ProtWeaver: Advanced Coevolutionary Analysis for Protein Function Prediction

University of Pittsburgh researchers have developed ProtWeaver, a software algorithm designed to predict functional associations between genes. ProtWeaver leverages multiple streams of evidence of coevolution and combines them using a machine learning algorithm to produce a comprehensive coevolutionary score. This innovative approach offers a faster and more cost-effective alternative to traditional wet lab analyses, significantly enhancing the accuracy of protein function inference.

Description

ProtWeaver is a bioinformatics tool that predicts functional associations between genes by analyzing coevolutionary signals. It integrates four main classes of algorithms: phylogenetic profiling, phylogenetic topology, colocalization, and residue covariation. By combining these evidence streams into an overall coevolutionary score, ProtWeaver provides a holistic view of the shared selective pressures between genes. This method improves upon existing algorithms by introducing statistical significance tests and dimensionality reduction techniques, resulting in enhanced performance and scalability.

Applications

• Protein function prediction
• Gene association studies
• Evolutionary biology research
• Bioinformatics tool development

Advantages

ProtWeaver offers several advantages over traditional methods, including improved computational efficiency and accuracy. It introduces novel statistical tests and dimensionality reduction techniques, making it faster and more scalable. The combination of multiple evidence streams into a single coevolutionary score provides a more comprehensive understanding of gene associations. Additionally, ProtWeaver is open source and freely available, promoting widespread adoption and external validation within the research community.

Invention Readiness

ProtWeaver is currently at the prototype stage. A prototype has been developed and validated, demonstrating the ability to predict functional associations between genes with high accuracy. The software has been presented at multiple research conferences and has received positive feedback from the bioinformatics community. Ongoing efforts include further validation and optimization of the algorithm, as well as collaborations with researchers to explore its applications in various biological contexts.

IP Status

Patent Pending