Structural validation and assessment of AlphaFold2 predictions for centrosomal and centriolar proteins and their complexes

Abstract

Obtaining the high-resolution structures of proteins and their complexes is a crucial aspect of understanding the mechanisms of life. Experimental structure determination methods are time-consuming, expensive and cannot keep pace with the growing number of protein sequences available through genomic DNA sequencing. Thus, the ability to accurately predict the structure of proteins from their sequence is a holy grail of structural and computational biology that would remove a bottleneck in our efforts to understand as well as rationally engineer living systems. Recent advances in protein structure prediction, in particular the breakthrough with the AI-based tool AlphaFold2 (AF2), hold promise for achieving this goal, but the practical utility of AF2 remains to be explored. Focusing on proteins with essential roles in centrosome and centriole biogenesis, we demonstrate the quality and usability of the AF2 prediction models and we show that they can provide important insights into the modular organization of two key players in this process, CEP192 and CEP44. Furthermore, we used the AF2 algorithm to elucidate and then experimentally validate previously unknown prime features in the structure of TTBK2 bound to CEP164, as well as the Chibby1-FAM92A complex for which no structural information was available to date. These findings have important implications in understanding the regulation and function of these complexes. Finally, we also discuss some practical limitations of AF2 and anticipate the implications for future research approaches in the centriole/centrosome field.

Generation of Flag/DYKDDDDK Epitope Tag Knock-In Mice Using i-GONAD Enables Detection of Endogenous CaMKIIα and β Proteins

“…The N- and C-terminal portions are shown in red in the 3D structure with a low confidence score (pLDDT), but both portions that are not alpha-helical bundles and beta-sheet domains should be considered as flexible protein structures that are amenable to tagging. AlphaFold2 also contributes to several biological processes, such as protein–protein complexes [ 31 ], prediction of the transcriptional activator domain [ 32 ], pathogenicity of missense variants [ 33 , 34 ], and phenotypic effects of single mutations [ 35 ]. Thus, our prediction for flag/DYKDDDDK epitope tag knock-in mouse is an application that uses structural modeling with AlphaFold2 and is able to escape the disruption of protein function by epitope tag.…”

Meta-analysis of genotype-phenotype associations in Alström syndrome

“…In the case of mutations in exon 16 or adjacent, perhaps the NMD is not activated either, but the generated protein, despite not being functional, could have a semi-normal conformation that prevents the formation of aggregates. This hypothesis could be easily tested if these protein isoforms can be simulated by homology, but unfortunately the protein structure of the ALMS1 gene remains unknown and cannot be simulated using the artificial intelligence models such as AlphaFold (van Breugel et al, 2022).…”

Genome-wide identification and functional prediction of silicon (Si) transporters in poplar (Populus trichocarpa)

“…Conformational changes in proteins are often key to their function and regulation and are frequently triggered by binding to other molecules (e.g., proteins or small molecules/ligands) or by post-translational modifications. The current prediction algorithm is unable to model these conformational changes and is therefore largely unable to provide a crucial aspect of understanding the protein structure-function relationship (Jumper et al 2021;van Breugel et al 2022).…”