Computational Methods in Mass Spectrometry-Based Protein 3D Studies

Abstract

Mass Spectrometry (MS)-based strategies featuring chemical or biochemical probing represent powerful and versatile tools for studying structural and dynamic features of proteins and their complexes. In fact, they can be used both as an alternative for systems intractable by other established high-resolution techniques, and as a complementary approach to these latter, providing different information on poorly characterized or very critical regions of the systems under investigation (Russell et al., 2004). The versatility of these MS-based methods depends on the wide range of usable probing techniques and reagents, which makes them suitable for virtually any class of biomolecules and complexes (Aebersold et al., 2003). Furthermore, versatility is still increased by the possibility of operating at very different levels of accuracy, ranging from qualitative high-throughput fold recognition or complex identification (Young et al., 2000), to the fine detail of structural rearrangements in biomolecules after environmental changes, point mutations or complex formations (Nikolova et al.,1998; Millevoi et al., 2001; Zheng et al., 2007). However, these techniques heavily rely upon the availability of powerful computational approaches to achieve a full exploitation of the information content associated with the experimental data. The determination of three-dimensional (3D) structures or models by MS-based techniques (MS3D) involves four main activity areas: 1) preparation of the sample and its derivatives labelled with chemical probes; 2) generation of derivatives/fragments of these molecules for further MS analysis; 3) interpretation of MS data to identify those residues that have reacted with probes; 4) derivation of 3D structures consistent with information from previous steps. Ideally, this procedure should be considered the core of an iterative process, where the final model possibly prompts for new validating experiments or helps the assignment of ambiguous information from the mass spectra interpretation step. Both the overall MS3D procedure and its different steps have been the subject of several accurate review and perspective articles (Sinz, 2006; Back et al., 2003; Young et al., 2000; Friedhoff, 2005, Renzone, et al., 2007a). However, with the partial exception of a few recent papers (Van Dijk et al., 2005; Fabris et al., 2010; Leitner et al., 2010), the full computational detail behind 3D model building (step 4) has generally received less attention than the former three steps. Structural derivation in MS3D, in fact, is considered a special case of structural determination from sparse/indirect constraints (SD-SIC). Nevertheless, information for modelling derivable from MS-based experiments exhibits some peculiar