Speaker
Description
In my talk I will present our approach for modeling macromolecular
flexibility of large molecular assemblies and how it can be combined with
sparse experimental data obtained with small-angle and cross-linking
experiments.
Large macromolecular machines, such as proteins and their complexes, are
typically very flexible at physiological conditions, and this flexibility is
important for their structure and function. Computationally, it can be often
approximated with just a few collective coordinates, which can be computed
e.g. using the Normal Mode Analysis (NMA). NMA determines low-frequency
motions at a very low computational cost and these are particularly
interesting to the structural biology community because they are commonly
assumed to give insight into protein function and dynamics [1].
One of the challenges in the community is the explanation of solution smallangle
scattering profiles. Very recently, we designed a computational scheme
that uses the nonlinear normal modes [2] as a low-dimensional representation
of the protein motion subspace and optimizes protein structures guided by the
SAXS and SANS profiles [3,4]. For example, in the CASP12 and CASP13
exercises, this scheme obtained best models for some (3 out of 9 in CASP12)
SAXS-assisted targets [5,6]. Overall, the flexible fitting scheme typically allows
a significant improvement of the goodness of fit to experimental profiles in a
very reasonable computational time. The NMA analysis also allows to
automatically split macromolecules into rigid domains, or to be used together
with the cross-linking data, as we demonstrated in the recent CASP13
challenge [7].
References:
[1] Grudinin, S., Laine, E., & Hoffmann, A. (2019). Predicting protein functional
motions: an old recipe with a new twist. bioRxiv, 703652.
[2] Hoffmann, A. & Grudinin, S. (2017). J. Chem. Theory Comput. 13, 2123 –
2134. For more information https://team.inria.fr/nano-d/software/nolbnormal-
modes/
[3] Grudinin, S. et al. (2017). Acta Cryst. D, D73, 449 – 464. For more
information https://team.inria.fr/nano-d/software/pepsi-saxs/
[4] https://team.inria.fr/nano-d/software/pepsi-sans/
[5] http://predictioncenter.org/casp13/zscores_final_assisted.cgi?target_flag=S
[6] Tamò, G. E., Abriata, L. A., Fonti, G., & Dal Peraro, M. (2018). Proteins:
Structure, Function, and Bioinformatics, 86, 215-227.
[7] http://predictioncenter.org/casp13/zscores_final_assisted.cgi?target_flag=X
