Protein-protein interactions are central to understanding the functional relationship between proteins. We have curated the available information and augmented with context-specific analysis to ease your research journey.
This is an indigenously developed web-based application designed by Dr. Samrat Chatterjee and his group, which can generate context-specific directional PPI networks from the input proteins and detect their biological and topological importance in the network. konnect2prot (k2p) contains data curated from various databases with multidimensional information to build a network relevant to the end user. This platform is also equipped with network algorithms that can further enhance the decision making process.
We study biological systems in their entirety or in fragments through the lens of system biology. The former covers topics such as network biology, machine learning-based drug discovery, and so forth, while the latter covers various differential equation based kinetic models. The group is supervised by Dr. Samrat Chatterjee and consists of post doctoral fellows, PhD student and research fellows. They are mainly from mathematical and computational backgrounds keen to work on biological problems in an interdisciplinary environment.Read More
Some significant recent publications from the group.
He is a Mathematician. He uses mathematical modelling and system biology to understand biological problems. He formulated and supervised the project.
He is a Computational Biophysicist. His main area of research is structural bioinformatics and drug discovery. He helped in the supervision of the project.
He is doing PhD in Computational Biology. His major work domain is to develop pattern recognition algorithms using AI/ML. He designed and developed the web application.
Other contributers: Ekant Sharma, Komal Sharma, Krishna
We would like to thank DBT for proving us the finanical support for this project.
Funding body by Govt. of India
We would like to thank OmniPath for providing directional relationship of protein pairs.
Directional database for PPI
We would like to thank signor for providing directional relationship of protein pairs.
Directional database for PPI
We would like to thank disgenet for providing disease information related to protein.
We would like to thank KEGG for providing information of biological pathways associated with proteins.
Biological pathway database
We would like to thank GO for providing information of biological process and molecular functions associated with proteins.
Biological ontology database
We would like to thank HPA for providing information of tissue specific expression and subcellular associated with proteins.
Human Protein Atlas
We would like to thank THSTI for providing the infrastructure to carry out the work.
We would like to thank uniprot for providing detailed information of protein charaterstics including its functions and pathways.
Protein information database
We would like to thank Enrichr for providing enrichment score of pathway, functions and diseases.
Protein enrichment webserver
We would like to thank PDB for providing structure related information.
Protein structure database
We would like to thank DGIdb for providing information of ligands and classes associated with proteins.
Drug target database
We would like to thank signalink for providing information of signalling pathways associated with proteins.
Signalling pathway database
We would like to thank DoCM for providing information of disease-causing mutations associated with proteins.
Disease mutation database
We would also like to pass our gratitude to open source software develpers of Flask, Python, Dash (Plotly), MongoDB.
k2p is a one-stop junction for identifying the spreaders in a PPI network constructed from user-given inputs. It is a web application of directional PPI which can be filtered according to user-driven context. Currently, context-specificity is defined as filtering the network through the use of localization, pathways, biological processes, molecular functions, and tissue specificity. Additionally, it also displays structural insights into the proteins, their available ligands, and disease-specific mutations.
Currently it contains only proteins from human interactome.
k2p extracts causal information data from the signor and omnipath. The cellular localization and tissue-specificity information is curated from The Human Protein Atlas, information of pathways are taken from the kegg, biological process, and molecular function information is curated from GO, protein class and ligands information is taken from DGIdb, disease-based information is curated from DisGeNet, the structural information is curated from the PDB database and finally the disease mutation data is taken from the D3 distal database.
Identifying a set of influential spreaders in complex networks plays a crucial role in effective information spreading which are identified using the voterank algorithm. In this approach, all nodes vote in a spreader in each turn, and the voting ability of neighbors of the elected spreader will be decreased in subsequent turns. During the investigation, the identified spreaders could be explored for various applications such as potential drug targets.
k2p only allows the extraction of various network images and statistical information generated from the network. The complete data is available upon request.
k2p has used Enrichr for the enrichment of biological pathways and ontological enrichment.
k2p is a non-commercial open-access web application for academic purposes only. It is copyright protected under Indian Copyright Dairy No.: 14303/2020-CO/SW and funded by the Department of Biotechnology (DBT) (Ministry of Science and Technology, Govt. of India). Grant number: BT/PR15426/BRB/10/1459/2015.
You don't require any permission, however, the citation to our manuscript will be highly appreciated.
Shivam Kumar, Dipanka Tanu Sarmah, Shailendra Asthana, Samrat Chatterjee, konnect2prot: A web application to explore the protein properties in a functional protein-protein interaction network, Bioinformatics, 2022;, btac815, https://doi.org/10.1093/bioinformatics/btac815
Please visit the tutorial page for more details.
We have made this far on our own, collectively we can accomplish much more !!!