Indian Scientists Develop AI Tool to Decode Disordered Proteins

 



Researchers at the National Centre for Biological Sciences, under the Tata Institute of Fundamental Research, Bengaluru, have developed a deep-learning tool that can predict how intrinsically disordered proteins bind to their partners. The breakthrough addresses a long-standing challenge in molecular biology and opens new possibilities in disease research and drug discovery.

Why Intrinsically Disordered Proteins Matter

Unlike most proteins that fold into stable three-dimensional structures, intrinsically disordered proteins (IDPs) lack a fixed shape. These shapeshifting molecules are central to cellular life. They regulate signalling networks, control gene expression, assist protein folding and quality control, and help form dynamic cellular structures known as condensates. Their flexibility, while biologically advantageous, has made them difficult to analyse using conventional structural biology techniques.

The Disobind Deep-Learning Tool

The newly developed tool, named Disobind, analyses protein sequences to predict which regions of a disordered protein will interact with a binding partner. It uses protein language models, a form of artificial intelligence trained on millions of known protein sequences. Crucially, Disobind does not require prior structural information or sequence alignments and explicitly accounts for the binding partner, a key factor in IDP interactions.

Performance and Benchmarking

The research team, led by Kartik Majila, benchmarked Disobind against existing predictors, including AlphaFold-Multimer and AlphaFold3. Disobind consistently showed higher accuracy, particularly when tested on previously unseen protein pairs. When used alongside AlphaFold-Multimer predictions, its performance improved further, highlighting its complementary value to structure-based approache

Applications in Disease and Drug Design

According to Shruthi Viswanath, who heads the Integrative Structural Biology Lab at NCBS, Disobind can help uncover disease-linked interaction motifs and identify new intervention points for therapy. The tool has been tested across systems ranging from immune signalling to proteins involved in cancer and neurodegeneration. Disobind has been released as open-source software, making it freely accessible to researchers worldwide.

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