Sunil Pranit Lal

SucStruct: Prediction of succinylated lysine residues by using structural properties of amino acids

Post-Translational Modification (PTM) is a biological reaction which contributes to diversify the proteome. Despite many modifications with important roles in cellular activity, lysine succinylation has recently emerged as an important PTM mark. It alters the chemical structure of lysines, leading to remarkable changes in the structure and function of proteins. In contrast to the huge amount of proteins being sequenced in the post-genome era, the experimental detection of succinylated residues remains expensive, inefficient and time-consuming. Therefore, the development of computational tools for accurately predicting succinylated lysines is an urgent necessity. To date, several approaches have been proposed but their sensitivity has been reportedly poor. In this paper, we propose an approach that utilizes structural features of amino acids to improve lysine succinylation prediction. Succinylated and non-succinylated lysines were first retrieved from 670 proteins and characteristics such as accessible surface area, backbone torsion angles and local structure conformations were incorporated. We used the k-nearest neighbors cleaning treatment for dealing with class imbalance and designed a pruned decision tree for classification. Our predictor, referred to as SucStruct (Succinylation using Structural features), proved to significantly improve performance when compared to previous predictors, with sensitivity, accuracy and Mathews correlation coefficient equal to 0.7334-0.7946, 0.7444-0.7608 and 0.4884-0.5240, respectively.

Protein Structural Class Prediction via k-Separated Bigrams Using Position Specific Scoring Matrix

Protein structural class prediction (SCP) is as important task in identifying protein tertiary structure and protein functions. In this study, we propose a feature extraction technique to predict secondary structures. The technique utilizes bigram (of adjacent and k-separated amino acids) information derived from Position Specific Scoring Matrix (PSSM). The technique has shown promising results when evaluated on benchmarked Ding and Dubchak dataset.

Low Reynolds number airfoil optimization for wind turbine applications using genetic algorithm

Optimization of a low Reynolds number airfoil for use in small wind turbines is carried out using a Genetic Algorithm (GA) optimization technique. With the aim of creating a roughness insensitive airfoil for the tip region of turbine blades, a multi-objective genetic algorithm code is developed. A review of existing parameterization and optimization methods is presented along with the strategies applied to optimize the airfoil in this study. A composite Bezier curve is used to parameterize the airfoil. The resulting airfoil, the USPT2 has a maximum thickness of 10% and shows insensitivity to roughness at the optimized angles and at other angles of attack as well. The characteristics of USPT2 are studies by comparing it against the popular SG6043 airfoil. While a slight loss in lift is noticed for both airfoils, the drag increments due to early transition are noticeable as well. The airfoil is also studied using computational fluid dynamics (CFD) and wind tunnel experiments during free and forced transitio...

Probabilistic expression of spatially varied amino acid dimers into general form of Chou׳s pseudo amino acid composition for protein fold recognition.

BACKGROUND Identification of the tertiary structure (3D structure) of a protein is a fundamental problem in biology which helps in identifying its functions. Predicting a protein׳s fold is considered to be an intermediate step for identifying the tertiary structure of a protein. Computational methods have been applied to determine a protein׳s fold by assembling information from its structural, physicochemical and/or evolutionary properties. METHODS In this study, we propose a scheme in which a feature extraction technique that extracts probabilistic expressions of amino acid dimers, which have varying degree of spatial separation in the primary sequences of proteins, from the Position Specific Scoring Matrix (PSSM). SVM classifier is used to create a model from extracted features for fold recognition. RESULTS The performance of the proposed scheme is evaluated against three benchmarked datasets, namely the Ding and Dubchak, Extended Ding and Dubchak, and Taguchi and Gromiha datasets. CONCLUSIONS The proposed scheme performed well in the experiments conducted, providing improvements over previously published results in literature.

Subcellular localization for Gram positive and Gram negative bacterial proteins using linear interpolation smoothing model

Protein subcellular localization is an important topic in proteomics since it is related to a protein׳s overall function, helps in the understanding of metabolic pathways, and in drug design and discovery. In this paper, a basic approximation technique from natural language processing called the linear interpolation smoothing model is applied for predicting protein subcellular localizations. The proposed approach extracts features from syntactical information in protein sequences to build probabilistic profiles using dependency models, which are used in linear interpolation to determine how likely is a sequence to belong to a particular subcellular location. This technique builds a statistical model based on maximum likelihood. It is able to deal effectively with high dimensionality that hinders other traditional classifiers such as Support Vector Machines or k-Nearest Neighbours without sacrificing performance. This approach has been evaluated by predicting subcellular localizations of Gram positive and Gram negative bacterial proteins.

Emergent Motion Characteristics of a Modular Robot through Genetic Algorithm

In this paper we present an approach to transform individual behaviour of homogenous sub-systems to yield desired global behaviour of the overall system. As a test bed we consider the brittle star robot as the system which is composed of homogenous modules (sub-systems). Using genetic algorithm, the rotational motion of the individual modules is translated into rectilinear motion of the robot. We argue that given a set of sub-system level behaviours, it is better to discover intermediate system level infinitesimal behaviours as a stepping stone to developing desired system level global behaviour.

PSSM-Suc: Accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction

Post-translational modification (PTM) is a covalent and enzymatic modification of proteins, which contributes to diversify the proteome. Despite many reported PTMs with essential roles in cellular functioning, lysine succinylation has emerged as a subject of particular interest. Because its experimental identification remains a costly and time-consuming process, computational predictors have been recently proposed for tackling this important issue. However, the performance of current predictors is still very limited. In this paper, we propose a new predictor called PSSM-Suc which employs evolutionary information of amino acids for predicting succinylated lysine residues. Here we described each lysine residue in terms of profile bigrams extracted from position specific scoring matrices. We compared the performance of PSSM-Suc to that of existing predictors using a widely used benchmark dataset. PSSM-Suc showed a significant improvement in performance over state-of-the-art predictors. Its sensitivity, accuracy and Matthews correlation coefficient were 0.8159, 0.8199 and 0.6396, respectively.

Evolving Motion Control for a Modular Robot

This paper documents our ongoing efforts in devising efficient strategies in motion control of the brittle star-typed robot. As part of the control framework, each robotic leg consisting of series of homogenous modules is modeled as a neural network. The modules representative of neurons are interconnected via synaptic weights. The principle operation of the module involves summing the weighted input stimulus and using a sinusoidal activation function to determine the next phase angle. Motion is achieved by propagating phase information from the modules closest to the main body to the remainder of the modules in the leg via the synaptic weights. Genetic algorithm was used to evolve near optimal control parameters. Simulations results indicate that the current neural network inspired control model produces better motion characteristics than the previous cellular automata-based control model as well as addresses other issues such as fault tolerance.

Studies on motion control of a modular robot using cellular automata

In this paper we report preliminary findings of using cellular automata (CA) as an underlying architecture in controlling the motion of a five-legged brittle star typed robot. Three control models were incrementally designed making use of genetic algorithm (GA) as well as co-evolutionary algorithm in finding appropriate rules for automaton. Simulations using Open Dynamics Engine (ODE) was used to verify the rules obtained for each of the models. The indications from the results are promising in support for CA as feasible means for motion control.

Improving protein fold recognition and structural class prediction accuracies using physicochemical properties of amino acids

Predicting the three-dimensional (3-D) structure of a protein is an important task in the field of bioinformatics and biological sciences. However, directly predicting the 3-D structure from the primary structure is hard to achieve. Therefore, predicting the fold or structural class of a protein sequence is generally used as an intermediate step in determining the proteins 3-D structure. For protein fold recognition (PFR) and structural class prediction (SCP), two steps are required - feature extraction step and classification step. Feature extraction techniques generally utilize syntactical-based information, evolutionary-based information and physicochemical-based information to extract features. In this study, we explore the importance of utilizing the physicochemical properties of amino acids for improving PFR and SCP accuracies. For this, we propose a Forward Consecutive Search (FCS) scheme which aims to strategically select physicochemical attributes that will supplement the existing feature extraction techniques for PFR and SCP. An exhaustive search is conducted on all the existing 544 physicochemical attributes using the proposed FCS scheme and a subset of physicochemical attributes is identified. Features extracted from these selected attributes are then combined with existing syntactical-based and evolutionary-based features, to show an improvement in the recognition and prediction performance on benchmark datasets.