Benchmarking predictions of allostery in liver pyruvate kinase in CAGI4. Issue 9 (2nd May 2017)

• Record Type: Journal Article
• Authors: Xu, Qifang; Tang, Qingling; Katsonis, Panagiotis; Lichtarge, Olivier; Jones, David; Bovo, Samuele; Babbi, Giulia; Martelli, Pier L.; Casadio, Rita; Lee, Gyu Rie; Seok, Chaok; Fenton, Aron W.; Dunbrack, Roland L.
• Is Part Of: Human mutation. Volume 38:Issue 9(2017)
• Page Start: 1123
• Page End: 1131
• Publication Date: 2017-05-02
• Subjects: allosteric effect -- CAGI experiment -- liver pyruvate kinase -- missense mutation; Human chromosome abnormalities -- Periodicals; Mutation (Biology) -- Periodicals; 616.04205
• DOI: 10.1002/humu.23222
• Languages: English
• ISSNs: 1059-7794

Abstract

Abstract : Homotetramer of human liver pyruvate kinase (PYK) with bound substrate (phosphoenol pyruvate), inhibiting allosteric effector (alanine), and activating allosteric effector (fructose‐1, 6‐bisphosphate), compiled from multiple crystal structures of PYK. Abstract: The Critical Assessment of Genome Interpretation (CAGI) is a global community experiment to objectively assess computational methods for predicting phenotypic impacts of genomic variation. One of the 2015–2016 competitions focused on predicting the influence of mutations on the allosteric regulation of human liver pyruvate kinase. More than 30 different researchers accessed the challenge data. However, only four groups accepted the challenge. Features used for predictions ranged from evolutionary constraints, mutant site locations relative to active and effector binding sites, and computational docking outputs. Despite the range of expertise and strategies used by predictors, the best predictions were marginally greater than random for modified allostery resulting from mutations. In contrast, several groups successfully predicted which mutations severely reduced enzymatic activity. Nonetheless, poor predictions of allostery stands in stark contrast to the impression left by more than 700 PubMed entries identified using the identifiers "computational + allosteric." This contrast highlights a specialized need for new computational tools and utilization of benchmarks that focus on allosteric regulation.