Catalytic and binding sites prediction in globular proteins through discrete Markov chains and network centrality measures.

In this work we use a discrete Markov chain approach combined with network centrality measures to identify and predict the location of active sites in globular proteins. To accomplish this, we use a three-dimensional network of proteinCαatoms as nodes connected through weighted edges which represent the varying interaction degree between protein's atoms. We compute the mean first passage time matrixH= {Hji} for this Markov chain and evaluate the averaged number of steps ⟨Hj⟩ to reach single nodenjin order to identify such residues that, on the average, are at the least distant from every other node. We also carry out a graph theory analysis to evaluate closeness centralityCc, betweenness centralityCband eigenvector centralityCemeasures which provide relevant information about the connectivity structure and topology of theCαprotein networks. Finally we also performed an analysis of equivalent random and regular networks of the same sizeNin terms of the average path lengthLand the average clustering coefficient⟨C⟩comparing these with the corresponding values forCαprotein networks. Our results show that the mean-first passage time matrixHand its related quantity ⟨Hj⟩ together withCc,CbandCecan not only predict with relative high accuracy the location of active sites in globular proteins but also exhibit a high feasibility to use them to predict the existence of new regions in protein's structure to identify new potential binding or catalytic activity or, in some cases, the presence of new allosteric pathways.

Translocation of non-interacting heteropolymer protein chains in terms of single helical propensity and size.

In this work we present an analytical framework to calculate the average translocation time τ required for an ideal proteinogenic polypeptide chain to cross over a small pore on a membrane. Translocation is considered to proceed as a chain of non-interacting amino acid residues of sequence {Xj} diffuses through the pore against an energy barrier Δℱ, set by chain entropy and unfolding-folding energetics. We analyze the effect of sequence heterogeneity on the dynamics of translocation by means of helical propensity of amino acid residues. In our calculations we use sequences of fifteen well-known proteins that are translocated which span two orders of magnitude in size according to the number of residues N. Results show non-symmetric free energy barriers as a consequence of sequence heterogeneity, such asymmetry in energy may be useful in differentiated directions of translocation. For the fifteen polypeptide chains considered we found conditions when sequence heterogeneity has not a significant effect on the time scale of translocation leading to a scaling law τ ∝ Nν, where ν ∼ 1.6 is an exponent that holds for most ground state energies. We also identify conditions when sequence heterogeneity has a great impact on the time scale of translocation, in consequence, no more scaling laws for τ there exist.

Role of single-point mutations and deletions on transition temperatures in ideal proteinogenic heteropolymer chains in the gas phase.

A coarse-grained statistical mechanics-based model for ideal heteropolymer proteinogenic chains of non-interacting residues is presented in terms of the size K of the chain and the set of helical propensities [Formula: see text] associated with each residue j along the chain. For this model, we provide an algorithm to compute the degeneracy tensor [Formula: see text] associated with energy level [Formula: see text] where [Formula: see text] is the number of residues with a native contact in a given conformation. From these results, we calculate the equilibrium partition function [Formula: see text] and characteristic temperature [Formula: see text] at which a transition from a low to a high entropy states is observed. The formalism is applied to analyze the effect on characteristic temperatures [Formula: see text] of single-point mutations and deletions of specific amino acids [Formula: see text] along the chain. Two probe systems are considered. First, we address the case of a random heteropolymer of size K and given helical propensities [Formula: see text] on a conformational phase space. Second, we focus our attention to a particular set of neuropentapeptides, [Met-5] and [Leu-5] enkephalins whose thermodynamic stability is a key feature on their coupling to [Formula: see text] and [Formula: see text] receptors and the triggering of biochemical responses.

Thermodynamics of ideal proteinogenic homopolymer chains as a function of the energy spectrum E, helical propensity ω and enthalpic energy barrier.

A reformulation and generalization of the Zwanzig model (ZW model) for ideal homopolymer chains poly-X, where X represents any of the twenty naturally occurring proteinogenic amino acid residues is presented. This reformulation and generalization provides a direct connection between coarse-grained parameters originally proposed in the ZW model with variables from the Lifson-Roig (LR) theory, such as the helical propensity per residue ω, and new variables introduced here, such as the energy gap Δ between unfolded and folded structures, as well as the ratio f of the energy scales involved. This enables us to discover the relevance of the energy spectrum E to the onset of configurational phase transitions. From the configurational partition function Q, thermodynamic properties such as the configurational entropy S, specific heat v and average energy are calculated in terms of the number of residues K, temperature T, helical propensity ω and energy barrier ΔH for different poly-X chains in vacuo. Results obtained here provide substantial evidence that configurational phase transitions for ideal poly-X chains correspond to first-order phase transitions. An anomalous behavior of the thermodynamic functions , Cv, S with respect to the number K of residues is also highlighted. On-going methods of solution are outlined.

Protein's native state stability in a chemically induced denaturation mechanism.

In this work, we present a generalization of Zwanzig's protein unfolding analysis [Zwanzig, R., 1997. Two-state models of protein folding kinetics. Proc. Natl Acad. Sci. USA 94, 148-150; Zwanzig, R., 1995. Simple model of protein folding kinetics. Proc. Natl Acad. Sci. USA 92, 9801], in order to calculate the free energy change Delta(N)(D)F between the protein's native state N and its unfolded state D in a chemically induced denaturation. This Extended Zwanzig Model (EZM) is both based on an equilibrium statistical mechanics approach and the inclusion of experimental denaturation curves. It enables us to construct a suitable partition function Z and to derive an analytical formula for Delta(N)(D)F in terms of the number K of residues of the macromolecule, the average number nu of accessible states for each single amino acid and the concentration C(1/2) where the midpoint of the N<==>D transition occurs. The results of the EZM for proteins where chemical denaturation follows a sigmoidal-type profile, as it occurs for the case of the T70N human variant of lysozyme (PDB code: T70N) [Esposito, G., et al., 2003. J. Biol. Chem. 278, 25910-25918], can be splitted into two lines. First, EZM shows that for sigmoidal denaturation profiles, the internal degrees of freedom of the chain play an outstanding role in the stability of the native state. On the other hand, that under certain conditions DeltaF can be written as a quadratic polynomial on concentration C(1/2), i.e., DeltaF approximately aC(1/2)(2)+bC(1/2)+c, where a,b,c are constant coefficients directly linked to protein's size K and the averaged number of non-native conformations nu. Such functional form for DeltaF has been widely known to fit experimental measures in chemically induced protein denaturation [Yagi, M., et al., 2003. J. Biol. Chem. 278, 47009-47015; Asgeirsson, B., Guojonsdottir, K., 2006. Biochim. Biophys. Acta 1764, 190-198; Sharma, S., et al., 2006. Protein Pept. Lett. 13(4), 323-329; Salem, M., et al., 2006. Biochim. Biophys. Acta 1764(5), 903-912] so EZM can shed some light into the physical meaning of the experimental values for the a,b,c coefficients.