[Colloquium] 1/25 Thesis Defense: Zhiyong Wang

[Mary Marre]

*When: *   Monday, January 25th; 12:00 - 4:00 p.m.

*Where:*   TTIC, 6045 S Kenwood Avenue, Room #526

*Who: *     Zhiyong Wang

*Title:* Knowledge-Based Machine Learning Methods for Macromolecular 3D
Structure Prediction


*Abstract:*

Predicting the 3D structure of a macromolecule, such as a protein or an RNA
molecule, is ranked

top among the most difficult and attractive problems in bioinformatics and
computational

biology. Its importance comes from the relationship between the 3D
structure and the function

of a given protein or RNA. 3D structures also help to find the ligands of
the protein, which are

usually small molecules, a key step in drug design. Unfortunately, there is
no shortcut to

accurately obtain the 3D structure of a macromolecule. Many physical
measurements of

macromolecular 3D structures cannot scale up, due to their large labor
costs and the

requirements for lab conditions.

In recent years, computational methods have made huge progress due to
advance in

computation speed and machine learning methods. These methods only need the
sequence

information to predict 3D structures by employing various mathematical
models and machine

learning methods. The success of computational methods is highly dependent
on a large

database of the proteins and RNA with known structures.

However, the performance of computational methods are always expected to be
improved.

There are several reasons for this. First, we are facing, and will continue
to face sparseness of

data. The number of known 3D structures increased rapidly in the fast few
years, but still falls

behind the number of sequences. Structure data is much more expensive when
compared with

sequence data. Secondly, the 3D structure space is too large for our
computational capability.

The computing speed is not nearly enough to simulate the atom-level fold
process when

computing the physical energy among all the atoms.

The two obstacles can be removed by knowledge-based methods, which combine
knowledge

learned from the known structures and biologists’ knowledge of the folding
process of protein

or RNA. In the dissertation, I will present my results in building a
knowledge-based method by

using machine learning methods to tackle this problem. My methods include
the knowledge

constraints on intermediate states, which can highly reduce the solution
space of a protein or

RNA, in turn increasing the efficiency of the structure folding method and
improving its

accuracy.



Thesis Advisor: Professor Jinbo Xu






Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 504*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*
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