Surbhi Dhingra

Surbhi Dhingra
University of Nantes | UNIV Nantes · Unit for functional Integeration of Proteins (UFIP)

PhD

About

10
Publications
1,276
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28
Citations
Introduction
The PhD project revolves around development of a de novo protein structure prediction pipeline using the structural alphabet type called Protein Blocks (PBs). To this end, a fragment generation pipeline has been standardised to generate PB based fragment libraries for a given protein sequence. Along with it, Initial experiments have been performed for construction of protein models using Modeller.
Additional affiliations
April 2017 - June 2020
University of Nantes
Position
  • PhD Student
April 2015 - April 2017
NCBS
Position
  • Research Assistant
Education
August 2013 - July 2015
Institute of Bioinformatics and Applied Biotechnology
Field of study
  • Biotechnology and Bioinformatics

Publications

Publications (10)
Conference Paper
Full-text available
An impressive number of modern proteins have been generated through mechanisms such as recombination, duplication, and accretion of smaller protein fragments. These subdomain-sized fragments are believed to be peptidal ancestors of the proteins that exist today (Romero-Romero et al., 2021). In an effort to locate all fragments shared within the kno...
Preprint
Full-text available
Motivation: Computational protein structure prediction has taken over the structural community in past few decades, mostly focusing on the development of Template-Free modelling (TFM) or ab initio modelling protocols. Fragment-based assembly (FBA), falls under this category and is by far the most popular approach to solve the spatial arrangements o...
Article
Full-text available
Prediction of protein structures using computational approaches has been explored for over two decades, paving a way for more focused research and development of algorithms in comparative modelling, ab intio modelling and structure refinement protocols. A tremendous success has been witnessed in template-based modelling protocols, whereas strategie...
Preprint
Full-text available
Prediction of protein structures using computational approaches has been explored for over two decades, paving a way for more focused research and development of algorithms in comparative modelling, ab intio modelling and structure refinement protocols. A tremendous success has been witnessed in template-based modelling protocols, whereas strategie...
Poster
Full-text available
This poster gives an overview of our discovery of complete olfactory receptor (OR) gene sets from two solitary bee genomes using computational approaches. Although the comparison with other social Hymenopteran and other insect species shows no evidence for increased olfactory receptor repertoires in social species, few subfamilies of olfactory rece...
Article
Full-text available
Olfactory/odorant receptors (ORs) probably govern eusocial behaviour in honey bees through detection of cuticular hydrocarbons (CHCs) and queen mandibular gland pheromones (QMP). CHCs are involved in nest-mate recognition whereas QMP acts as sex pheromone for drones and as retinue pheromone for female workers. Further studies on the effect of eusoc...

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Projects

Projects (2)
Project
Phylogenetic analysis of newfound ORs complemented with RNA-seq analysis, resulted in the identification of sex-pheromone receptors.