Kavraki Lab

Physical and Biological Computing

Keywords:

Abstract:

This work shows how to decrease the complexity of modeling flexibility in proteins by reducing the
number of dimensions necessary to model important macromolecular motions such as the induced fit
process. Induced fit occurs during the binding of a protein to other proteins, nucleic acids or small
molecules (ligands) and is a critical part of protein function. It is now widely accepted that
conformational changes of proteins can affect their ability to bind other molecules and that any progress
in modeling protein motion and flexibility will contribute to the understanding of key biological
functions. However, modeling protein flexibility has proven a very difficult task. Experimental
laboratory methods such as X-ray crystallography produce rather limited information, while
computational methods such as Molecular Dynamics are too slow for routine use with large systems. In
this work we show how to use the Principal Component Analysis method, a dimensionality reduction
technique, to transform the original high-dimensional representation of protein motion into a lower
dimensional representation that captures the dominant modes of motions of proteins. For a medium-sized
protein this corresponds to reducing a problem with a few thousand degrees of freedom to one with less
than fifty. Although there is inevitably some loss in accuracy, we show that we can obtain conformations
that have been observed in laboratory experiments, starting from different initial conformations and
working in a drastically reduced search space.