MOTIVATION: Allostery enables changes to the dynamic behavior of a protein at distant positions induced by binding. Here, we present APOP, a new allosteric pocket prediction method, which perturbs the pockets formed in the structure by stiffening pairwise interactions in the elastic network across the pocket, to emulate ligand binding. Ranking the pockets based on the shifts in the global mode frequencies, as well as their mean local hydrophobicities, leads to high prediction success when tested on a dataset of allosteric proteins, composed of both monomers and multimeric assemblages. RESULTS: Out of the 104 test cases, APOP predicts known allosteric pockets for 92 within the top 3 rank out of multiple pockets available in the protein. In addition, we demonstrate that APOP can also find new alternative allosteric pockets in proteins. Particularly interesting findings are the discovery of previously overlooked large pockets located in the centers of many protein biological assemblages; binding of ligands at these sites would likely be particularly effective in changing the protein's global dynamics. AVAILABILITY AND IMPLEMENTATION: APOP is freely available as an open-source code (https://github.com/Ambuj-UF/APOP) and as a web server at https://apop.bb.iastate.edu/.

SUMMARY: A new dynamic community identifier (DCI) is presented that relies upon protein residue dynamic cross-correlations generated by Gaussian elastic network models to identify those residue clusters exhibiting motions within a protein. A number of examples of communities are shown for diverse proteins, including GPCRs. It is a tool that can immediately simplify and clarify the most essential functional moving parts of any given protein. Proteins usually can be subdivided into groups of residues that move as communities. These are usually densely packed local sub-structures, but in some cases can be physically distant residues identified to be within the same community. The set of these communities for each protein are the moving parts. The ways in which these are organized overall can aid in understanding many aspects of functional dynamics and allostery. DCI enables a more direct understanding of functions including enzyme activity, action across membranes and changes in the community structure from mutations or ligand binding. The DCI server is freely available on a web site (https://dci.bb.iastate.edu/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computational prediction of cancer associated SNPs from the large pool of SNP dataset is now being used as a tool for detecting the probable oncogenes, which are further examined in the wet lab experiments. The lack in prediction accuracy has been a major hurdle in relying on the computational results obtained by implementing multiple tools, platforms and algorithms for cancer associated SNP prediction. Our result obtained from the initial computational compilations suggests the strong chance of Aurora-A G325W mutation (rs11539196) to cause hepatocellular carcinoma. The implementation of molecular dynamics simulation (MDS) approaches has significantly aided in raising the prediction accuracy of these results, but measuring the difference in the convergence time of mutant protein structures has been a challenging task while setting the simulation timescale. The convergence time of most of the protein structures may vary from 10 ns to 100 ns or more, depending upon its size. Thus, in this work we have implemented 200 ns of MDS to aid the final results obtained from computational SNP prediction technique. The MDS results have significantly explained the atomic alteration related with the mutant protein and are useful in elaborating the change in structural conformations coupled with the computationally predicted cancer associated mutation. With further advancements in the computational techniques, it will become much easier to predict such mutations with higher accuracy level.

The poster is based on the in silico identification and analysis of the variation in the gene network related to the pancreatic cancer.Pancreatic cancer has been a major cause of death in Asia and European countries.This cancer is not easily detectable in its initial stages and at the later stages it becomes very hard to cure this disease.So the in-depth understanding of the variation in the genetic pathway of this disease is very important.We selected 5 candidate genes from various published journals which are involved in the pancreatic cancer pathway (KRAS, CDKN2A, MADH4, TP53 and ARMET) in generating their interaction network using Agilent literature search plugin in cytoscape.The organic layout of interaction revealed the cross interaction between these genes and the other neighbour genes.Merging the expression profile data of the pancreatic cancer to the parent network helped us in understanding the variation of the network in the diseased state.Using the merged profile network we found out the importance of the KRAS & CDKN2A gene interaction with other 21 neighbour genes among which PIK3CA and TP53 interactions were showing major variation on their expression pattern.This study reveals the importance of change in expression level of candidate genes(KRAS,CDKN2A and TP53) in causing pancreatic cancer.The results obtained in our study will be very much useful in detecting the disease in its initial stages and in finding the cure.

Mobile communication systems are being developed operating both in outdoor and indoor environments. This calls for establishment of effective communications. IEEE-802.11b also provides wireless connectivity in both of these environments. We have made study in the scenario when a mobile user enters into an indoor environment from outside environment and establishes communication inside. This paper reports propagation measurements for two situations at 2000 MHz. In order to simulate, a source at 2000 MHz is used to illuminate the building from outside. Theoretical models have been used to calculate penetration loss, Rician factor 'K' and other channel parameters. It has been observed that when the transmitter illuminates the building from outside, the received signals inside the building attenuates severely. The received signal distribution follows d/sup -m/ power law. Measurements reveal that the received signals are attenuated by approximately 40-45 dB in the corridors of different floors. The movements of people greatly vary amplitudes of the transmitted signal and make the indoor channel nonstationary even when either both the transmitter and receiver are stationed at a particular position or transmitter is fixed and receiver moves. This aspect has also been reported in this paper.