Showing papers by "Mansi Sharma published in 2018"

Homology Modeling, Molecular Docking and Molecular Dynamics Based Functional Insights into Rice Urease Bound to Urea

Abstract: Urease (EC 3.5.1.5) is an important member of most popular amidohydrolases superfamily that is well known for catalyzes the hydrolysis of urea into ammonia and carbon dioxide. Urease protein exclusively found in a wide range of living organisms including plant, algae, bacteria, fungi and some invertebrates. In plants, urease play an important role of recapturing the nitrogen from urea. Despite its critical interplay in plants the structural and functional aspects of urease in O. sativa are still unresolved. In the present study, a three-dimensional structure of rice urease was deduced by using homology modelling based approach. Molecular dynamics simulations were performed to gain further insight into the molecular mechanism and mode of action of urease of rice. Further, the possible binding interactions of modeled structure of urease with urea were assessed by using a geometry-based molecular docking algorithm. The study reveals the role of Ser324, Ala329 and Val385 of rice urease enzyme in binding with the substrate urea. In conclusion, this study presents a 3D model of rice urease and helps understanding the molecular basis for the mechanism of urease interaction with substrate urea at atomic level.

An Integrated Optimization Approach for Depth Map Enhancement on Special Riemannian Manifold

Abstract: Depth images captured by consumer depth sensors like ToF Cameras or Microsoft Kinect are often noisy and incomplete. Most existing methods recover missing depth values from low quality measurements using information in the corresponding color images. However, the performance of such methods is susceptible when color image is noisy or correlation between RGB-D is weak. This paper presents a depth map enhancement algorithm based on Riemannian Geometry that performs depth map de-noising and completion simultaneously. The algorithm is based on the observation that similar RGB-D patches lie in a very low-dimensional subspace over the Riemannian quotient manifold of varying-rank matrices. The similar RGB-D patches are assembled into a matrix and optimization is performed on the search space of this quotient manifold with Kronecker product trace norm penalty. The proposed convex optimization problem on a special quotient manifold essentially captures the underlying structure in the color and depth patches. This enables robust depth refinement against noise or weak correlation between RGB-D data. This non-Euclidean approach with Kronecker product trace-norm constraints and cones in the non-linear matrix spaces provide a proper geometric framework to perform optimization. This formulates depth map enhancement as a matrix completion problem in the product space of Riemannian manifolds. This Riemannian submersion automatically handles ranks that change over matrices, and ensures guaranteed convergence over constructed manifold. The experiments on public benchmarks RGB-D images show that proposed method can effectively enhance depth maps.