Biology subjects

Bryant, P.

Publications and source records attributed to Bryant, P..

3 recordsLinked to original sources

bioRxiv2021

A structural biology community assessment of AlphaFold 2 applications

Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods have led to protein structure predictions that have reached the accuracy of experimentally determined models. While this has been independently verified, the implementation of these methods across structural biology applications remains to be tested. Here, we evaluate the use of AlphaFold 2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modelling of interactions; and modelling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modelled when compared to homology modelling, identifying structural features rarely seen in the PDB. AF2-based predictions of protein disorder and protein complexes surpass state-of-the-art tools and AF2 models can be used across diverse applications equally well compared to experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life science research.

biophysics↗

bioRxiv2021

Improved prediction of protein-protein interactions using AlphaFold2 and extended multiple-sequence alignments

Predicting the structure of interacting protein chains is a fundamental step towards understanding protein function. Unfortunately, no computational method can produce accurate structures of protein complexes. AlphaFold2, has shown unprecedented levels of accuracy in modelling single chain protein structures. Here, we apply AlphaFold2 for the prediction of heterodimeric protein complexes. We find that the AlphaFold2 protocol together with optimized multiple sequence alignments, generate models with acceptable quality (DockQ[≥]0.23) for 63% of the dimers. From the predicted interfaces we create a simple function to predict the DockQ score which distinguishes acceptable from incorrect models as well as interacting from non-interacting proteins with state-of-art accuracy. We find that, using the predicted DockQ scores, we can identify 51% of all interacting pairs at 1% FPR. The protocol can be found at: https://gitlab.com/ElofssonLab/FoldDock.

bioinformatics↗

bioRxiv2021

The relationship between ageing and changes in the human blood and brain methylomes

Changes in DNA methylation have been found to be strongly correlated with age, enabling the creation of "epigenetic clocks". Previously, studies on the relationship between ageing and DNA methylation have assumed a linear relationship, but no study has shown this always to be the case. We show that the relationships between significant methylation changes and ageing are different in different tissues, and that these changes show variable rates across life. Further, we observe a tendency for saturation as ageing proceeds. We provide a straightforward method of assessing all methylation-age relationships and cluster them according to their relative change rates based on fold change. Less than 13% of the significant markers selected here are used in the most common epigenetic clocks. When the significant markers display largely non-linear relationships, our fold change selection outperforms the most common epigenetic clocks in predicting age. We also show that the saturation observed at older ages explains the earlier observations that the epigenetic clock consistently underestimates the age in older samples. The findings imply that markers selected from linear adjustments or correlations do not represent the most meaningful biological changes.

bioinformatics↗