TIGP--Computational antibody rational design
- LecturerDr. Yang, An-Suei (Genomics Research Center, Academia Sinica)
Host: Miss Elsa Pan - Time2010-09-23 (Thu.) 14:00 – 15:00
- LocationAuditorium 106 at new IIS Building
Abstract
Abstract:
Antibody is becoming one of the most prominent molecular classes in
both therapeutics and diagnostics. As the importance of protein
therapeutics and diagnostics expands due to the rapid development of
proteomics and genomics in medical applications, antibody engineering
technologies leading to rapid antibody discoveries and to optimal
physicochemical properties of the antibody molecules will hold a
strategically prominent position in the biotechnology industry. We aim
to build a superior antibody engineering platform overtaking the
current state-of-the-art methodologies and to develop new antibody
applications made possible only by the new antibody engineering
development.
Current antibody discovery relies on animal immunization-hydridoma or
phage/yeast display technologies, which can now frequently uncover
useful antibody molecules of human origin. But the shortcoming of the
current methodology is the reliance of nature antibody sequence
repertoires as sources for the lead antibodies, and thus the antibodies
discovered are limited by the uncontrollable animal immune systems.
One of the key developments in our lab is to construct protein
recognition databases from the already vast collection of protein
structures in the public domain. Machine learning and informatics
algorithms built on the basis of the databases organize the knowledge
extracted from the information and provide sequence designs that are
more likely to be functional as antibodies. The information of
successes as well as failures in sequence designs will form an integral
part in the database and eventually feed back to the next round of
computational designs. This cumulative enrichment in
computer-organized knowledge will eventually result in a
rationale-based methodology in synthetic antibody design previously
unseen.