Abstract: With drug resistance becoming widespread in
Plasmodium falciparum infections, the development of the alternative
drugs is the desired strategy for prevention and cure of malaria. Three
drug targets were selected to screen promising drug molecules from
the GSK library of 13469 molecules. Using an in silico structure-based
drug designing approach, the differences in binding energies of
the substrate and inhibitor were exploited between target sites of
parasite and human to design a drug molecule against Plasmodium.
The docking studies have shown several promising molecules from
GSK library with more effective binding as compared to the already
known inhibitors for the drug targets. Though stronger interaction has
been shown by several molecules as compared to the reference, few
molecules have shown the potential as drug candidates though in
vitro studies are required to validate the results. In case of
thymidylate synthase-dihydrofolatereductase (TS-DHFR), three
compounds have shown promise for future studies as potential drugs.
Abstract: In this work we report the recent progresses that have been achieved by our group in the last half decade on the field of computational proteomics. Specifically, we discuss the application of Molecular Dynamics Simulations and Electronic Structure Calculations in drug design, in the clarification of the structural and dynamic properties of proteins and enzymes and in the understanding of the catalytic and inhibition mechanism of cancer-related enzymes. A set of examples illustrate the concepts and help to introduce the reader into this important and fast moving field.
Abstract: Severe acute respiratory syndrome (SARS) is a respiratory disease in humans which is caused by the SARS coronavirus. The treatment of coronavirus-associated SARS has been evolving and so far there is no consensus on an optimal regimen. The mainstream therapeutic interventions for SARS involve broad-spectrum antibiotics and supportive care, as well as antiviral agents and immunomodulatory therapy. The Protein- Ligand interaction plays a significant role in structural based drug designing. In the present work we have taken the receptor Angiotensin converting enzyme 2 and identified the drugs that are commonly used against SARS. They are Lopinavir, Ritonavir, Ribavirin, and Oseltamivir. The receptor Angiotensin converting enzyme 2 (ACE-2) was docked with above said drugs and the energy value obtained are as follows, Lopinavir (-292.3), Ritonavir (-325.6), Oseltamivir (- 229.1), Ribavirin (-208.8). Depending on the least energy value we have chosen the best two drugs out of the four conventional drugs. We tried to improve the binding efficiency and steric compatibility of the two drugs namely Ritonavir and Lopinavir. Several modifications were made to the probable functional groups (phenylic, ketonic groups in case of Ritonavir and carboxylic groups in case of Lopinavir respectively) which were interacting with the receptor molecule. Analogs were prepared by Marvin Sketch software and were docked using HEX docking software. Lopinavir analog 8 and Ritonavir analog 11 were detected with significant energy values and are probable lead molecule. It infers that some of the modified drugs are better than the original drugs. Further work can be carried out to improve the steric compatibility of the drug based upon the work done above for a more energy efficient binding of the drugs to the receptor.
Abstract: Chikungunya virus (CHICKV) is an arboviruses belonging to family Tagoviridae and is transmitted to human through by mosquito (Aedes aegypti and Aedes albopictus) bite. A large outbreak of chikungunya has been reported in India between 2006 and 2007, along with several other countries from South-East Asia and for the first time in Europe. It was for the first time that the CHICKV outbreak has been reported with mortality from Reunion Island and increased mortality from Asian countries. CHICKV affects all age groups, and currently there are no specific drugs or vaccine to cure the disease. The need of antiviral agents for the treatment of CHICKV infection and the success of virtual screening against many therapeutically valuable targets led us to carry out the structure based drug design against Chikungunya nSP2 protease (PDB: 3TRK). Highthroughput virtual screening of publicly available databases, ZINC12 and BindingDB, has been carried out using the Openeye tools and Schrodinger LLC software packages. Openeye Filter program has been used to filter the database and the filtered outputs were docked using HTVS protocol implemented in GLIDE package of Schrodinger LLC. The top HITS were further used for enriching the similar molecules from the database through vROCS; a shape based screening protocol implemented in Openeye. The approach adopted has provided different scaffolds as HITS against CHICKV protease. Three scaffolds: Indole, Pyrazole and Sulphone derivatives were selected based on the docking score and synthetic feasibility. Derivatives of Pyrazole were synthesized and submitted for antiviral screening against CHICKV.
Abstract: The worldwide prevalence of H3N2 influenza virus
and its increasing resistance to the existing drugs necessitates for the
development of an improved/better targeting anti-influenza drug.
H3N2 influenza neuraminidase is one of the two membrane-bound
proteins belonging to group-2 neuraminidases. It acts as key player
involved in viral pathogenicity and hence, is an important target of
anti-influenza drugs. Oseltamivir is one of the potent drugs targeting
this neuraminidase. In the present work, we have taken subtype N2
neuraminidase as the receptor and probable analogs of oseltamivir as
drug molecules to study the protein-drug interaction in anticipation of
finding efficient modified candidate compound. Oseltamivir analogs
were made by modifying the functional groups using Marvin Sketch
software and were docked using Schrodinger-s Glide. Oseltamivir
analog 10 was detected to have significant energy value (16% less
compared to Oseltamivir) and could be the probable lead molecule. It
infers that some of the modified compounds can interact in a novel
manner with increased hydrogen bonding at the active site of
neuraminidase and it might be better than the original drug. Further
work can be carried out such as enzymatic inhibition studies;
synthesis and crystallizing the drug-target complex to analyze the
interactions biologically.
Abstract: The small interfering RNA (siRNA) alters the
regulatory role of mRNA during gene expression by translational
inhibition. Recent studies show that upregulation of mRNA because
serious diseases like cancer. So designing effective siRNA with good
knockdown effects plays an important role in gene silencing. Various
siRNA design tools had been developed earlier. In this work, we are
trying to analyze the existing good scoring second generation siRNA
predicting tools and to optimize the efficiency of siRNA prediction
by designing a computational model using Artificial Neural Network
and whole stacking energy (%G), which may help in gene silencing
and drug design in cancer therapy. Our model is trained and tested
against a large data set of siRNA sequences. Validation of our results
is done by finding correlation coefficient of experimental versus
observed inhibition efficacy of siRNA. We achieved a correlation
coefficient of 0.727 in our previous computational model and we
could improve the correlation coefficient up to 0.753 when the
threshold of whole tacking energy is greater than or equal to -32.5
kcal/mol.
Abstract: Every 2-3 years the influenza B virus serves
epidemics. Neuraminidase (NA) is an important target for influenza
drug design. Although, oseltamivir, an oral neuraminidase drug, has
been shown good inhibitory efficiency against wild-type of influenza
B virus, the lower susceptibility to the R152K mutation has been
reported. Better understanding of oseltamivir efficiency and
resistance toward the influenza B NA wild-type and R152K mutant,
respectively, could be useful for rational drug design. Here, two
complex systems of wild-type and R152K NAs with oseltamivir
bound were studied using molecular dynamics (MD) simulations.
Based on 5-ns MD simulation, the loss of notable hydrogen bond and
decrease in per-residue decomposition energy from the mutated
residue K152 contributed to drug compared to those of R152 in wildtype
were found to be a primary source of high-level of oseltamivir
resistance due to the R152K mutation.
Abstract: Data mining incorporates a group of statistical
methods used to analyze a set of information, or a data set. It operates
with models and algorithms, which are powerful tools with the great
potential. They can help people to understand the patterns in certain
chunk of information so it is obvious that the data mining tools have
a wide area of applications. For example in the theoretical chemistry
data mining tools can be used to predict moleculeproperties or
improve computer-assisted drug design. Classification analysis is one
of the major data mining methodologies. The aim of thecontribution
is to create a classification model, which would be able to deal with a
huge data set with high accuracy. For this purpose logistic regression,
Bayesian logistic regression and random forest models were built
using R software. TheBayesian logistic regression in Latent GOLD
software was created as well. These classification methods belong to
supervised learning methods.
It was necessary to reduce data matrix dimension before construct
models and thus the factor analysis (FA) was used. Those models
were applied to predict the biological activity of molecules, potential
new drug candidates.