Motions described by the essential subspace have been mapped on average 3D-structures by representing with ellipsoids (at proba- bility 0.1) the computed anisotropic temperature factors obtained from the per-residue Ca anisotropic U-tensors of cross-correlations of motion. The average 3D-structure is defined as the sampled conformation with the least distance (in terms of main-chain rmsd) from the ensemble average of atomic positions. The cosine content and overlap indexes as root mean square inner product have been monitored to evaluate the sampling quality, according to standard procedures applied in our laboratory [78]. The per-residue Ca root mean square fluctuation (rmsf) was calculated with respect to the average structure, after projection of the trajectories on the essential subspace. To properly assess the rmsf convergence and the consistency of flexibility profiles, per- residue rmsf profiles have been computed on partially overlapping time-windows, starting from the first frame of the macro- trajectories and moving onwards by steps of 500 ps. This calculation has been repeated for different window lengths (1, 2, 5, 10, 15 and 20 ns) and an average rmsf profile for each time- window has been calculated (Figure S10 in Supporting Informa- tion S1). Correlation plots were obtained by first computing Ca correlation matrices [26] C(i,j), using non-overlapping averaging windows of 1 ns (Figure S11 in Supporting Information S1), and also compared, for validation, to correlations on averaging windows of 2 and 3 ns. C(i,j) has been calculated according to, Ci,j ~ c(i,j) c(i,i)1=2c(j,j)1=2 Where c(i,j) is the covariance matrix of protein fluctuations between residues i and j. Only the most significant (|C(i,j)|.0.4) long range (|i2j|.12) correlations have been considered. The cutoff of sequence distance has been selected to exclude from the analysis correlations relative to a-helices structure and contiguous residues in the primary sequence. Moreover, since an average C(i,j) matrix has been considered for the analysis (even if the single C(i,j) matrix relative to each 1 ns window have been compared to the average one), a cutoff of 0.4 in absolute value have been selected to identify significant correlations and to exclude pairs of residues which are poorly communicating each other and likely to be characterized by uncoupled motions. In particular, on 1 ns time-scale, only positively correlated motions are identified above this cutoff. Moreover, in order to clearly identify differences in the patterns of coupled motions of AHA and PPA functional regions, in the correlation plots the correlations relative to secondary structural elements (correlations within the b-barrel and correlations between a-helices) have been discarded. Correlations were then plotted on the 3D structures by connecting atoms i and j with lines, with thickness proportional to C(i,j). References 1. Trivedi S, Gehlot HS, Rao SR (2006) Protein thermostability in Archaea and Eubacteria. Genet Mol Res 5: 816–827. 2. D’Amico S, Collins T, Marx J-C, Feller G, Gerday C (2006) Psychrophilic microorganisms: challenges for life. EMBO Rep 7: 385–389. 3. Rodrigues DF, Tiedje JM (2008) Coping with our cold planet. Appl Environ Microb 74: 1677–1686. 4. Gerday C, Aittaleb M, Bentahir M, Chessa JP, Claverie P, et al. (2000) Cold- adapted enzymes: from fundamentals to biotechnology. Trends Biotechnol 18: 103–107. 5. Joseph B, Ramteke PW, Thomas G (2008) Cold active microbial lipases: Some hot issues and recent developments. Biotechnol Adv 26: 457–470. 6. Siddiqui KS, Cavicchioli R (2006) Cold-adapted enzymes. Annu Rev Biochem 75: 403–433. Molecular Dynamics of Psychrophilic Enzyme Mutants Salt bridge interactions and networks Salt bridges were identified as oppositely charged groups at less than 0.4 nm of distance in at least 24% of the macro-trajectory frames. In order to check the validity of the results also other distance (0.45 and 0.5 nm) and persistence (20%) cutoffs were employed (data not shown). The cut-off of 24%, in particular, was selected as the persistence value which best divided the intera- ctions dataset in well-separated groups, defined as signal and noise (Figure S12 in Supporting Information S1), according to a protocol previously applied [48] and summarized in Figure S12 in the Supporting Information S1. In particular, the distribution of charge-charge pairs at a defined cutoff of distance has been analyzed in terms of probability density function. It turns out that there are several ion pairs at low persistence (,10%) which are likely to be not relevant for protein structure and dynamics and identified as ‘‘noise’’ signal. Whereas, at persistence greater of 30% the number of ionic pairs is generally constant. Therefore, the selected cutoff of 24% should be able to divide the dataset in the two regions of low and high significance. This cutoff has been calculated by two supervised classification methods, trained on a set composed by two classes: noise (interactions below 10%) and signal (interactions above 30%). A Support Vector Machine (SVM) and a k-Nearest Neighbours (kNN, k = 4) classifiers, as implemented in Matlab suite, have been trained on this set and used to classify all the interactions between 10 and 30%. To identify the clusters of spatial proximity of salt-bridges, the residues involved in salt bridges have been represented as nodes of an unrooted unoriented graph, in which two nodes were connected by arcs if an interaction was found between them or if two of them were at less than 5 residues of distance in the sequence. An exhaustive search procedure was carried out on the graph to isolate the networks of interactions, which have been represented as interaction clusters (indicated by different colors in Figure 6 and 7). Supporting Information Supporting Information S1 This file contains all the supporting tables and figures for this article. (PDF) Acknowledgments We thank Dr. Gaetano Invernizzi for fruitful discussion and comments, along with a careful reading of the manuscript. Author Contributions Conceived and designed the experiments: EP. Performed the experiments: EP MP MT. Analyzed the data: EP MP MT. Wrote the paper: EP MP LDG. 7. Gianese G, Bossa F, Pascarella S (2002) Comparative structural analysis of psychrophilic and meso- and thermophilic enzymes. Proteins 47: 236–249. 8. Casanueva A, Tuffin M, Cary C, Cowan DA (2010) Molecular adaptations to psychrophily: the impact of ‘omic’ technologies. Trends Microbiol 18: 374–381. 9. Georlette D, Blaise V, Collins T, D’Amico S, Gratia E, et al. (2004) Some like it cold: biocatalysis at low temperatures. FEMS Microbiol Rev 28: 25–42. 10. Smalas AO, Leiros HK, Os V, Willassen NP (2000) Cold adapted enzymes. Biotechnol Annu Rev 6: 1–57. 11. Fields PA (2001) Review: Protein function at thermal extremes: balancing stability and flexibility. Comp Biochem Physiol A Mol Integr Physiol 129: 417–431. 12. Feller G (2010) Protein stability and enzyme activity at extreme biological temperatures. J Phys-Condens Mat 22: 323101–323116. PLoS ONE | www.plosone.org 11 September 2011 | Volume 6 | Issue 9 | e24214