NMD SERVER: NATURAL MEDICINES DATABASE FOR DRUG DISCOVERY

Cancer is the most frequently diagnosed disease globally and the second leading cause of the death. Natural Medicines are the alternative form of treatment that includes use of various plants. It is one of the safe treatment option to treat cancer and safer than allopathic medicines in order to reduce side effects. NMD Server: Natural Medicines Database for Drug Discovery is a unique and significant database of its kind, giving researchers, medical practitioners, pharmaceutical industries and students of Life Sciences an instant access to over 354 records of Natural Medicines which may be developed and used for treatment of Cancer. This database constitutes the specific information related to Natural Medicines and their respective target sites. NMD Server: Natural Medicines Database for Drug Discovery provides all the information (database fields) regarding the physiological parameters of database and is considered to be the linked table with pre-determined values and names that are included to aid in populating the fields of the linked tables. There have been many different types of fields with its respective data types that have been designated on the basis of data provided. NMDdock Tools have been integrated in this database for convenience for users like docking analysis of target and natural medicine, Sensitivity & Specificity analysis of natural medicine, Linear Correlation and Regression tool, Sequence Manipulation of target, Statistical Analysis. For the precise information about any particular drug, connectivity has been made with other databases and applications based highly bioinformatics tools have been embedded for convenience of users.&nbsp

New Trends in Artificial Intelligence: Applications of Particle Swarm Optimization in Biomedical Problems

Optimization is a process to discover the most effective element or solution from a set of all possible resources or solutions. Currently, there are various biological problems such as extending from biomolecule structure prediction to drug discovery that can be elevated by opting standard protocol for optimization. Particle swarm optimization (PSO) process, purposed by Dr. Eberhart and Dr. Kennedy in 1995, is solely based on population stochastic optimization technique. This method was designed by the researchers after inspired by social behavior of flocking bird or schooling fishes. This method shares numerous resemblances with the evolutionary computation procedures such as genetic algorithms (GA). Since, PSO algorithms is easy process to subject with minor adjustment of a few restrictions, it has gained more attention or advantages over other population based algorithms. Hence, PSO algorithms is widely used in various research fields like ranging from artificial neural network training to other areas where GA can be used in the system

Artificial Neural Networks for Prediction of Tuberculosis Disease

Background: The global burden of tuberculosis (TB) and antibiotic resistance is attracting the attention of researchers to develop some novel and rapid diagnostic tools. Although, the conventional methods like culture are considered as the gold standard, they are time consuming in diagnostic procedure, during which there are more chances in the transmission of disease. Further, the Xpert MTB/RIF assay offers a fast diagnostic facility within 2 h, but due to low sensitivity in some sample types may lead to more serious state of the disease. The role of computer technologies is now increasing in the diagnostic procedures. Here, in the current study we have applied the artificial neural network (ANN) that predicted the TB disease based on the TB suspect data.Methods: We developed an approach for prediction of TB, based on an ANN. The data was collected from the TB suspects, guardians or care takers along with samples, referred by TB units and health centers. All the samples were processed and cultured. Data was trained on 12,636 records of TB patients, collected during the years 2016 and 2017 from the provincial TB reference laboratory, Khyber Pakhtunkhwa, Pakistan. The training and test set of the suspect data were kept as 70 and 30%, respectively, followed by validation and normalization. The ANN takes the TB suspect’s information such as gender, age, HIV-status, previous TB history, sample type, and signs and symptoms for TB prediction.Results: Based on TB patient data, ANN accurately predicted the Mycobacterium tuberculosis (MTB) positive or negative with an overall accuracy of >94%. Further, the accuracy of the test and validation were found to be >93%. This increased accuracy of ANN in the detection of TB suspected patients might be useful for early management of disease to adopt some control measures in further transmission and reduce the drug resistance burden.Conclusion: ANNs algorithms may play an effective role in the early diagnosis of TB disease that might be applied as a supportive tool. Modern computer technologies should be trained in diagnostics for rapid disease management. Delays in TB diagnosis and initiation treatment may allow the emergence of new cases by transmission, causing high drug resistance in countries with a high TB burden

Insights into unbound–bound states of GPR142 receptor in a membrane-aqueous system using molecular dynamics simulations

<p>G protein coupled receptors (GPCRs) are source machinery in signal transduction pathways and being one of the major therapeutic targets play a significant in drug discovery. GPR142, an orphan GPCR, has been implicated in the regulation of insulin, thereby having a crucial role in Type II diabetes management. Deciphering of the structures of orphan, GPCRs (O-GPCRs) offer better prospects for advancements in research in ion translocation and transduction of extracellular signals. As the crystallographic structure of GPR142 is not available in PDB, therefore, threading and <i>ab initio</i>-based approaches were used for 3D modeling of GPR142. Molecular dynamic simulations (900 ns) were performed on the 3D model of GPR142 and complexes of GPR142 with top five hits, obtained through virtual screening, embedded in lipid bilayer with aqueous system using OPLS force field. Compound 1, 3, and 4 may act as scaffolds for designing potential lead agonists for GPR142. The finding of GPR142 MD simulation study provides more comprehensive representation of the functional properties. The concern for Type II diabetes is increasing worldwide and successful treatment of this disease demands novel drugs with better efficacy.</p

3D Structure Modeling of Catalase Enzyme from Aspergillus fumigatus

The respiratory diseases in humans, such as aspergilloma, allergic bronchopulmonary aspergillosis and invasive aspergillosis are caused by the fungal pathogen Aspergillus fumigatus (A. fumigatus) The enzyme catalase of A. fumigatus provides a putative virulence to this fungal pathogen against the toxic effects of human hydrogen peroxide, which they cleave into water and molecular oxygen. The 3-D tructure of this protein in A. fumigatus is not known, while it is very important for understanding the molecular mechanism of action of this enzyme and development of new drugs for various respiratory diseases. This article proposes the 3-D structure of catalase enzyme from A. fumigatus which has been predicted and validated using different computational programs. Based on the percentage of residue occurrence in helical, strand and loop regions, four structural domains have been identifi ed in the modeled structure. The structure and function relationship for all identifi ed structural domains have been also described. This study will be helpful for in silico drug discovery against the virulence nature of Aspergillus fumigatus.</p

Nano-particle mediated inhibition of Parkinson's disease using computational biology approach

Parkinson's disease (PD) arises as neurodegenerative disorder and characterized by progressive deterioration of motor functions due to forfeiture of dopamine-releasing neurons. During PD, neurons at stake loss their functionality that results into cognition impairment and forgetfulness, commonly called as dementia. Recently, nanoparticles (NPs) have been reported for easy drug delivery through blood-brain barrier (BBB) into the central nervous system (CNS) against the conventional drug delivery systems. However, present study attempted to elucidate the α-synuclein activity, a major factor casing PD, in presence of its inhibitor cerium oxide (CeO2) nanoparticle via computational biology approach. A computational analysis was also conducted for the α-synuclein activity with biocompatible metal NPs such as GOLD NPs and SPIONs to scrutinize the efficacy and degree of inhibition induced by the CeO2 NP. The obtained results concluded that CeO2 NP fit best in the active site of α-synuclein with good contacts and interaction, and potentially inhibited the PD against L-DOPA drug selected as positive control in the designed PD biochemical pathway. Hence, CeO2 NP has been purposed as potential inhibitor of α-synuclein and can be employed as nano-drug against the PD

Receptor thermodynamics of ligand–receptor or ligand–enzyme association

Experimental techniques that directly assess the thermodynamics of ligand–receptor or ligand–enzyme association, such as isothermal titration calorimetry, have been improved in recent years and can provide thermodynamic details of the binding process. Parallel to the continuous increase in computational power, several classes of computational methods have been developed that can be used to get a more detail insight into the mode and affinity of compounds (drug) to their target (off). Such methods are affiliated with a qualitative and/or quantitative assessment of binding free energies, and differently trade off speed versus physical accuracy. With the current wealth of available three-dimensional structures of proteins and their complexes with ligands, structure-based drug design studies can be used to identify the key ligand interactions and free energy calculations, and can quantify the thermodynamics of binding between ligand and the target of interest

Thermodynamic cycles and their application in protein targets

A key part of drug design and development is the optimization of molecular interactions between an engineered drug candidate and its binding target. Thermodynamic characterization provides information about the balance of energetic forces driving binding interactions and is essential for understanding and optimizing molecular interactions. Comprehensive thermodynamic evaluation is vital in the drug development process to speed drug development towards an optimal energetic interaction profile while retaining good pharmacological properties. Practical thermodynamic approaches, such as enthalpic optimization, thermodynamic optimization plots and the enthalpic efficiency index, have now been developed to provide proven utility in design process. Improved throughput in calorimetric methods remains essential for even greater integration of thermodynamics into drug design

Biological data analysis program (BDAP): a multitasking biological sequence analysis program

Exploration of mysterious facts from the sequences and structures of biomolecules of an organism is the essential requirement for understanding their molecular and evolutionary processes. Sequence analysis approach is an exciting choice for exploring those mysterious facts from biological data at genomic, transcriptomic and proteomic level. Development of bioinformatics tools is the most challenging task for analyzing these biological data at above three levels. In this communication, an attempt has been made to develop a bioinformatics program “Biological Data Analysis Program (BDAP)” having the ability to analyze the DNA/RNA/protein sequence data at molecular level. It also includes the links of various online databases, tools, search engines and many of the prestigious journals. The coding of the program has been done in Perl language. BDAP is freely available at https://sites.google.com/site/dwivediplanet/bdap under the terms and conditions of GNU General Public License