  Proteins are the basic building blocks of life. They usually perform
functions by folding to a particular structure. Understanding the folding
process could help the researchers to understand the functions of proteins and
could also help to develop supplemental proteins for people with deficiencies
and gain more insight into diseases associated with troublesome folding
proteins. Experimental methods are both expensive and time consuming. In this
thesis I introduce a new machine learning based method to predict the protein
structure. The new method improves the performance from two directions:
creating accurate protein alignments and predicting accurate protein contacts.
First, I present an alignment framework MRFalign which goes beyond
state-of-the-art methods and uses Markov Random Fields to model a protein
family and align two proteins by aligning two MRFs together. Compared to other
methods, that can only model local-range residue correlation, MRFs can model
long-range residue interactions and thus, encodes global information in a
protein. Secondly, I present a Group Graphical Lasso method for contact
prediction that integrates joint multi-family Evolutionary Coupling analysis
and supervised learning to improve accuracy on proteins without many sequence
homologs. Different from single-family EC analysis that uses residue
co-evolution information in only the target protein family, our joint EC
analysis uses residue co-evolution in both the target family and its related
families, which may have divergent sequences but similar folds. Our method can
also integrate supervised learning methods to further improve accuracy. We
evaluate the performance of both methods including each of its components on
large public benchmarks. Experiments show that our methods can achieve better
accuracy than existing state-of-the-art methods under all the measurements on
most of the protein classes.
