Application and implementation of probabilistic profile-profile comparison methods for protein fold recognition
Fold recognition is a method of fold detecting and protein tertiary structure prediction
applied for proteins lacking homologues sequences of known fold and structure
deposited in the Protein Data Bank. They are based on assumption that there is strictly
limited number of different protein folds in nature, mostly as a result of evolution and
due to basic physical and chemical constraints of polypeptide chains.
Fold recognition methods are useful for protein structure prediction, evolutionary
analysis, metabolic pathways and enzymatic efficiency prediction, molecular docking
and drug design.
Currently there are about 1300 discovered and characterized protein folds in SCOP and
CATH databases. Every newly discovered protein sequence has significant chances to
be classified into one of those folds. Many different approaches have been proposed for
finding the correct fold for a new sequence and it is often useful to include evolutionary
information for query as well as for target proteins. One of the methods of including this
information is a comparison of a query and target sequences profiles. These fold
recognition techniques are called profile-profile methods.
Profile-profile alignments can be calculated using a dot-product, a probabilistic model,
stochastic or theoretical measures. Here are presented applications and
implementations of probabilistic profile-profile comparison methods and advantages of
usage of probabilistic scoring function over comparable fold recognition techniques.
The purpose of this comparison is to show that probabilistic profile-profile methods may
outperform other fold recognition methods in comparison in analysis of distantly related
proteins and that they can be applied not only for fold recognition but also for slightly
different purposes like gene identification, detection of domain boundaries and
modeling of complex proteins.
Full text of my Ph.D. thesis can be downloaded here
Full text of my Ph.D. thesis can be downloaded here
Comments