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| About Maricel Kann (University of Maryland, Baltimore County) |
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Dr. Maricel Kann is an Assistant Professor at the University of Maryland,
Baltimore County. She received a B.Sc. degree in Chemistry and a graduate
degree in Pharmaceutical Chemistry from the Universidad de la Republica in
Montevideo (Uruguay), where she was a research assistant in the Quantum
Chemistry Department. In 2001, she obtained a doctoral degree from the
University of Michigan in Chemistry. Her thesis work under the guidance of
Dr. Richard A. Goldstein focused on the theory, statistics and methods for
protein sequence alignment. After completing her Ph.D., Dr. Kann joined the
Structure group at the National Center for Biotechnology Information (NIH)
as a postdoctoral fellow. In August 2007, she joined the Department of
Biological Sciences at UMBC as an Assistant Professor. Dr. Kann's research
focuses on computational approaches to annotate the human genome with the
goal of revealing the molecular underpinning of human diseases. One of the
crucial steps after sequencing the genome is to classify and assign function
to gene-encoded proteins. Dr. Kann's work addresses these challenges
studying new computational methodologies to align, classify and predict
interactions of proteins as well as to identify the role of certain
mutations in the disease mechanisms. Dr. Kann is one of the leading experts
in the area of translational Bioinformatics and has chaired several
international conference sessions at the Pacific Symposium on Biocomputing
(PSB), the Intelligent Systems and Molecular Biology (ISMB) and the American
Medical Informatics Association (AMIA) Summit in Bioinformatics. She is a
member of AMIA, the American Association for the Advancement of Science and
the International Society of Computational Biology. Dr. Kann is part of the
editorial boards of the Journal of Biomedical Informatics and the
International Journal of Computational Models and Algorithms in Medicine and
she is an advisory board member of the PubMedCentral National Committee.
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Using Correlated Evolution of Interacting Protein Domains to Predict their Interactions
Maricel Kann (University of Maryland, Baltimore County)
A clear understanding of the malfunctions that ultimately cause
disease can only be achieved when the molecular details of the relevant
protein interactions are known. Many protein interactions are mediated by
protein units of compact structure. Computational tools to predict
domain-domain interactions provide a detailed molecular view of the protein
interactions and complements expensive and laborious experimental techniques
to identify such interactions. The evolutionary distances of interacting
proteins often display a higher level of similarity than those of
non-interacting proteins. This finding indicates that interacting proteins
are subject to common evolutionary constraints and constitute the basis of a
method to predict protein interactions known as mirrortree. It has been
difficult, however, to identify the direct cause of the observed
similarities between evolutionary trees. One possible explanation is the
existence of compensatory mutations between partners' binding sites to
maintain proper binding. This explanation, however, has been recently
challenged. It has been suggested that the signal of correlated evolution
uncovered by the mirrortree method is unrelated to any correlated evolution
between binding sites. We have addressed this controversial debate in the
field by studying the contribution of binding sites to the correlation
between evolutionary trees of interacting domains. We showed that binding
neighborhoods of interacting proteins have, on average, higher
co-evolutionary signal compared to the regions outside binding sites;
although when the binding neighborhood was removed, the remaining domain
sequence still contained some co-evolutionary signal. These results provide
evidence of the role of compensatory mutations in protein co-evolution and
contribute to our understanding of co-evolution of interacting proteins.
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