A recent study by MIT’s Department of Biology, in collaboration with the Howard Hughes Medical Institute, has introduced an innovative AI-driven method to predict protein-protein interactions.
Published in the Proceedings of the National Academy of Sciences, this research utilizes machine learning to identify small protein fragments capable of binding to and inhibiting full-length proteins in Escherichia coli (E. coli). The study was led by Associate Professor Gene-Wei Li and Professor Amy Keating, head of the Department of Biology and Biological Engineering.
Advancing Protein Interaction Research with AI
The newly developed AI program, FragFold, builds upon AlphaFold’s groundbreaking protein-folding predictions. Unlike conventional applications, this study explores AlphaFold’s ability to identify fragment inhibitors, a novel approach in the field. The researchers confirmed that over half of FragFold’s predictions accurately identified protein binding or inhibition, even without prior structural data.
According to co-first and corresponding author Andrew Savinov, a postdoctoral researcher in the Li Lab, “Our findings demonstrate a generalizable approach to discovering protein interactions and inhibitors, even for proteins with unknown structures or functions.”
Unraveling Complex Protein Interactions
One key example examined was FtsZ, a crucial protein in bacterial cell division. Though extensively studied, FtsZ contains a dynamic, disordered region that traditional structural biology methods struggle to analyze. FragFold successfully identified new binding interactions within this region, shedding light on previously unknown mechanisms.
This advancement is particularly significant as it demonstrates FragFold’s ability to generate insights without solving the disordered region’s structure, showcasing its potential for broader applications. “AlphaFold is revolutionizing molecular and cellular biology,” says Keating. “Innovative AI-driven tools like FragFold open new research avenues and unexpected capabilities.”
Expanding the Potential of Protein Fragments
To generate predictions, the team computationally fragmented proteins and analyzed how these fragments interacted with potential binding partners. They then compared these computational models to experimental data obtained from high-throughput screening of millions of bacterial cells expressing protein fragments.
Unlike traditional AlphaFold methods, which require time-consuming multiple sequence alignments (MSAs) for every prediction, FragFold streamlines this process by pre-calculating MSAs for full-length proteins and using them to predict fragment interactions.
Among the proteins studied, the team explored the lipopolysaccharide transport system (LptF-LptG) in E. coli. They identified a fragment of LptG that inhibited this interaction, potentially disrupting essential bacterial processes. “We were surprised by how accurately we could predict binding, and in many cases, inhibition,” says Savinov. “Every protein we analyzed yielded effective inhibitors.”
Unlocking New Applications for Protein Engineering
Initially, the study focused on identifying inhibitory protein fragments due to the ease of measuring their effects on essential cellular functions. Moving forward, the researchers aim to explore other fragment functions, such as stabilizing proteins, modifying their activity, or triggering degradation.
By analyzing thousands of mutated protein fragments using deep mutational scanning, the team pinpointed key amino acids responsible for inhibition. In some cases, modified fragments were even more effective than their natural counterparts.
“This work surpasses previous methods by identifying functional protein fragments without relying on existing structural data,” says study co-author Sebastian Swanson. “The synergy between high-throughput inhibition data and AI-generated structural models provides a powerful framework for studying protein interactions.”
A New Era in Protein Design
This research paves the way for a deeper understanding of protein behavior and interaction networks. “Now that we can predict them, we aim to uncover the underlying principles guiding AlphaFold’s learning process,” says Savinov.
The study’s findings have broad implications, from designing compact, genetically encoded protein binders to developing new tools for studying cell biology and potential therapeutic applications. “FragFold has the potential to modify native proteins, alter their localization, and even reprogram them,” Li explains. “This opens up exciting possibilities for both fundamental research and medical advancements.”
Source : India ai
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