Amit Kumar Banerjee

Brief Communication: Classification and identification of mosquito species using artificial neural networks

An artificial neural network method is presented for classification and identification of Anopheles mosquito species based on the internal transcribed spacer2 (ITS2) data of ribosomal DNA string. The method is implemented in two different multi-layered feed-forward neural network model forms, namely, multi-input single-output neural network (MISONN) and multi-input multi-output neural network (MIMONN). A number of data sequences in varying sizes of different Anopheline malarial vectors and their corresponding species coding are employed to develop the neural network models. The classification efficiency of the network models for untrained data sequences is evaluated in terms of quantitative performance criteria. The results demonstrate the efficiency of the neural network models to extract the genetic information in ITS2 sequences and to adapt to new data. The method of MISONN is found to exhibit superior performance over MIMONN in distinguishing and identification of the mosquito vectors.

Comparative characterization of commercially important xylanase enzymes

Xylanase is an industrially important enzyme having wide range of applications especially in paper industry. It is crucial to gain an understanding about the structure and functional aspects of various xylanases produced from diverse sources. In this study, a bioinformatics and molecular modeling approach was adopted to explore properties and structure of xylanases. Physico-chemical properties were predicted and prediction of motifs, disulfide bridges and secondary structure was performed for functional characterization. Apart from these analyses, three dimensional structures were constructed and stereo-chemical quality was evaluated by different structure validation tools. Comparative catalytic site analysis and assessment was performed to extract information about the important residues. Asn72 was found to be the common residue in the active sites of the proteins P35809 and Q12603.

New Targets, New Hope: Novel Drug Targets for Curbing Malaria

Malaria continues to plague the tropical and subtropical regions causing high morbidity and mortality. Every year, millions die due to lack of affordable and effective anti-malarial drugs. Malaria poses significant threat to half of the worlds population and our arsenal to combat this disease is nearly empty. Pharmaceutical companies shy away from investing in research and development for anti-malarial drugs and have shunned it as non-profitable venture. In wake of emergence and spread of drug resistant malaria to newer territories, there is imperative need to develop new drugs for curbing malaria. This underscores the need of exploring new drug targets and reevaluation of existing drug targets. Availability of genome sequence of both parasite and human host has greatly facilitated the search for novel drug targets. This endeavor is complemented well by advances in functional genomics, structure - based drug design and high throughput screening methods and raises much optimism about winning this battle against malaria. This review discusses potential drug targets in the malarial parasite for designing intervention strategies and suitable chemotherapeutic agents.

Analyzing a Potential Drug Target N-Myristoyltransferase of Plasmodium falciparum Through In Silico Approaches

Background: Despite concerted global efforts to combat malaria, malaria elimination is still a remote dream. Fast evolution rate of malarial parasite along with its ability to respond quickly to any drug resulting in partial or complete resistance has been a cause of concern among researcher communities. Materials and Methods: Molecular modeling approach was adopted to gain insight about the structure and various analyses were performed. Modeller 9v3, Protparam, Protscale, MEME, NAMD and other tools were employed for this study. PROCHECK and other tools were used for stereo-chemical quality evaluation. Results and Conclusion: It was observed during the course of study that this protein contains 32.2% of aliphatic amino acids among which Leucine (9.5%) is predominant. Theoretical pI of 8.39 identified the protein as basic in nature and most of the amino acids present in N-Myristoyltransferase are hydrophobic (46.1%). Secondary structure analysis shows predominance of alpha helices and random coils. Motif analyses revealed that this target protein contains 2 signature motifs, i.e., EVNFLCVHK and KFGEGDG. Apart from motif search, three-dimensional model was generated and validated and the stereo-chemical quality check confirmed that 97.7% amino acid residues fall in the core region of Ramachandran plot. Molecular dynamics simulation resulted in maximum 1.3 Å Root Mean Square Deviation (RMSD) between the initial structure and the trajectories obtained later on. The template and the target molecule has shown 1.5 Å RMSD for the C alpha trace. A docking study was also conducted with various ligand molecules among which specific benzofuran compounds turned out to be effective. This derived information will help in designing new inhibitor molecules for this target protein as well in better understanding the parasite protein.

In silico characterization of Shikimate Kinase of Shigella flexneri: A potential drug target

Shigella flexneri is a major pathogen responsible for Shigellosis causing massive morbidity among young population and imposes huge socio-economic burden. In this study, Shikimate Kinase (SK) from S. flexneri was characterized in silico and disordered regions were predicted. Motifs and domains were calculated using computational tools. A three dimensional model of Shikimate Kinase of S.flexneri was constructed using Shikimate Kinase of E.coli (PDBID: 1KAG_A) as template by comparative modeling approach. Molecular dynamics calculations were carried out to check the stable conformation embedded in water sphere with least RMSD possible. Perusal of backbone conformation of the modeled structure by PROCHECK revealed that more than 98% of the residues fell in the allowed regions and ERRAT results confirmed good quality of modeled structure. Active site and its important residues were predicted for the derived model. Disulphide bridges were estimated by computational method and most probable pattern of cysteine residues was found in the pairs 8–22. Results of this study will shed light on the structural aspects of Shikimate Kinase of S. flexneri and will aid in rational drug designing.

Application of Kohonen maps for solving the classification puzzle in AGC kinase protein sequences

Availability of enormous number of sequences in public domain databases warrants the need for effective tools for clustering and classification of such data. AGC protein kinase family is known to contain many enzymes involved in important cellular processes. In the present study, 21 important physicochemical parameters were calculated for 115 sequences of AGC kinase family belonging to mouse and human. Kohonen maps, also known as Self Organizing Maps (SOM) were employed for the identification of clusters of similar sequences, projection and visualization of high dimensional data spaces owing to their capability of preserving topological relationships between the features. This simplistic approach can provide a method not only for studying intricate interplay of features and minute differences even in the members of same protein family but also for recognition of certain unifying common features. Each cluster obtained using SOM in this study has a distinct characteristic that sets it apart from the other clusters.

Targeting Strategies for Human Immunodeficiency Virus: A Combinatorial Approach

The battle between human and the Human immunodeficiency virus (HIV) is on, with both of them rapidly improving their attacking and defense strategies. Many therapeutic agents for HIV infection have been designed and developed, However there are various aspects, like novel targets against HIV, which are yet to be unfolded with a goal of designing and developing novel drug molecules against HIV. This article reviews the current status and innovative new options for antiretroviral therapy for HIV and also discusses the various mechanisms of action for each class of drugs, and the problems yet to be solved with respect to HIV as a target for improvised treatment against AIDS.

Keratin protein property based classification of mammals and non-mammals using machine learning techniques

Keratin protein is ubiquitous in most vertebrates and invertebrates, and has several important cellular and extracellular functions that are related to survival and protection. Keratin function has played a significant role in the natural selection of an organism. Hence, it acts as a marker of evolution. Much information about an organism and its evolution can therefore be obtained by investigating this important protein. In the present study, Keratin sequences were extracted from public data repositories and various important sequential, structural and physicochemical properties were computed and used for preparing the dataset. The dataset containing two classes, namely mammals (Class-1) and non-mammals (Class-0), was prepared, and rigorous classification analysis was performed. To reduce the complexity of the dataset containing 56 parameters and to achieve improved accuracy, feature selection was done using the t-statistic. The 20 best features (parameters) were selected for further classification analysis using computational algorithms which included SVM, KNN, Neural Network, Logistic regression, Meta-modeling, Tree Induction, Rule Induction, Discriminant analysis and Bayesian Modeling. Statistical methods were used to evaluate the output. Logistic regression was found to be the most effective algorithm for classification, with greater than 96% accuracy using a 10-fold cross validation analysis. KNN, SVM and Rule Induction algorithms also were found to be efficacious for classification.

Study of platelet aggregation in acute coronary syndrome with special reference to metabolic syndrome

Background/Context: Antiplatelet drug resistance increases the risk of adverse events like stent thrombosis in acute coronary syndrome (ACS). Metabolic syndrome (MS) is a prothrombotic state and presence of MS further increases the risk of antiplatelet drug resistance. Aims and Objectives: We studied platelet aggregation characteristics in patients of ACS for aspirin or clopidogrel resistance. We studied the relation of drug resistance with blood markers like high sensitivity C-reactive protein (hsCRP). We also studied for any relation of drug resistance with presence of MS. Materials and Methods: We studied platelet aggregation characteristics by optical aggregometry using platelet-rich plasma (PRP) of patients. Collagen (2 μg/mL) and adenosine diphosphate (ADP; 10 μmol) were used. Greater than 50% aggregation in PRP of patients was taken as an evidence of drug resistance. Suitable blood tests were done including newer risk markers like hsCRP, apolipoprotein B, and fibrinogen. Statistical test: Statistical tests included Students t-test and Kendalls rank correlation coefficient. Results: We had a total of 94 patients of ACS with 47 (50%) having MS. MS patients showed higher blood levels of hsCRP and fibrinogen. Twenty-eight (59.5%) patients with MS showed antiplatelet drug resistance compared to 12 patients without MS. Serum fibrinogen showed strongest correlation with drug resistance. HsCRP levels showed correlation with aspirin resistance (r = 0.53) only in the MS group. Discussion and Conclusion: We found significantly high prevalence of antiplatelet drug resistance. Aspirin and clopidogrel resistance was comparable. MS was a significant risk factor for drug resistance. The prothrombotic and proinflammatory markers showed strong correlation with drug resistance. A larger randomized trial is needed to better characterize this clinical problem.

Targeting Tuberculosis: A Glimpse of Promising Drug Targets

Tuberculosis caused by Mycobacterium tuberculosis has emerged as the biggest curse of our time causing significant morbidity and mortality. Increasing resistance in mycobacterium to existing drugs calls for exploration of metabolic pathways for finding novel drug targets and also for prioritization of known drug targets. Recent advances in molecular biology, bioinformatics and structural biology coupled with availability of M. tuberculosis genome sequence have provided much needed boost to drug discovery process. This review provides a glimpse of attractive drug targets for development of anti-mycobacterial drug development.