In the present work, final deliverable of my Ph. D. course, new possibilities offered by SAXS in solution for studying flexible biological systems are addressed. New data analysis algorithms utilizing an interdisciplinary approach have been here developed and presented in details. The work is aimed to extend the capability of a widely shared modus operandi for the studying of flexible particles in solution, namely ensemble characterization. As no crystals are needed, SAXS in solution is one of the very few structural techniques suitable for the analysis of macromolecular flexibility. In some cases, especially given the absence of limitations on the molecular mass of the solute, SAXS is the only technique able to provide qualitative as well quantitative structural information about flexible particles. This fact made SAXS a method of choice for studying flexibility over the last years. Recent improvements in this technique have helped in the characterization of local flexibility within the particles allowing, in some cases, to link the partially unstructured regions to important biological functions in applications ranging from basics research to pharmaceutical applications (see Chapter 3 and Chapter 4). These studies substantially contributed to the reevaluation of the concept stating that a predefined 3D shape is necessary for the particle to be functionally active.
As a result, new and challenging research areas are presently being actively developed including extensive studies of IDPs. A major breakthrough was marked by the advent of the methods employing ensemble analysis allowing for quantitative analysis of flexible systems. These methods demonstrated their power for simple (e.g. single-chain) macromolecules but had some limitations for the characterization of complicated scenarios where flexibility analysis had to account for specific structural features, e.g. the presence of point group symmetry or specific protein-protein or protein-nucleic acids interaction. Extensions of the existing methods were therefore requested by the biological community.
In this dissertation, the original ensemble analysis method EOM has been entirely re-designed and made capable of handling for complicated studies of flexibility. The new algorithm, called EOM 2.0 fully maintains all the capabilities of the previous one while being much more efficient and having important new features. Special attention was paid to the integration of the information coming from complementary techniques into the SAXS data analysis procedure. An artificial intelligence based strategy for the generation
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of missing regions was implemented making the modelling of the ensemble a highly efficient procedure. A revisited genetic algorithm of EOM 2.0 is able to identify an optimum ensemble size minimizing potential problems due to over- as well as under-fitting of the experimental data, typical for the ensemble approaches. Another important innovation is represented by the introduction of two quantitative metrics, Rflex and Rcheck. Adapting a concept coming from the field of information theory, these metrics have been designed to provide objective assessments of flexibility. Thanks to these developments, systematic characterizations of the flexibility are now possible as potential artifacts are automatically detected and reported to the user. Moreover, in EOM 2.0 these metrics are complemented by a detailed representation of the behavior of the particles in solution.
Finally and very importantly, EOM 2.0 offers the unique opportunity to model particles that show disorder as well as symmetry. This possibility required the generation step to be entirely re-designed and perhaps represents the highest impact development among those presented in this dissertation, given that none of the presently available methods can offer such modeling. Additionally, the possibility to search for the best ensemble through multiple pools (e.g. containing oligomeric species) has also been introduced allowing one to employ EOM 2.0 for tracking of oligomerization processes of flexible particles. The practical examples in Chapters 5 and 6 provide good illustrations of the advanced studies that can be now conducted thanks to the capabilities of EOM 2.0.
EOM 2.0 can be freely downloaded by academic users as part of the package ATSAS 2.5 publicly available at the EMBL BioSAXS website (http://www.embl-hamburg.de/
biosaxs/software.html). The program runs on all major hardware platforms and contains an exhaustive documentation and test examples. The beta-releases of EOM 2.0 have already been used in numerous structural studies as the final version is to be released soon (paper in preparation). We expect that EOM 2.0 will significantly contribute to further advancement of SAXS studies of flexible macromolecules and complexes in solution.
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