Phosphorylation Mechanism Switching in Histidine Kinases Is a Tool for Fast Protein Evolution: Insights From AlphaFold Models.

Histidine kinases (HKs) are a central part of bacterial environmental-sensing two-component systems. They provide their hosts with the ability to respond to a wide range of physical and chemical signals. HKs are multidomain proteins consisting of at least a sensor domain, dimerization and phosphorylation domain (DHp), and a catalytic domain. They work as homodimers and the existence of two different autophosphorylation mechanisms (cis and trans) has been proposed as relevant for pathway specificity. Although several HKs have been intensively studied, a precise sequence-to-structure explanation of why and how either cis or trans phosphorylation occurs is still unavailable nor is there any evolutionary analysis on the subject. In this work, we show that AlphaFold can accurately determine whether an HK dimerizes in a cis or trans structure. By modeling multiple HKs we show that both cis- and trans-acting HKs are common in nature and the switch between mechanisms has happened multiple times in the evolutionary history of the family. We then use AlphaFold modeling to explore the molecular determinants of the phosphorylation mechanism. We conclude that it is the difference in lengths of the helices surrounding the DHp loop that determines the mechanism. We also show that very small changes in these helices can cause a mechanism switch. Despite this, previous evidence shows that for a particular HK the phosphorylation mechanism is conserved. This suggests that the phosphorylation mechanism participates in system specificity and mechanism switching provides these systems with a way to diverge.

Knockout mice with pituitary malformations help identify human cases of hypopituitarism.

BACKGROUND: Congenital hypopituitarism (CH) and its associated syndromes, septo-optic dysplasia (SOD) and holoprosencephaly (HPE), are midline defects that cause significant morbidity for affected people. Variants in 67 genes are associated with CH, but a vast majority of CH cases lack a genetic diagnosis. Whole exome and whole genome sequencing of CH patients identifies sequence variants in genes known to cause CH, and in new candidate genes, but many of these are variants of uncertain significance (VUS). METHODS: The International Mouse Phenotyping Consortium (IMPC) is an effort to establish gene function by knocking-out all genes in the mouse genome and generating corresponding phenotype data. We used mouse embryonic imaging data generated by the Deciphering Mechanisms of Developmental Disorders (DMDD) project to screen 209 embryonic lethal and sub-viable knockout mouse lines for pituitary malformations. RESULTS: Of the 209 knockout mouse lines, we identified 51 that have embryonic pituitary malformations. These genes not only represent new candidates for CH, but also reveal new molecular pathways not previously associated with pituitary organogenesis. We used this list of candidate genes to mine whole exome sequencing data of a cohort of patients with CH, and we identified variants in two unrelated cases for two genes, MORC2 and SETD5, with CH and other syndromic features. CONCLUSIONS: The screening and analysis of IMPC phenotyping data provide proof-of-principle that recessive lethal mouse mutants generated by the knockout mouse project are an excellent source of candidate genes for congenital hypopituitarism in children.

Local energetic frustration conservation in protein families and superfamilies.

Energetic local frustration offers a biophysical perspective to interpret the effects of sequence variability on protein families. Here we present a methodology to analyze local frustration patterns within protein families and superfamilies that allows us to uncover constraints related to stability and function, and identify differential frustration patterns in families with a common ancestry. We analyze these signals in very well studied protein families such as PDZ, SH3, ɑ and ß globins and RAS families. Recent advances in protein structure prediction make it possible to analyze a vast majority of the protein space. An automatic and unsupervised proteome-wide analysis on the SARS-CoV-2 virus demonstrates the potential of our approach to enhance our understanding of the natural phenotypic diversity of protein families beyond single protein instances. We apply our method to modify biophysical properties of natural proteins based on their family properties, as well as perform unsupervised analysis of large datasets to shed light on the physicochemical signatures of poorly characterized proteins such as the ones belonging to emergent pathogens.