Forthcoming articles

International Journal of Computational Biology and Drug Design

International Journal of Computational Biology and Drug Design (IJCBDD)

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International Journal of Computational Biology and Drug Design (30 papers in press)

Regular Issues

  • Data Acquisition and Electrical Instrumentation Engineering Modelling for Intelligent Learning and Recognition
    by Jun Qin, Yuhao Jiang 

  • Development of interactive computer learning program for genetics and molecular biology applications
    by Xiaoli Yang, Bin Chen, Yifan Cai, Charles Tseng 

  • Human Caveolin-1 a potent inhibitor for prostate cancer therapy: a computational approach   Order a copy of this article
    by Uzma Khanam, Balwant Kishan Malik, Puniti Mathur, Bhawna Rathi 
    Abstract: Caveolin-1 (Cav-1) is 22 kDa caveolae protein, acts as a scaffold within caveolar membranes. It interacts with alpha subunits of G-protein and thereby regulates their activity. Earlier studies reported elevated or up-regulated levels of caveolin-1 in the serum of prostate cancer patients. Secreted Cav-1 promotes angiogenesis, cell proliferation and anti-apoptotic activities in prostate cancer patients. Cav-1 upregulation is mainly related to prostate cancer metastasis. Keeping above facts in view, the present study was designed to explore Cav-1 as a target for prostate cancer therapy using computational approach. Molecular docking, structural base molecular modelling and molecular dynamics simulations were performed to investigate Cav-1 inhibitors. A predictive model was generated and validated to establish a stable structure. ZINC database of biogenic compounds was used for induced fit docking (IFD) and high throughput virtual screening. The H-bond interactions of the compounds with active site residues of Cav-1 was estimated by IFD and 100 ns long molecular dynamic simulations. The reported compounds showed significant binding and thus can be considered as potent therapeutic inhibitors of Cav-1. This study provides a valuable insight into biochemical interactions of Cav-1 for therapeutic applications and warrants for experimental validation of the predicted active(s).
    Keywords: Molecular dynamics simulation; virtual screening; molecular docking; prostate cancer; caveolin-1; induced fit docking; protein-protein interaction network.

  • An in silico approach to design a potential drug for Haemophilia A   Order a copy of this article
    by Srishti Munjal, Gaurav Jaisawal, Navodit Goel, Udai Pratap Singh, Ajay Vishwakrma, Abhinav Srivastava 
    Abstract: Haemophilia A has been known as a disease since the late 20th century but till date, there has not been developed a cure for it. Treatments that temporarily relieve bleeding episodes include new factor replacement therapies with longer half-lives delaying the frequency of blood transfusions. There is a need to devise a new drug for the same. in silico drug designing comes as a powerful tool in designing a molecule to be used as drug in comparatively less time. In this study a new drug molecule was designed using Bioinformatic tools. The causative gene was found out to be X-linked F8 and the corresponding protein as coagulation factor VIII. Material and Methods: Target proteins were identified from protein databases and their structures were observed. Cavities in the protein were determined using SPDBV (Swiss PDB Viewer). Ligands and its isomers, following the Lipinskis rule of five, were prepared through Molinspiration. Docking between the ligands and target proteins were performed using Molegro Virtual Docker. Results: Thirteen proteins were selected and twelve ligands were prepared. Docking studies were performed and two criteria were compared MolDock score and hydrogen bond score. The most appropriate values as -836.722 for MolDock score and -55.02 for H-Bond score were obtained with 1SDD and ligand 1.
    Keywords: Haemophilia A; Factor VIII; X-linked disease; drug designing.

  • De novo Drug Design, Pharmacophore Search and Molecular Docking for Inhibitors to treat TB and HIV co-infection   Order a copy of this article
    by Satheeshkumar Sellamuthu, Ashok Kumar, Sushil Singh 
    Abstract: Novel molecules were designed as possible inhibitors of ATP synthase through de novo drug design, but were not drug-like molecules. Hence, ZINC database was searched for drug-like molecules from the common pharmacophore of the designed molecules. A total of 472 hits were obtained, among them, ZINC39552534, ZINC39371747, and ZINC38959526 produced better docking than the standard drug Bedaquiline. The vulnerability of TB and HIV co-infection has necessitated the search for inhibitors effective against both the diseases. Hence, the hits obtained were further screened for possible interaction with HIV reverse transcriptase. ZINC63941671, ZINC05858010, and ZINC05857787 were found better over the standard drug Rilpivirine, but their interaction was least against ATP synthase. Further, ZINC38959526 (lead against ATP synthase) and ZINC05858010 (lead against reverse transcriptase) share some common chemical features and based on this, new hybrid molecules were designed to inhibit both the targets. The possibility of hERG toxicity was also checked to eliminate unwanted cardiotoxicity.
    Keywords: ATP synthase inhibitors; De novo drug design; HIV; hERG toxicity; Molecular docking; Pharmacophore search; Reverse transcriptase; Tuberculosis; ZINC database.

  • Improving the nerve regeneration ability by inhibiting the orchestral activity of the myelin associated repair inhibitors: An In Silico Approach   Order a copy of this article
    by Sumaira Kanwal, Shazia Perveen 
    Abstract: Spinal cord injury (SCI) causes severe neurological modifications that significantly interrupt the physical, emotional and economical stability of affected individuals. Unluckily, the repairing ability of the central nervous system is very restricted because of reduced intrinsic growth capacity and non-permissive environment for axonal elongation. After injury, axonal regeneration of the adult central nervous system (CNS) is inhibited by myelin-derived growth-suppressing proteins. On contrary the regeneration capability of axons in peripheral nervous system is much better. These axonal growth inhibitory proteins are mediated via activation of Rho, a small GTP-binding protein.Reticulen4, myelin associated glycoprotein and Oligodendrocyte-myelin glycoprotein are the most influential axonal regeneration inhibitors. In the present study, a hybrid approach of comparative modeling and molecular docking followed by inhibitor identification and structure modeling was employed. Docking analysis showed that the two important drugs which are widely used have the potential to block the Rho-Rock pathways. Here, we report inhibitors which showed maximum binding affinity for the three most important axonal regeneration inhibitors. These two compounds at three stages and can block the activity of the inhibitors of axon regeneration. Three step approaches can be used to defeat the axonal neuropathies that especially in the CMT disease. However further studies are required to find the applications of these drugs.
    Keywords: Axonapathy; CMT2; NOGO; Rho-Rock pathways; Nonsteroidal anti-inflammatory drugs,Neurological disorder; Spinal Cord Injury; Multiple Sclerosis.

  • Deep Convolutional Neural Network for Laser Forward Scattering Image Classification in Microbial Source Tracking   Order a copy of this article
    by Bin Chen 
    Abstract: The colony-based laser scatter imaging for microbial source tracking heavily relies on the power of optical scattering image classification. While carefully handcraft feature extraction achieved excellent results for the colonies with certain sizes for optimal classification results, the classification accuracy drops quickly for smaller or larger colonies outside of the colony size range. In this study, a deep convolutional neural network was implemented for laser scattering image feature extraction and classification. The results show that the deep learning classification method clearly outperforms the traditional clustering methods with high accuracy and consistency for host species with a wide range of colony sizes. It also provides comparable accuracy for the colonies with the optimal sizes.
    Keywords: deep learning; convolutional neural network; microbial source tracking; laser imaging.

  • Computational prediction of binding of monocrotophos and its analogues on Human acetylcholine esterase, oxyhaemoglobin and IgE antibody   Order a copy of this article
    by Nathiya Soundararajan, Durga Mohan, Devasena Thiyagarajan 
    Abstract: In the present study, computational approach has been employed to study the interactions of human acetylcholine esterase, human oxyhaemoglobin and human high-affinity IgE receptor with an organophosphate pesticides and the comparative binding affinity, interacting residues of protein, H-bond distance and fitness score has been evaluated using GOLD software. Monocrotophos and its analogs bind to AchE with the highest fitness score. The analog RPR-II binds to the receptor with a highest fitness score: 42.17 when compared to RPR-V (fitness score: 40.62) and monocrotophos (fitness score: 35.25). Monocrotophos, RPR-II and RPR-V interact with oxyhaemoglobin with a fitness score of about 17.68, 20.16 and 24.62 respectively. Monocrotophos, RPR-II and RPR-V interact with human high-affinity IgE receptor with a fitness score of about 18.29, 19.05 and 22.57 respectively. The above results indicate that RPR series are highly toxic than monocrotophos, hence there is need for complete evaluation of the toxicological effect of new pesticides.
    Keywords: Monocrotophos; RPR series; Acetylcholine esterase; Oxyhaemoglobin; IgE receptor; Toxicology.

  • The unique QA domain of Runx2 causes conformational change in the Runt DNA binding domain which may result in alteration in its function   Order a copy of this article
    by Arpita Devi 
    Abstract: Runt-related transcription factors (RUNX) are a family of proteins expressed by RUNX genes. In mammals, there are three members in this family- RUNX1, RUNX2 and RUNX3. There is high sequence similarity in the three members. However, there is a presence of QA domain in the N-terminal of Runx2. The structural aspect of this domain has not been elucidated till now. Here, we model the structures RUNX1, RUNX2 and RUNX2 without the QA domain (RUNX2Δqa)from its N-terminal to DNA binding domain. It has been found that there is a significant difference in structure of RUNX2 and RUNX2Δqa. The structure of RUNX2Δqa resembles that of RUNX1. Also, RUNX2Δqa seems to bind to the consensus DNA sequence of RUNX1 with higher affinity than that of RUNX2. The presence of QA domain also decreases the affinity of Runx2 towards CBFbeta. Thus, we find that the QA domain structurally and functionally diverts RUNX2 from that of RUNX1.
    Keywords: Runx; Docking; Molecular dynamics simulation; QA domain.

  • Exploration of Cyclooxygenase-1 Binding modes of some Chiral Anti-inflammatory Drugs using Molecular Docking and Dynamic Simulations   Order a copy of this article
    by Meriem Meyar, Samira Feddal, Zohra Bouakouk, Safia Kellou-Tairi 
    Abstract: The profens represent an important class of chiral anti-inflammatory drugs. They are often marketed as racemic mixtures, but one of their enantiomers R or S can be inactive or toxic. With the aim of evaluating the anti-inflammatory activity of each enantiomer, it would be useful to first theoretically predict the enantiomer responsible for this activity. For that, three well known profens: ibuprofen, flurbiprofen, naproxen and some of their derivatives have been selected from the literature and were studied through docking and molecular dynamic (MD) simulations. Analysis of the recognition modes, through interactions with relevant residues of the cyclooxygenase-1(COX-1), can predict and explain which enantiomer is the most active. MD study highlights that water molecules play an important role in ligand-receptor interactions. Also, our combined study showed the preference of the profen's S-enantiomer towards the COX-1 active site in contrast to R-enantiomer.
    Keywords: COX-1; Profens; Chiral NSAIDs; Molecular docking; MD simulations; Binding Modes.

  • A Novel Approach for Identification of possible GSK-3 inhibitors using computational virtual screening analysis of Drugs   Order a copy of this article
    by KAKARLA NAGA MADHAVI LATHA 
    Abstract: GSK-3 has a prominent role in glucose uptake and was investigated using more specific, ATP-competitive GSK-3 inhibitors. This multifunctional kinase apart from the ability to phosphorylate glycogen synthase and regulate glucose metabolism was subsequently found to be a critical component in numerous cellular functions including regulation of different cell signaling, cell division, differentiation, proliferation and growth as well as apoptosis. In this work, we report molecular docking analysis of 2035 approved drugs from DrugBank database based on the hypothesis that certain medications would decrease the risk of diabetes and evaluated the characteristic properties of drugs and their potential to bind against type-2 diabetes protein target, GSK-3β. The crucial amino acids responsible for stable interaction with ligands were found to be Lys85, Asp133 and Val135. Molecular docking analysis revealed several new classes of drugs reported to exhibit inhibitory properties against GSK-3β. Apart from crucial amino acid interactions, several other amino acids are found to be interacted with drug compounds such as Asn64, Arg141, Cys19 and Asp200, respectively. Out of 13 best drugs resulted from the analysis, top three (Venetoclax, Cobicistat and Atorvastatin) were selected based on consensus scoring using six scoring schemes such as MolDock score of Molegro, mcule, Pose&Rank, MTiAutoDock, DockThor and DSX respectively.
    Keywords: virtual screening; molecular docking; DrugBank; type-2 diabetes; GSK-3β.

  • Asymmetric glycan recognition among alpha and beta monomers of Spatholobus parviflorus lectin: An insilico insight   Order a copy of this article
    by Surya Sukumaran, Haridas M 
    Abstract: Protein-carbohydrate recognition, an important form of inter-cell communication, plays promising role in several biological events. Extensive studies were already done in this area of protein-carbohydrate recognition using legume lectin molecules for drug targeting. It comprises huge processes, and many challenges still have to be solved. In this study, an attempt was made to reveal the interaction homogeneity of various carbohydrate residues and their comparative analysis of binding mode towards alpha and beta monomers of Spatholobus parviflorus lectin (SPL) as a model. An array of sugars based on their structural and functional roles in information coding were selected for virtual screening. Based on the glidescore, 20 sugars were screened and the extra precision docking exercises were carried out to explore the variability and stability in their binding affinity towards the SPL monomers. Among the studied sugars, raffinose exhibited highest affinity towards the alpha and beta monomers with glide scores of -11.43 and -10.65 kcal/mol respectively. When compared to alpha, the beta monomer showed higher glide score by favoring stable interactions. The low binding affinity of alpha subunit is featured by the extra cleft seen in close proximity of the known sugar binding pocket of alpha subunit for accommodating structurally miniatured sugars. These alterations exhibited by alpha and beta monomers may be due to its asymmetry in the pairing of α and β subunits. This prediction, deciphered the in silico binding report of sugars with SPL, along with their inconsistency in binding with monomeric units, may contribute towards more specific and precise drug targeting.
    Keywords: SPL; monomers; sugars.

  • Functional Module Extraction by Ensembling the Ensembles of Selective Module Detectors   Order a copy of this article
    by Monica Jha, Pietro Guzzi, Pierangelo Veltri, Swarup Roy 
    Abstract: A group of functionally related genes constitutes a functional module taking part in similar biological activities. Such modules can be employed for interpretation of biological and cellular processes or their involvement in associated diseases. Detection of such modules from co-expression network is a difficult task, different methods have been employed to date for detecting such modules, such as clustering, biclustering and network-based techniques. In this work, we discuss and compare selective module finding methods and their ensemble. We use RNA Sequence (RNASeq) data to evaluate the performances of few network based module finding techniques. It could be observed that ensemble technique increases the accuracy and stability.
    Keywords: Next generation Sequencing;RNA Seq; Ensemble; Functional Module; Gene Ontology; Pathway analysis.

  • The potential inhibitory role of teucrolivins against human Dipeptidyl peptidase 4 protein as a promising strategy for treatment of type 2 diabetes   Order a copy of this article
    by Ateeq Al-Zahrani 
    Abstract: Inhibition of disease-related proteins by natural inhibitors revealed its efficiency and became a promising step in drug discovery. With hundreds of advanced web servers and software, it is possible to predict potential drugtarget in order to reduce laboratory cost and time. In the current study, computational simulations were performed to investigate the possible role of teucrolivins, isolated from Teucrium oliverianum plant, as natural inhibitors against dipeptidyl peptidase 4 protein (DP4) which is related to type 2 diabetes. The docking results revealed that teucrolivins A, B, D and E showed higher binding affinities compared to the native inhibitor PF2. Teucrolivin D exhibited the highest interactions among teucrolivins with the minimum binding energy of -144.16. Sitagliptin, vildagliptin and omarigliptin are antidiabetic drugs for inhibition of dipeptidyl peptidase 4 protein. These drugs were used as negative controls. They gave minimum binding energy of -120.19, -103.1 and -104.69 respectively, and showed a lower binding affinity compared to teucrolivin D. Evaluation of ADMET confirmed the capability of teucrolivin D as an effective inhibitor against DP4 and its promising potential as an antidiabetic drug. This study highlights the medical importance of teucrolivins and the possibility of using this class of inhibitors for the treatment of type 2 diabetes.
    Keywords: teucrolivins; Teucrium oliverianum; dipeptidyl peptidase 4; antidiabetic inhibitors; molecular docking.

  • MOLECULAR DOCKING, IN-SILICO ADMET SCREENING, MM-GBSABINDING FREE ENERGYOF SOME NOVEL ISOXAZOLE SUBSTITUTED 9-AMNOACRIDINES AS HER2 INHIBITORS TARGETTING BREAST CANCER   Order a copy of this article
    by Kalirajan Rajagopal, Pandiselvi A, Gowramma B 
    Abstract: 9-aminoacridines play an important role in the field of antitumor DNA-intercalating agents, due to their antiproliferative properties. Several anticancer agents with 9-anilinoacridines such as amascrine, and nitracrine have been developed. To get insight of intermolecular interactions, the molecular docking studies are performed at active site of HER2. Aim: In the present study, for identification of potential ligands of isoxazole substituted 9-amino acridines as selective HER2 inhibitors (PDB id- 3PP0) targeting breast cancer by using Schrodinger suit-2016-2, Maestro 9.6 version. Molecular docking targeted against HER2 by Glide module, insilco ADMET screening also performed by qikprop module and free binding energy of compounds was calculated by Prime-MMGBSA module. The binding affinity of the designed molecules towards HER2 (PDB id- 3PP0) was selected on the basis of GLIDE score and interaction patterns. Many compounds showed strong hydrophobic interactions and hydrogen bonding interactions and other parameters with amino acid residues and also explain their potency to inhibit HER2 (3PP0). The isoxazole substituted 9-amino acridine derivatives 1a- 1x have good binding affinity with Glide score in the range of -6.6 to -9.7 when compared with the standard ledacrine (-6.3) and tamoxifen (-3.7). The ADMET screening of the designed molecules have almost all the ADMET properties of the compounds are within the recommended values. MM-GBSA binding results of most potent inhibitor displayed stable and favourable. The results reveals that, this study provides evidence for consideration of valuable ligands in isoxazole substituted 9-amino acridine derivatives as potential HER2 inhibitor and the compounds, 1o,f,n,d,m,w with good Glide score may produce significant anti breast cancer activity for further in vitro and in vivo investigations may prove their therapeutic potential.
    Keywords: Acridine; Isoxazole; docking studies; Insilico ADMET screening; MM-GBSA.

  • Identifying drug-like Inhibitors of Mycobacterium tuberculosis H37Rv Seryl tRNA Synthetase based on bioassay dataset: Homology modelling, docking and molecular dynamics simulation   Order a copy of this article
    by ADARSH V. K., Santhiagu Arockiasamy 
    Abstract: Resistance to existing drugs of tuberculosis bacteria demands an immediate requirement to develop effective new chemical entities acting on emerging targets. Seryl-tRNA synthetase (SerRS) is essential for the viability of Mycobacterium tuberculosis (MTB) due to its crucial role in protein biosynthesis. In this study, we have attempted to develop the tertiary structure of SerRS through homology modelling and to elucidate the active site interactions of inhibitor compounds aided by docking. Homology modelling using PDB ID: 2DQ3: A chain as template and validation of the model was carried out with Modeller V9.13 and SAVES online server respectively. About 1248 compounds from a putative kinase compound library of PubChem database found active in whole cell bioassay (AID2842) on MTB - H37Rv was used in docking studies using AutoDock. Out of the tested molecules, nine showed docking scores ≤ 10 kcal/mol with good drug-like properties were further subjected to molecular dynamics (MD) simulations and found three out of the nine compounds have stable interaction with the enzyme. We believe these molecules with the knowledge about their docked poses, interaction patterns, and scaffolds may provide hinds for further target specific screening and design.
    Keywords: drug design; homology modelling; Modeller; AutoDock; multidrug-resistant Mycobacterium tuberculosis; MDR-TB; Seryl-tRNA synthetase; SerRS; PubChem; molecular docking; molecular dynamics.

  • Boosting Gene Expression Clustering with System-Wide Biological Information: A Robust Autoencoder Approach   Order a copy of this article
    by Hongzhu Cui, Chong Zhou, Xinyu Dai, Yuting Liang, Randy Paffenroth, Dmitry Korkin 
    Abstract: Gene expression analysis provides genome-wide insights into the transcriptional activity of a cell. One of the first computational steps in exploration and analysis of the gene expression data is clustering. With a number of standard clustering methods routinely used, most of the methods do not take prior biological information into account. Here, we propose a new approach for gene expression clustering analysis. The approach benefits from a new deep learning architecture, Robust Autoencoder, which provides a more accurate high-level representation of the feature sets, and from incorporating prior system-wide biological information into the clustering process. We tested our approach on two gene expression datasets and compared the performance with two widely used clustering methods, hierarchical clustering and k-means, and with a recent deep learning clustering approach. Our approach outperformed all other clustering methods on the labeled yeast gene expression dataset. Furthermore, we showed that it is better in identifying the functionally common clusters than k-means on the unlabeled human gene expression dataset. The results demonstrate that our new deep learning architecture can generalize well the specific properties of gene expression profiles. Furthermore, the results confirm our hypothesis that the prior biological network knowledge is helpful in the gene expression clustering.
    Keywords: gene expression; protein-protein interactions; clustering; deep learning.

  • Mathematical modelling of hepatitis C virus dynamics response to therapeutic effects of interferon and ribavirin   Order a copy of this article
    by Jean Marie Ntaganda 
    Abstract: This paper aims at designing a two compartmental mathematical model for determining the response of protein (Interferon) and drug (Ribarivin) for a patient who is suffering from hepatitis C virus (HVC). The stability of developed mathematical model is established. Using inverse techniques, model parameters and functions are identified. To test efficiency and response to interferon and ribavirin as HCV treatment, the validation of the mathematical model is achieved by considering a patient on treatment during 12 months. The results obtained are rather satisfactory since model parameters vary around their corresponding value that is equilibrium values for healthy subjects. Furthermore, the reaction of the disease to treatment can be modeled and a feedback can be approximated by the solution of an optimal control problem. The increasing necessity to interpret the meaning of measurable variables such as interferon and ribavirin under both physiological and pathological conditions for a patient has imposed the need for relatively simple models that should be able to describe as accurately as possible the mechanical behavior of the disease.
    Keywords: HVC; Treatment; Interferon; Ribavirin; Parameters identification; Stability; Equilibrium value; Healthy subjects; Numerical simulation.

Special Issue on: ICIBM 2018 Intelligent Biology and Medicine

  • Drug-Drug Interaction Prediction based on Co-Medication Patterns and Graph Matching   Order a copy of this article
    by Wen-Hao Chiang, Li Shen, Lang Li, Xia Ning 
    Abstract: High-order Drug-Drug Interactions (DDIs) and associated Adverse Drug Reactions (ADRs) are common, particularly for elderly people, and therefore represent a significant public health problem. Currently, high-order DDI detection primarily relies on the spontaneous reporting of ADR events. However, proactive prediction of unknown DDIs and their ADRs has indispensable benefit for protective health care. In this manuscript, the problem of predicting whether a drug combination of arbitrary orders is likely to induce adverse drug reactions is considered. The prediction problem becomes highly non-trivial when arbitrary orders of drug combinations have to been accommodated by the prospective computational methods. To solve this problem, novel kernels over drug combinations of arbitrary orders are developed within support vector machines for the prediction. Graph matching methods are used in the novel kernels to measure the similarities among drug combinations, in which drug co-medication patterns are leveraged to measure single drug similarities. The experimental results on a real-world dataset demonstrated that the new kernels achieve an area under the curve (AUC) value 0.912 for the prediction problem. The new methods with drug co-medication based single drug similarities can accurately predict whether a drug combination is likely to induce adverse drug reactions of interest.
    Keywords: drug-drug interaction prediction; drug combination similarity; co-medication; graph matching; arbitrary order; adverse drug reaction; myopathy; single drug similarity; support vector machines; binary classification problem.

  • Pessimistic Optimization For Modeling Microbial Communities With Uncertainty   Order a copy of this article
    by Meltem Apaydin, Liang Xu, Bo Zeng, Xiaoning Qian 
    Abstract: It is important to understand the complicated interactions of microbial communities who play critical roles in the ecological system, human health and diseases. Optimization-based mathematical models provide ways to analyze and obtain predictions on microbial communities. However, there are inherent model and data uncertainties from the existing knowledge and experiments about different microbial communities so that the imposed models may not exactly reflect the reality in nature. Here, addressing these challenges and aiming to have a flexible framework to model microbial communities with uncertainty, we introduce P-OptCom, an extension of an existing method OptCom, based on the ideas from the pessimistic bilevel optimization literature. This framework relies on the coordinated decision making between the single upper-(communitylevel) and multiple lower-level (multiple microorganisms or guilds) decision makers to support robust solutions to better approximate microbial community steady states even when the individual microorganisms behavior deviate from the optimum in terms of their cellular fitness criteria. We formulate the problem by considering suboptimal behavior of the individual members, and relaxing the constraints denoting the interactions within communities to obtain a model flexible enough to deal with potential uncertainties. Our study demonstrates that without experimental knowledge in advance, we are able to analyze the tradeoffs among the members of microbial communities and closely approximate the actual experimental measurements.
    Keywords: Microbial communities; Pessimistic bilevel optimization; Stoichiometric-based genome-scale metabolic modeling.

  • TopQA: A Topological Representation for Single-Model Protein Quality Assessment with Machine Learning   Order a copy of this article
    by John Smith, Matthew Conover, Natalie Stephenson, Jesse Eickholt, Dong Si, Miao Sun, Renzhi Cao 
    Abstract: Correctly predicting the complex three-dimensional structure of a protein from its sequence would allow for a superior understanding of the function of specific proteins. Thus, advancements could be made in drug discovery, nanotechnology, and many other biological fields. We propose a novel method aimed to tackle a crucial step in the protein prediction problem, assessing the quality of generated predictions. Previously, some research has focused on qualities of proteins, such as the distance between amino acids or energy functions. Our method, to the best of our knowledge, is the first to analyze the topology of the predicted structure. We confirmed our representation with a widely used visualization tool, Chimera, and found that it provided accurate information regarding the location of the protein\'s backbone. Using this information, we implemented a novel algorithm to process this information based on Convolutional Neural Network (CNN) to predict the GDT\\_TS score (a metric for assessing the quality of a model) for given protein models. Our method has shown promising results, achieving an overall correlation of 0.41 on testing dataset of CASP12. Future work will aim to implement additional features into our representation.The software is freely available at GitHub: https://github.com/caorenzhi/TopQA.
    Keywords: Convolutional Neural Network; protein single-model quality assessment; topological representation.

  • A Hidden Markov Model-based approach to reconstructing double minute chromosome amplicons   Order a copy of this article
    by Ruslan Mardugalliamov, Kamal Al Nasr, Matthew Hayes 
    Abstract: Double minute chromosomes (DMs) are circular fragments of extrachromosomal DNA. They are a mechanism for extreme gene amplification in the cells of some malignant tumors. Their existence strongly correlates with malignant tumor cell behavior and drug resistance. Locating DMs is important for informing precision therapy to cancer treatment. Furthermore, accurate detection of double minutes requires precise reconstruction of their amplicons, which are the highly-amplified gene-carrying contiguous segments that adjoin to form DMs. This work presents AmpliconFinder -- a Hidden-Markov Model-based approach to detect DM amplicons. To assess its efficacy, AmpliconFinder was used to augment an earlier framework for DM detection (DMFinder), thus improving its robustness to noisy sequence data, and thus improving its sensitivity to detect DMs. Experiments on simulated genomic data have shown that augmenting DMFinder with AmpliconFinder significantly increased the sensitivity of DMFinder on these data. Moreover, DMFinder with AmpliconFinder found all previously reported DMs in three pediatric medulloblastoma datasets, whereas the original DMFinder framework found none.
    Keywords: next generation sequencing; double minute chromosome; double minute; structural variation; amplicon; tumor genome reconstruction;.

  • High scoring segment selection for pairwise whole genome sequence alignment with the maximum scoring subsequence and GPUs   Order a copy of this article
    by Abdulrhman Aljouie, Ling Zhong, Usman Roshan 
    Abstract: Whole genome alignment programs use exact string matching with hash tables to quickly identify high scoring fragments between a query and target sequence around which a full alignment is then built. In a recent large-scale comparison of alignment programs called Alignathon it was discovered that while evolutionary similar genomes were easy to align, divergent genomes still posed a challenge to existing methods. As a first step to fill this gap we explore the use of more exact methods to identify high scoring fragments which we then pass on to a standard pipeline. We identify such segments between two whole genome sequences with the maximum scoring subsequence instead of hash tables. This is computationally extremely expensive and so we employ the parallelism of a Graphics Processing Unit to speed it up. We split the query genome into several fragments and determine its best match to the target with a previously published GPU algorithm for aligning short reads to a genome sequence. We then pass such high scoring fragments on to the LASTZ program which extends the fragment to obtain a more complete alignment. Upon evaluation on simulated data, where the true alignment is known, we see that this method gives an average of at least 20% higher accuracy than the alignment given by LASTZ at the expense of a few hours of additional runtime. We make our source code freely available at url{web.njit.edu/~usman/MSGA}.rn
    Keywords: genome alignment; anchor selection; LASTZ; GPU.

  • Brain-wide structural connectivity alterations under the control of Alzheimer risk genes   Order a copy of this article
    by Jingwen Yan, Vinesh Raja V, Zhi Huang, Enrico Amico, Kwangsik Nho, Shiaofen Fang, Olaf Sporns, Yu-chien Wu, Andrew Saykin, Joaquin Goni, Li Shen 
    Abstract: Background: Alzheimer's disease is the most common form of brain dementiarncharacterized by gradual loss of memory followed by further deterioration of otherrncognitive function. Large-scale genome-wide association studies have identi edrnand validated more than 20 AD risk genes. However, how these genes are relatedrnto the brain-wide breakdown of structural connectivity in AD patients remainsrnunknown.rnMethods: We used the genotype and di usion tensor imaging (DTI) data in thernAlzheimer's Disease Neuroimaging Initiative (ADNI) database. After constructingrnthe brain network for each subject, we extracted three types of link measures,rnincluding ber anisotropy, ber length and density. We then performed a targetedrngenetic association analysis of brain-wide connectivity measures using generalrnlinear regression models. Age at scan and gender were included in the regressionrnmodel as covariates. For fair comparison of the genetic e ect on di erentrnmeasures, ber anisotropy, ber length and density were all normalized withrnmean as 0 and standard deviation as one.We aim to discover the abnormalrnbrain-wide network alterations under the control of 34 AD risk SNPs identi ed inrnprevious large-scale genome-wide association studies.rnResults: After enforcing the stringent Bonferroni correction, rs10498633 inrnSLC24A4 were found to signi cantly associated with anisotropy, total numberrnand length of bers, including some connecting brain hemispheres. rs429358 inrntop AD risk gene APOE shows nominal signi cance of association with therndensity of fibers between Subcortical and Cerebellum (p=2.71e-6).
    Keywords: brain connectivity; imaging genetics association; Alzheimer's disease.

  • A De-Novo drug design and ADMET study to design small molecule stabilizers targeting mutant (V210I) human prion protein against familial Creutzfeldt-Jakob disease (fCJD).   Order a copy of this article
    by Rafat Alam, G.M. Sayedur Rahman, Nahid Hasan, Abu Sayeed Chowdhury 
    Abstract: The purpose of our project was to computationally design small molecule stabilizers targeting mutant (V210I) human prion protein (HuPrP) using combined De-novo, pharmacophore, molecular docking and ADMET study to cure familial Creutzfeldt-Jakob disease (fCJD). Successful Development of drugs against familial CJD might provide valuable insight for design and development of new antiprion drugs and understand their mechanisms. We collected the target protein structure from Protein Data Bank (RCSB PDB). After that, we minimized the energy using Yasara energy minimization webserver and validated the structure using RAMPAGE webserver. We used KV Finder, a plug-in of Pymol to identify the drug binding pockets in the target protein. The pocket information was used for de-novo ligand design using the e-LEA3D webserver. Those ligands were used to generate a pharmacophore using LigandScout for the selected pocket. The designed pharmacophore was implied to the webserver Pharmit for virtual screening of small molecules from Pubchem database and the screened small molecules were docked into the target pocket of the protein using the software Autodock Vina. Best 5 molecules were identified with binding affinities of 7.7, 7.2, 7.2, 7.1 and 7.1 kcal mol-1 respectively. Finally, we analyzed the ADMET properties of the best five ligands using the webserver SwissADME. All the five small molecules were proven to be the ideal candidates for further drug development.
    Keywords: ADMET; de-novo drug design; Docking; Prion; PDB; Pharmacophore.

  • Modeling of Hypoxia gene expression for three different cancer cell lines   Order a copy of this article
    by Babak Soltanalizadeh 
    Abstract: Gene dynamic analysis is essential in identifying target genes involved in the pathogenesis of various diseases, including cancer. Hypoxia often influences cancer prognosis. We applied a multi-step pipeline to study dynamic gene expressions in response to hypoxia in three cancer cell lines: prostate (DU145), colon (HT29), and breast (MCF7). We identified 26 distinct temporal expression patterns in DU145 and 29 patterns in HT29 and MCF7. Module-based dynamic networks were developed for each cell line. Because our analyses exploited the time-dependent nature of gene expression for identifying significant genes novel significant genes and transcription factors were identified. Our gene network returned significant information regarding biologically important modules of genes. In particular, results suggest that changes expression of BMP6 and ARSJ might play a key role in the time-dependent response to hypoxia in breast cancer. Furthermore, the network can potentially learn the regulatory path between transcription factors and the downstream genes.
    Keywords: gene expression; hypoxia; colon cancer; breast cancer; prostate cancer; significant genes; BMP6; ARSJ.

Special Issue on: CMBH 18 Computational Approaches in Biology And Medicine

  • Comparative In-Silico Parmacokinetics and Molecular Docking Study on Gedunin Isolated from Azadirachta indica, its Modified Derivatives and Selected Antifolate Drugs as Potential Dihydrofolate Reductase Inhibitors of Plasmodium falciparum   Order a copy of this article
    by Samuel Cosmas, Olanrewaju Durojaye, Parker Joshua, Joyce Ogidigo, Collins Difa, Justus Nwachukwu 
    Abstract: Introduction: Malaria is one of the most common diseases that threaten many of the subtropical and tropical regions. Countries where the risk of transmission of malaria is at the high rate are over a hundred currently and these counties are being visited by over 125, 000, 000 international travelers on yearly basis. Plasmodium, a protozoan parasite is the cause of malaria. The Plasmodium parasites that cause the human malaria are of four different species: P. falciparum, P. malariae, P. ovale and P. vivax. The folate metabolism of the malaria parasite which leads to the synthesis and the use up of folate cofactors is inhibited by antifolate drugs hence, the reason behind their use as antimalarials. Gedunin, a bioactive product from a natural origin such as the Azadirachta indica possess potential antimalarial activities and as such can be developed into drugs to target the folate synthesis pathway of the Plasmodium parasite. Materials and Methods: Chemical structures of ligands were drawn with the MarvinSketch software and converted into SMILES strings for the calculation of pharmacokinetic parameters. This was achieved by utilizing the SwissADME server. Ligand chemical structures were minimized and viewed using the Chimera and Pymol software respectively. Minimized ligand structures were saved as Mol2 files in preparation for docking while the binding energies between each experimental ligands and the Plasmodium falciparum DHFR enzyme was predicted using the AutoDock Vina software. Polar interactions were also viewed through the Pymol and this was used in the prediction of the bind pockets. Sequence alignment between the human and Plasmodium falciparum DHFR was performed using the Clustal Omega alignment software. Results: The predicted binding energies between the three selected antifolate drugs (cycloguanil, proguanil, pyrimethamine), gedunin, its derivatives (C=O, C2H5, C3H6O2, C4H8O2, CONH2, NH2, OCH3, OH) and the Plasmodium falciparium DHFR enzyme were -8.0, -7.5, -8.0, -9.5, -9.0, -8.4, -8.9, -8.2, -8.9, -8.7, -8.3, -8.4Kcal/mol respectively. The experimental ligands were viewed to form weak interactions with a total of 15 amino acids, leading to the prediction of 2 binding pockets while the sequence alignment result showed 32% identity between the human and P. falciparum DHFR enzyme. Conclusion: The results from the experiment showed that gedunin and its modified derivatives might be better antimalarial agents than the antifolate drugs as revealed by the predicted binding energies between the target enzyme and the ligands.
    Keywords: Plasmodium; Azadirachta indica; Antifolate drugs; Parasite; Dihydrofolate Reductase.

  • EXPLORING THE ANTINEOPLASTIC EFFECT OF PHYTOCHEMICALS FROM IPOMEA SEPIARIA AGAINST MATRIX METALLOPEPTIDASES A PHARMACOINFORMATICS APPROACH   Order a copy of this article
    by Ariya S S, Baby Joseph, Jemmy Christy H 
    Abstract: Cancer is one of the leading causes of death worldwide. Though advanced treatment options are available, a cure for this disease is still unidentified. This work is aimed at the identification of drugs to target the matrix metallopeptidase, a major protein overexpressed in this disease. 247 active phytochemicals from the medicinal plant Ipomea sepiaria were taken as the lead drugs and molecular docking analysis as well as interaction studies were carried out. The binding affinity for each ligand with the target was determined along with Molecular dynamic simulation of the complex to determine the stability of the complex in the system. Thus eight drugs namely Tetradecanoic acid, Nerolidol, Ipomeanine, Dibutyl phthalate, Cis-Caffeic acid, Caffeic acid, Moupinamide and N-Cis-Feruloyltyramine were found to be the most promising drugs for treating cancer. They outperformed the scores of four different drugs available in the market.
    Keywords: Cancer; docking; simulation; phytochemicals; ADMET; matrix metallopeptidase; receptor; ligands.

  • Inter k-shell Connectivity: A Novel Computational Approach to Identify Drug Targets   Order a copy of this article
    by Praveen K. Singh 
    Abstract: Central lethality rule suggests that hub proteins are the most important and their deletion leads to more damage to biological networks as compared to non-hub proteins. Hub proteins present towards the core of Protein-Protein Interaction (PPI) network are considered to be more important in comparison to proteins at the periphery. The k-shell decomposition method generates k-shell index which indicates the local and global importance of a protein in PPI network. But there is no method till now reported which can differentiate among the proteins with same k-shell index. In this work attempt has been made to add parameter Inter k-shell connectivity to differentiate proteins with same k-shell index and exploring their biological importance.
    Keywords: Centrality lethality rule; Protein networks; k-shell decomposition; Mycobacterium.

  • In- silico analysis of peptidoglycan hydrolases from Serratia marcescens and other Serratia species   Order a copy of this article
    by Aditi Rathee, Kanika Gupta, Seema Kumari, Sanjay Chhibber, Ashok Kumar 
    Abstract: Bacteria possess a protective extracytoplasmic glycopeptide polymer i.e. peptidoglycan. In case of Gram-positive bacteria, it acts as scaffolds to many virulence factors whereas in Gram-negative bacteria, it serves as an anchor to outer membrane. Many antibiotics act on bacteria by inhibiting the activity of enzymes involved in the synthesis of peptidoglycan. However during the years, overexposure of antibiotics has led to modification of peptidoglycan chain by bacteria viz. N-deacetylation, N-glycolylation, O-acetylation etc. Peptidoglycan hydrolases are known to play an important role in the suppression of bacterial infections as a component of the innate immune system as well as disintegrating peptidoglycan which is an important factor in the pathogenesis of various organisms. Present study explicates computational analysis of a peptidoglycan hydrolase enzyme from a total of 41 fully sequenced genomes of Serratia marcescenes and other Serratia species.75 unique motifs were identified among the protein sequences of peptidoglycan hydrolase.
    Keywords: Antibiotic resistance; Peptidoglycan hydrolase; Sequence analysis; multiple sequence alignment; conserved motif.