Vanessa Ortiz

Emerging computational approaches for the study of protein allostery

Allosteric regulation of protein function is key in controlling cellular processes so its underlying mechanisms are of primary concern to research in areas spanning protein engineering and drug design. However, due to the complex nature of allosteric mechanisms, a clear and predictive understanding of the relationship between protein structure and allosteric function remains elusive. Well established experimental approaches are available to offer a limited degree of characterization of mechanical properties within proteins, but the analytical capabilities of computational methods are evolving rapidly in their ability to accurately define the subtle and concerted structural dynamics that comprise allostery. This review includes a brief overview of allostery in proteins and an exploration of relevant experimental methods. An explanation of the transition from experimental toward computational methods for allostery is discussed, followed by a review of existing and emerging methods.

A Chemical Perspective on Allostery

Much work has been done in the past decade to quantify the phenomenon of allosteric communication in proteins. Every new study unveils an extra piece of the puzzle in our search for an understanding of allostery that allows us to make predictions on the response of a protein to medically relevant stimuli such as pathological mutations or drug binding. This review summarizes recent advances in the analysis of mechanisms of allosteric communication in proteins, and combines this new knowledge to offer a perspective of allostery which is consistent with chemical views of molecular processes. First, we review recent work, particularly computational, on the characterization of signal propagation and conformational changes in allosteric proteins. We then compare different models of allostery, and discuss the significance of the concept of an allosteric pathway. We argue that allostery can be rationalized in terms of pathways of residues that efficiently transmit energy between different binding sites. We then provide examples that show how this picture could account for most of the observed data, since energy flow may be manifested as changes in both structure and dynamics. We conclude by acknowledging that the proposed view is still a simplification and should not be taken as a rigorous model of allosteric communication in proteins. Nevertheless, simple pictures like this can go a long way in improving our understanding of many complex phenomena observed in nature.

Determination of Signaling Pathways in Proteins through Network Theory: Importance of the Topology

Network theory methods are being increasingly applied to proteins to investigate complex biological phenomena. Residues that are important for signaling processes can be identified by their condition as critical nodes in a protein structure network. This analysis involves modeling the protein as a graph in which each residue is represented as a node and edges are drawn between nodes that are deemed connected. In this paper, we show that the results obtained from this type of network analysis (i.e., signaling pathways, key residues for signal transmission, etc.) are profoundly affected by the topology of the network, with normally used determination of network edges by geometrical cutoff schemes giving rise to substantial statistical errors. We propose a method of determining protein structure networks by calculating inter-residue interaction energies and show that it gives an accurate and reliable description of the signal-propagation properties of a known allosteric enzyme. We also show that including covalent interactions in the network topology is essential for accurate results to be obtained.

Energy Propagation and Network Energetic Coupling in Proteins

Understanding how allosteric proteins respond to changes in their environment is a major goal of current biological research. We show that these responses can be quantified by analyzing protein energy networks using a method recently developed in our group. On the basis of this method, we introduce here a quantity named energetic coupling, which we show is able to discriminate allosterically active mutants of the lactose repressor (LacI) protein, and of the catabolite activator protein (CAP), a dynamically driven allosteric protein. Our method assumes that allostery and signal transmission can be more accurately described as efficient energy propagation, and not as the more widely used atomic motion correlations. We demonstrate the validity of this assumption by performing energy-propagation simulations. Finally, we present results from energy-propagation simulations performed on folded and fully extended conformations of the postsynaptic density protein 95 (PSD-95). They show that the protein backbone provides a more efficient route for energy transfer, when compared to secondary or tertiary contacts. On the basis of this, we propose energy propagation through the backbone as a possible explanation for the observation that intrinsically disordered proteins can efficiently transmit signals while lacking a well-defined tertiary structure.

MDN: A Web Portal for Network Analysis of Molecular Dynamics Simulations

We introduce a web portal that employs network theory for the analysis of trajectories from molecular dynamics simulations. Users can create protein energy networks following methodology previously introduced by our group, and can identify residues that are important for signal propagation, as well as measure the efficiency of signal propagation by calculating the network coupling. This tool, called MDN, was used to characterize signal propagation in Escherichia coli heat-shock protein 70-kDa. Two variants of this protein experimentally shown to be allosterically active exhibit higher network coupling relative to that of two inactive variants. In addition, calculations of partial coupling suggest that this quantity could be used as part of the criteria to determine pocket druggability in drug discovery studies.

Local elastic constants of LacI and implications for allostery.

Allostery connects subtle changes in a proteins potential energy surface, induced by perturbations like ligand-binding, to significant changes in its function. Understanding this phenomenon and predicting its occurrence are major goals of current research in biophysics and molecular biology. In this paper we introduce a novel approach for studying complex structural transformations such as those typical for allostery. We show that the calculation and analysis of atomic elastic constants of a known allosterically regulated protein, lac repressor, highlights regions that are particularly prone to suffer structural deformation and are experimentally linked to allosteric function. The calculations are based on a high resolution, all-atom description of the protein. We also show that, for the present system, modifying the description of the system from an all-atom forcefield to an elastic network model yields qualitatively different results, indicating the importance of adequately describing the local environment surrounding the different parts of the protein.