Gohlke, H.

Radestock, S.

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John von Neumann Institute for ComputingConstraint Network Analysis: Exploiting the LinkBetween Protein Rigidity and ThermostabilityS. Radestock, H. Gohlkepublished inFrom Computational Biophysics to Systems Biology (CBSB08),Proceedings of the NIC Workshop 2008,Ulrich H. E. Hansmann, Jan H. Meinke, Sandipan Mohanty,Walter Nadler, Olav Zimmermann (Editors),John von Neumann Institute for Computing, Ju¨lich,NIC Series, Vol. 40, ISBN 978-3-9810843-6-8, pp. 357-360, 2008.c© 2008 by John von Neumann Institute for ComputingPermission to make digital or hard copies of portions of this work forpersonal or classroom use is granted provided that the copies are notmade or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. To copy otherwiserequires prior specific permission by the publisher mentioned above.http://www.fz-juelich.de/nic-series/volume40Constraint Network Analysis: Exploiting the LinkBetween Protein Rigidity and ThermostabilitySebastian Radestock1,2 and Holger Gohlke1,21 Fachbereich Biowissenschaften, Molekulare Bioinformatik,Johann Wolfgang Goethe-Universita¨t, Frankfurt am Main, Germany2 Mathematisch-Naturwissenschaftliche Fakulta¨t, Pharmazeutisches Institut,Christian-Albrechts-Universita¨t, Kiel, GermanyE-mail: {radestock, gohlke}@pharmazie.uni-kiel.deUnderstanding the relationship between microscopic structure and macroscopic stability is im-portant for developing strategies to improve protein stability in the reaction media used in indus-trial processes, e.g., at high temperatures. Protein thermostability has been repeatedly linked toan enhanced structural rigidity of the folded state. Here, we used constraint network analysis fordirectly probing the rigidity of protein structures from mesophilic and thermophilic organismsalong a thermal unfolding trajectory. The approach allowed for identifying structural featuresfrom which a destabilization of the structure originates upon thermal unfolding. These predic-tions showed a good agreement with experiment. The information might thus be exploited indata-driven protein engineering by pointing to residues that should be varied to obtain a proteinwith higher thermostability.1 IntroductionStable proteins are important for broadening the industrial applicability of enzymes.1 Nat-urally occurring enzymes have usually not evolved to be tolerant to the presence of organicsolvents, extremes of pH or high temperatures that might occur in industrial processes.The identification or the development of enzymes with higher stability will thus increasethe adoption of biocatalytic syntheses in industrial production. Understanding the rela-tionship between microscopic structure and macroscopic stability is essential for this. Inthis context, computational approaches that allow for identifying structural features fromwhich a destabilization of the structure originates should provide valuable guidance.2Of all potentially destabilizing factors that might occur in industrial production, tem-perature is the best studied.1 As an approach to understand the determinants of thermosta-bility, proteins from thermophilic organisms with optimum growth temperatures of morethan 60◦C have been investigated. These proteins show a substantially higher intrinsicthermostability than their counterparts from mesophilic organisms, while retaining the ba-sic fold characteristics of the particular protein family.3 Protein thermostability has beenrepeatedly linked to an enhanced structural rigidity of the folded state.32 Materials and MethodsCrystallographic models of 20 homologous pairs of mesophilic and thermophilic proteinstructures were collected from the Protein Data Bank (PDB).4 Protein structures were mod-eled as constraint networks, where vertices represent atoms and edges represent covalent357Figure 1. Mesophilic (a, b) and thermophilic (c, d) thermolysin-like protease (TLP) directly before (a, c) andafter (b, d) the phase transition. Rigid clusters are depicted as uniformly colored bodies. The blue body in (a) and(c) represents the giant cluster. Arrows in (b) and (d) indicate potential unfolding nuclei. Roman numbers referto the numbering of the unfolding nuclei in Table 1. The N- and C-termini are marked.and non-covalent bond constraints as well as angular constraints. The network was con-structued using the FIRST software (version 6.2).5 A fast combinatorial algorithm canbe applied to determine the number and spatial distribution of bond-rotational degrees offreedom in the network and, hence, the local network rigidity. Such a rigidity analysis isavailable with FIRST.5By diluting non-covalent constraints in the protein structure network starting from thenative state, FIRST has been applied to simulate thermal unfolding of proteins.6 Here,heating was simulated by removing hydrogen bonds from the network in the order of in-creasing interaction energy. The energy of a hydrogen bond relates to the temperature atwhich the bond breaks. In going from a rigid to a flexible network, a phase transition can beobserved that defines the rigidity percolation threshold. To identify the temperature of thephase transition Tp, concepts from percolation theory and network science were applied.7, 83 Results and DiscussionIn a first step, the general percolation behavior of the constraint networks was analyzed.The phase transition can be viewed as a rigid to flexible transition of the kind observed innetwork glasses. It is characterized by the decay of a large rigid cluster (the giant cluster)in the network.4 The temperature of the phase transition relates to the melting temperatureof the protein. A higher phase transition temperature was observed for approximately two-358thirds of the proteins from thermophilic organisms from our data set compared to theirmesophilic counterparts (data not shown).In a second step, the microscopic structure of the networks was related to their observedmacroscopic behavior, in order to characterize stability features of the protein structures.For this, networks directly before and after the phase transition were compared. In Figure1, the rigid cluster decomposition of mesophilic and thermophilic thermolysin-like protein(TLP) is shown. Figure 1a and c show the networks from mesophilic and thermophilic TLPdirectly before the phase transition, respectively. Apparently, the giant cluster dominatesthe system in both cases. Moreover, the giant cluster is located in the same region ofthe proteins: It extends over the N-terminal domain and comprises the β-sheet regionand an α-helix in the N-terminal domain. After the phase transition, the giant clusterdecays into smaller rigid clusters and regions that are flexible (Figure 1b and d). The closecorrespondence of the rigid cluster distribution in the networks of the homologous proteinsbefore and after the phase transition is an intriguing result of our analysis.Unfolding nucleus Predicted sites Experimentallyverified sites9I β-sheet region in theN-terminal domain21-24, 29, 31-34, 39-42, 44, 101-107, 114-115,122-123–II N-terminus of the α-helix in theN-terminal domain68-70 69III Region around F63 in the N-terminal domain54, 56-62 4, 56, 58, 63, 65Table 1. Comparison of predicted with experimentally verified unfolding sites9 for thermolysin-like protein(TLP).In analogy to experimental protein unfolding, where initial unfolding of local regionsprecedes the denaturation of the entire protein, the loss of rigidity in certain regions isconsidered to precede the phase transition. These regions were identified as parts of thegiant cluster that become flexible upon the phase transition, each representing an unfoldingnucleus. In case of TLP, three unfolding nuclei could be found (Table 1, Figure 1). Thepredicted unfolding nuclei were compared with experimental data (Table 1). Notably, thepredicted unfolding nuclei are in good agreement with sites where stabilizing mutationshave successfully been introduced into TLP.9 A likewise good agreement between ourpredictions and experimental data was found for many other proteins from our data set.4 Concluding RemarksOur findings strongly support the notion that the stability of thermophilic proteins is ingeneral linked to an enhanced structural rigidity of the folded native state.3 Furthermore,direct support is found for the corresponding states concept which states that homologousproteins exist in corresponding states of similar flexibility at their respective optimal tem-perature.3 To the best of our knowledge, this is the first theoretical study addressing this359issue by directly probing the rigidity of protein structures along a thermal unfolding tra-jectory for a comprehensive dataset. Regarding the identification of regions that becomeflexible when approaching the phase transition (unfolding nuclei), we were encouragedto see the good (albeit not perfect) agreement between predicted sites and experimentalmutations that led to higher structural stability. The result demonstrates that our approachwill indeed be helpful to guide data-driven protein engineering to regions where mutationsmost likely will have a notable effect on thermostability.References1. D. C. Demirjian, F. Moris-Varas, C. S. Cassidy, Enzymes from extremophiles, Curr.Opin. Chem. Biol. 5, 144–151, 2001.2. J. F. Chaparro-Riggers, K. M. Polizzi, A. S. Bommarius, Better library design: Data-driven protein engineering, Biotechnol. J. 2, 180-191, 2007.3. R. Jaenicke, G. Bo¨hm, The stability of proteins in extreme environments, Curr. Opin.Struct. Biol. 8, 738-748, 1998.4. S. Radestock, H. Gohlke, Exploiting the link between protein rigidity and thermosta-bility for data-driven protein engineering, submitted.5. D. J. Jacobs, A. J. Rader, L. A. Kuhn, M. F. Thorpe, Protein flexibility predictionsusing graph theory, Proteins 4, 150–165, 2001.6. A. J. Rader, B. M. Hespenheide, L. A. Kuhn, M. F. Thorpe, Protein unfolding: Rigid-ity lost, Proc. Natl. Acad. Sci. U.S.A. 99, 3540–3545, 2002.7. D. Stauffer, A. Aharony, Introduction to percolation theory, 2nd ed., Taylor and Fran-cis, London, 1994.8. I. R. Tsang, I. J. Tsang, Cluster size diversity, percolation, and complex systems, Phys.Rev. E 60, 2684-2698, 1999.9. B. van den Burg, B. W. Dijkstra, G. Vriend, B. Vandervinne, G. Venema, V. G. H.Eijsink, Protein stabilization by hydrophobic interactions at the surface, Eur. J.Biochem. 220, 981-985, 1994.360

Constraint Network Analysis: Exploiting the Link Between Protein Rigidity and Thermostability

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Constraint Network Analysis: Exploiting the Link Between Protein Rigidity and Thermostability

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