Pub Date : 2020-05-10DOI: 10.1504/ijcbdd.2020.10029441
Anindita Roy Chowdhury, H. G. Nagendra, A. Seal
Hydrophobic force as one of the fundamental forces contributes in folding of the primary sequence of amino acids into a functional three dimensional protein structure. Hydrophobic interactions of side-chains provide maximum stability to correctly folded proteins. Earlier, the authors identified the aromatic and aliphatic residues contributing maximum and minimum hydrophobicity in all the six enzyme classes. The present investigation examines the relative contributions towards hydrophobicity of the different hydrophobic amino acids in both aromatic and aliphatic categories. Notably in a sequence, inverse relationship between residues of similar hydrophobic strength both in aromatic and aliphatic categories seems to exist. This analysis is likely to provide insight for finer analysis of the enzyme molecule.
{"title":"Correlation among hydrophobic aromatic and aliphatic residues in the six enzyme classes","authors":"Anindita Roy Chowdhury, H. G. Nagendra, A. Seal","doi":"10.1504/ijcbdd.2020.10029441","DOIUrl":"https://doi.org/10.1504/ijcbdd.2020.10029441","url":null,"abstract":"Hydrophobic force as one of the fundamental forces contributes in folding of the primary sequence of amino acids into a functional three dimensional protein structure. Hydrophobic interactions of side-chains provide maximum stability to correctly folded proteins. Earlier, the authors identified the aromatic and aliphatic residues contributing maximum and minimum hydrophobicity in all the six enzyme classes. The present investigation examines the relative contributions towards hydrophobicity of the different hydrophobic amino acids in both aromatic and aliphatic categories. Notably in a sequence, inverse relationship between residues of similar hydrophobic strength both in aromatic and aliphatic categories seems to exist. This analysis is likely to provide insight for finer analysis of the enzyme molecule.","PeriodicalId":13612,"journal":{"name":"Int. J. Comput. Biol. Drug Des.","volume":"4 1","pages":"209-223"},"PeriodicalIF":0.0,"publicationDate":"2020-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90417417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-10DOI: 10.1504/ijcbdd.2020.10029442
Charaf Eddine Bailoul, N. Alaa
In this paper, we present a new mathematical model that explains the transmission along a biological neuron. We also present a numerical scheme based on the four order β-method to simulate numerically the transmission. The idea is to couple the β-method of high order with Runge Kutta method in order to get high order schemes without oscillations. Furthermore, various numerical experiments are presented to show the power and efficiency of our proposed model.
{"title":"Modelling and simulation of transmission lines in a biological neuron","authors":"Charaf Eddine Bailoul, N. Alaa","doi":"10.1504/ijcbdd.2020.10029442","DOIUrl":"https://doi.org/10.1504/ijcbdd.2020.10029442","url":null,"abstract":"In this paper, we present a new mathematical model that explains the transmission along a biological neuron. We also present a numerical scheme based on the four order β-method to simulate numerically the transmission. The idea is to couple the β-method of high order with Runge Kutta method in order to get high order schemes without oscillations. Furthermore, various numerical experiments are presented to show the power and efficiency of our proposed model.","PeriodicalId":13612,"journal":{"name":"Int. J. Comput. Biol. Drug Des.","volume":"24 1","pages":"224-234"},"PeriodicalIF":0.0,"publicationDate":"2020-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76694817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-10DOI: 10.1504/ijcbdd.2020.10029439
P. Antony, Bincy Baby, Zahrah Al Homedi, Walaa Al Halabi, Amanat Ali, Ranjit Vijayan
Prostate cancer is one of the most frequently diagnosed forms of cancer. Over expression of several non-receptor tyrosine kinases (NRTKs) have been observed in prostate cancer. Three NRTKs - Bruton's tyrosine kinase (BTK), focal adhesion kinase (FAK) and Src kinase - were considered in this study. Virtual screening of the InterBioScreen natural compounds library identified four compounds - STOCK1N 32236, STOCK1N 30449, STOCK1N 24193 and STOCK1N 23077 - that are structurally similar and possessed polypharmacological properties by interacting with all the three NRTKs in a similar manner by orienting one naphthalene group towards the hinge region and another towards the activation loop. Binding score and interactions of these natural compounds were better than currently available kinase inhibitors. 100 ns molecular dynamics simulation showed that these molecules bound stably in the active site. The screened natural molecules could be a framework for developing novel kinase inhibitors for the treatment of prostate cancer.
{"title":"Polypharmacological potential of natural compounds against prostate cancer explored using molecular docking and molecular dynamics simulations","authors":"P. Antony, Bincy Baby, Zahrah Al Homedi, Walaa Al Halabi, Amanat Ali, Ranjit Vijayan","doi":"10.1504/ijcbdd.2020.10029439","DOIUrl":"https://doi.org/10.1504/ijcbdd.2020.10029439","url":null,"abstract":"Prostate cancer is one of the most frequently diagnosed forms of cancer. Over expression of several non-receptor tyrosine kinases (NRTKs) have been observed in prostate cancer. Three NRTKs - Bruton's tyrosine kinase (BTK), focal adhesion kinase (FAK) and Src kinase - were considered in this study. Virtual screening of the InterBioScreen natural compounds library identified four compounds - STOCK1N 32236, STOCK1N 30449, STOCK1N 24193 and STOCK1N 23077 - that are structurally similar and possessed polypharmacological properties by interacting with all the three NRTKs in a similar manner by orienting one naphthalene group towards the hinge region and another towards the activation loop. Binding score and interactions of these natural compounds were better than currently available kinase inhibitors. 100 ns molecular dynamics simulation showed that these molecules bound stably in the active site. The screened natural molecules could be a framework for developing novel kinase inhibitors for the treatment of prostate cancer.","PeriodicalId":13612,"journal":{"name":"Int. J. Comput. Biol. Drug Des.","volume":"70 1","pages":"181-199"},"PeriodicalIF":0.0,"publicationDate":"2020-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89363940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1089/cmb.2020.29027.ljc
L. Cowen
{"title":"Preface Special Issue: RECOMB 2019","authors":"L. Cowen","doi":"10.1089/cmb.2020.29027.ljc","DOIUrl":"https://doi.org/10.1089/cmb.2020.29027.ljc","url":null,"abstract":"","PeriodicalId":13612,"journal":{"name":"Int. J. Comput. Biol. Drug Des.","volume":"19 6 1","pages":"441"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85417233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-07DOI: 10.1504/ijcbdd.2020.10026790
Rafat Alam, G. M. S. Rahman, Nahid Hasan, Abu Sayeed Chowdhury
The purpose of our project was to computationally design small molecule stabilisers targeting mutant (V210I) human prion protein (HuPrP) using combined De-novo pharmacophore based drug design and virtual molecular docking. The newly designed molecules were also analysed so it might qualify as a new cure for the familial Creutzfeldt-Jakob disease (fCJD). We collected the target protein structure from protein data bank (RCSB PDB). and minimised the energy using Yasara energy minimisation 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 pockets. The pharmacophores were used as the search templates using Pharmit for the virtual screening of small molecules from Pubchem database followed by the docking of the screened small molecules in the pockets using Autodock Vina. Best five molecules were selected for ADMET properties using SwissADME. All the five small molecules were proven to be the ideal candidates for further drug development.
{"title":"A De-Novo drug design and ADMET study to design small molecule stabilisers targeting mutant (V210I) human prion protein against familial Creutzfeldt-Jakob disease (fCJD)","authors":"Rafat Alam, G. M. S. Rahman, Nahid Hasan, Abu Sayeed Chowdhury","doi":"10.1504/ijcbdd.2020.10026790","DOIUrl":"https://doi.org/10.1504/ijcbdd.2020.10026790","url":null,"abstract":"The purpose of our project was to computationally design small molecule stabilisers targeting mutant (V210I) human prion protein (HuPrP) using combined De-novo pharmacophore based drug design and virtual molecular docking. The newly designed molecules were also analysed so it might qualify as a new cure for the familial Creutzfeldt-Jakob disease (fCJD). We collected the target protein structure from protein data bank (RCSB PDB). and minimised the energy using Yasara energy minimisation 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 pockets. The pharmacophores were used as the search templates using Pharmit for the virtual screening of small molecules from Pubchem database followed by the docking of the screened small molecules in the pockets using Autodock Vina. Best five molecules were selected for ADMET properties using SwissADME. All the five small molecules were proven to be the ideal candidates for further drug development.","PeriodicalId":13612,"journal":{"name":"Int. J. Comput. Biol. Drug Des.","volume":"1 1","pages":"21-35"},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89918602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-07DOI: 10.1504/ijcbdd.2020.10026784
John Smith, Matthew Conover, Natalie Stephenson, Jesse Eickholt, Dong Si, Miao Sun, Renzhi Cao
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 with many applications. We propose a novel method aimed to tackle a crucial step in the protein prediction problem, assessing the quality of generated predictions. Unlike traditional methods, our method, to the best of our knowledge, is the first to analyse the topology of the predicted structure. We found that our new representation provided accurate information regarding the location of the protein's backbone. Using this information, we implemented a novel algorithm based on convolutional neural network (CNN) to predict GDT_TS score for given protein models. Our method has shown promising results - overall correlation of 0.41 on CASP12 dataset. Future work will aim to implement additional features into our representation. The software is freely available at GitHub: https://github.com/caorenzhi/TopQA.
{"title":"TopQA: a topological representation for single-model protein quality assessment with machine learning","authors":"John Smith, Matthew Conover, Natalie Stephenson, Jesse Eickholt, Dong Si, Miao Sun, Renzhi Cao","doi":"10.1504/ijcbdd.2020.10026784","DOIUrl":"https://doi.org/10.1504/ijcbdd.2020.10026784","url":null,"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 with many applications. We propose a novel method aimed to tackle a crucial step in the protein prediction problem, assessing the quality of generated predictions. Unlike traditional methods, our method, to the best of our knowledge, is the first to analyse the topology of the predicted structure. We found that our new representation provided accurate information regarding the location of the protein's backbone. Using this information, we implemented a novel algorithm based on convolutional neural network (CNN) to predict GDT_TS score for given protein models. Our method has shown promising results - overall correlation of 0.41 on CASP12 dataset. Future work will aim to implement additional features into our representation. The software is freely available at GitHub: https://github.com/caorenzhi/TopQA.","PeriodicalId":13612,"journal":{"name":"Int. J. Comput. Biol. Drug Des.","volume":"117 1","pages":"144-153"},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79942630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-07DOI: 10.1504/ijcbdd.2020.10026786
Ruslan T. Mardugalliamov, K. Nasr, Matthew Hayes
Double minute chromosomes (DMs) are circular fragments of extrachromosomal DNA. They cause extreme gene amplification in the cells of malignant tumours. Their existence correlates with malignant tumour cell behaviour 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 sensitivity and robustness to noisy sequence data. Experiments on simulated genomic data show 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.
{"title":"A hidden Markov model-based approach to reconstructing double minute chromosome amplicons","authors":"Ruslan T. Mardugalliamov, K. Nasr, Matthew Hayes","doi":"10.1504/ijcbdd.2020.10026786","DOIUrl":"https://doi.org/10.1504/ijcbdd.2020.10026786","url":null,"abstract":"Double minute chromosomes (DMs) are circular fragments of extrachromosomal DNA. They cause extreme gene amplification in the cells of malignant tumours. Their existence correlates with malignant tumour cell behaviour 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 sensitivity and robustness to noisy sequence data. Experiments on simulated genomic data show 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.","PeriodicalId":13612,"journal":{"name":"Int. J. Comput. Biol. Drug Des.","volume":"13 1","pages":"5-20"},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85414851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-07DOI: 10.1504/ijcbdd.2020.10026787
Abdulrhman Aljouie, Ling Zhong, Usman Roshan
Whole genome alignment programs use string matching with hash tables to identify high scoring fragments between a query and target sequence around which a full alignment is then built. A recent study comparing alignment programs showed that while evolutionary similar genomes were easy to align, divergent genomes still posed a challenge to existing methods. To fill this gap we explore the use of the maximum scoring subsequence to identify high scoring fragments. We split the query genome into several fragments and align them to the target with a previously published parallel algorithm for short read alignment. We then pass such high scoring fragments on to the LASTZ program to obtain a more complete alignment. On simulated data we obtain an average of at least 20% higher accuracy than the alignment given by LASTZ at the expense of few hours of additional runtime. Our source code is freely available at http://web.njit.edu/usman/MSGA
{"title":"High scoring segment selection for pairwise whole genome sequence alignment with the maximum scoring subsequence and GPUs","authors":"Abdulrhman Aljouie, Ling Zhong, Usman Roshan","doi":"10.1504/ijcbdd.2020.10026787","DOIUrl":"https://doi.org/10.1504/ijcbdd.2020.10026787","url":null,"abstract":"Whole genome alignment programs use string matching with hash tables to identify high scoring fragments between a query and target sequence around which a full alignment is then built. A recent study comparing alignment programs showed that while evolutionary similar genomes were easy to align, divergent genomes still posed a challenge to existing methods. To fill this gap we explore the use of the maximum scoring subsequence to identify high scoring fragments. We split the query genome into several fragments and align them to the target with a previously published parallel algorithm for short read alignment. We then pass such high scoring fragments on to the LASTZ program to obtain a more complete alignment. On simulated data we obtain an average of at least 20% higher accuracy than the alignment given by LASTZ at the expense of few hours of additional runtime. Our source code is freely available at http://web.njit.edu/usman/MSGA","PeriodicalId":13612,"journal":{"name":"Int. J. Comput. Biol. Drug Des.","volume":"24 1","pages":"71-81"},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75228895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-07DOI: 10.1504/ijcbdd.2020.10026781
Meltem Apaydin, Liang Xu, Bo Zeng, Xiaoning Qian
Optimisation-based mathematical models provide ways to analyse and obtain predictions on microbial communities who play critical roles in the ecological system, human health and diseases. However, there are inherent model and data uncertainties from the existing knowledge and experiments so that the imposed models may not exactly reflect the reality in nature. Here, we aim to have a flexible framework to model microbial communities with uncertainty, and introduce P-OptCom, an extension of an existing method OptCom, based on pessimistic bilevel optimisation. This framework relies on the coordinated decision making between the single upper-level and multiple lower-level decision makers to better approximate community steady states even when the individual microorganisms' behavior deviate from the optimum in terms of their cellular fitness criteria. Our study demonstrates that without experimental knowledge in advance, we are able to analyse the trade-offs among the members of microbial communities and closely approximate the actual experimental measurements.
{"title":"Pessimistic optimisation for modelling microbial communities with uncertainty","authors":"Meltem Apaydin, Liang Xu, Bo Zeng, Xiaoning Qian","doi":"10.1504/ijcbdd.2020.10026781","DOIUrl":"https://doi.org/10.1504/ijcbdd.2020.10026781","url":null,"abstract":"Optimisation-based mathematical models provide ways to analyse and obtain predictions on microbial communities who play critical roles in the ecological system, human health and diseases. However, there are inherent model and data uncertainties from the existing knowledge and experiments so that the imposed models may not exactly reflect the reality in nature. Here, we aim to have a flexible framework to model microbial communities with uncertainty, and introduce P-OptCom, an extension of an existing method OptCom, based on pessimistic bilevel optimisation. This framework relies on the coordinated decision making between the single upper-level and multiple lower-level decision makers to better approximate community steady states even when the individual microorganisms' behavior deviate from the optimum in terms of their cellular fitness criteria. Our study demonstrates that without experimental knowledge in advance, we are able to analyse the trade-offs among the members of microbial communities and closely approximate the actual experimental measurements.","PeriodicalId":13612,"journal":{"name":"Int. J. Comput. Biol. Drug Des.","volume":"36 1","pages":"82-97"},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90390656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Heta P. Desai, Anuja P. Parameshwaran, Rajshekhar Sunderraman, M. Weeks
Bacterial 16S ribosomal gene was used to classify bacteria because it consists of both highly conservative region, as well as a hypervariable region, in its sequence. This hypervariable region serv...
{"title":"Comparative Study Using Neural Networks for 16S Ribosomal Gene Classification","authors":"Heta P. Desai, Anuja P. Parameshwaran, Rajshekhar Sunderraman, M. Weeks","doi":"10.1089/cmb.2019.0436","DOIUrl":"https://doi.org/10.1089/cmb.2019.0436","url":null,"abstract":"Bacterial 16S ribosomal gene was used to classify bacteria because it consists of both highly conservative region, as well as a hypervariable region, in its sequence. This hypervariable region serv...","PeriodicalId":13612,"journal":{"name":"Int. J. Comput. Biol. Drug Des.","volume":"60 1","pages":"248-258"},"PeriodicalIF":0.0,"publicationDate":"2020-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86062139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：