Researchers develop AI tool to predict how shapeshifting proteins connect inside cells

Researchers at the National Centre for Biological Sciences (NCBS), Tata Institute of Fundamental Research, Bengaluru, have developed a deep-learning tool that can predict how intrinsically disordered proteins (IDP) latch on to their binding partners.

Shapeshifters, central to life

Proteins usually fold into specific shapes; However, IDPs, which are shapeshifting molecules vital to cellular communication, don’t form a fixed structure. Central to life, they guide signalling networks, help proteins move and find partners within the cell, regulate which genes are switched on or off, support protein folding and quality control, and assemble flexible cellular hubs called condensates.

But their flexibility has made it challenging to figure out how they attach to other molecules and to map them using traditional structural biology or computational tools.

On the other hand, it is crucial to understand this interaction to decode cell communication, understand disease mechanisms and design more precise treatments.

Protein language models

The tool, named Disobind and developed by the researchers, analyses the protein sequences and uses protein language models (a form of AI trained on millions of known protein sequences) to forecast which parts of a floppy, flexible protein will make contact with another protein molecule. Unlike current structure prediction methods, Disobind does not require any structural information or sequence alignments, and importantly, considers the binding partner. This is crucial because context influences interaction in the case of these floppy proteins.

Kartik Majila, lead author of the study, and his team benchmarked Disobind against predictors including AlphaFold-multimer and AlphaFold3. Disobind delivered consistently higher accuracy when tested on new protein pairs it had not seen before. When combined with AlphaFold-multimer predictions, performance improved even further.

Disease biology to drug design

“Applications span from disease biology to drug design. With Disobind, we can begin to reveal new interaction motifs linked to disease, suggest intervention points for regulating IDR-mediated interactions across the proteome, and better position disordered segments within large molecular assemblies,” said Shruthi Viswanath, who leads the Integrative Structural Biology Lab at NCBS. 

The team demonstrated Disobind’s capability across diverse biological systems from immune signalling molecules to repair proteins implicated in cancer and neurodegeneration. Disobind is open-source and freely available for researchers worldwide.