Learning Pipeline called PIONEER
Biomedical research is once again one step closer to improving precision medicine! A new deep-learning-based pipeline called PIONEER (Protein-protein InteractiOn iNtErfacE pRediction) generates a partner-specific human 3D interactome, solving the problem of lack of reliable structural information for most protein interactions. By identifying disease-associated mutations at protein-protein interfaces, PIONEER helps to better understand disease mechanisms at the atomic and allele level (quick explanation for better understanding, also for non-biomedics: alleles are alternative forms of a gene that occur at the same location on a chromosome). Read on to learn more about this breakthrough technology and its potential to accelerate biological research!
Precision medicine aims to identify actionable genetic variants in patients through whole-genome or exome sequencing and statistical analysis. However, traditional statistical approaches may be inaccurate in identifying functional variants or disease risk-genes and drug targets due to small sample sizes. To understand the causes of disease mutations, it is crucial to locate all protein interaction interfaces across the entire proteome scale, as most disease mutations affect specific protein-protein interactions. Unfortunately, only a small percentage of protein interactions have structural models determined by experimental or traditional homology modeling approaches.
New deep-learning-based pipeline PIONEER generates high-quality partner-specific human interactome for improved disease research
A recent study presented a deep-learning-based ensemble learning pipeline called PIONEER (Protein-protein InteractiOn iNtErfacE pRediction) that generates a next-generation partner-specific 3D human interactome with improved quality and coverage.
By leveraging the available atomic-resolution co-crystal structures along with homology models, we established a comprehensive multiscale 3D structural interactome, which consists of 282,095 interactions from humans and seven other commonly studied organisms, and includes all 146,138 experimentally determined PPIs for 16,232 human proteins.
The PIONEER pipeline addresses the challenge of a lack of reliable structural information for most protein interactions by incorporating a comprehensive set of features, including biophysical, evolutionary, structural, and sequence information. The PIONEER framework is available as a software package to accelerate biological research.
The authors evaluated the effectiveness of their new model designs by comparing the performance of PIONEER and ECLAIR on a benchmark testing dataset. They found that PIONEER models outperformed their previous ECLAIR models, indicating that their unique hybrid-architecture deep learning models captured more information in the features than the previous random forest-based models.
PIONEER algorithm identifies disease-associated mutations in protein-protein interfaces for enhanced understanding of disease mechanisms
The study identified a large number of disease-associated mutations located in the predicted protein-protein interfaces by the PIONEER algorithm. These mutations were found in various disease categories and affected PPIs among 5,684 proteins. The study provided examples of three PPI interfaces with germline mutations associated with different diseases, highlighting the usefulness of PIONEER-predicted interfaces in studying disease mechanisms at the atomic and allele levels. The study concluded that PIONEER-predicted protein-protein interface mutations convey crucial structural information in delineating the functional consequences for disease mechanisms.
Furthermore, the study investigated somatic mutations from cancer patients in the context of PPI interfaces inferred by PIONEER.