Many protein structure prediction programs exist and they can efficiently generate a number of protein models of a varying quality. One of the problems is that it is difficult to know which model is the best one for a given target sequence. Selecting the best model is one of the major tasks of Model Quality Assessment Programs (MQAPs). These programs are able to predict model accuracy before the native structure is determined. The accuracy estimation can be divided into two parts: global (the whole model accuracy) and local (the accuracy of each residue). ProQ2 is one of the most successful MQAPs for prediction of both local and global model accuracy and is based on a Machine Learning approach.

In this thesis, I present my own contribution to Model Quality Assessment (MQA) and the newest developments of ProQ program series. Firstly, I describe a new ProQ2 implementation in the protein modelling software package Rosetta. This new implementation allows use of ProQ2 as a scoring function for conformational sampling inside Rosetta, which was not possible before. Moreover, I present two new methods, ProQ3 and ProQ3D that both outperform their predecessor. ProQ3 introduces new training features that are calculated from Rosetta energy functions and ProQ3D introduces a new machine learning approach based on deep learning. ProQ3 program participated in the 12th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP12) and was one of the best methods in the MQA category. Finally, an important issue in model quality assessment is how to select a target function that the predictor is trying to learn. In the fourth manuscript, I show that MQA results can be improved by selecting a contact-based target function instead of more conventional superposition based functions.

Proteins are the basic molecular machines of the cell, performing a broad range of tasks, from structural support to catalysisof chemical reactions. Their function is determined by their 3D structure, which in turn is dictated by the order of their components, the amino acids.

This thesis is dedicated to applications of machine learning to the problems of contact prediction, ab-initio, and model quality assessment. In particular, my research has been focused on developing methods that are both effective, and easy to use.

In the first paper, we improved the already state-of-the-art model quality assessment (MQA) program ProQ3 replacing the underlying machine learning algorithm from svm to Deep Learning, baptised ProQ3D. The correlation between predicted and true scores was improved from 0.85 to 0.90, using the same training data and features.

The second paper joined several programs into a single pipeline for ab-initio structure prediction: contact prediction,folding, and model selection. We attempted to predict the structures of all 6379 PFAM families with unknown structure, ofwhich 558 we believe to be accurate. Of these, 415 had not been reported before.

The third paper uses advances in machine learning to build a contact predictor, PconsC4, that is fast and easy to deployin large-scale studies, since it requires a single Multiple Sequence Alignment (MSA), and no external dependencies. The predictions are state-of-the-art, yielding a 12% improvement in precision over PconsC3, and 244 times faster.

With ProQ4, in the fourth paper, we introduce a novel way of training deep networks for MQA in a way that minimises the bias of the training data, and emphasises model ranking, and demonstrate its viability with a minimal description ofthe protein. The ranking correlation was improved with respect to ProQ3D from 0.82 to 0.90.

Lastly, in the fifth paper, weshow the results of ProQ3D and ProQ4 in a completely blind test: CASP13.