Background and Objective: AD is a progressive neurodegenerative disorder characterized by memory ... more Background and Objective: AD is a progressive neurodegenerative disorder characterized by memory loss. Due to the advancement in next-generation sequencing technologies, an enormous amount of AD-associated genomics data is available. However, the information about the involvement of these genes in AD association is still a research topic because all these algorithms are based on statistical techniques. Therefore, AlzGenPred is developed to identify the AD-associated genes from a large set of data. Methods: To develop the AlzGenPred, we have compiled a benchmark dataset consisting of 1086 AD and non-AD genes and used them as positive and negative datasets. We have generated several features including the fused features and evaluated them through machine learning methods. Then hyperparameter tuning approach was also applied and the final model was selected. The proposed method was validated by using the AlzGene and transcriptomics datasets and proposed as a standalone tool. Results: T...
Protein methyltransferases (PMTs) are a group of enzymes that help to catalyze the transfer of a ... more Protein methyltransferases (PMTs) are a group of enzymes that help to catalyze the transfer of a methyl group to its substrates. These enzymes play an important role in epigenetic regulation and are able to methylate various substrates with DNA, RNA, protein, and smallmolecule secondary metabolites. Dysregulation of methyltransferases is involved in different types of human cancers. However, in light of the well-recognized significance of PMTs, it becomes crucial to have reliable and fast methods for identifying these proteins. In the present work, we propose a machine-learning-based method for the identification of PMTs. Various sequence-based features were calculated and prediction models were develped using different machine-learning methods. A ten-fold cross-validation technique was used for model training. The SVM-based CKSAAP model gave the best prediction and achieved the highest accuracy of 87.94% with balance sensitivity (88.8%) and specificity (87.11%) with MCC of 0.759 an...
The meaningful data extraction from the biological big data or omics data is a remaining challeng... more The meaningful data extraction from the biological big data or omics data is a remaining challenge in bioinformatics. The deep learning methods, which can be used for the prediction of hidden information from the biological data, are widely used in the industry and academia. The authors have discussed the similarity and differences in the widely utilized models in deep learning studies. They first discussed the basic structure of various models followed by their applications in biological perspective. They have also discussed the suggestions and limitations of deep learning. They expect that this chapter can serve as significant perspective for continuous development of its theory, algorithm, and application in the established bioinformatics domain.
Journal of Biomolecular Structure & Dynamics, Feb 21, 2019
Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and characterized b... more Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and characterized by brain cell death, memory loss and is the most common form of dementia. Although AD has devastating effects, however, drugs which can treat the AD remain limited. The cyclin-dependent kinase 5 (CDK5) has been recognized as being involved in the pathological hyperphosphorylation of tau protein, which leads to the formation of neurofibrillary tangles (NFTs). We utilized the structure-based virtual screening (SBVS) approach to find the potential inhibitors against HsCDK5. The natural compound subset from the ZINC database (n = 167,741) was retrieved and screened by using SBVS method. From here, we have predicted 297 potent inhibitors. These 297 compounds were evaluated through their pharmacokinetic properties by ADMET (absorption, distribution, metabolism, elimination/excretion and toxicity) descriptors. Finally, 17 compounds were selected and used for re-docking. After the refinement by molecular docking and by using drug-likeness analysis, we have identified four potential inhibitors (ZINC85877721, ZINC96114862, ZINC96115616 and ZINC96116231). All these four ligands were employed for 100 ns MDS study. From the root mean square deviation (RMSD), root mean square fluctuation (RMSF), Rg, number of hydrogen bonds, solvent accessible surface area (SASA), principal component analysis (PCA) and binding free energy analysis we have found that out of four inhibitors ZINC85877721 and ZINC96116231 showed good binding free energy of −198.84 and −159.32 kJ.mol−1, respectively, and also good in other structural analyses. Both compounds displayed excellent pharmacological and structural properties to be the drug candidates. Collectively, these findings recommend that two compounds have great potential to be a promising agent against AD to reduce the CDK5 induced hyperphosphorylation and could be considered as therapeutic agents for the AD. Communicated by Ramaswamy H. Sarma
Background SMYD2 is a protein of the SET and MYND domain-containing family SMYD. It can methylate... more Background SMYD2 is a protein of the SET and MYND domain-containing family SMYD. It can methylate the lysine residue of various histone and nonhistone cancer-related proteins and plays a critical role in tumorigenesis. Although emerging evidence supports the association of SMYD2 in the progression of cancers, but its definitive effect is not yet clear. Therefore, further study of the gene in relation with cancer progression needs to be conducted. In the current study, investigators used TCGA data to determine the potential carcinogenic effect of SMYD2 in 11 cancer types. The transcriptional expression, survival rate, mutations, enriched pathways, and Gene Ontology of the SMYD2 were explored using different bioinformatics tools and servers. In addition, we also examined the correlation between SMYD2 gene expression and immunocyte infiltration in multiple cancer types. Results Findings revealed that higher expression of SMYD2 was significantly correlated with cancer incidents. In CESC...
With the increase in biological data in online databases, there is a need of optimization techniq... more With the increase in biological data in online databases, there is a need of optimization techniques to handle data complexity. In the previous decade, theoretical development in computer science opened new windows for system modeling. A growing interest of physicist to solve complex biological processes and regulation mechanism underlying unknown pathway made a significant contribution in optimization techniques. In this chapter, we focused on applications of optimization in systems biology, where optimization plays a vital role to define regulation or inhibition flux for accurate information flow prediction. Optimization methods are used for model building, network construction, and optimization of flux in metabolic and synthetic biology. We believe that a deeper insight of the theory and logic behind the concept of optimization in this chapter will help in the proper implementation of optimization techniques and investigation of complex biological networks including insertion of new ideas to control biosystems in the much effective way.
Abstract Epigenetics concerns the genetic alterations and results at phenotypic level that are fr... more Abstract Epigenetics concerns the genetic alterations and results at phenotypic level that are free from modified DNA successions. Various studies have proven that methylation at cytosine position in DNA reduces with age in all tissues and therefore is considered as an important factor of epigenetics by neuroscientists. Epigenetic alterations can take place in various diseases and also are considered to be involved in various types of dementia. Alzheimer's disease (AD) is a complex disorder that is confirmed by the occurrence of amyloid beta plaques and neurofibrillary tangles. Hyperphosphorylation in tau protein leads to imperfections in neuronal activity. The source of AD is not yet known and research continues in attempts to identify the underlying mechanisms of action. In this chapter, we present the key factors associated with epigenetic modifications that may help us increase our knowledge of AD pathogenesis and help in the treatment of neurodegenerative disorders.
Background and Objective: AD is a progressive neurodegenerative disorder characterized by memory ... more Background and Objective: AD is a progressive neurodegenerative disorder characterized by memory loss. Due to the advancement in next-generation sequencing technologies, an enormous amount of AD-associated genomics data is available. However, the information about the involvement of these genes in AD association is still a research topic because all these algorithms are based on statistical techniques. Therefore, AlzGenPred is developed to identify the AD-associated genes from a large set of data. Methods: To develop the AlzGenPred, we have compiled a benchmark dataset consisting of 1086 AD and non-AD genes and used them as positive and negative datasets. We have generated several features including the fused features and evaluated them through machine learning methods. Then hyperparameter tuning approach was also applied and the final model was selected. The proposed method was validated by using the AlzGene and transcriptomics datasets and proposed as a standalone tool. Results: T...
Protein methyltransferases (PMTs) are a group of enzymes that help to catalyze the transfer of a ... more Protein methyltransferases (PMTs) are a group of enzymes that help to catalyze the transfer of a methyl group to its substrates. These enzymes play an important role in epigenetic regulation and are able to methylate various substrates with DNA, RNA, protein, and smallmolecule secondary metabolites. Dysregulation of methyltransferases is involved in different types of human cancers. However, in light of the well-recognized significance of PMTs, it becomes crucial to have reliable and fast methods for identifying these proteins. In the present work, we propose a machine-learning-based method for the identification of PMTs. Various sequence-based features were calculated and prediction models were develped using different machine-learning methods. A ten-fold cross-validation technique was used for model training. The SVM-based CKSAAP model gave the best prediction and achieved the highest accuracy of 87.94% with balance sensitivity (88.8%) and specificity (87.11%) with MCC of 0.759 an...
The meaningful data extraction from the biological big data or omics data is a remaining challeng... more The meaningful data extraction from the biological big data or omics data is a remaining challenge in bioinformatics. The deep learning methods, which can be used for the prediction of hidden information from the biological data, are widely used in the industry and academia. The authors have discussed the similarity and differences in the widely utilized models in deep learning studies. They first discussed the basic structure of various models followed by their applications in biological perspective. They have also discussed the suggestions and limitations of deep learning. They expect that this chapter can serve as significant perspective for continuous development of its theory, algorithm, and application in the established bioinformatics domain.
Journal of Biomolecular Structure & Dynamics, Feb 21, 2019
Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and characterized b... more Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and characterized by brain cell death, memory loss and is the most common form of dementia. Although AD has devastating effects, however, drugs which can treat the AD remain limited. The cyclin-dependent kinase 5 (CDK5) has been recognized as being involved in the pathological hyperphosphorylation of tau protein, which leads to the formation of neurofibrillary tangles (NFTs). We utilized the structure-based virtual screening (SBVS) approach to find the potential inhibitors against HsCDK5. The natural compound subset from the ZINC database (n = 167,741) was retrieved and screened by using SBVS method. From here, we have predicted 297 potent inhibitors. These 297 compounds were evaluated through their pharmacokinetic properties by ADMET (absorption, distribution, metabolism, elimination/excretion and toxicity) descriptors. Finally, 17 compounds were selected and used for re-docking. After the refinement by molecular docking and by using drug-likeness analysis, we have identified four potential inhibitors (ZINC85877721, ZINC96114862, ZINC96115616 and ZINC96116231). All these four ligands were employed for 100 ns MDS study. From the root mean square deviation (RMSD), root mean square fluctuation (RMSF), Rg, number of hydrogen bonds, solvent accessible surface area (SASA), principal component analysis (PCA) and binding free energy analysis we have found that out of four inhibitors ZINC85877721 and ZINC96116231 showed good binding free energy of −198.84 and −159.32 kJ.mol−1, respectively, and also good in other structural analyses. Both compounds displayed excellent pharmacological and structural properties to be the drug candidates. Collectively, these findings recommend that two compounds have great potential to be a promising agent against AD to reduce the CDK5 induced hyperphosphorylation and could be considered as therapeutic agents for the AD. Communicated by Ramaswamy H. Sarma
Background SMYD2 is a protein of the SET and MYND domain-containing family SMYD. It can methylate... more Background SMYD2 is a protein of the SET and MYND domain-containing family SMYD. It can methylate the lysine residue of various histone and nonhistone cancer-related proteins and plays a critical role in tumorigenesis. Although emerging evidence supports the association of SMYD2 in the progression of cancers, but its definitive effect is not yet clear. Therefore, further study of the gene in relation with cancer progression needs to be conducted. In the current study, investigators used TCGA data to determine the potential carcinogenic effect of SMYD2 in 11 cancer types. The transcriptional expression, survival rate, mutations, enriched pathways, and Gene Ontology of the SMYD2 were explored using different bioinformatics tools and servers. In addition, we also examined the correlation between SMYD2 gene expression and immunocyte infiltration in multiple cancer types. Results Findings revealed that higher expression of SMYD2 was significantly correlated with cancer incidents. In CESC...
With the increase in biological data in online databases, there is a need of optimization techniq... more With the increase in biological data in online databases, there is a need of optimization techniques to handle data complexity. In the previous decade, theoretical development in computer science opened new windows for system modeling. A growing interest of physicist to solve complex biological processes and regulation mechanism underlying unknown pathway made a significant contribution in optimization techniques. In this chapter, we focused on applications of optimization in systems biology, where optimization plays a vital role to define regulation or inhibition flux for accurate information flow prediction. Optimization methods are used for model building, network construction, and optimization of flux in metabolic and synthetic biology. We believe that a deeper insight of the theory and logic behind the concept of optimization in this chapter will help in the proper implementation of optimization techniques and investigation of complex biological networks including insertion of new ideas to control biosystems in the much effective way.
Abstract Epigenetics concerns the genetic alterations and results at phenotypic level that are fr... more Abstract Epigenetics concerns the genetic alterations and results at phenotypic level that are free from modified DNA successions. Various studies have proven that methylation at cytosine position in DNA reduces with age in all tissues and therefore is considered as an important factor of epigenetics by neuroscientists. Epigenetic alterations can take place in various diseases and also are considered to be involved in various types of dementia. Alzheimer's disease (AD) is a complex disorder that is confirmed by the occurrence of amyloid beta plaques and neurofibrillary tangles. Hyperphosphorylation in tau protein leads to imperfections in neuronal activity. The source of AD is not yet known and research continues in attempts to identify the underlying mechanisms of action. In this chapter, we present the key factors associated with epigenetic modifications that may help us increase our knowledge of AD pathogenesis and help in the treatment of neurodegenerative disorders.
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Papers by Tiratha Singh