Speaker: Sergei Grudinin
Title: Novel algorithms for rapid modeling and analysis of flexibility and symmetry in macromolecules
Abstract: 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, this flexibility 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.
We have recently introduced a new conceptually simple and computationally efficient method for nonlinear normal mode analysis called NOLB [1]. It is an extension of a very popular rotation translation blocks (RTB) approach [2]. Overall, the NOLB method produces structures with a better local geometry compared to the standard techniques, especially at large deformation amplitudes, and it also predicts better structural transitions between conformational states of macromolecules. Also, the NOLB method is scalable and robust, it typically runs at interactive time rates, and can be applied to very large molecular systems, such as ribosomes. NMA can be combined with other computational techniques for various applications, such as fitting [3] and docking [4].
Many protein complexes in the Protein Data Bank (PDB) are symmetric homo-oligomers. Indeed, it appears that large symmetrical protein structures have evolved in many organisms because they carry specific morphological and functional advantages compared to small individual protein molecules. There is therefore considerable interest in studying and modeling these structures. Recently we have proposed a novel free-docking method for protein complexes with arbitrary point-group symmetry [5]. It assembles complexes with cyclic symmetry, dihedral symmetry, and also those of high order (tetrahedral, octahedral, and icosahedral). Later on we discovered that the inverse problem, i.e. identification of symmetry in a protein assembly, is even more interesting. Given a structure of the assembly, it consists in the identification of the symmetry measure, and also of the computations of the symmetry axes [6-8]. We tackled this problem using two orthogonal approaches, (i) analytical minimization of a geometrical mismatch score over transformation operators within a symmetry group [6-7]; and (ii) convolutional neural networks trained on 3D density maps [8]. Using these tools, we performed exhaustive analysis of all symmetric structures in the PDB and found some organization patterns that I am going to present.
[2] Tama et al. (2000). Proteins: Structure, Function and Genetics 41, p1-7.
[3] Hoffmann et al (2017). J. Appl. Cryst. 50, 1036-1047.
[4] Neveu et al. (2018). Bioinformatics, 34, 2757–2765.
[5] Ritchie, D. W. & Grudinin, S (2016). J. Appl. Cryst., 49, 1-10.
[6] Pages, G., Kinzina, E, & Grudinin, S (2018). J. Struct.Biology, 203 (2), 142-148.
[7] Pages, G. & Grudinin, S (2018). J. Struct.Biology, 203 (3), 185-194.
[8] Pages, G. & Grudinin, S (2019). Bioinformatics,10.1093/bioinformatics/btz454.
The Hylleraas seminars alternate between Oslo (room V205) and Tromsø (room C302), and are broadcasted by video conference to the other place. It is possible to follow the seminars externally upon request.
