19
Modeling of Biomolecular Systems Interactions, Dynamics, and Allostery Session II Abstracts
Soman Induced Conformational Changes of Human Acetylcholine Esterase
Sebnem Essiz Gokhan
1
, Brian Bennion
2
, Edmond Y. Lau
2
, Felice C. Lightstone
2
.
1
Kadir Has University, Istanbul, Turkey,
2
Lawrence Livermore National Lab, Livermore, CA,
USA.
Permanent inhibition of acetylcholine esterase, AChE, results in “runaway” neurotransmission
leading to cognitive deficiencies, seizures, paralysis, and eventually death depending on the
exposure. We present data from quantum mechanics/molecular mechanics (QM/MM) and 100 ns
(MD) simulations of the apo and soman-adducted forms of hAChE to investigate the effects on
the dynamics and protein structure when the catalytic Serine 203 is phosphonylated. By using
correlation and principal component analysis of MD trajectories, we identified the allosteric sites
in addition to segments of the protein, which are loosing flexibility due to the presence of soman
in the binding pocket. The altered motions and resulting structures provide for alternative
pathways into and out of the enzyme active site through the side-door in the soman-adducted
protein.
Evolution Teaches Predicting Protein Interactions from Sequence
Burkhard Rost
.
Technical University of Munich, Garching, Germany.
The physical protein-protein interaction (PPI) between two proteins A and B can be predicted
from sequence alone. However, methods perform poorly on this difficult task when both proteins
A and B have not been in the training set. Tobias Hamp in our group has developed a new
approach that improves significantly over state-of-the-art methods. We machine learned highly
reliable human PPIs from the Hippie resource through a new profile-kernel based SVM. This use
of evolutionary information in combination with predicted sub-cellular localization raises
precision even for low recall levels (most reliable predicted few interactions). A new rigorous
way to reduce PPI redundancy reveals that only a fraction of the available PPIs is needed to build
more accurate classifiers.
