COMPLX: a computer algorithm for the detection of protein-ligand and other macromolecular complexes in mass spectra

Bioinformatics in mass spectrometry data analysis for proteomics studies

Mass spectrometry is a technique widely employed for the identification and characterization of proteins. The role of bioinformatics is fundamental for the elaboration of mass spectrometry data due to the amount of data that this technique can produce. To process data efficiently, new software packages and algorithms are continuously being developed to improve protein identification and characterization in terms of high-throughput and statistical accuracy. However, many limitations exist concerning bioinformatics spectral data elaboration. This review aims to critically cover the recent and future developments of new bioinformatics approaches in mass spectrometry data analysis for proteomics studies.

Protein structure and dynamics studied by mass spectrometry: H/D exchange, hydroxyl radical labeling, and related approaches

Mass spectrometry (MS) plays a central role in studies on protein structure and dynamics. This review highlights some of the recent developments in this area, with focus on applications involving the use of electrospray ionization (ESI) MS. Although this technique involves the transformation of analytes into highly nonphysiological species (desolvated gas-phase ions in the vacuum), ESI-MS can provide detailed insights into the solution-phase behavior of proteins. Notably, the ionization process itself occurs in a structurally sensitive manner. An increased degree of solution-phase unfolding is correlated with a higher level of protonation. Also, ESI allows the transfer of intact noncovalent complexes into the gas phase, thereby yielding information on binding partners, stoichiometries, and even affinities. A particular focus of this article is the use of hydrogen/deuterium exchange (HDX) methods and hydroxyl radical (.OH) labeling for monitoring dynamic and structural aspect of solution-phase proteins. Conceptual similarities and differences between the two methods are discussed. We describe a simple method for the computational simulation of protein HDX patterns, a tool that can be helpful for the interpretation of isotope exchange data recorded under mixed EX1/EX2 conditions. Important aspects of .OH labeling include a striking dependence on protein concentration, and the tendency of commonly used solvent additives to act as highly effective radical scavengers. If not properly controlled, both of these factors may lead to experimental artifacts.

Performance of the computer algorithm COMPLX for the detection of protein complexes in the mass spectra of simulated biological mixtures

The performance of the algorithm COMPLX for detecting protein-ligand or other macromolecular complexes has been tested for highly complex data sets. These data contain m/z values for ions of proteins of the SWISS-PROT database within simulated biological mixtures where each component shares a similar molecular weight and/or isoelectric point (pI). As many as 1600 ion signals were entered to challenge the algorithm to identify ion signals associated with a single protein complex that has been ionised and detected within a mass spectrometer. Despite the complexity of such data sets, the algorithm is shown to be able to identify the presence of individual bimolecular complexes. The output data can be re-evaluated by the user as necessary in light of any additional information that is known concerning the nature of predicted associations, as well as the quality of the data-set in terms of errors in m/z values as a direct consequence of the mass calibration or resolution achieved. The data presented illustrates that the best results are obtained when output results are ranked according to the largest continuous series of ion pairs detected for a protein or macromolecule and its complex for which the ligand mass is assigned the lowest mass error.