title: Improvements to methods for the quality assessment of three-dimensional models of proteins
Creators: name: Maghrabi, Ali Hassan Ahmed
Publisher: name: University of Reading
Year: 2020

description: After water, proteins are the most abundant substances in the human body, forming around 80% of 
its dry mass. Understanding protein function is beneficial for life needs, such as finding medicines, 
producing healthy foods and combating infectious diseases. Each protein molecule has its own 
unique sequence which is comprised of linear chains of amino acids. These amino acid chains fold 
to form tertiary structures, which confer the protein’s function. It is important that we can 
characterise protein structures in order to better understand their functions. Several experimental 
methods such as X-ray Crystallography and Nuclear Magnetic Resonance have been applied to 
solve protein structures. However, such methods are costly and time consuming, and some proteins
are also problematic or impossible to solve using these methods. Consequently, the process of 
growing protein structure data is relatively slow in comparison to the speed of sequencing genomes 
and their encoded proteins, which has kept increasing especially after breakthroughs in the genetic 
sequencing technology. As a result, a gap has grown between known protein sequences and their 
resolved structures and it has been necessary to find other solutions. Computational methods for 
predicting the structures of proteins directly from own sequences have become fast and effective 
alternatives to experimental methods. Over the past 20 years there has been an emergence of 
different types of protein structure predicting methods, the most accurate type being the 
comparative modelling method, which consists of a number of steps including: template 
recognition, alignment, quality assessment, and ending with refinement. Each of these steps 
contribute to successful modelling pipelines, but perhaps the most critical step for the wider 
acceptance of 3D models of proteins has been the quality assessment step, where the predicted 
models are evaluated in terms of their likely accuracy, prior to the availability of an experimental 
structure. Numerous approaches to the quality estimation problem have been developed over the 
years including the use of statistical potentials, stereochemistry checks and machine learning 
techniques. Such methods have traditionally been referred to as Model Quality Assessment 
Programs (MQAPs). One of the leading MQAPs has been the ModFOLD method which has been 
developed by our group. Since its inception, ModFOLD has been continuously improved, going 
through many upgrades until its latest version, ModFOLD7. This study was conducted during a 
major development cycle, beginning with the benchmarking of ModFOLD6, the most powerful 
MQAP method compared to its other competitors at that time. The study starts with the 
investigation of the integration of ten MQAP scoring methods in an attempt to enhance 
performance. The study also explores the implementation of deep neural networks on the MQAP method’s pipeline, and how this technique can be used to improve the MQAP scoring approach. In 
the later stage of our research, we managed to improve our method significantly leading to the 
latest upgrade, ModFOLD7. During this project, we also participated in a number of independent 
blind experiments and competitions to verify our improvements. We also undertook several 
collaborations in order to apply our methods in practical contexts. The overall results have shown 
incremental but significant improvements in ModFOLD performance during this study, with an 
approximate 5% improvement over previous versions. However, there are still plenty of room for 
ModFOLD to improve further and a number of suggestions for further developments will be 
addressed throughout this thesis.