Attività di Ricerca
Recent research activities
Oxygen-oxygen distances in protein-bound crystallographic water suggest the presence of protonated clusters.
BACKGROUND: The availability of high-resolution X-ray structures has shown that proteins contain numerous water molecules, but their role is still not fully understood. Protonated and deprotonated water species are often involved in biochemical reactions. However protons are exceedingly difficult to detect directly because they are electron-poor species.
METHODS: The oxygen‑oxygen distance of the crystallographic water molecules was analyzed in a large high-resolution data set. Moreover, a detailed analysis was carried out on the protein-bound water in the available structures of carbonic anhydrase II and cytochrome c oxidase, chosen as protein models in which protonated and deprotonated water species play a significant role.
RESULTS: The analysis shows an excess of water-water distances below the expected value for hydrogen bond. In the cavities and on the surface of the considered model proteins, clusters of water molecules are found, whose structure suggests the presence of chemical species deriving from self-ionization of water.
CONCLUSIONS: The presence of a small maximum below the hydrogen bond threshold in the oxygen‑oxygen distance distribution of crystallographic water molecules, along with the location of many of these water clusters, suggest the presence of Zundel-like structures in, or near, the proteins. Particularly significant is the presence of such structures in protein regions which have been identified as proton antennae or channels.
GENERAL SIGNIFICANCE: This work shows the possibilities, still unexplored, offered by this type of analysis in detecting in structures obtained by X-ray diffraction the presence of aqueous protons or hydroxide ions, which are chemical species as important as elusive.
DOI:10.1016/j.bbagen.2019.129480
Freely available (until January 10 2020) at https://authors.elsevier.com/a/1a64y15Dr%7EVouf
Explaining leak states in the proton pump of heme-copper oxidases observed in single-molecule experiments.
Heme-copper oxidases couple the exergonic oxygen reduction with the endergonic proton translocation. Redox-linked structural changes have been localized in deeply buried regions of the protein, near the low-potential heme. How these movements can modulate distant gating events along the intramolecular proton path, where the entry (exit) of pumped proton occurs, is a major concern for the proton pump models. Generally, these models associate, more or less directly, all translocation events with redox transitions. Although they can account for many phenomenological aspects of the pump, evidences from single-molecules experiments about leak states of the pump represent a formidable challenge. Disconnecting the redox-linked pKa shifts of the proton loading site from the external barriers, we obtain a simple stochastic mechanism which behaves similarly to the real enzyme, able to reverse the flow of the proton transfer.
DOI: 10.1016/j.bpc.2019.106276
Freely available (until January 1 2020) at https://authors.elsevier.com/a/1a2vt14YsaVgf9
Cytochrome c oxidase structures suggest a four-state stochastic pump mechanism.
Cytochrome c oxidase catalyses the terminal step of cellular respiration in eukaryotes and in many prokaryotes. This enzyme reduces molecular oxygen by means of a process coupled with proton pumping. Models for proton pumping activity in cytochrome c oxidase can be divided in two groups, which are still strongly debated to date: one in which haem a is the key player, and another where this role is covered by the oxygen reduction site. Current models share the fact of requesting, more or less explicitly, an ordered sequence of events. Here we show that all the available subunit I structures of this enzyme can be clustered in four groups. Starting from these structural observations, and considering the large corpus of available experimental data and theoretical considerations, a simple four-state (stochastic) pump model is proposed. This model implies a series of characteristics that reflect the behavior of the real enzyme in a natural way, where strictly sequential models require ad hoc assumptions (e.g. slipping mechanisms). Our results suggest that the stochastic conformational coupling could be a mechanism for energy transduction used by the protein machines.
DOI: 10.1039/C8CP07365A
A random version of principal component analysis in data clustering.
Principal component analysis (PCA) is a widespread technique for data analysis that relies on the covariance/correlation matrix of the analyzed data. However, to properly work with high-dimensional data sets, PCA poses severe mathematical constraints on the minimum number of different replicates, or samples, that must be included in the analysis. Generally, improper sampling is due to a small number of data respect to the number of the degrees of freedom that characterize the ensemble. In the field of life sciences it is often important to have an algorithm that can accept poorly dimensioned data sets, including degenerated ones. Here a new random projection algorithm is proposed, in which a random symmetric matrix surrogates the covariance/correlation matrix of PCA, while maintaining the data clustering capacity. We demonstrate that what is important for clustering efficiency of PCA is not the exact form of the covariance/correlation matrix, but simply its symmetry.
- DOI: 10.1016/j.compbiolchem.2018.01.009
- preliminary version published as arXiv:1610.08664
Conformations of the HIV-1 protease: A crystal structure data set analysis.
The HIV protease is an important drug target for HIV/AIDS therapy, and its structure and function have been extensively investigated. This enzyme performs an essential role in viral maturation by processing specific cleavage sites in the Gag and Gag-Pol precursor polyproteins so as to release their mature forms. This 99 amino acid aspartic protease works as a homodimer, with the active site localized in a central cavity capped by two flexible flap regions. The dimer presents closed or open conformations, which are involved in the substrate binding and release. Here the results of the analysis of a HIV-1 protease data set containing 552 dimer structures are reported. Different dimensionality reduction methods have been used in order to get information from this multidimensional database. Most of the structures in the data set belong to two conformational clusters. An interesting observation that comes from the analysis of these data is that some protease sequences are localized preferentially in specific areas of the conformational landscape of this protein.
Protein States as Symmetry Transitions in the Correlation Matrices.
Over the last few years, there has been significant progress in the knowledge on protein folding. However, some aspects of protein folding still need further attention. One of these is the exact relationship between the folded and unfolded states and the differences between them. Whereas the folded state is well known, at least from a structural point of view (just think of the thousands of structures in online databases), the unfolded state is more elusive. Also, these are dynamic states of matter, and this aspect cannot be overlooked. Molecular dynamics-derived correlation matrices are an invaluable source of information on the protein dynamics. Here, bulk eigenvalue spectra of the correlation matrices obtained from the Trp-cage dynamics in the folded and unfolded states have been analyzed. The associated modes represent localized vibrations and are significantly affected by the fine details of the structure and interactions. Therefore, these bulk modes can be used as probes of the protein local dynamics in different states. The results of these analyses show that the correlation matrices describing the folded and unfolded dynamics belong to different symmetry classes. This finding provides new support to the phase-transition models of protein folding.
Random Matrix Theory in molecular dynamics analysis.
It is well known that, in some situations, principal component analysis (PCA) carried out on molecular dynamics data results in the appearance of cosine-shaped low index projections. Because this is reminiscent of the results obtained by performing PCA on a multidimensional Brownian dynamics, it has been suggested that short-time protein dynamics is essentially nothing more than a noisy signal. Here we use Random Matrix Theory to analyze a series of short-time molecular dynamics experiments which are specifically designed to be simulations with high cosine content. We use as a model system the protein apoCox17, a mitochondrial copper chaperone. Spectral analysis on correlation matrices allows to easily differentiate random correlations, simply deriving from the finite length of the process, from non-random signals reflecting the intrinsic system properties. Our results clearly show that protein dynamics is not really Brownian also in presence of the cosine-shaped low index projections on principal axes.