Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule

Background: Different isoforms of Cytochrome P450 (CYP) metabolized different types of substrates (or drugs molecule) and make them soluble during biotransformation. Therefore, fate of any drug molecule depends on how they are treated or metabolized by CYP isoform. There is a need to develop models for predicting substrate specificity of major isoforms of P450, in order to understand whether a given drug will be metabolized or not. This paper describes an in-silico method for predicting the metabolizing capability of major isoforms (e.g. CYP 3A4, 2D6, 1A2, 2C9 and 2C19). Results: All models were trained and tested on 226 approved drug molecules. Firstly, 2392 molecular descriptors for each drug molecule were calculated using various softwares. Secondly, best 41 descriptors were selected using general and genetic algorithm. Thirdly, Support Vector Machine (SVM) based QSAR models were developed using 41 best descriptors and achieved an average accuracy of 86.02%, evaluated using fivefold cross-validation. We have also evaluated the performance of our model on an independent dataset of 146 drug molecules and achieved average accuracy 70.55%. In addition, SVM based models were developed using 26 Chemistry Development Kit (CDK) molecular descriptors and achieved an average accuracy of 86.60%. Conclusions: This study demonstrates that SVM based QSAR model can predict substrate specificity of major CYP isoforms with high accuracy. These models can be used to predict isoform responsible for metabolizing a drug molecule. Thus these models can used to understand whether a molecule will be metabolized or not. This is possible to develop highly accurate models for predicting substrate specificity of major isoforms using CDK descriptors. A web server MetaPred has been developed for predicting metabolizing isoform of a drug molecule http://crdd.osdd.net/raghava/metapred/

AntiBP2: improved version of antibacterial peptide prediction

<p>Abstract</p> <p>Background</p> <p>Antibacterial peptides are one of the effecter molecules of innate immune system. Over the last few decades several antibacterial peptides have successfully approved as drug by FDA, which has prompted an interest in these antibacterial peptides. In our recent study we analyzed 999 antibacterial peptides, which were collected from Antibacterial Peptide Database (APD). We have also developed methods to predict and classify these antibacterial peptides using Support Vector Machine (SVM).</p> <p>Results</p> <p>During analysis we observed that certain residues are preferred over other in antibacterial peptide, particularly at the N and C terminus. These observation and increased data of antibacterial peptide in APD encouraged us to again develop a new and more robust method for predicting antibacterial peptides in protein from their amino acid sequence or given peptide have antibacterial properties or not. First, the binary patterns of the 15 N terminus residues were used for predicting antibacterial peptide using SVM and achieved accuracy of 85.46% with 0.705 Mathew's Correlation Coefficient (MCC). Then we used the binary pattern of 15 C terminus residues and achieved accuracy of 85.05% with 0.701 MCC, latter on we developed prediction method by combining N & C terminus and achieved an accuracy of 91.64% with 0.831 MCC. Finally we developed SVM based model using amino acid composition of whole peptide and achieved 92.14% accuracy with MCC 0.843. In this study we used five-fold cross validation technique to develop all these models and tested the performance of these models on an independent dataset. We further classify antibacterial peptides according to their sources and achieved an overall accuracy of 98.95%. We further classify antibacterial peptides in their respective family and got a satisfactory result.</p> <p>Conclusion</p> <p>Among antibacterial peptides, there is preference for certain residues at N and C terminus, which helps to discriminate them from non-antibacterial peptides. Amino acid composition of antibacterial peptides helps to demarcate them from non-antibacterial peptide and their further classification in source and family. Antibp2 will be helpful in discovering efficacious antibacterial peptide, which we hope will be helpful against antibiotics resistant bacteria. We also developed user friendly web server for the biological community.</p

Choice of sedative for deep brain stimulation in Parkinson’s disease: Our experience and comparison of two cases

AbstractIntroductionParkinson’s disease (PD) is a severe, debilitating disease of the extra pyramidal central nervous system, which has a significant effect on lifestyle and day to day living of the affected population. Statistically, more of the elderly are now going to present with this disease. Moving ahead from older procedures such as cingulotomy, pallidotomy and thalamotomy which had irreversible side effects, deep brain stimulation (DBS) has emerged as a new, safer and more attractive option for such patients. Anaesthetic concerns for such procedures mainly incorporate principles of awake craniotomy, for which the basic requirement is a cooperative patient. Although Propofol was somewhat of a gold standard for this purpose until a few years back, Dexmedetomidine has emerged as the new drug of choice.CaseWhile conducting two surgeries for DBS over two days, we had an obverse experience with these drugs. We describe the pre-operative assessment and intra-operative management of the two cases and a discussion of the factors which might have contributed to this contradiction.ConclusionThe choice of sedation for DBS in PD should take into consideration factors such as patient cooperation, ‘drug off’ state due to pre-op medication stoppage, GABA versus non-GABA mediated mechanism of drugs, amount of dependence on PD drugs, severity of disease and finally requirement of the testing team. No drug can be singled out to be better and must be chosen based on individual merits of the patient and disease

Identification of ATP binding residues of a protein from its primary sequence

<p>Abstract</p> <p>Background</p> <p>One of the major challenges in post-genomic era is to provide functional annotations for large number of proteins arising from genome sequencing projects. The function of many proteins depends on their interaction with small molecules or ligands. ATP is one such important ligand that plays critical role as a coenzyme in the functionality of many proteins. There is a need to develop method for identifying ATP interacting residues in a ATP binding proteins (ABPs), in order to understand mechanism of protein-ligands interaction.</p> <p>Results</p> <p>We have compared the amino acid composition of ATP interacting and non-interacting regions of proteins and observed that certain residues are preferred for interaction with ATP. This study describes few models that have been developed for identifying ATP interacting residues in a protein. All these models were trained and tested on 168 non-redundant ABPs chains. First we have developed a Support Vector Machine (SVM) based model using primary sequence of proteins and obtained maximum MCC 0.33 with accuracy of 66.25%. Secondly, another SVM based model was developed using position specific scoring matrix (PSSM) generated by PSI-BLAST. The performance of this model was improved significantly (MCC 0.5) from the previous one, where only the primary sequence of the proteins were used.</p> <p>Conclusion</p> <p>This study demonstrates that it is possible to predict 'ATP interacting residues' in a protein with moderate accuracy using its sequence. The evolutionary information is important for the identification of 'ATP interacting residues', as it provides more information compared to the primary sequence. This method will be useful for researchers studying ATP-binding proteins. Based on this study, a web server has been developed for predicting 'ATP interacting residues' in a protein <url>http://www.imtech.res.in/raghava/atpint/</url>.</p

Prediction of GTP interacting residues, dipeptides and tripeptides in a protein from its evolutionary information

Background: Guanosine triphosphate (GTP)-binding proteins play an important role in regulation of G-protein. Thus prediction of GTP interacting residues in a protein is one of the major challenges in the field of the computational biology. In this study, an attempt has been made to develop a computational method for predicting GTP interacting residues in a protein with high accuracy (Acc), precision (Prec) and recall (Rc). Result: All the models developed in this study have been trained and tested on a non-redundant (40% similarity) dataset using five-fold cross-validation. Firstly, we have developed neural network based models using single sequence and PSSM profile and achieved maximum Matthews Correlation Coefficient (MCC) 0.24 (Acc 61.30%) and 0.39 (Acc 68.88%) respectively. Secondly, we have developed a support vector machine (SVM) based models using single sequence and PSSM profile and achieved maximum MCC 0.37 (Prec 0.73, Rc 0.57, Acc 67.98%) and 0.55 (Prec 0.80, Rc 0.73, Acc 77.17%) respectively. In this work, we have introduced a new concept of predicting GTP interacting dipeptide (two consecutive GTP interacting residues) and tripeptide (three consecutive GTP interacting residues) for the first time. We have developed SVM based model for predicting GTP interacting dipeptides using PSSM profile and achieved MCC 0.64 with precision 0.87, recall 0.74 and accuracy 81.37%. Similarly, SVM based model have been developed for predicting GTP interacting tripeptides using PSSM profile and achieved MCC 0.70 with precision 0.93, recall 0.73 and accuracy 83.98%. Conclusion: These results show that PSSM based method performs better than single sequence based method. The prediction models based on dipeptides or tripeptides are more accurate than the traditional model based on single residue. A web server "GTPBinder" http://www.imtech.res.in/raghava/gtpbinder/ webcite based on above models has been developed for predicting GTP interacting residues in a protein

Red Panda: A Novel Method for Detecting Variants in Single-Cell RNA Sequencing

BACKGROUND: Single-cell sequencing enables us to better understand genetic diseases, such as cancer or autoimmune disorders, which are often affected by changes in rare cells. Currently, no existing software is aimed at identifying single nucleotide variations or micro (1-50 bp) insertions and deletions in single-cell RNA sequencing (scRNA-seq) data. Generating high-quality variant data is vital to the study of the aforementioned diseases, among others. RESULTS: In this study, we report the design and implementation of Red Panda, a novel method to accurately identify variants in scRNA-seq data. Variants were called on scRNA-seq data from human articular chondrocytes, mouse embryonic fibroblasts (MEFs), and simulated data stemming from the MEF alignments. Red Panda had the highest Positive Predictive Value at 45.0%, while other tools-FreeBayes, GATK HaplotypeCaller, GATK UnifiedGenotyper, Monovar, and Platypus-ranged from 5.8-41.53%. From the simulated data, Red Panda had the highest sensitivity at 72.44%. CONCLUSIONS: We show that our method provides a novel and improved mechanism to identify variants in scRNA-seq as compared to currently existing software. However, methods for identification of genomic variants using scRNA-seq data can be still improved

A Neurally Inspired Robotic Control Algorithm for Gait Rehabilitation in Hemiplegic Stroke Patients

Abstract-Cerebrovascular accident or stroke is one of the major brain impairments that affects numerous people globally. After a unilateral stroke, sensory motor damages contralateral to the brain lesion occur in many patients. As a result, gait remains impaired and asymmetric. This paper describes and simulates a novel closed loop algorithm designed for the control of a lower limb exoskeleton for post-stroke rehabilitation. The algorithm has been developed to control a lower limb exoskeleton including actuators for the hip and knee joints, and feedback sensors for the measure of joint angular excursions. It has been designed to control and correct the gait cycle of the affected leg using kinematics information from the unaffected one. In particular, a probabilistic particle filter like algorithm has been used at the top-level control to modulate gait velocity and the joint angular excursions. Simulation results show that the algorithm is able to correct and control velocity of the affected side restoring phase synchronization between the legs

Detection of Human Papillomavirus among Cervical Cancer Patients by Qualitative Polymerase Chain Reaction on Formalin- fixed, Paraffin-embedded Tissue: A Retrospective Observational Analysis

Introduction: Cervical cancer is second most commonly diagnosed and third most common cause for cancer death among women in the developing countries. It is now-established that Human Papillomavirus (HPV) infection is responsible for pathogenesis of cervical cancer. HPV Deoxyribonucleic Acid (DNA) detection is generally done on cytology specimens to triage women undergoing cervical cancer screening, but testing of Formalin-fixed, Paraffin- embedded tissue (FFPE) is not yet widely used. Aim: To study the detection of HPV by Qualitative PCR by extraction of DNA from FFPE. Materials and Methods: This retrospective observational analysis was carried out at Department of Pathology, Armed Forces Medical College, Pune, Maharashtra, India, managed at the centre from 2008 to 2015. The data analysis was done Aug 2013-Oct 2015 on 35 patients of cervical cancer which were reconfirmed by histopatholgical study of sections. Tissue blocks were obtained from the selected subjects and 3-5 micron sections were taken and prepared for Haematoxylin and Eosin (H&E) staining. Qualitative PCR was run on DNA extracted from FFPE tissue for evaluation of HPV. The amplified DNA varied between 230-270 base pairs (bp) and was analysed for oncogenic HPV type 16, 18, 31, 33, 35, 45, 52b and 58 by gel electrophoresis. Data was tabulated in Microsoft excel and mean, frequency and percentages were calculated. Pearson’s Chi-square test was used to calculate the significance. Results: Out of the total 35 samples analysed (mean age: 51.08±10.6 years),15 cases were large cell non keratinising carcinoma, 12 cases of keratinising squamous cell carcinoma, 5 cases of carcinoma in-situ and 3 cases were adenocarcinoma. A total of 13 cases out of 35 showed the bands of HPV genomes, indicating either of the HPV strain. Conclusion: Although molecular diagnostics on FFPE tissue is need of hour, stringent protocols for timing of fixation, technical expertise for extraction of DNA or Ribonucleic Acid (RNA), careful handling of the sample and quality control is of paramount importance. Fragmentation is a problem in DNA extracted from FFPE tissue, so primers having small base pairs should be used to maximise the yield