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Shiv Nadar University

Center for Informatics

Majority of proteins undergo important post-translational modifications (PTM) that may alter physical and chemical properties of the protein and mainly their functions. Laboratory processes of determining PTM sites in proteins are laborious and expensive. On the contrary, computational approaches are far swifter and economical; and the models for prediction of PTMs can be quite accurate too. Among the PTMs, Protein N- terminal N-myristoylation by myristoyl-CoA protein N-myristoyltransferase (NMT) is an important lipid anchor modification of eukaryotic and viral proteins; occurring in about 0.5% encoded NMT substrates. Reliable recognition of myristoylation capability from the substrate amino acid sequence is useful for proteomic functional annotation projects as also in building therapeutics targeting the NMT. Using computational techniques, prediction-based models can be developed and new functions of protein substrates can be identified. In this study, we employ Biogeography based Optimization (BBO) for feature selection along with Support Vector Machines (SVM) and Random Forest for classification of N-myristoylation sequences. The simulations indicate that N-myristoylation sites can be identified with high accuracy using hybrid BBO wrappers in combination with weighted filter methods. © 2012 Springer-Verlag.

World University root node

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Hybrid biogeography based simultaneous feature selection and prediction of N-myristoylation substrate proteins using support vector machines and random forest classifiers

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Journal of Materials Science

Recent advances in first principles computations in materials research

Molecular Modeling for the Design of Novel Performance Chemicals and {\ldots}

14 First Principles Modeling of

Undoped cages of (ZnO)n nanoclusters are magic with n = 12 and 34. However with Mn doping, our ab initio calculations show that O rich, nonstoichiometric Zn12MnO15 and Zn34MnO 37 nanoclusters obtained from n = 13 and 35, become magic with 1μB magnetic moment. The Mn-doped nanoclusters can also be viewed as composite structures where a MnOx species (x = 1-4) is attached to (ZnO)n (n = 12 and 34) cages. Mn atoms can exist in a high oxidation state in these nanoclusters, with the highest binding energy for x = 3. Further replacement of Mn by Cr leads to a neutral, nonmagnetic Zn12CrO 15 nanocluster with a large highest occupied molecular orbital and lowest unoccupied molecular orbital gap of 2.24 eV. These results provide new ground to understand doped nanostructures of ZnO and may also shed light on magnetism in Mn-doped ZnO thin films. © 2012 American Physical Society.

Physical Review B - Condensed Matter and Materials Physics

Nonstoichiometric Mn-doped ZnO magic nanoclusters and their composite structures from ab initio calculations

Defensins are considered to play an important role in the innate immune system of virtually all life forms, from insects and plants to amphibians and mammals. They are classified into alpha, beta and theta-defensins. Fast and accurate computational prediction of defensin and defensin types will help in annotating unidentified defensin novel peptides. Identified defensins, owing to their small length and potent antimicrobial activity can be used effectively for development of new clinically applicable antibiotics. Thus predicting the defensin candidates will aid in accurate identification of novel peptide drugs. Support vector machines prediction model accuracy was 99% for defensin and defensin types. The results indicate that it is most accurate and efficient prediction method for defensin peptides. User friendly defensin web server is provided at www.defensinpred.cdac.in for the benefit of scientific community.

Protein and peptide letters

Defensinpred: defensin and defensin types prediction server.

Small nanoparticles of III-V compound semiconductors have often been considered to have open-cage structures. However, using first-principles calculations, we report the finding of a structural transition from empty-cage structures for (AlN) n and (GaN) n nanoparticles up to n = 34 that we studied, to a filled-cage structure for (InN) 32. Further, phosphides and arsenides of Al and In have an early transition to three-dimensional (3D) filled-cage structures. Our results show that a 3D (AlP) 13 is strongly magic with high binding energy and large highest occupied-lowest unoccupied molecular orbital gap. But nanoparticles of GaP show a transition from an empty cage for n = 13 to a strongly magic 3D filled cage for n = 32. The latter has a cage of (GaP) 28 and a (GaP) 4 squashed cube inside, the atoms on which are well connected with the cage. The bonding characteristics and the reason for structural transition are discussed. {\textcopyright} 2012 American Physical Society.

Empty cage to three-dimensional structural transition in nanoparticles of III-V compound semiconductors: The finding of magic (AlP) 13 and (GaP) 32

The ground state structures of neutral and anionic clusters of Na nSim (1 ≤ n ≤ 3, 1 ≤ m ≤ 11) have been determined using genetic algorithm incorporated in first principles total energy code. The size dependence of the structural and electronic properties is discussed in detail. It is found that the lowest-energy structures of Na nSim clusters resemble those of the pure Si clusters. Interestingly, Na atoms in neutral NanSim clusters are usually well separated by the Sim skeleton, whereas Na atoms can form Na-Na bonds in some anionic clusters. The ionization potentials, adiabatic electron affinities and photoelectron spectra are also calculated and the results compare well with the experimental data. {\textcopyright} 2011 American Institute of Physics.

Journal of Chemical Physics

Lowest-energy structures and electronic properties of Na-Si binary clusters from ab initio global search

This paper addresses the issue of identifying important input features in building an intrusion detection system (IDS). Elimination of the insignificant and useless inputs leads to a simplification of the problem, faster and more accurate systems. The aim is to achieve maximum detection accuracy and to minimize computational complexity. We use algorithms like Ant Colony Optimization (ACO), Genetic Algorithm (GA), Recursive Frequent Elimination (SVM-RFE) and Minimal-Redundancy-Maximal-Relevance (mRMR) for identifying important set of features from the IDS data. We have also used information gain and chi square statistics tool of WEKA (machine learning toolset) to rank the features and identified the important ones. Ant Miner is used for classification and prediction of the data after the feature selection. After identification of the relevant features, the input data is taken as per the reduced feature subset and the optimum parameters for Ant Miner are selected. The experiments were performed using KDDCUP-99 dataset, which is the benchmark dataset for intrusion detection. The prediction/detection accuracy achieved and the time taken for training and testing after use of each hybrid system on the data is finally compared.