title: Development and application of novel bioinformatics tools for protein function prediction
Creators: name: Brackenridge, Danielle Allison
Publisher: name: University of Reading
Year: 2021

description: Pearson Correlation Coefficient and provides a value between -1 to 1, with -1 being a total 
negative correlation, 0 is no correlation and 1 is a total positive correlation based on the 
observed and predicted ligand-binding site residues. Scores of 0.40 to 0.69 are strong 
positive relationships and 0.70 and higher are strong positive relationships. The downside of 
MCC is that it does not take into consideration the overall 3D structure of the protein model. 
Therefore, BDT will also be utilised as this score, which is also scored from -1 to 1, to take 
into consideration the 3D structure. Both MCC and BDT are only possible to produce when 
there is an observed (actual) structure available with bound ligands to compare against the 
predicted structure and hence why MCC and BDT are objective measures of ligand-binding 
site prediction. The average MCC and BDT score from CASP11 was 0.42 and 0.51, 
respectively. CASP12 saw the prediction of ligands for low annotation level proteins with no 
known ligands, demonstrating the potential use of FunFOLD3 in novel protein prediction. 
The average MCC and BDT score from CASP13 was 0.47 and 0.53. CAFA3 showed 
FunFOLDQ can be used in the prediction of GO terms, however further refinements are 
needed to increase specificity of the term predictions. The development option this thesis 
has explored is the use of docking (preferred orientation of interacting partners) with 
AutoDock Vina to improve the accuracy of ligand-binding residues by FunFOLD3, as the 
problem with TBM methods can be that predicted ligand(s) from a similar template will be 
forced to fit within the ligand-binding pocket. However, with docking, the aim of this method 
is to predict the preferred orientation of the ligand within the ligand-binding space. Utilisation 
of docking has also added to the novelty of this research, as different grid box calculations 
around the ligand-binding space was explored, with varying degrees of success with each 
grid box calculation. Examples of two CASP targets which had improvements in MCC and 
BDT score following docking were CASP11 target T0783 (2-C-methyl-D-erythritol 4-
phosphate cytidylyltransferase) the MCC and BDT scores by FunFOLD3 were 0.17 and 
0.21, respectively. Following docking the MCC and BDT scores increased to 0.63 and 0.45, 
respectively. CASP13 target T1016 (alpha-ribazole-5'-P phosphatase) had MCC and BDT 
scores of 0.556 and 0.646 by FunFOLD3, respectively. Following docking the MCC and BDT 
increased to 0.85 and 0.91, respectively. 
Lastly, CASP_Commons, a community-wide experiment to find the consensus structures, 
explored the role of FunFOLD3 with predicting ligands and ligand-binding sites for the novel 
protein and proteins domains of SARS-CoV-2. The protein domains were non-structural 
proteins 2, 4 and 6, open reading frames 3a, 6, 7b, 8 and 10, membrane protein and papain�like protease. FunFOLD3 predicted ligands for ten of the protein domains, of which there 
were a total of 32 targets due to domains being split into smaller residues and subsequent 
rounds of 3D modelling improvement. 
Increased understanding of protein structures can provide further insight into a protein’s 
function, particularly if ligands are bound and identified, an example in this thesis is the 
prediction of chlorophyll A for non-structural protein 4 (nsp4). Chlorophyll A, like 
haemoglobin is a porphyrin ring and templates related to nsp4 show a role in blood clotting. 
Therefore, whilst chlorophyll A might not be the exact ligand, similarities between 
haemoglobin and chlorophyll A can clearly be determined and assist in understanding the 
role of nsp4 in the pathology of COVID-19. Identification of GO terms can provide more 
detailed understanding into the function or functions of proteins and, in proteins with limited 
annotation information this can assist with comprehending their role. 
This thesis has focused on improving and developing a function prediction method, 
FunFOLD3, to better understand the role and function of proteins. The new method of 
FunFOLD3 which utilises docking will be integrated into the McGuffin group prediction 
servers and will be benchmarked in subsequent CASP competitions, to critically assess the 
performance of the developed method.