title: Improvement of alphaFold2-based methods for modelling quaternary structures of proteins
Creators: name: Genc, Ahmet Gurkan
Publisher: name: University of Reading
Year: 2025

description: The functions of proteins are determined by their 3D structures, hence different methods have 
been developed in order to predict protein structures as a stepping stone to better 
understanding their functions and interactions. Protein structure modelling was a process that 
often involved two different stages: modelling and refinement. However, the release of the 
deep neural network-based AlphaFold2 (AF2) as a protein modelling tool in 2020 has enabled 
significant advances in protein bioinformatics. These advances have made it possible to 
predict models of monomeric structures that are close to structures derived experimentally.
Thus, the effective application of machine learning approaches has reduced the need for the 
traditional refinement process. Instead, modellers use end-to-end processes for improvements
covering both modelling and refinement. One of the most important developments in for such 
processes was the open-access release of the AF2 code. As a result, almost all recent tools 
have integrated the AF2 code into their own pipelines using different methods and parameters, 
aiming to obtain better models than those produced by the default AF2 method. However, the 
successes achieved for monomeric globular structures have not yet been realised for 
multimeric globular structures, and this has increased the need for the development of new 
modelling tools. Although many AF2 versions have been introduced in the process, the full 
effectiveness of AF2 - and indirectly, which structures it can accurately predict - is not yet fully 
understood. Therefore, the basis of this research is to investigate the features of this black box 
and to explore how to use it most effectively for the improvement of quaternary structure 
models. In this direction, we aimed to design an improved AF2-based multimeric protein 
modelling pipeline.
The effect of recycling, a key part of the AF2 algorithm, on the refinement of models is
investigated in Chapter 2. The results show that in both AF2 versions (AF2_Advanced and 
AF2_Multimer (AF2M)) the quality of the predicted protein model improves as the number of 
recycles increases. It is also shown that while 3 cycles is the default value for the AF2 versions, 
12 cycles may be more effective for both main versions. With the integration of the custom 
template option into the AF2M code, the effect of custom templates and recycling methods on 
protein modelling are examined in Chapter 3. It is shown that providing initial structural 
information to AF2M as an input and further recycling can lead to better quality structure
models. It is also emphasised that using multiple sequence alignment (MSA) inputs is more 
effective in AF2M compared to providing a single sequence (SS). Another new parameter 
introduced for AF2M for improving modelling was the custom MSA option. Although the 
effectiveness of custom templates and custom MSA options have been supported by many 
studies, the effect of altering these input features on AF2M rather than using the defaults had
not been fully revealed. In Chapter 4, we discovered that when multimeric custom template 
structures are given to AF2M as a “single-chain” protein structure, a cumulative improvement 
in TM-scores and IDDT scores are observed, although there is no improvement in interface 
scores (QS-scores and DockQ_wave scores). Furthermore, in order to obtain custom MSAs, 
disordered residues in homologous sequences were deleted within MSAs, so that AF2M made 
its predictions only from residues corresponding to ordered regions. As a result, it was also
observed that AF2M obtained higher quality protein structures in more than half of the targets. 
These two major results emphasise that input changes to AF2M can be more effective for 
target-specific protein modelling than for general protein modelling. Finally, based on the 
results from the previous chapters, we designed two successive versions of a protein modelling 
tool called MultiFOLD, which aims to create a pool of models with conformational sampling 
using custom template recycling followed by ranking and selection. In Chapter 5, through 
extensive analysis of benchmarking data we demonstrate that MultiFOLD is particularly 
effective in modelling multimeric globular structures, and the latest version, MultiFOLD2, 
outperformed all other servers including AlphaFold3 (AF3) that are participating in the CAMEO�BETA project. With the acquisition of better-quality protein structures, it is now possible to 
better infer function and to model protein-ligand interactions in downstream analyses.