nature of the plant serinc domain-containing proteins, sharing physiochemical and structural properties with yeast and human homologues. The study found that Arabidopsis SERINC proteins are divided into cytoplasmic and secreted/chloroplast groups, distinguished by the physiochemical properties, localization and phylogeny. Analysis of the protein-protein interaction databases suggests that SERINC proteins could play an important regulatory and physiological role in plant metabolism. Further investigation on the SERINC proteins on plants will help to define their role in plant development, exact biochemical function and affected physiological processes. Also, it would enhance our comprehension of the evolutionally conserved mechanisms of antiretroviral protection, shared between yeast, plants and human. Potentially, plant serinc domain-containing proteins could be used as a model to study antiretroviral response. Such knowledge could provide important insight into the creation of the effective antiretroviral vaccine for medical use.
Keywords: serinc; retrovirus; HIV; Arabidopsis; in silico; phylogeny.
Conformers of a novel lipopeptide antibiotic, Kannurin inhibits SARS-Cov2 replication via interfering with RNA-dependent-RNA polymerase activation and function
by Shabeer Ali H, Prajosh P, Sreejith K, Divya Lakshmanan M
Abstract: In the context of COVID-19 outbreak, the key component nsp12 of RNA-dependent-RNA polymerase of Corona virus is considered as a primary target for drug intervention purposes. The broad spectrum antimicrobial lipopeptide Kannurin revealed favorable interactions against nsp12 in preliminary in silico experiment. Among the different target sites on nsp12 selected for docking study, the cyclic form of Kannurin was found to interact with the residues Phe 407, Leu 544 and Lys 511 present in the finger subdomain of nsp12 (docking score -5.983 kCal/mol). These are the key residues facilitating the binding of nsp7 and nsp8 cofactors. With this mechanism, it is proposed that Kannurin can act by inhibiting the binding of cofactors with nsp12 and ultimately leading to its inactivation. The second mechanism was proposed by observing the interaction of linear form of Kannurin with the palm subdomain cavity (docking score -6.04 kCal/mol) especially with the residue Arg 555 that involved in receiving the incoming nucleotides during replication. The mechanism is closely related to that of a standard antiviral agent Remdesivir. Due to the presence of diverse chemical groups and the ability to attain different conformational states, Kannurin can easily adapt with a number of surfaces and cavities with varying chemical nature. These preliminary findings and the attractive structural properties of Kannurin suggest that currently available lipopeptide antibiotics can also be considered for ongoing clinical trials to counteract the emerging SARS-COV viruses.
Keywords: SARS-COV; Kannurin; Lipopeptide; nsp12; nsp7; nsp8.
Probing into the genetic factors responsible for bladder cancer prognosis
by Kavinkumar Nirmala Karunakaran, Jeevitha Priya Manoharan, Subramanian Vidyalakshmi
Keywords: Bladder cancer; TCGA; GEO; DEMs; Protein Protein Interaction network; KM Plotter.
Analysis Of The Codon Usage Pattern In 2019-nCoV
by Satyabrata Sahoo
Abstract: Abstract: upon a viral outbreak of novel coronavirus (2019-nCoV), recognized as Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2) which causes a severe acute respiratory illness with a high mortality rate, it is very important to understand the molecular characteristics of the SARS-CoV-2 genome and to detect the causative agent of the deadly viral infection. In the present study, we comprehensively analyzed the codon usage pattern of SARS-CoV-2 strains to gain an insight into the mechanism of viral pathogenesis. The codon usage has a measurable role in the adaptation of viruses to hosts. The phenomenon of unequal use of synonymous codons in SARS-COV-2 is found to be common. The codon usage bias not only plays an important regulatory role at the level of gene expression, but also helps in improving the accuracy and efficiency of translation. Meanwhile, the codon usage pattern of coronavirus(CoV) genomes is important for interpreting evolutionary characteristics in species. The correspondence analysis (CA), ENC-plot, the relationship between GC12 versus GC3, and the RSCU of overall/separated genomes show that the codon usage bias exists in CoV genomes. We found that SARS-CoV-2 showed a low codon usage bias. Both mutation and natural selection pressure have contributed to this low codon usage bias, with the former being the main determining factor. Lastly, the relationship between codon usage index(CUI) and codon adaptation index(CAI) and the gene expression profile of SARS-CoV-2 genes along with other CoV genes have been discussed in reference to codon bias score(CBS) for further study on virus-host relationship and their evolutionary phenomenon.
Keywords: Keywords: CAI • Codon usage bias• GC content • gene expression • RSCU • SARS-CoV-2.
Docking based screening of curcumin derivatives: A novel approach in the inhibition of tubercular DHFR
by Somdutt Mujwar, Kamal Shah, Jeetendra Kumar Gupta, Alekh Gour
Abstract: Tuberculosis is one of the main causes of morbidity and mortality across the globe. Mycobacterium tuberculosis can attack any part of the human body and can cause treacherous effects also. It maltreats not only the respiratory system but also many vital organs of the sufferers. Tuberculosis is one of the prevalent diseases and known for its deleterious effect on human beings. The regimen for its cure consists of multiple drugs. The resistance to these drugs is one of the crucial problems that exacerbate this muddle. This paper aims to identify the receptor dihydrofolate reductase as a potential drug target present in the Mycobacterium tuberculosis which adds a breakthrough in the alleviation of this illness and may help a bid to fight against resistant strains. The present study consists of the molecular docking simulation of thirty-two reported curcumin derivatives against bacterial DHFR to identify potential lead molecules for inhibition of tubercular bacteria. On the basis of the in-silico studies, the best five ligand molecules for bacterial DHFR were identified. The present study gives a novel insight into the management of tuberculosis through curcumin derivatives.
Keywords: Curcumin; Dihydrofolate reductase; Mycobacterium tuberculosis; tuberculosis; autodock; docking.
Molecular docking and pharmacophore modeling of phytoconstituents of Vaccinium secundiflorum for antidiabetic and antioxidant activity
by Jainey James, Afiya Abdul Aziz, Dhanya Krishnan, Pankaj Kumar, Abhishek Kumar
Abstract: Vaccinium secundiflorum of the Ericaceae family, a shrub in Madagascar, is used by the localities to treat diabetes mellitus. The study's main aim was to determine the binding interactions of the thirty phytoconstituents with the target proteins, 4GQQ and 1HD2, to assess their antidiabetic and antioxidant activities, respectively. In silico approaches by Schr
Keywords: Phytoconstituents; Molecular docking; Pharmacophore modeling; ADMET; Antidiabetic; Vaccinium secundiflorum.
DSP Techniques for Protein Coding Region Identification based on Background Noise and Nonlinear Phase Delay Reduction from Period-3 Spectrum using Zero Phased Anti-Notch Filter and Savitzky-Golay (S-G) Filter
by Amit Kumar Singh, Vinay Kumar Srivastava
Abstract: Identifying protein-coding regions from a given DNA sequence has always been a challenging task in bioinformatics. Spectrum analysis techniques, such as short time discrete Fourier transform (STDFT) and anti-notch filters (ANF), have been successfully applied to solve this problem. The ANF techniques are comparatively faster than transform techniques. However, the filter based methods performance is still limited because of the excessive background noise and nonlinear phase delay in their outputs. The drawback with existing de-noising techniques is that it identically treats the coding regions spectra and noncoding regions spectra. Consequently, the discriminative spectral measure of protein coding regions is also diminished, which reduces the overall accuracy. A Savitzky-Golay (SG) acts as a weighted moving average filter that de-noise the signal without distortion. This paper investigated the de-noising performance of Savitzky-Golay (SG) filter with both the STDFT and ANF techniques. It is demonstrated that the ANF technique is more prone to background noise. To overcome the nonlinear phase delay in ANF spectrum, the zero phased anti-notch filter (ZANF) technique is used. The performance of the proposed method is compared with other de-noising techniques at the nucleotide level. The findings of this investigation encourage the combined use of ZANF followed and S-G filter as it provides the best prediction results than other methods.
Keywords: Protein coding region prediction; 3-base periodicity; Short time discrete Fourier transform; Moving average filter; Savitzky-Golay(S-G) filter.
Telemetric Drug Injection System with Centralized Monitoring and Control of Multiple Injectors
by Maham Sarvat, Suhaib Masroor, Muhammad Muzammil Khan
Abstract: A Syringe pump is a device used for the administration of medicines, having a predetermined amount of doses, into the patients with prolonging injection time. The problem of continuous pump monitoring, drug status, injection time, and manual control are common drawbacks of all existing syringe pumps. In this study, all the aforesaid problems are addressed by the proposed syringe pump which offers wireless control of the pump from the nursing station via any android device having authorized access to the pump. Moreover, this study also proposes a centralized control of multiple syringe pumps, within 30-meter range, such that a single android device can access multiple syringe pumps installed in ICU/CCU, and perform all the drug injection control tasks. The proposed concept is executed by controlling two syringe pumps from a single android device. In this new design, a medical personal become able to control the entire multiple syringe pump system while staying at the nursing counter via an android device, instead of moving bed to bed to monitor and control the pumps. Thus, the proposed design also offer cost reduction in the term of wages paid to the medical personals required to control the pumps in the manual system
Keywords: Telemedicine; Biomedical Instrumentation; Centralized Control; Syringe Pump.