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Abstract In recent years, significant advancements have been made in deep learning‐based computational modeling of proteins, with DeepMind's AlphaFold2 standing out as a landmark achievement. These computationally modeled protein structures not only provide atomic coordinates but also include self‐confidence metrics to assess the relative quality of the modeling, either for individual residues or the entire protein. However, these self‐confidence scores are not always reliable; for instance, poorly modeled regions of a protein may sometimes be assigned high confidence. To address this limitation, we introduce Equivariant Quality Assessment Folding (EQAFold), an enhanced framework that refines the Local Distance Difference Test prediction head of AlphaFold to generate more accurate self‐confidence scores. Our results demonstrate that EQAFold outperforms the standard AlphaFold architecture and recent model quality assessment protocols in providing more reliable confidence metrics. Source code for EQAFold is available athttps://github.com/kiharalab/EQAFold_public. 

Driving mechanisms of many biological functions in a cell include physical interactions of proteins. As protein-protein interactions (PPIs) are also important in disease development, protein-protein interactions are highlighted in the pharmaceutical industry as possible therapeutic targets in recent years. To understand the variety of protein-protein interactions in a proteome, it is essential to establish a method that can identify similarity and dissimilarity between protein-protein interactions for inferring the binding of similar molecules, including drugs and other proteins. In this study, we developed a novel method, protein-protein interaction-Surfer, which compares and quantifies similarity of local surface regions of protein-protein interactions. protein-protein interaction-Surfer represents a protein-protein interaction surface with overlapping surface patches, each of which is described with a three-dimensional Zernike descriptor (3DZD), a compact mathematical representation of 3D function. 3DZD captures both the 3D shape and physicochemical properties of the protein surface. The performance of protein-protein interaction-Surfer was benchmarked on datasets of protein-protein interactions, where we were able to show that protein-protein interaction-Surfer finds similar potential drug binding regions that do not share sequence and structure similarity. protein-protein interaction-Surfer is available at https://kiharalab.org/ppi-surfer .