Year: 2024
Type: dissertation
Abstract: The deep learning revolution has contributed to a great leap in protein structure prediction, but predicting the multiple conformational states that proteins can hold remains an open problem. This thesis approaches this problem through the use of MSA Transformer, a protein language model pre-trained on multiple sequence alignments, that has demonstrated the ability to learn protein properties such as contacts and secondary structure. The aim was to investigate if MSA Transformer has learned to represent flexibility features of proteins during pre-training and if that information can be captured somehow. This mainly took the shape of training neural networks on the outputs of the transformer model to predict the local RMSD flexibility metric of flexible proteins in the PDBFlex database. None of the attempted networks succeeded with classifying flexibility, most likely because of problems with the chosen architectures, evaluation metric and dataset. Much more promising was the discovery of patterns in the attention maps of MSA Transformer which displayed correlation to the local RMSD metric. The findings were expanded upon by the identification of certain attention heads that seemingly correlated with specific types of flexibility, but further investigation is required to draw any conclusions. Prior studies have shown that multiple sequence alignments can be clustered and modified in order to sample multiple conformations with AlphaFold2. While not attempted in this project, the groundwork has been laid for expanding on these approaches with the use of MSA Transformer features to select residues and sequences of specific significance.
Author: Karlin, Vera
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7009808766