- New tools to search for drugs and drug targets
- Improved interface for 3D visualisation using Jmol/JSmol
- An update to the representation of protein symmetry and stoichiometry.
- Improvements when performing sequence searches.

Reference

http://www.rcsb.org/pdb/static.do?p=general_information/whats_new.jsp?b=1308

The group is both computational and experimental.

We ask biological questions to large datasets made using novel genomics techniques, with the help of computers. One of the strengths in the group are the many connections to high-profile experimental laboratories which supply data to be analyzed.

Lab webpage @ http://people.binf.ku.dk/albin/Sandelin_group_at_the_Bioinformatic_Centre/The_Sandelin_group.html

Translational recoding.

RNA editing.

Evolution of the genetic code and translation.

More at http://lapti.ucc.ie/research.html

Lab page http://lapti.ucc.ie/index.html

Job Description: We are interested in finding an excellent postdoc with interests in protein functional annotation, machine learning and computer grids. The position is open for 3.5 years at the Université Pierre et Marie Curie, in the heart of paris.

Research topic: Protein function annotation, multiple probabilistic models, domain architecture, machine learning, combinatorial optimization, computer grid.

Title: A novel integrative platform for large scale protein annotation that exploits a multitude of diversified probabilistic models in several protein signature databases.

We propose a novel integrated approach for large scale protein annotation that will exploit an unprecedented amount of genomic data as well as sophisticated machine learning techniques and combinatorial optimization approaches taking advantages of High Performance Computing (HPC) environments. The idea is to uncover as much as possible the evolutionary processes of protein sequences that took place throughout the whole tree of life and that affected the evolution of a protein family. We have already demonstrated in a previous work that the problem of functional annotation is inherent to the ability of uncovering such paths. Now, we shall extend this approach to large scale genome annotation by considering 11 different protein databases, constituted by about 10^9 protein sequences, and by producing a large pool of diversified probabilistic models coding for about 10^7 evolutionary protein pathways. Such models will be used to search for specific domains in genomes to be annotated. Our previous methodology needs to be fundamentally improved to deal with this large amount of biological data. In this project, we shall work on the algorithms to reduce the space of models and the search complexity, and we shall implement some important algorithmic changes towards the realization of a powerful integrated annotation tool.

Where: This project is run on the Laboratoire de Biologie Computationnelle et Quantitative UMR7238 CNRS-UPMC – Analytical Genomics team, headed by A.Carbone. It is co-advised with Pierre-Henri Wuillemin, Laboratoire d’Informatique de Paris 6 – Equipe DECISION.

Start date: September 1st, 2014
Contact Person: Alessandra Carbone
Contact: alessandra.carbone@lip6.fr

Essential qualifications: First class M. Sc. in any branch of life sciences and qualified CSIR-UGC NET/GATE Examination.

Desirable qualifications: Practical experience in biochemical and biophysical studies of proteins

Emoluments: as per DBT norms

The project is tenable for two years, initially for one year.

Age: Below 28 years (relaxable in the case of SC/ST/OBC/women candidates)

Candidates are requested to bring two sets of complete applications on plain paper furnishing bio-data and copies of attested certificates along with originals (for verification) on the date of interview.

No TA/DA is admissible for candidates appearing at the interview.

Dr. Rajat Banerjee
Assistant Professor
Department of Biotechnology and
Dr. B. C. Guha Centre for Genetic Engineering and Biotechnology
University College of Science
35, Ballygunge Circular Road
Kolkata 700019

Advertisement: www.caluniv.ac.in/news/jrf_biotech_2.pdf

 http://metapred2cs.ibers.aber.ac.uk/

Address of the bookmark: https://github.com/martinjvickers/MetaPred2CS

Following are the list of tools commonly used to PPIs predictions:

Protein-Protein Interaction Sites

PPISP

A consensus neural network method for predicting protein-protein interaction sites

HOMCOS

A server to predict interacting protein pairs and interacting sites by homology modeling of complex structures

HotPOINT

Prediction of protein interfaces using an empirical model

ISIS

Prediction of interaction hotspots from sequence

KFC server

Automated decision-tree approach to predicting protein-protein interaction hot spots

meta-PPISP

A meta server for predicting protein-protein interaction sites. meta-PPISP is built on three individual web servers: cons-PPISP, PINUP, and Promate

ODA

Identification of optimal surface patches with the lowest docking desolvation energy values

PINUP

Protein binding site prediction with an empirical scoring function

Other Sites (DNA, RNA, Metals)

CHED 

Web server for predicting soft metal binding sites in proteins

DBD-Hunter

A knowledge-based method for the prediction of DNA-protein interactions

DISPLAR

Given the structure of a protein known to bind DNA, the method predicts residues that contact DNA using neural network method

iDBPs

Predicts DNA binding proteins for proteins with known 3D structure.

PFplus

More http://www.nieduszynski.org/index.php
http://www.path.ox.ac.uk/research/cell-biology-and-pathology/conrad-nieduszynski-group