Revolutionizing Drug Discovery: AI Tool PIONEER Identifies Protein-Protein Interaction Mutations for Disease Treatment

Scientists from Cleveland Clinic and Cornell University have developed a new AI tool called PIONEER (Protein-protein InteractiOn iNtErfacE pRediction) to predict mutations in protein-protein interactions. This tool could potentially lead to more effective treatments for hundreds of diseases, including cancer. This publicly available software and web database simplifies identifying crucial protein-protein interactions that can be targeted with medications, offering a way to accelerate drug discovery for complex diseases.

Genomic research plays a crucial role in drug discovery. Still, as Dr. Feixiong Cheng, co-lead author and director of Cleveland Clinic's Genome Center, explains, it often isn't enough. Developing medications based on genomic data can take 10 to 15 years, from identifying a disease-causing gene to entering clinical trials. Dr. Cheng explains, "In theory, making new medicines based on genetic data is straightforward: mutated genes make mutated proteins. We try to create molecules that stop these proteins from disrupting critical biological processes by blocking them from interacting with healthy proteins. Still, in reality, that is much easier said than done."

New AI tool predicts protein-protein interaction mutations in hundreds of diseases

Proteins in the human body interact in complex networks called interactomes, where each protein can interact with hundreds of others. The complexity increases when DNA mutations are introduced into this network, particularly in disease-causing genes. A single disease can be associated with many gene mutations, resulting in various interactomes. Drug developers are left with tens of thousands of potential disease-causing interactions to evaluate, made more difficult by the sheer volume of possible mutations and protein structures.

To address this, Dr. Cheng, his team, and Dr. Haiyuan Yu from Cornell University developed PIONEER. This AI tool integrates vast data, including genomic sequences from nearly 100,000 individuals with disease-causing mutations, over 16,000 human protein structures, and known interactions between almost 300,000 protein-protein pairs. This data allows PIONEER to generate ranked lists of protein-protein interactions likely to contribute to a particular disease and possibly be targeted by drugs.

Researchers can input a known mutation into PIONEER to receive a ranked list of interactions to explore further in research or drug development. Additionally, scientists can search for diseases by name to access potential protein interactions that could cause disease. PIONEER applies to various diseases, including autoimmune disorders, cancer, cardiovascular, metabolic, neurological, and pulmonary diseases.

The power of PIONEER lies in its ability to reduce the entry barrier for interactome studies, a field that often requires significant resources. The team validated PIONEER's predictions by creating nearly 3,000 mutations on over 1,000 proteins and testing their impact on almost 7,000 protein-protein interaction pairs. This confirmed PIONEER's ability to accurately predict protein-protein interaction mutations that could lead to new therapeutic targets. Preliminary research is underway to develop treatments for lung and endometrial cancers.

In addition to its broader applications, PIONEER demonstrated that mutations in protein-protein interactions can predict critical outcomes in cancer. The team showed that their AI model could predict survival rates and prognoses for various cancers, including sarcoma. PIONEER also predicted anti-cancer drug responses using pharmacogenomics databases. Researchers validated that mutations in interactions between proteins NRF2 and KEAP1 could predict tumor growth in lung cancer, offering a target for new therapies.

Dr. Cheng noted that interactome studies often challenge genetic researchers, but PIONEER may overcome these barriers. "We hope PIONEER can lessen the burden on researchers and give more scientists the ability to advance new therapies," Dr. Cheng said.

In summary, PIONEER represents a significant advancement in AI-driven drug discovery. By integrating genomic, proteomic, and interaction data, this tool helps researchers navigate complex networks of protein-protein interactions and identify promising targets for treating diseases. With the potential to accelerate new therapies across various medical fields, PIONEER is set to become a valuable resource for researchers worldwide.