Development and assessment of methods for membrane protein structure prediction

National Institute Of Neurological Disorders And Stroke

Investigators

Linked publications, trials & patents

Abstract

During the course of evolution, proteins often retain shared three-dimensional structural features, even as the sequence of amino acids that they consist of can undergo significant divergence. These relationships can also manifest internally within a single protein structure, originating from duplications and repetitions of defined elements. Consequently, the identification of evolutionary relationships between two proteins, or two different regions within the same protein, despite substantial evolutionary distance, holds immense value. With the availability of experimentally-determined structures, such relationships can revealed by superposing common regions, a technique referred to as structure alignment. While these structural alignment methods have been adapted to explore relationships within individual structures and to uncover symmetries or pseudo-symmetries, they come with limitations. Notably, they do not account for a protein's positioning within the cellular membrane or its conformational changes. To address these challenges, we have taken established approaches for detecting symmetry and tailored them to enhance their applicability to membrane proteins. The initial outcomes from this work were compiled into a database called EncoMPASS (Encyclopedia for Membrane Proteins Analyzed by Structure and Symmetry). We have continued to spend considerable efforts improving the procedures for generating the EncoMPASS database, broadening the pool of structures that can be analyzed, while also introducing greater levels of automation and computational efficiency. With the aid of the NINDS Bioinformatics Core (Yavaktar and Kumar), we have focused on improving the visualization of the data currently available through a public webserver hosted at https://encompass.ninds.nih.gov. We are on the verge of publishing the updated dataset. Our overarching goal is to create a user-friendly resource that can be readily updated with the latest data, and to which new features can easily be incorporated in the future.