Dressel, F.

Labudde, D.

Marsico, A.

Schroeder, M.

Tuukkanen, A.

Winnenburg, R.

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John von Neumann Institute for ComputingStabilizing Regions in Membrane ProteinsF. Dressel, A. Marsico, A.Tuukkanen, R. Winnenburg,M. Schroeder, D. Labuddepublished 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. 197-200, 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/volume40Stabilizing Regions in Membrane ProteinsFrank Dressel, Annalisa Marsico, Anne Tuukkanen,Rainer Winnenburg, Michael Schroeder, and Dirk LabuddeBiotechnologisches Zentrum , TU Dresden,Tatzberg 47/49, 01307 Dresden, GermanyE-mail: firstname.lastname@biotec.tu-dresden.deAround one third of a typical genome consists of membrane proteins. Misfolding of membraneproteins can often be linked to diseases, so that it is of great importance to understand, whichresidues and interactions are crucial for its stability. We developed a coarse-grained modelto predict stabilizing regions in membrane proteins. We compare the model to experimentaldata from Single Molecule Force Spectroscopy (SMFS) and literature to evaluate the effectsof mutations on function and stability of five membrane proteins. The aim of this study is todescribe all these data in an unified context, the interaction energies of amino acids in a coarsegrained model to gain a deeper insight into membrane proteins.1 IntroductionIntegral membrane proteins account for about 20%-30% of the open reading frames ina typical genome but, despite their central importance for all organisms, the number ofknown structures remains small giving raise to the need for sequence based methods.Single Molecule Force Spectroscopy (SMFS) allows detecting and locating interac-tions stabilizing a membrane protein. During continuous stretching of the molecule, theapplied forces and the extension are measured. The resultant force-distance curve reflectssubsequent unfolding events1, the so called force peaks. The so-called Worm-like-chainmodel (WLC)2 relates the position of these unfolding barriers to positions in the primarysequence of the protein. Besides the SMFS data, there are a lot of mutation experiments inthe literature, where the effect of a certain mutation on stability and function of a membraneprotein is discussed.In this work, we developed a coarse grained model, which is able to describe the abovementioned experimental results. To the best of our knowledge, this is the first time, thatsuch a simplified model is used for predicting correctly unfolding barriers and estimatingthe influence of mutations based on sequence.2 MethodsFor the representation of a protein, we constrained ourself to the Cα/Cβ atoms of eachamino acid. To estimate the energy of the interaction between amino acids, we defined asolvation energy ei based on the probability distribution for each amino acid in an mem-brane protein to be inside (solved) or outside of the protein3. Inside means facing otheramino acids, outside means facing the phospholipids. The combination of two solva-tion energies results in a contact energy for an interaction between two spatial neighboredamino acids.197Based on the tertiary structure we estimate the energyEi with which each amino acid iinteracts with the rest of the protein by summing up the interaction energies between i andall other amino acids. Two amino acids interact if they are not direct neighbors in sequenceand if their Cβ atoms are less than 8A˚ apart. This gives an energy profile containing theenergy Ei for each amino acid in the protein. A deep valley in this profile gives raise to anenergy barrier in the model.To estimate the influence of a mutation based on the sequence only, we calculate a socalled solvation energy profile, which contains for each amino acid i the solvation energyei. This profile is, in contrast to the energy profile, independent from the tertiary structureof a protein.3 ResultsFor our analysis, we used the following five membrane proteins: Bacteriorhodopsin,Halorhodopsin, Bovine rhodopsin, Na+/H+ antiporter and Aquaporin-1. Up to now, theseare the only proteins, for which SMFS data are available.3.1 Prediction of Unfolding BarriersFor the five proteins mentioned above we predicted the barriers based on their energyprofiles. For the comparison with the experiment, we considered the published barrierposition from the experiment plus an experimental error of ∆exp = ±4 amino acids. Thisis due to systematic errors in the experiment like intrinsic movement of the cantilever.These errors are reported to be in the range of 3-7 amino acids4, depending on the usedcantilever.On average 61% of the barriers are detected by our method. A match between a pre-dicted barrier and an experimentally determined one is established if their boundaries arewithin a certain distance.A remarkable result is the following: all unfolding barriers for all proteins that, accord-ing to SMFS measurements, are found having 100% of probability of occurrence (mainpeaks) are correctly predicted by our method. This suggests that major unfolding barriersin the experiments are due to energetic reasons. Minor events are more difficult to detectwith this approach.Energy profiles can also be used for detecting smaller stabilizing regions in a protein.The helix C in bacteriorhodopsin for example is known for its important part in the protonpumping pathway5. The mutations T90A and T90V in helix C for example decrease theactivity down to 10% and 20% compared to the wild type6. This is thought to be partlydue to the missing stabilizing interaction of the threonine. Interestingly, alanine and va-line are both more repulsive in our model than threonine itself. These mutations have adestabilizing effect on this region in helix C.3.2 Predict Effects of Mutations Based on the SequenceTo quantify the ability of the model to predict the influence of mutations based on sequencealone, we screened the literature for single point mutations, which influence stability orfunction of the used membrane proteins. For this, we applied a text mining approach,198which searches in PubMed abstracts. We only used the publications, where a single pointmutation was discussed in the context of stability or stability related function. If an ap-propriate mutation was found in the literature, we compared the solvation energies of bothresidues to decide, if the mutation was stabilizing, slightly stabilizing, slightly destabiliz-ing and destabilizing. 25 out of 35 mutational effects reported in the literature correlatewith the predictions from the energy profiles and thus can be related to stability issues interms of amino acid interactions.4 SummaryWe used a coarse grained model to assess the influence of interactions between aminoacids in membrane proteins. It is based on the probability distribution for an amino acidto be inside or outside of an membrane protein. We found that major SMFS unfoldingbarriers are related to strongly interacting amino acids, whereas minor barriers are relatedto non-energetic reasons. The activity of bacteriorhodopsin and the effect of mutations atsite Thr90 is related to an increase of the interaction energy of this amino acid if mutatedto Ala or Val. Although these two use cases require knowledge about the tertiary structureof a protein, it is possible to extend the scope of the model to a purely sequence basedmethod. Compared with a list compiled from literature, 25 out of 35 mutations can berelated to energetic reasons in the model by this sequence-only approach. Concluding, theproposed coarse grained interaction scheme can be applied successfully to a wide range ofapplications in the context of membrane proteins.References1. F Oesterhelt, D Oesterhelt, M Pfeiffer, A Engel, HE Gaub, and DJ Muller, Unfold-ing pathways of individual bacteriorhodopsins., Science, 288, no. 5463, 143–6, Apr2000.2. C Bustamante, J F Marko, E D Siggia, and S Smith, Entropic elasticity of lambda-phage DNA, Science, 265, no. 5178, 1599–1600, Sep 1994, Letter.3. F Dressel, A Marsico, A Tuukkanen, R Winnenburg, M Schroeder, and D Labudde,Analyzing stable regions in membrane proteins using a coarse grained energy model,submitted, 2008.4. A Kedrov, C Ziegler, H Janovjak, W Kuehlbrandt, and DJ Muller, Controlled unfold-ing and refolding of a single sodium-proton antiporter using atomic force microscopy.,J Mol Biol, 340, no. 5, 1143–52, Jul 2004.5. H Luecke, HT Richter, and JK Lanyi, Proton transfer pathways in bacteriorhodopsinat 2.3 angstrom resolution., Science, 280, no. 5371, 1934–7, Jun 1998.6. A Peralvarez-Marin, M Marquez, JL Bourdelande, E Querol, and E Padros, Thr-90plays a vital role in the structure and function of bacteriorhodopsin., J Biol Chem,279, no. 16, 16403–9, Apr 2004.199

Stabilizing Regions in Membrane Proteins

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Stabilizing Regions in Membrane Proteins

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