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Fuglebakk et al.  PNAS | December 21, 2016 | vol. XXX | no. XX | 1

Structural characteristics of membrane binding sites on peripheral membrane proteins

Fuglebakk E^{a,b} and Reuter, N^{a,b,2}

a Department of Molecular Biology, University of Bergen; ^{b} Computational Biology Unit, University of Bergen

This manuscript was compiled on December 21, 2016

{Please provide an abstract of no more than 250 words in a single paragraph. Abstracts should explain to the general reader the major contributions of the article. References in the abstract must be cited in full within the abstract itself and cited in the text.}

{Keyword 1} | {Keyword 2} | {Keyword 3} | ...

The lipid bilayer is an ancient and crucial component in

the organisation of life. It defines boundaries between cells and cell compartments, and is stage to a myriad of interfacial processes that are necessary for the cooperation of cells and interactions between organisms. Biological membranes provide many of the restrictions and possibilities that shape processes essential to complex life, such as signalling, recognition and trafficking. A range of proteins have evolved to facilitate and regulate these processes. Some of them are completely embedded in the membrane, while others are highly soluble and interact with the membrane only shallowly and transiently. This class of shallowly binding proteins, or peripheral membrane proteins, play important roles in interfacial biology. They are for example involved in trafficking (? ), immune recognition (? ), toxicity (? ), morphology (? ? ) and signalling (? ? ). A detailed understanding of these processes inevitably involves understanding the binding modes of peripheral membrane proteins. Elucidation of the binding mechanism for individual proteins can for example open the door for exciting new ways of manipulating their function, such as the development of membrane binding inhibitors (? ). Of particular interest, however, is the development of general binding models, which is pertinent for extracting knowledge from the large data sets that are being developed in the emerging field of lipidomics (? ).

The embedded and transmembrane proteins typically have well defined regions of hydrophobic surface, clearly distinguishing their membrane interacting segments. This is not typically the case for the peripheral membrane proteins. To be soluble, they have to be restricted in how large hydrophobic patches they expose. Their membrane binding are in fact typically understood in terms of electrostatic complementary with the head group charges of membrane lipids (? ), rather than in terms of desolvation of hydrophobes. Other specific binding modes have also been identified, such as conformational change, binding of specific lipids, and covalent attachment of lipids or other hydrophobic moieties (? ). Yet, the general properties of biological membranes, their hydrophobic core and anionic surface, is of particular interest. These factors are universally present in the environment shaping peripheral membrane proteins, and their explanatory power generally carries over from the wide range of in vitro model membranes to their biological context. However, as both small hydrophobic patches and charged residues are frequently found on the surfaces of most proteins, it is not trivial to distinguished membrane binding sites from the rest of the surface of peripheral membrane proteins. Even when such residues are found in close proximity to the membrane, their energetic contribution to binding is difficult to determine both experimentally and theoretically. This is particularly true for hydrophobes, as the hydrophobic effect influence the systems free energy via the interactions between solvent molecules, and can therefore not easily be isolated in theoretical treatments. When an amino acids energetic contribution to binding can not be deduced with high confidence, their expected importance for binding can be addressed more qualitatively based on general descriptions of membrane binding motifs. Some such motifs have been identified such as amphipathic helices {cite} or cationic clusters {cite}.

The purpose of this contribution is to identify structural criteria that distinguish exposed hydrophobes at membrane binding sites from those that are frequently found on any protein surface. Hydrophobes that protrude from the protein globule at membrane binding sites have been observed in both case studies (? ? ) and systematic surveys (? ). We have made a simple definition that captures this informal concept of protruding hydrophobes, which can be easily computed once the protein structure has been determined. This definition allows us to systematically investigate to what extent protruding hydrophobes are characteristic of membrane binding sites, and to identify structural criteria for recognising exposed hydrophobes that are likely to be important for membrane binding. Amphipathic helices are also frequently positioned as elements protruding from the protein globule, and we view the concept of protruding hydrophobes as a generalisation of this.

A major obstacle in developing general association models for peripheral membrane proteins, is the scarcity of experimentally verified binding sites. So far, computational studies on the role of hydrophobes on membrane binding sites have been based on small sets of proteins with known bind-

.. Significance Statement {Authors must submit a 120-word maximum statement about the significance of their research paper written at a level under- standable to an undergraduate educated scientist outside their field of speciality. The primary goal of the Significance State- ment is to explain the relevance of the work in broad context to a broad readership. The Significance Statement appears in the paper itself and is required for all research papers.}

{Please provide details of author contributions here.} {Please declare any conflict of interest here.}

2To whom correspondence should be addressed. E-mail: Nathalie.Reutercbu.uib.com

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ing sites (? ? ? ), and generalisation of these effects can therefore be hard to justify. To get around this problem, we perform statistical analysis of protein surfaces, comparing peripheral membrane proteins with non-binding reference surfaces. Given a classification of proteins that separates membrane binders from non-binders, this approach allows us to circumvent the difficult problem of experimentally identifying exact binding sites. With this we can extend our analysis to hundreds of protein families, rather than the few dozens for which binding sites have been experimentally identified.

With our simple definition of structural protrusion, and through a statistical approach not dependent on exact binding site verification, we find several characteristics of protruding hydrophobes that support their general role in membrane association. We find that hydrophobes are much more frequent on the protruding sites of peripheral membrane proteins than in the reference proteins, and that they have a strong tendency to cluster on positions that can simultaneously interact with the membrane. We also derive membrane binding site predictors that are highly indicative of both experimentally identified membrane binding residues, and binding orientations predicted by other computational models. This allows us to confidently assert that the membrane binding motif of protruding hydrophobes do generalise from the examples that motivated our definition. Despite the attention generally offered to electrostatic interactions in membrane binding, we find protruding hydrophobes to be a distinct signature of peripheral membrane proteins, and estimate that they participate in membrane binding in at least half of the 326 {check} protein families we have analysed.

Results and Discussion

When visually inspecting peripheral membrane proteins, we find that their binding sites frequently contain hydrophobes located on local protrusions on the protein surface. Similar observations has been made by others, studying specific proteins (? ) {cite} or sets of computationally predicted binding sites (? ). While small hydrophobic patches are common on protein surfaces, we find the frequent localisation of hydrophobes on protruding structural elements to be striking. Motivated by this observation we defined the concept of protrusions as detailed in section 1 and illustrated in Figure 1. This definition encodes the idea that protrusions should be highly exposed residues that have their side-chains accessible for membrane insertion. This membrane accessibility is modelled by requiring protrusions to be vertex residues of the convex hull (blue faces in in Figure 1), which implies that their side chains can be inserted in a flat membrane, without inserting or deforming any part of the protein backbone. The model thus implicitly assumes that proteins interact with the membrane without appreciable conformational change, or prior to such change. High solvent exposure, which is necessary for the hydrophobic effect to contribute to binding, is modelled by restrictions on the local protein density, defined in section 1. Regions of low local protein density are also likely to have few steric conflicts with the membrane upon insertion.

In what follows, we present results of application of this model to characterise hydropathic properties of protrusions in peripheral membrane proteins. We do this by comparing a dataset of peripheral membrane proteins and a reference set of protein surfaces not interacting with the membrane, as described in section 1.

Protruding hydrophobes on peripheral membrane proteins.

First we compared the frequency of hydrophobes on protrusions in different protein families. In Figure 3, we observe a stark contrast between this frequency in the set of peripheral proteins and in the reference set (Figures 3 A and 3 C). Hydrophobes occur with high frequency, and in almost all families, on protrusions on peripheral proteins. In the reference set on the other hand, hydrophobes on protrusions are much less tolerated, reflected in a histogram mode of zero. This trend is special for protruding positions, and is not reflecting a general difference in surface amino-acid composition between the data sets. If we consider the frequency of hydrophboes on all solvent exposed residues, it is clear that the distributions look quite similar, with both sets having histogram modes close to 0.2 (Figures 3 B and 3 D). This is similar to the fraction of the surface of globular proteins typically reported to be hydrophobic (for instance 0.19 in Ref. (? )). The surfaces of the references set are in some cases very small, due to the way we ensure that these surfaces are not interacting with the membrane (see section 1). While these small surfaces are relevant samples for calculating average frequencies, the fraction of hydrophobes on such surfaces tend to take on more extreme values (close to zero and close to 1). For this reason the tails of the histograms for the reference set are somewhat fatter than those for the peripheral membrane proteins.

In our model, the differences presented in Figure 3 is naturally ascribed to two factors; the availability of residues for membrane insertion (they are vertex residues of the convex hull) and their low local protein density. We explored the dependence of this difference on the model parameters, by grouping vertex residues by their local protein density d, which is defined in section 1. As explained there, we have required protrusions to have local protein density less than n = 22. In Figure 5 we show the contrast between the data sets with respect to the frequencies of hydrophobes on vertex residues, for different ranges of the local protein density d. The leftmost bar (0 ≤ d ≤ 6) correspond to chain terminals. The other bars covered by our definition of protruding residues (7 ≤ d < 22) shows that hydrophobic residues are more frequently found at vertex residues with low local protein density. This also justifies our choice of n.

If the over-representation of hydrophobes on protrusions in peripheral membrane proteins stems from membrane binding sites, we would expect them to frequently occur on positions that can be simultaneously inserted in the membrane. We investigate this by estimating their tendency to be coinsertable as defined in section 1. Figure 7 shows estimates for this tendency in the two data sets. We note that peripheral membrane proteins does indeed tend to have hydrophobes on co-insertable protrusions to a significantly larger extent than what would be expected from just randomly scattering hydrophobes among protruding positions. For the reference set, this tendency is much lower, even when considering the extremities of the error bars, which are wide precisely because there are very few protruding hydrophobes in this set.

We further explored how the concept of protruding hydrophobes and the concept of co-insertability can be combined to identify likely membrane binding proteins. Figure 4 shows the fraction of proteins that have protruding hydrophobes

Fuglebakk et al.  PNAS | December 21, 2016 | vol. XXX | no. XX | 3

somewhere on their surface. We have differentiated here between protrusions that have at least one co-insertable protruding hydrophobe, and those that have not, which we have labelled isolated in the figure. Note that it is protrusions and not proteins that are grouped in this way, so that a protein may contribute to both groups. As we do not enjoy the normalisation with respect to protein size that we do for our previous analysis, we also grouped the proteins by total number of surface protrusions (regardless of hydropathic properties). While we see weak discrimination in the case of isolated protruding hydrophobes, the co-insertable ones proves to be very strong indicators of which proteins surfaces are membrane binding. We consider the frequency in the reference set to be a reasonable estimate of a false positive rate for predicting membrane binding function based on the presence of protruding hydrophobes. Based on Figure 4 we estimate this false positive rate to be around 15% for co-insertable protrusions, in both the size ranges we have analysed. For the peripheral membrane proteins, we estimate that 65% and 75% {Get exact numbers.} of the peripheral membrane proteins in the respective groups have co-insertable protruding hydrophobes. Assuming that such motifs occur by chance at a rate no higher than it does in the reference set, and that the over-representation is due to the membrane binding function defining the data sets, we conservatively estimate that coinsertable protruding hydrophobes are membrane-interacting motifs in around half of the peripheral membrane proteins we have analysed.

Protruding hydrophobes on known membrane binding sites.

The analysis presented in Figures 5 and 4 suggests that membrane binding residues can indeed be identified on individual proteins, based on the concepts of protruding hydrophobes and co-insertability. Using these concepts, we therefore defined The Likely Inserted Hydrophobe as a predictor of membrane binding sites. The Likely Inserted Hydrophobe is identified by ranking protruding hydrophobes by decreasing number of co-insertable protruding hydrophobes and increasing local protein density, as defined in section 1. Figure 10 illustrates that this simple definition is able to identify binding sites on domains that are known to facilitate peripheral binding {cite}. A more quantitative comparison between predicted and verified membrane interacting residues is complicated by the absence of negative assertions from either method. Experiments rarely investigate all membrane binding residues, and we certainly do not expect the Likely Inserted Hydrophobe to be the only one interacting with the membrane. We can however approximate binding orientations from these partial binding site identifications. As defined in Eq. 11, we represent the orientation of the protein on the membrane by vectors connecting the mean position of all residues in the protein with the mean position of the membrane interacting residues. The membrane interacting residues can be identified through experiment (I_{e}) or modelled as the Likely Inserted Hydrophobe (I_{p}), and the angle between the corresponding vectors (t_{I_{e}} and t_{I_{p}} ) represents a rough measure of how well these binding site identifications agree. We have gathered what we could find of experimental verification of binding sites for peripheral proteins, and present in Figure 9 such comparisons of protein orientations. This is a coarse approximation to the protein orientation, which is sensitive to both protein shape, the selection of residues included in the partial biding sites, and any difference in backbone conformation between bound and unbound protein. Even so, we do expect that wrong binding site predictions should provide angles in the entire range from 0°to 180°with roughly uniform probability. But, we observe that almost all angles are sharper than 90°, indicating that not many predictions are wrong. We also observe a similar range of angles for cases where the membrane interaction of the Likely Inserted Hydrophobe has been experimentally verified (red marks) and the cases where it has not. We would like to emphasise at this point that the Likely Inserted Hydrophobes that are not yet found to be membrane interacting might very well never have been tested. To assure ourselves that the skew towards lower angles in Figure 9 is indeed driven by the hydrophathic properties of the identified protrusions, we also calculated all angles between the set of experimentally identified residues and protruding amino acids of all kinds. These results are displayed as box-plots in the top panel in Figure 9. While they vary a bit between families, we note that all medians are close to 90°, confirming that the statistical expectation for protrusions in general is to have roughly equally many observations larger than and smaller than 90°. The complete list of experimentally identified partial binding sites can be found in supplementary material. It overlaps with the list provided by Lomize et al. (? ), but our binding site definitions are not always equal to theirs, as we include membrane interacting residues even when they are not inserted in the hydrophobic core of the membrane.

Note that not all families in Figure 9 are classified in the category of peripheral membrane proteins in OPM. Peptidase S26 included in the examples analysed is a periplasmic domain of a transmembrane protein {cite}; the Pore-forming Equinatoxin is analysed in its soluble monomeric state, while it forms a transmembrane pore upon oligomerisation {cite}; and the Lactoferrin B example is classified as a peptide, but NMR experiments suggest a stable fold so we have included it in our analysis {cite} {check}.

Protruding hydrophobes on predicted membrane binding

sites. The continuum-model presented by Lomize et al. (? ) forms the basis of a systematic effort to provide likely binding orientations for peripheral membrane proteins via the OPM database (? ). As we have identified protruding hydrophobes to be important for membrane association, we find it interesting to investigate to what extent protruding hydrophobes are captured by the model proposed by Lomize et al.. In order to compare predictions made from our analysis of surface residues with the predictions found in OPM, we identify The Likely Inserted Hydrophobe for each protein, and extract the insertion coordinate for this residue in the OPM prediction. The insertion coordinate is described in section 1, and reflect the distance from the hydrocarbon boundary of the membrane with negative. It is negative on either side of the membrane, and positive in the hydrocarbon core. Histograms of the median insertion coordinate of the Likely Inserted Hydrophobe in each family are shown in Figure 8. We note that the majority of the protruding hydrophobes identified in this way are located close to the interface of the membrane model in the OPM-predictions (Figure 8 A). Around 75% {get exact number} of the families in the set of peripheral membrane proteins have the median insertion coordinate for the Likely Inserted Hydrophobe within a margin of 0.5 nm from the membrane. This fraction is similar to the estimated fraction of proteins

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that have co-insertable protruding hydrophobes (Figures 4 A and B. We allow this margin of 0.5 nm to compensate for the assumptions of rigid protein, flat membrane, and the distance between C_{α} -atoms and side-chain atoms. Fractions for other margins can be read from the cumulative histogram shown in Figure 8 C. By representing position with the insertion coordinate, we effectively project residue coordinates onto the membrane normal. We therefore do not expect surface amino acids to be uniformly distributed along the insertion coordinate axis, and present control statistics for randomly chosen protruding amino acids of all hydropathic properties (Figure 8 B and 8 D). It is clear that protein shape with respect to the membrane normal explains only a very small part of this result.

Composition of protruding hydrophobes. Figure 5 indicates

that the ability to discriminate the data sets based on the frequency of hydrophobes on protrusions gets lower as the local protein density gets higher. The secondary structures that best allows for low local protein density are loops, turns and bends. These secondary structures typically mark a clear change in direction of the backbone trace, getting neighbouring residues out of the way of the protruding hydrophobe. Figure 6 A shows the secondary structure for the protruding hydrophobes in the set of peripehral proteins. We note that loops, turns and bends are indeed abundant. In Figure 6 B we show the comparison between the data sets of hydrophobes on protrusions that have specific secondary structures. We see that hydrophobes on turns and bends are not only common in the peripheral membrane proteins as we saw in Figure 6Ã, but they are also significantly more frequent than in the reference set. Interestingly, this is not the case for loops. A reason for this might be that these secondary structure elements provide a rigid scaffold for exposing the hydrophobe, which could otherwise rearrange to desolvate when exposed to solvent, and thereby likely reducing the free-energy gain of membrane insertion. As the definition of loop here is simply absence of any of the other secondary structure definitions, we would expect this category to contain less regular, more flexible structures. We also expect this property of rigid scaffolding from amphipathic helices, which is an established motif for membrane association. Figure 6 illustrates however that protrusions are not dominantly helices, confirming that the concept of protruding hydrophobes provide a useful generalisation.

For purposes of isolating the structural component of hydrophobic membrane association, we have used a dichotomous definition of hydrophobicity. Yet, we do of course expect that different amino acids have different average contributions to the free energy of binding. We have therefore also assessed the relative importance of different amino acids, for discriminating between our two data sets. Figure 2 B shows the comparison of the frequencies of different hydrophobic amino acids on protrusions in the two data sets. As might be expected, we find non-polar residues with large aliphatic or aromatic side chains to be significantly more frequent at the protrusions of peripheral proteins. While the error bars in Figure 2 B are not corrected for multiple testing, the signal for the hydrophobes as a group is quite clear. They all occur as over-represented in the set of peripheral proteins and the odds-ratio is much larger for phenylalanine, leucine and tryptophan than for any of the amino-acids that are over-represented in the reference set. Recall that lnR (Eq.10) is symmetric around 0, so the bar representing phenylalanine on one end, can be directly compared to the bar representing threonine in the negative direction. Tyrosine on the other hand discriminates the sets poorly compared to its high hydrophobicity score. We consider this a possible consequence of the orientational restrictions on the binding sites of peripheral membrane proteins. The typical orientations consistent with shallow binding, has the residue anchored above the membrane. This probably allows less freedom for the hydroxyl group of tyrosine to orient towards regions of higher water density, than it has in the peptides used for the Wimley-White experiments or in transmembrane proteins. We also note with interest that proline is among the residues somewhat over-represented in the set of peripheral proteins. In general, prolines are conformationally important protein components, that restricts the backbone with respect to its immediate neighbours along the peptide chain, and are therefore likely to promote local rigidity. They also serve to induce sharp changes in the backbone trace, which we facilitates solvent exposure of neighbouring side-chains, as discussed above. Specifically, they are frequently found on turns.

Comments on data sets. The set of peripheral membrane proteins have been carefully obtained by the maintainers of the OPM database. While they have strict criteria for inclusion (? ), membrane binding is not asserted by experiment in all cases, and the set might contain some false positives. For the reference set, we extracted data from the solvent-exposed parts of trans-membrane complexes in the OPM, which has been computationally inserted into a single flat membrane. When transmembrane proteins interact with secondary membranes or interact with membranes of extremely high curvature, it is not captured by the OPM model, and the assumption that these surfaces are not interacting with membrane may be violated. We have assumed that such issues are exceptional. We do consider these assumptions to be conservative, in the sense that inclusion of non-binding proteins in our set of peripheral membrane proteins would likely weaken any general signal in that set, and inclusion of secondary membrane interactions sites in the reference set would probably inflate the number of hydrophobes on protrusions in that set. These protein structures are obtained by X-ray crystallography and NMR spectroscopy and we have assumed that at least the backbone coordinates are representative of the solvated state of the proteins. As the source of structural information for this database is the Protein Data Bank (PDB)(? ), the database authors have decided on oligomer models, upon which we rely. These are taken from PDBe (? ), generated by PISA (? ) or obtained from literature as described by Lomize et al. (? ). As weak protein-protein interaction interfaces may also contain exposed hydrophobic patches, we expect our analysis to be sensitive to how protein quaternary structure is modelled. We have performed analysis relying solely on computationally predicted quaternary structures, which is included in the supplementary material. This analysis confirms the dependency on quaternary structure modelling, but preserves all relative trends between the data sets that we have interpreted.

Conclusion

Typically, protein-membrane interactions are studied in vitro or in silico and inference to their biological context have to carry over from greatly simplified membrane models. To make

Fuglebakk et al.  PNAS | December 21, 2016 | vol. XXX | no. XX | 5

sense of such experiments and simulations, it is essential to formulate general models that explain protein association in terms of factors that are present in both model systems and the relevant in vivo counterpart. In pursuit of such general models for membrane recognition, we have formulated the concepts of protruding hydrophboes and co-insertability. We have analysed more than 300 families of proteins that are classified as peripheral membrane binders and identified this model to be a good fit to more than half of them (Figure 4). The generality of the model is corroborated by three important points. Hydrophobes are clearly over-represented on protrusions on peripheral membrane proteins (compare Figure 3 A and 3 C, and see Figure 5), they tend to locate on co-insertable protrusions (see Figure 7 and Figure 4), and they are generally positioned consistent with experimentally identified binding sites (Figure 10 and Figure 9). While our definition of protrusion is designed to also capture exposed amphipathic helices, a well known membrane binding motif, we find that the majority of identified protruding hydrophobes are not helices (Figure 6 A) and that hydrophobes are also highly over-represented on protruding turns and bends (Figure 6 B).

Participation of hydrophobic amino acids in binding of peripheral membrane proteins is known for several well studied cases, but as a general model of membrane recognition it faces strong competition from the well developed theory of electrostatic mechanisms for membrane binding. In both cases, a major obstacle to determining their importance for membrane biding is that both small hydrophobic patches and charged amino acids are quite common on solvent exposed surfaces in general. Barring a detailed investigation of their energetic contributions to binding, their presence on binding sites is therefore not very indicative of their importance for binding. In this contribution we have showed that subtle consideration of protein structure strongly distinguishes the small hydrophobic patches on peripheral membrane proteins from other protein surfaces, and provide good reason to assume importance for binding. An important advantage to identifying important binding residues through statistical analysis of the kind presented here, is that we do not rely on experimental verification of exact binding site locations. Such experiments can be costly and difficult even when it is already well established that a protein bind to the membrane. Moreover, resources for systematic and comprehensive annotation of known binding sites have yet to be developed. This strategy therefore overcomes a major obstacle to learning about the binding of peripheral membrane proteins, and should be applicable to the study of other general binding mechanisms as well.

The concept of protruding hydrophobes provides a simple framework for interpreting the role hydrophobes and their supporting structures in the binding of peripheral membrane proteins. We have shown that binding sites and important residues in many cases can be identified from this simples structural analysis, without explicit treatment of protein dynamics or detailed models of the hydrophobic effect. The prevalence of co-insertable protruding hydrophobes in so many families of peripheral membrane proteins, adds to an accumulating body of work challenging the paradigm of electrostatic binding mechanisms in the study of peripheral membrane proteins. As small hydrophobic patches are common on all protein surfaces, it has been challenging to determine exactly when they are positioned to form a likely binding site. The work presented here provides the means to make that kind of distinction and should open the doors to understand hydrophobic binding sites where they have previously been overlooked.

Materials and Methods

Data sets. We obtained data sets from the collection of proteins in the OPM-database (? ). Our set of peripheral proteins are all proteins in OPM classified as type: Monotopic/peripheral

The reference set consist of fragments of transmembrane complexes. We obtained these protein fragments from all proteins classified as type: Transmembrane in OPM. The fragments analysed are composed of all amino acids whose C_{α} -coordinates are at least 1.5 nm from the hydrocarbon region of the membrane model (The parameter Z_{HDC} in the OPM model (? )). We rely here on membrane models positioned by the OPM, which we deem very reliable for transmembrane proteins. While the entire protein complex was considered when calculating structural properties, only the fragments meeting this distance criteria were considered in the statistical analyses. A few structures meeting the above criteria, were not included in the analysis for technical reasons, such as issues with formatting of PDB files. After exclusion of these cases, the final set of peripheral proteins contains 1013 protein structures classified into 326 families. The final reference set contains 495 protein structures classified into 158 families. {check}

As a quality control, we also performed our analysis on a different reference set based on the SCOPe-classification (? ). This control reproduced qualitatively all observations that we have interpreted, and is presented as Supplementary Material.

Based on experiments reported in available literature (? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ), we made a data set of partially identified membrane binding sites on proteins with resolved structures. This set contains membrane interacting residues of 34 protein structures, classified into 22 families {check}. A detailed description is provided in the Supplementary Material (Table ?? and ?? ).

Definitions.

Structural characteristics of protein surfaces. We characterise the

surface of proteins with different criteria designed to capture solventexposed residues, protruding residues and co-insertable protruding residues. The two latter are illustrated in Figure 1.

Exposed amino acids are defined as all amino acids which has a solvent accessible side-chain area greater than 0.2 nm^{2}, as calculated with a probe with a radius of 0.14 nm, following the procedure described in Eisenhaber et al. (? ) using van der Waals radi reported by Bondi (? ).

We identify a protrusion or a protruding residue, via calculation of the convex hull of the C_{α} - and C_{β} -coordinates of the protein. The convex hull of a set of points S is the smallest possible convex set containing S. We define vertex residues as residues whose C_{β} - atom is a vertex of this convex hull. A protrusion or a protruding residue, is defined as a vertex residue, that also have low local protein density. For the purposes of this work, we will define the local protein density d of a residue, as the number of C_{α} - or C_{β} -atoms within a distance c of its C_{β} -atom. We will designate a local protein density as low, if d < n, with n = 22 and c = 1 nm. These parameters were manually chosen based on a set of six different families of peripheral membrane proteins (C2-domain, PX-domain, Discodin domain, ENTH domain, Lipoxygenases and a Bacterial Phospholipase C). A list of these proteins are provided as supplementary material (Table ?? )

We define two residues to be co-insertable or a co-insertable pair, if they are adjacent vertex residues of the convex hull. That is, all points on the straight line connecting their C_{β} -atoms are also vertices of the convex hull.

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Hydrophobic residues. An amino acids is defined to be hydrophobic, or a hydrophobe, if it contributes favourably to membrane interface partitioning of peptides, as determined in the Wimley-White scale for interfacial insertion (? ). These amino acids are: leucine, isoleucine, phenylalanine, tyrosine, tryptophan, cysteine and methionine.

Secondary structure. We use DSSP definitions (? ) for protein secondary structure. DSSP codes H, G or I are reported as helix, DSSP codes B or E as β, DSSP code T as bend and DSSP code S as turn. All other residues are considered to be in loops.

Likely Inserted Hydrophobe. The Likely Inserted Hydrophobe is defined as the protruding hydrophobe with the largest number of coinsertable protruding hydrophobes in a protein. Ties are resolved by choosing the likely inserted hydrophobe with the smallest local protein density d. Further ties are resolved by random selection, so that each protein has exactly one Likely Inserted Hydrophobe, unless it has no protruding hydrophobes at all.

Insertion coordinate. For comparing with OPM predictions, we define the insertion coordinate of atoms. This coordinate measures how deeply into the OPM membrane model an atom is inserted, and is therefore negative on the solvated side of the membrane. The membrane perimeter, where the insertion coordinate is 0, is the end of the hydrocarbon region, which is identified by the parameter Z_{HDC} in the model used to predict the OPM orientations (? ).

Measures.

Averages of residues. We compare protein surfaces with respect to structural and hydropathic properties, reflected in different selection criteria and averaged over families or the entire data sets.

The mean fraction of residues having property s with respect to a reference property r in a family is:

f̂_{s|r} = \frac{1}{|C|} ∑_{S∈C} \frac{|S_{s} ∩ S_{r} |}{|S_{r} |}  [1]

where C is a family |C| is the number of proteins in that family, S_{s} is the set of residues on a protein meeting criteria s and S_{r} is the set meeting criteria r. We will specify s and r according to the definitions above, using intersect notation to combine criteria when necessary. f̂_{hydrophobe |protrusion ∩helix} , for instance, should be interpreted as the mean fraction of hydrophobes out of all protruding amino acids that are in helices.

We estimate weighted data set counts of amino acids with property s as:

N̂_{s} = ∑_{C∈D} ( \frac{1}{|C|} ∑_{S∈C} |S_{s} |)  [2]

where D is a data set, such as the set of peripheral proteins or the reference set. Similarly we quantify the weighted count of proteins that has at least one amino acid with property s as:

M̂_{s} = ∑_{C∈D} ( \frac{1}{|C|} ∑_{S∈C} H (|S_{s} |))  [3]

where H is the Heaviside step function. Given a property s and reference property r, we estimate the weighted fraction in a data set, F̂_{s|r} :

F̂_{s|r} = \frac{N̂_{s∩r}}{N̂_{r}}  [4]

or the weighted fraction of proteins that has at least one residue with the given property s:

Ê_{s} = \frac{M̂_{s}}{|D|}  [5]

With |D| being the number of families in the data set. When such fractions (Eqs. 4 or 5) are reported, we estimate 95%-confidence intervals using a normal approximation to the binomial distribution, with |D| the total number of trials, or N̂_{r} serving as a real-number analog to the total number of trials.

Averages of co-insertable pairs. To analyse co-insertable residues, we estimate weighted data set counts of co-insertable pairs of residues with property s, as:

N̂_{s}^{pair} = ∑_{C∈D} ( \frac{1}{|C|} ∑_{S∈C} |S_{s}^{pair} |)  [6]

where |S_{s}^{pair} | are the number of co-insertable amino acids pairs with property s. For quantification of the weighted count of proteins that have at least one co-insertable pair with property s, we calculate:

M̂_{s}^{pair} = ∑_{C∈D} ( \frac{1}{|C|} ∑_{S∈C} H (|S_{s}^{pair} |))  [7]

Considering the set of co-insertable amino acid pairs in a protein, S^{pair} , we will denote the set of pairs where at least one of the amino acids is a protruding hydrophobe as S_{one}^{pair} , and the set where both are protruding hydrophobes as S_{both}^{pair} . We will report the weighted fraction of proteins that have co-insertable protruding hydrophobes as:

Ê_{both}^{pair} = \frac{M̂_{both}^{pair}}{|D|}  [8]

and the weighted frequency of co-insertion of protruding hydrophobes as:

F̂_{both |one}^{pair} = \frac{N̂_{both}^{pair}}{N̂_{one}^{pair}}  [9]

Note that F̂_{both |one}^{pair} estimates the conditional probability that both amino acids of a co-insertable pair are protruding hydrophobes, given that one of them is. The tendency for protruding hydrophobes to be located at co-insertable positions can then be quantified by comparing with a null model for each set. We obtain these null models by randomly reassigning the hydrophobic amino acids to other protruding locations in the same protein.

Comparison between data sets. The frequency of properties in different data sets, are compared via weighted fractions. For two data sets, A and B, we compare a certain weighted fraction F̂ using the odds ratio, R(A, B, F̂) :

R(A, B, F̂) = \begin{matrix}F̂^{A} & (1 − F̂^{B} ) \\ F̂^{B} & (1 − F̂^{A}) \end{matrix}  [10]

where F̂^{A} denotes the fraction F̂ obtained for data set A. We will report ln R, which is symmetric around 0, so that ln R(A, B, F̂) =

− ln R(B, A, F̂) . Wald 95%-confidence intervals for ln R are calcu-

lated with N̂_{s∩r} and (N̂_{r} − N̂_{s∩r} ) serving as real number analogs for the count of successes and failures in the data sets compared. When F̂_{both |one}^{pair} is compared, the corresponding counts of successes

and failures are N̂_{both}^{pair} and N̂_{both}^{pair} − N̂_{one}^{pair} , respectively.

Comparison of binding site identifications. For comparing partially

identified binding sites of proteins, we infer an idealised protein orientation with respect to the membrane. Given a set of identified or predicted membrane interacting resides, I, we compute the vector, t_{I} :

t_{I} = \frac{1}{|I|} ∑_{a∈I} V_{a} − \frac{1}{|S|} ∑_{a∈S} V_{a}  [11]

where V_{a} denotes the C_{α} -coordinate of residue a, and S is the set of all residues in the protein. We will denote vectors obtained for experimentally identified membrane binding residues as t_{I_{e}} , and those obtained for predicted membrane interacting residues as t_{I_{p}} . We then quantify their agreement by the angle between these vectors, which we will denote as ∠t_{I_{e}} t_{I_{p}} .

Implementation. Solvent accessible area was calculated with MMTK (? ) (version 2.9.0), and the convex hull was calculated with Qhull (? ) via scipy (? ) (version 0.13.3). Proportion test

Fuglebakk et al.  PNAS | December 21, 2016 | vol. XXX | no. XX | 7

confidence intervals was calculated with R (? ) (Version 2.12.0), odds ratios and corresponding confidence intervals was calculated with the R-package epitools (? ) (version 0.5-6). Secondary structure annotations was calculated with the CMBI DSSP implementation (? ) (version 2.0.4). Otherwise the analyses was implemented by us, using Python and R. Plots were produced with R, and other visualisations using VMD (Visual Molecular Dynamics) (? ).

ACKNOWLEDGMENTS. {Please include your acknowledgments here, set in a single paragraph. Please do not include any acknowledgments in the Supporting Information, or anywhere else in the manuscript.}

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DRAFT

Figures

{Figures are to be incorporated in text, when reformatted}

Fuglebakk et al.  PNAS | December 21, 2016 | vol. XXX | no. XX | 9

Fig. 1. Illustration of the definitions of protrusions and co-insertable protruding hydrophobes. 1(a) shows a cartoon representation of the C2 domain of phospholipase A_{2} (PDBID: 1RLW). All C_{α} - and C_{β} -atoms are shown as spheres. Hydrophobes are coloured orange. 1(b) illustrates the model used to define protrusions for the same protein. The convex hull of the C_{α} - and C_{β} -coordinates are shown as blue faces. C_{β} -atoms are illustrated with speheres exactly as in 1(a) and are visible in this illustration when they are vertex residues. Protrusions are defined as vertex residues with low local protein density, and depicted with large spheres. Co-insertable protruding hydrophobes are protruding hydrophobes that are adjacent on the convex hull, shown connected by orange lines. Small spheres are vertex residues that have high local density, and does therefore not meet the criteria for protrusions. {Use different colors in stead, Hard to see size difference due to perspective representation.}

Fig. 2. Comparison of frequencies of different amino acids at protruding positions. Panel A shows the weighted fractions (Eq. 4) in peripheral proteins (cyan) and the reference set (red). In panel B, the contrast between the sets are quantified by the odds ratio (Eq. 10), so that positive values reflect higher frequencies in the peripheral proteins. More precisely the vertical axis denote ln R (peripheral , reference , F̂_{aa,protruding} ), with aa representing each of the standard amino acids. Error bars are 95% confidence intervals.

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Fig. 3. The frequency of hydrophobes on different surface residues in peripheral proteins and the reference set. Panel A and C show the distributions for hydrophobes on protrusions, panel B and D for all solvent exposed residues. The histograms show the mean fraction (Eq.1) of hydrophobic amino acids in a family. The vertical axes denote the fraction of families in each data set.

Fig. 4. Occurance of co-insertable protruding hydrophobes on protein surfaces. Panel A and B shows the weighted fraction of proteins (Eq. 5) in the peripheral proteins (cyan) and the reference set (red). The analysis is done separately for two groups of proteins according to the total number of protrusions on the protein surface ([0, 25⟩ in panel A, [25, 50⟩ in panel B). We have differentiated here between protrusions that have at least one co-insertable protruding hydrophobe (labeled “Co-ins.”), and those that have not (labeled “isolated”). Panel C shows the frequency distribution of total number of protruding residues (“# protrusions”) for all proteins. The selections analysed in panel A and B are found between the dashed lines in panel C. Error bars in panel A and B are 95% confidence intervals.

Fuglebakk et al.  PNAS | December 21, 2016 | vol. XXX | no. XX | 11

Fig. 5. Comparing the weighted frequencies (Eq.4) of hydrophobes on vertex residues of varying local protein density (d). For each bin, the frequencies are compared between the peripheral membrane proteins and the reference set, using the odds ratio (Eq.10), so that positive values reflect higher frequencies in the peripheral proteins. More precisely, the vertical axis show

R (peripheral , reference , F̂_{hydrophobe |vertex ∩l<d≤u} )

where l and u denote the lower and upper limits of the ranges given on the vertical axis, and d is the local protein density defined in section 1. Error bars are 95% confidence intervals

.

Fig. 6. Panel A shows the weighted number (Eq. 2) of protruding hydrophobes with the given secondary structures., grouped by their number of co-insertable protruding hydrophobes. We have differentiated between protrusions that have at least one co-insertable protruding hydrophobe (labeled “Co-ins.”), and those that have not (labeled “isolated”). Panel B compares the weighted frequencies (Eq. 4) of hydrophobes on protruding secondary structures. The frequencies are compared between the peripheral membrane proteins and the reference set, using the odds ratio (Eq. 10), so that positive values reflect higher frequencies in the peripheral proteins. More precisely, panel A show the values N_{hydrophobe |protrusion ∩sse} ,

and  panel  B  the  comparisons
R (peripheral , reference , F̂_{hydrophobe |protrusion ∩sse} )

where sse specifies the different secondary structures given on the horisontal axis. Error bars in panel B are 95% confidence intervals.

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Fig. 7. The tendency for protruding hydrophobes to be co-insertable. The weighted frequency of co-insertion (Eq. 9) is compared between each data set and a null model, using the odds ratio, so that positive values reflect higher frequencies of co-insertion than in the null model. More precisely, we show the comparisons R (set, null, F̂_{one ,both}^{pair} ) , where set represents the set of peripheral proteins (cyan) and the reference set (red), and null represent their respective null models where hydrophobes have been relocated randomly among protrusions as described in the methods section. Error bars are 95% confidence intervals.

Fig. 8. Comparing with OPM. The plots show distributions of the median insertion coordinate in each family, as measured at the C_{α} -atom of predicted membrane binding residues. Values close to zero indicates agreement with OPM, and negative insertion coordinates are on the solvated side of the membrane model. Panel A show data for the Likely Inserted Hydrophobes and panel B for a null model of randomly selected protruding residues. Panel C and D show the histogram bins in panel A and B, accumulated with decreasing insertion coordinates.

Fuglebakk et al.  PNAS | December 21, 2016 | vol. XXX | no. XX | 13

Fig. 9. Comparison of predicted binding sites with experiment. Experimental and predicted orientations are compared via the angle of the vectors connecting the protein centre of mass with the binding site (Eq.11). The predicted binding site is taken to be the Likely Inserted Hydrophobe. Red marks are proteins where the Likely Inserted Hydrophobe has been experimentally identified to be interacting with the membrane. Proteins with no protruding hydrophobes are marked with a cross at 180°. Experimetnal data is summarized in Table ?? . The boxplot in the top panel shows the angles of the experimental orientation compared with all protruding amino acids on the protein, not regarding hydropathic properties. {label subfigures: a and b}

{Clarify distinction from 1rlw, also a PLA_{2} }

domain  C2  PX  PLA_{2}
PDBID  1RLW (? )  1H6H (? )  1POA (? )

domain  C1  ENTH  FYVE
PDBID  1PTR (? )  1H0A (? )  1VFY (? )

Fig. 10. Convex hull (blue faces) of the C_{α} and C_{β} -atoms of selected peripheral membrane binding domains, and the C_{β} -atoms of the Likely Inserted Hydrophobe (red or orange). C_{β} -atoms of residues indicated by experiment to interact with membrane lipids are shown in green, or red if it is the Likely Inserted Hydrophobe. I_{p} is the Likely Inserted Hydrophobe, I_{e} is the set of experimentally identified residues, and t_{Ip} and t_{Iv} are the corresponding vectors (Eq.11) quantifying the similarity of the experimental and predicted orientation. Some listed residues, not on the convex hull, may be hidden or partly hidden from view, such as the case of Y110 in P LA_{2} .

14 | www.pnas.org/cgi/doi/10.1073/pnas.XXXXXXXXXX  Fuglebakk et al.