International Journal of Computational Biology and Drug Design (12 papers in press)
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
Identification of Novel Compounds against Lower Urinary Tract Symptoms and cystitis through Docking and Virtual screening.
by Balwant K. Malik, Rajni Jaiswal, Poonam Malik, Neha Malik, Uzma Khanam
People suffering from infection in their urinary tract are more likely to get disease like cancer. LUTS is commonly witnessed in males, but females can harbor it as well. Cystitis is a common type of urinary tract infection in females, caused by bacteria; whereas LUTS is caused by bacteria, viruses or undesirable microorganisms that invade into the lower urinary tract and causes infection. To treat these diseases drugs viz. alpha blocker and 5- alpha-reductase inhibitors for LUTS; and several antibiotics viz. nitrofurantoin, trimethoprim sulfamethoxazole, etc. for Cystitis are used. However these drugs also associated with adverse effects viz. nausea, diarrhea, headache, drowsiness, dizziness. Therefore to overcome the adverse effects and to develop low cost drugs, all natural compounds and some compounds with known anticancer properties need to be employed in various novel target proteins.
The study was done with few novel proteins for LUTS and cystitis as targets and predicted their 3D- structure using different tools. Predicted structures were validated and selected for the docking and virtual screening with all 50,000 natural compounds and few anti-cancer compounds.
In this study we have selected four proteins with best predicted structure viz. eNOS, PDE5, TRPV1 and ERK for further studies. We have also identified few natural compounds out of 50,000 and some anticancer compound by analyzing their binding affinity to target compounds. Therefore we can conclude that these few natural compounds and anticancer compounds can be promising molecules for further drug development including improvement of treatment with low cost and devoid of side effects for all.
Keywords: Cystitis; LUTS; Docking; Drug designing; Homology modeling; Molecular modeling; Virtual screening.
MDM2 case study: Computational protocol utilizing protein flexibility and data mining improves ligand binding mode predictions
by Anthony Ascone, Ridwan Sakidja
Abstract: Recovery of the P53 tumor suppressor pathway via small molecule inhibitors of onco-protein MDM2 highlights the critical role of computational methodologies in targeted cancer therapies. Molecular docking programs in particular, have become essential during computer-aided drug design by providing a quantitative ranking of predicted binding geometries of small ligands to proteins based on binding free energy. In this study, we found improved ligand binding mode predictions of small medicinal compounds to MDM2 based on RMSD values using AutoDock and AutoDock Vina employing protein binding site flexibility. Additional analysis suggests a data mining protocol using linear regression can isolate the particular flexible bonds necessary for future optimum docking results. The implementation of a flexible receptor protocol based on a priori knowledge obtained from data mining will improve accuracy and reduce costs of high throughput virtual screenings of potential cancer drugs targeting MDM2.
Keywords: MDM2; autodock; autodock vina; molecular docking; data mining; drug design; molecular dynamics; high throughput virtual screenings.
CoMFA, CoMSIA Analysis of 4-[5-(4-Fluoro-benzyl-1H-pyrazol-3-yl]-pyridine Derivatives as CYP3A4 inhibitors
by Neelamma Mantri, Seshagiri Bandi, Anuradha GH, Jaheer Mohmed, Mounica Bandela
Abstract: 3D QSAR studies were performed on a series of 4-[5-(4-Fluoro-benzyl-1H-pyrazol-3-yl]-pyridine derivatives as CYP3A4 inhibitors. The molecular superimposition of most active compound structure was performed by atom/shape-based RMS fit. The statistically significant model was established from 95 molecules, which were validated by evaluation of test set of 24 compounds and training set of 71 compounds. The atom-based RMS alignment yielded best predictive CoMFA model q2= 0.784, r2 = 0.974, F value = 479.682 with 5 components, while the CoMSIA model yielded q2 = 0.797, r2 = 0.987, F value= 791.297 with 6 components. Contour maps obtained from 3D-QSAR CoMFA, CoMSIA studies were evaluated for the biological activity trends of the molecules analyzed. The statistical analysis results indicate that the steric, electrostatic, hydrogen bond donor and acceptor substituents play significant role in CYP3A4 activity. The contribution of the ligand-based study approach is expected to become more significant and effective in the future.
Keywords: CYP3A4; 4-[5-(4-Fluoro-benzyl-1H-pyrazol-3-yl]-pyridine; CoMFA; CoMSIA.
MEEPTOOLS: A maximum expected error based FASTQ read filtering and trimming toolkit
by Vishal Koparde, Hardik Parikh, Steven Bradley, Nihar Sheth
Abstract: Read trimming is one of the most important data preprocessing steps of almost all Next Generation Sequencing (NGS) analyses. The presence of low quality or erroneous base in a read may lead to a missed alignment downstream or generate false k-mers in de novo assembly process causing misassemblies. Most algorithms, like sickle, trimmomatic, etc. either depend on a running sum of quality of bases; or rely on average base quality of a sliding window. These tools consider the PHRED quality, which is exponentially related to the probability of an erroneous base call. Here we present MEEPTOOLS, which is a collection of open-source tools based on maximum expected error as a percentage of readlength (MEEP score) to filter, trim, truncate and assess next generation DNA sequencing data in FASTQ file format. MEEPTOOLS retains more reads at comparatively longer readlengths. MEEPTOOLS is available for download under the GNU GPLv3 at https://github.com/nisheth/meeptools.
Keywords: Read trimming; FASTQ; QC; read processing; Illumina.
Evaluation of predictive models based on Random Forest, Decision Tree, and Support Vector Machine classifiers and virtual screening of anti-mycobacterial compounds
by Madhulata Kumari, Neeraj Tiwari, Naidu Suubarao, Subhash Chandra
Abstract: In present work, we used three machine learning classifiers: Random Forest, Decision Tree, and Support Vector Machine to build three predictive models of an anti-mycobacterial dataset obtained from ChEMBL database and evaluated for their predictive capability. Before the development of predictive models, data pre-processing was carried out to fix an issue such as class imbalance problem by applying cost sensitive classifier, and filtration of data instance by supervised SMOTE, Spread subsample and resample method. During the predictive model's development, 10-fold cross-validation was used to validate predictive models. As compare Decision Tree and Support Vector Machine, the RF classifier showed the higher area under the curve (AUC), accuracy, sensitivity, and specificity in the cross-validation tests. The statistical results indicated that Random forest predictive model is the best model as it shows with 93.83% accuracy and 0.984 ROC value. Additionally, we built toxicity predictive models based on the SingleCellcall DSSTox carcinogenicity database (AID1189) using same classifiers. The result showed that Random Forest model was the best predictive model. The RF predictive model of ChEMBL anti-mycobacterial and DSSTox CPDBAS dataset (AID 1189) were deployed on two unknown datasets for the screening of nontoxic and anti- mycobacterial compounds. Total 1317 compounds out of 1554 approved drugs and 2234 compounds out of 18746 from ChEMBL anti-malarial dataset were classified as nontoxic and anti-mycobacterial compounds which could be better drug candidates. Thus machine learning models present highly efficient methods to find out novel hit anti-mycobacterial compounds. We suggest that such machine learning techniques could be very useful to screen drug candidates not only for tuberculosis but also for other diseases.
Keywords: Machine learning; Random Forest; DT; SVM; J48; Mycobacterium tuberculosis; Drug discovery.
3D-QSAR (CoMFA, CoMSIA) and molecular docking studies of natural flavonoid derivatives to explore structural requirements for MCF-7 cell line inhibition
by Shravan Kumar Gunda, Manasa Reddy Gummi, Mohmed Jaheer, Ayub Shaik
Abstract: In the present study, molecular modeling studies have been reported on a series of natural flavonoid derivatives to analyze structure activity relationship studies of MCF-7 inhibitors using CoMFA and CoMSIA based QSAR methods. Eighty four compounds were used as training set to establish the model and twenty compounds were used as external test set to validate these models. The generated models exhibited good statistical results such as q2, r2. CoMFA analysis yielded the q2 of 0.765 and r2 of 0.968 with five components. CoMSIA model generated using steric, electrostatic, hydrophobic, donor and acceptor fields with q2 value of 0.592 and r2 value of 0.932 with six components was found to be the optimal model among the various models generated. According to contour map results we have recommended the critical sites for modification in structure which will be useful in designing potent compounds with improved activity.
Keywords: Flavonoids; MCF-7; CoMFA; CoMSIA; Molecular docking.
Comparative conformational variation and flexibility analysis of binding site D-loop and its importance in designing of potential tankyrases inhibitors
by Rakesh D. Nimbalkar, Sujit R. Tangadpalliwar, Prabha Garg
Abstract: The principle point of this study is to know the dynamics of D-loop affecting the inhibitory action of tankyrase inhibitors. The knowledge of D-loop conformational variations, flexibility analysis, and its significance in the binding of tankyrase inhibitors (TNKSIs) has not been explored earlier. This study at first focused on observing the conformational changes of D-loop in tankyrases crystal structures. These conformational changes were observed by overlapping of D-loop residues for their open inward and open outward conformation as well as to describe the movement of D-loop towards the neighboring loop (N-loop) or not. Comparative analysis of D-loop of TNKS1 and TNKS2 shows that D-loop of TNKS2 has its conformation more towards the neighboring N-loop. Flexibility analyses of both the isoforms of TNKSs have indicated the most extreme movement of D-loop in the presence of adenosine and nicotinamide site inhibitors. The presence of dual site inhibitor doesn't influence the movement of D-loop significantly. To achieve this, relative residue fluctuation profile examination of undocked and docked complexes were performed. Further, MM/GBSA free energy calculations were used to expose the more prominent binding strength of strong inhibitors and selectivity compared to other respective site inhibitors. Molecular dynamics simulation search revealed that RMSF profile of D-loop of TNKSs affects the binding potential of inhibitors. Thus conformational changes in the D-loop affected by inhibitors can be used to screen the potent tankyrase inhibitors (TNKSIs). The molecular docking, D-loop flexibility analysis, molecular dynamics and MM/GBSA binding energy calculations were carried out.
Keywords: Tankyrase-1; TNKS1; Tankyrase-2; TNKS2; Prime MM/GBSA; molecular docking; Molecular dynamic simulations; D-loop; residue fluctuation.
Interaction of Curcumin with Different Target Proteins of Alzheimers Disease: Docking and MD Simulation Studies
by Sujata Roy, Shraddha Sriraman, Nanda Gopal Saha
Abstract: Curcumin, commonly called turmeric, is a polyphenol derived from the rhizome of the plant, Curcuma longa. Curcumin has been extensively used in the treatment of various medical conditions, including arthritis, cystic fibrosis, and cancer and so on. In addition, curcumin also has a potential role in the prevention and treatment of Alzheimer's disease (AD). Although the effect of curcumin with regards to AD has been studied experimentally, the molecular mechanism is still unknown. Many targets of AD have been identified. In this analysis, the interactions of curcumin with eight different targets of AD have been studied, in order to locate the binding site of curcumin. Based on docking energy, three potential targets such as acetylcholinesterase, cholinesterase and inducible nitric oxide synthase have been selected. Then, MD simulation was performed for those three docked structures. It was found that acetylcholinesterase was the best target of curcumin. Existing experimental results support this finding. The dynamics of interaction at the atomic level was studied to understand the main chemical property of curcumin that can be exploited in treating AD.
Keywords: Alzheimer's disease; Curcumin; Acetylcholinesterase; Molecular Docking and Molecular Dynamics.
An Improved Convex and Concave Index for Revealing the Exposure Degree of Atoms in Protein 3D Structure
by Xiao Wang, Jian Zhao, Yujiao Yan, Jingye Qian, Ping Han, Xiaofeng Song
Abstract: Geometry property of protein surface contributes largely to the protein function in the cells, descriptors of measuring the convexity and concavity of protein surfaces can help to understand the protein function. Motivated by CX algorithm, we developed an improved surface structural parameter named convex-and-concave index (CCI) to describe geometric properties of protein surface. The proposed CCI eliminates the defect of coarse computing for the overlap volume of two adjacent atom spheres in CX algorithm, by dividing the probe sphere into many cubic lattices and labelling the cubic lattices inside the atoms or not respectively. The results indicated that the CCI algorithm improved the accuracy of CX, and not increased the computing complexity. The proposed CCI is a fast and simple method that can accurately describe the exposure degree of atoms in protein and reveal protein functional sites, such as active sites and ubiquitination site.
Keywords: structural parameter; convex-and-concave index; degree of exposure; protein surface; active site; ubiquitination site.
Special Issue on: ICIBM 2016 Recent Advances in Computational Systems Biology and Bioinformatics
A Flexible Approach to Reconstruct the Genomic Spatial Structure by the Genetic Algorithm
by Yan Zhang, William Hoskins, Ruofan Xia, Xiya Xia, Jim W. Zheng, Jijun Tang
Abstract: The 3D structures of the chromosomes play fundamental roles in essential cellular functions, e.g. gene regulation, gene expression, evolution. HiC technique provides the interaction density between loci on chromosomes.\r\nSeveral approaches have been developed to reconstruct the 3D model of the chromosomes from HiC data. However, all of the approaches are based on a particular mathematical model and lack of flexibility for new development.\r\nWe introduce a novel approach using the genetic algorithm. Our approach is\r\nflexible to accept any mathematical models to build a 3D chromosomal structure.\r\nAlso, our approach outperforms current techniques in accuracy
Keywords: : Genome; Spatial Structure; Genetic Algorithm; HiC.