Biology subjects

Pak, M. A.

Publications and source records attributed to Pak, M. A..

2 recordsLinked to original sources

bioRxiv2021

Using AlphaFold to predict the impact of single mutations on protein stability and function

AlphaFold changed the field of structural biology by achieving three-dimensional (3D) structure prediction from protein sequence at experimental quality. The astounding success even led to claims that the protein folding problem is "solved". However, protein folding problem is more than just structure prediction from sequence. Presently, it is unknown if the AlphaFold-triggered revolution could help to solve other problems related to protein folding. Here we assay the ability of AlphaFold to predict the impact of single mutations on protein stability ({Delta}{Delta}G) and function. To study the question we extracted metrics from AlphaFold predictions before and after single mutation in a protein and correlated the predicted change with the experimentally known {Delta}{Delta}G values. Additionally, we correlated the AlphaFold predictions on the impact of a single mutation on structure with a large scale dataset of single mutations in GFP with the experimentally assayed levels of fluorescence. We found a very weak or no correlation between AlphaFold output metrics and change of protein stability or fluorescence. Our results imply that AlphaFold cannot be immediately applied to other problems or applications in protein folding.

bioinformatics↗

bioRxiv2021

Best templates outperform homology models in predicting the impact of mutations on protein stability

MotivationPrediction of protein stability change upon mutation ({Delta}{Delta}G) is crucial for facilitating protein engineering and understanding of protein folding principles. Robust prediction of protein folding free energy change requires the knowledge of protein three-dimensional (3D) structure. Unfortunately, protein 3D structure is not always available. In this case, one can still predict the protein stability change by constructing a homology model of the protein; however, the accuracy of homology model-based {Delta}{Delta}G predictions is unknown. The perspectives of using 3D structures of the best templates are also unclear. ResultsTo investigate these questions, we used the most popular and accurate publicly available tools: FoldX for stability change prediction and I-Tasser for homology modeling. We found that both homology models and best templates worsen the {Delta}{Delta}G prediction, with best templates performing 1.5 times better than homology models. For AlphaFold models, we also found that the best templates seem to outperform protein models. Our findings imply using the 3D structures of the best templates for {Delta}{Delta}G prediction if the 3D protein structure is unavailable. Contactd.ivankov@skoltech.ru

bioinformatics↗