| Summary: | Protein structure prediction is concerned with the prediction of a
protein's three dimensional structure from its amino acid sequence.
Such predictions are commonly performed by searching the possible
structures and evaluating each structure by using some scoring
function. If it is assumed that the target protein structure
resembles the structure of a known protein, the search space can be
significantly reduced. Such an approach is referred to as
comparative structure prediction. When such an assumption is
not made, the approach is known as ab initio structure
prediction. There are several difficulties in devising efficient
searches or in computing the scoring function. Many of these
problems have ready solutions from known mathematical methods.
However, the problems that are yet unsolved have hindered structure
prediction methods from more ideal predictions.
The objective of this study is to present a complete framework for
ab initio protein structure prediction. To achieve this, a new
search strategy is proposed, and better techniques are devised for
computing the known scoring functions. Some of the remaining
problems in protein structure prediction are revisited. Several of
them are shown to be intractable. In many of these cases, approximation
methods are suggested as alternative solutions. The primary issues addressed in this thesis
are concerned with local structures prediction, structure assembly
or sampling, side chain packing, model comparison, and structural
alignment. For brevity, we do not elaborate on these problems here;
a concise introduction is given in the first section of this thesis.
Results from these studies prompted the development of several
programs, forming a utility suite for ab initio protein
structure prediction. Due to the general usefulness of these
programs, some of them are released with open source licenses to
benefit the community.
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