Short Curriculum Vitae:

 

Malgorzata Biczysko is full Professor at University of Wrocław, where she also obtained her Master degree in Chemistry in 1994 and PhD in Theoretical Chemistry in 2000. She has been Professor at Shanghai University (2015-2024), as well as EU Marie Curie Networks post-doc at the Universities of Bologna, Helsinki, Coimbra, Naples and Scuola Normale Superiore in Pisa and researcher at Italian Research Council (ICCOM-CNR).

MB works on the development of computational protocols for molecular structure and spectroscopy for molecular systems ranging from small molecules and complexes in the gas phase to large biomolecules. Her main research interests are the development and validation of computational protocols to simulate vibrational and vibronic spectra for medium-sized molecular systems of increasing complexity, featuring dispersion interactions, hydrogen bonding, variable local stereochemistry-conformation, and chirality. Recently she also develops integrated computational model for protein structure and function as a part of the Quantum Refinement project.

 

Lecture 48: Malgorzata Biczysko

Optimal Clustering for Quantum-Based Refinement of Biomacromolecules

Malgorzata Biczysko1, Holger Kruse2, Nigel W. Moriarty3, Mark P. Waller2, Pavel V. Afonine3

1 Faculty of Chemistry, University of Wrocław, Poland

2Pending AI Pty Ltd., Eveleigh, NSW 2015, Australia

3MBIB Lawrence Berkeley National Laboratory, Berkeley, CA 94720-8235, USA

malgorzata.biczysko@uwr.edu.pl

 

Quantum refinement (Q|R) of crystallographic or cryo-EM-derived structures of biomolecules within the Q|R project aims at using quantum chemical (QM) computations instead of library-based chemical restraints. Advantage of using QM is to describe equally well typical structural properties for standard protein molecules as well as unusual local arrangements of residues in the Ramachandran space, novel ligands, or non-covalent interactions such as 𝝅 stacking, halogen, hydrogen or salt bridges.

The methods we are developing in the Q|R project [1-5], an open-source software package (http://github.com/qrefine), combine experimental data with chemical restraints derived from QM [1-6]. These procedures allow at present rather straightforward quantum refinement of proteins containing amino acids and organic ligands, in conjunction to both X-ray crystallography and Cryo-EM experiments. Replacing standard restraints by the ones derived from QM has shown to significantly improve model geometry, considering both the overall aspects of model and model-to-data fit statistics, as well as specific detailed structural features, in particular the hydrogen bonding. However, extension of the Q|R procedures to very large proteins still poses challenges on the employed algorithms and QM-based computations, in particular with relation to tackling the size of macromolecules. A solution to this problem adopted in Q|R is to divide the molecular system into manageable parts and do computations for these parts rather than using the whole macromolecule. I will discuss in detail the validation and optimization of the automatic divide-and-conquer procedure developed within the Q|R project with an atomic gradient error score that can be easily examined with common molecular visualization programs [5].

 

Figure . Q|R: blend of expertise and software tools

References

  1. M. Zheng, J. R. Reimers, M. P. Waller, P. V. Afonine, Acta Cryst. D 73. (2017). 45.

  2. M. Zheng, N. W. Moriarty, Y. Xu, J. R. Reimers, P. V. Afonine, M. P. Waller, Acta Cryst. D 73. (2017). 1020.

  3. M. Zheng, M. Biczysko, Y. Xu, N. W. Moriarty, H. Kruse, A. Urzhumtsev, M. P. Waller, P. V. Afonine, Acta Cryst. D 76. (2020). 41.

  4. L. Wang, H. Kruse, O. V. Sobolev, N. W. Moriarty, M. P. Waller, P. V. Afonine, M. Biczysko, Acta Cryst. D 76 (2020). 1184.

  5. Y. Wang, H. Kruse, N. W. Moriarty, M. P. Waller, P. V. Afonine, M. Biczysko, TCA submitted, bioRxiv DOI: 10.1101/2022.11.24.517825 (2022)

  6. Y. Liu, M. Biczysko, N. W. Moriarty, Acta Cryst. D 78 (2022). 43.