A membrane insertion code for intrinsically disordered proteins

We first collected 10 aromatic-centered motifs that previous studies have classified as either membrane inserters or non-inserters (Figure 1A). This initial set includes an F-centered motif from TrkA (sequence 1), which inserted deeply into the acyl chain region in MD simulations (Wang et al., 2019); a W-centered motif in Drp1 (sequence 2), which was indicated by solution NMR to be an inserter (Mahajan et al., 2021); two Y-centered segments composing the ITAM in CD3ε (sequences 3 and 4), which were estimated to have 20% and 0% probabilities, respectively, of reaching the acyl chain region based on modeling paramagnetic relaxation enhancement data (Jin et al., 2024); and six Y-centered segments composing three ITAMs in CD3ζ (sequences 5–10), which did not bind to membranes according to binding assays (Sigalov and Hendricks, 2009).

For each sequence, we generated an initial disordered conformation using TraDES (Feldman and Hogue, 2000) and placed it near a membrane, with the central aromatic residue just above the phosphate plane. The peptide-proximal leaflet was composed of 70% POPC (zwitterionic), 25% POPS (net charge –1), and 5% PIP₂ (net charge –4). MD simulations were conducted in 20 replicates, each for 1 µs. Figure 1B presents a simulation where the central aromatic side chain (sequence 1), after ~90 ns, is stably inserted into the acyl chain region. We use a value of –3.1 Å (indicated by a red dashed line), corresponding to the mean position of glycerol C2 carbon atoms, for the Z coordinate of the aromatic tip carbon atom (Z_tip) to demarcate insertion into the acyl chain region. In contrast, Figure 1C presents a simulation where the peptide (sequence 6) moves away from the membrane.

We label a simulation as membrane-inserting if >80% of the frames in the last 100 ns have Z_tip ≤–3.1 Å. Using this cutoff, the F- and W-centered peptides (sequences 1 and 2) have 3 and 8 simulations, respectively, out of a total of 20 each, as membrane-inserting (Figure 1D). Among all their respective 20 simulations, the F- and W-centered peptides have 20.6% and 56.6%, respectively, membrane-inserting frames (Table 1). By contrast, none of the 20 simulations of each of the 8 Y-centered peptides (sequences 3–10) is membrane-inserting. We classify sequences 1 and 2 as membrane inserters (with at least one membrane-inserting simulation), whereas sequences 3–10 as non-inserters. Sequence 3 is special among the non-inserters: in two of its simulations, the percentage of membrane-inserting frames is substantial, at 57.0% and 22.4%, although not reaching the 80% threshold (to be called ‘partly inserting’). Correspondingly, the overall percentage of membrane-inserting frames in 20 simulations of this peptide is the highest, at 4.5%, among all 8 Y-centered peptides (Table 1). These outcomes are in complete agreement with the previous characterizations of the membrane-insertion propensities of these peptides (Mahajan et al., 2021; Sigalov and Hendricks, 2009; Wang et al., 2019; Jin et al., 2024).

The simulations of sequences 1–3 revealed distinct membrane-bound states and pathways to membrane insertion (see Appendix 1 for more details). For the F- and W-centered peptides (sequences 1 and 2), a peak of the aromatic Z_tip distributions at –6.5 Å defines the inserted state (Figure 2A and B), where the aromatic side chains form hydrophobic interactions with acyl chains. The aromatic ring is mostly oriented parallel to the membrane normal, but can also turn sideways (Figure 2—figure supplement 1A–C). In both populations, the indole nitrogen atom of W can hydrogen bond with glycerol oxygen atoms. In comparison, the Y-centered sequence 3 features a partially inserted peak at Z_tip = –2.5 Å, where the side chain hydroxyl can hydrogen bond with glycerol oxygen atoms (Figure 2C) and, correspondingly, the Y side chain and the glycerol C2 carbon atom of a partner lipid rise and fall together (Figure 2—figure supplement 1E, F). For each peptide, a second peak in aromatic Z_tip constitutes an intermediate state. Figure 2D illustrates the aromatic side chains in their respective (partially) inserted states. The aromatic side chains sample either exclusively the (partially) inserted state (Figure 2—figure supplement 2A, B, red curves; Figure 2—figure supplement 2C, solid red curve) or both the intermediate and (partially) inserted states (Figure 2—figure supplement 2A, B, insets; Figure 2—figure supplement 2C, dashed red curve). They have a tendency to form cation-π interactions with a lipid choline group, especially at their respective intermediate states (Figure 2A–C), occurring in 13.8, 26.4, and 24.2% of all the membrane-interacting frames for sequences 1–3, respectively (Figure 2—figure supplement 2).

Membrane insertion is not an act of the central aromatic side chain alone. Rather, it is helped or hindered by the flanking residues. Figure 2—figure supplement 3A, B displays the Z_tip coordinates of all side chains in F-centered sequence 1 and W-centered sequence 2 in the last 100 ns of the inserted simulations; Figure 2—figure supplement 3C displays the corresponding results for Y-centered sequence 3 in the two partly inserting simulations. For all three sequences, the central aromatic side chain is consistently inserted deeper than flanking side chains, as shown by the average over replicate simulations (Figure 2—figure supplement 3, solid black curves), justifying our focus on aromatic residues. For F-centered sequence 1 (Figure 2—figure supplement 3A), the inserted state is stabilized by intermittent penetration of I[+2] (two residues upstream of the central aromatic residue) into the acyl chain region and snorkeling of R[+4], whereby its aliphatic portion contacts acyl chains while its terminal guanidinium group interacts with phosphates. Additional stabilization is provided by salt-bridge formation with lipid phosphates by all the basic side chains. In the case of W-centered sequence 2 (Figure 2—figure supplement 3B), membrane penetration by M[+3] and L[+4] and snorkeling and salt-bridge formation by R[+1] are the major stabilizing factors for the inserted state. For Y-centered sequence 3 (Figure 2—figure supplement 3C), membrane penetration by L[+3] and salt-bridge formation by R[–3] reinforce membrane binding.

Inspecting the entire trajectories of sequences 1–3 (illustrated by Figure 1B), we arrive at the following pathways for membrane insertion of aromatic-centered motifs. The pathways generally consist of three steps: initial membrane contact, further stabilization at the membrane surface, and insertion by the central aromatic side chain (Figure 3). These steps lead to poses illustrated and labeled as 1, 2, and 3 in circles in Figure 3; poses 2 and 3 correspond to the intermediate and (partially) inserted states, respectively, in Figure 2A–C. Pose 3 is already described in the preceding paragraph; we now describe poses 1 and 2 (Figure 3—figure supplement 1). For F-centered sequence 1, R[–4], R[–3], and K[–1] make the initial membrane contact, via long-range electrostatic interactions with phosphates, especially those on acidic lipids (Figure 3—figure supplement 1A; Figure 3A pose 1). R[+4] then tethers the opposite end of the peptide to the membrane, priming the F side chain for cation-π interaction with a choline (Figure 3A pose 2). For W-centered sequence 2, R[+1] makes the initial membrane contact, followed by membrane penetration by L[+4] (or M[+3]) (Figure 3—figure supplement 1B; Figure 3B pose 1). With deeper penetration by L[+4] and M[+3], the W side chain now forms cation-π interaction in the headgroup region (Figure 3B pose 2). For Y-centered sequence 3, the initial membrane contact can occur through R[–3] via electrostatic interaction or the Y side chain itself via cation-π interaction (Figure 3—figure supplement 1C; Figure 3C pose 1). Then, R[–3] and L[+3] pin the peptide to the membrane, placing the Y side chain for cation-π interaction with a choline (Figure 3C pose 2).

Previous MD and NMR studies using longer constructs provide validation of our all-atom simulations using 9-residue fragments. Our F-centered sequence 1 was taken from the disordered juxtamembrane domain (residues 440–465) of TrkA; Wang et al., 2019 used all-atom MD simulations to characterize the insertion of this 26-residue region into membranes with a simplified representation. The insertion distance profile in that study is quantitatively reproduced by our simulations (Figure 2—figure supplement 4), both showing the deepest insertion by the central F residue. Likewise, our W-centered sequence 2 was taken from the disordered variable domain (residues 497–607) of Drp1. Solution NMR data of Mahajan et al., 2021 on this 111-residue region indicated that residues W₅₅₂RG are partially helical and insert into cardiolipin-containing membranes. In one simulation of sequence 2, WRGML form an α-helix lying parallel to the membrane surface (Figure 2—figure supplement 5A), much like an amphipathic helix. In other membrane-inserting simulations, sequence 2 does not form an α-helix (Figure 2—figure supplement 5B, C), showing that the helical structure is only partially stable. When averaged between simulations with helical and non-helical structures, the secondary chemical shifts have similar magnitudes (0.2–2 ppm; Figure 2—figure supplement 5D) to those reported by Mahajan et al.

As demonstrated above, all-atom MD simulations are very effective in separating membrane inserters from non-inserters and providing mechanistic insights, but they are time-consuming and therefore not feasible to apply to a very large set of sequences in order to reach a comprehensive understanding of the contributions of flanking residues to membrane insertion. To address this limitation, we developed a fast workflow based on the PPM method (Lomize et al., 2011), originally developed for membrane placement of structured proteins. We adapted this method for disordered peptides, by using a batch of random conformations generated by the TraDES algorithm (Feldman and Hogue, 2000) to represent each peptide (Figure 4A). Each PPM run, using a peptide conformation as input, produced a pose of the peptide with respect to the membrane (Figure 4B) and a membrane interaction free energy, ΔG. Figure 4C displays a scatter plot of ΔG and aromatic Z_tip data for a batch of 100 conformations of the F-centered sequence 1. We choose a cutoff of –1.5 Å to label a peptide conformation as membrane-inserting. This cutoff is less strict than in the all-atom MD simulations, due to the rigid treatment of peptides and implicit treatment of the membrane by PPM, but we compensate by adding a cutoff on ΔG, at –4.1 kcal/mol (Figure 4C, region bordered by red dashed lines).

We measure the membrane-insertion status of each peptide by the percentage of membrane-inserting conformations in a batch of 100 conformations, and denote this quantity as Φ. Replicate PPM runs yield highly reproducible results for each of the 10 sequences. The minimum Φ values of sequences 1 and 2 are 11% and 38%, respectively, while the maximum Φ value of sequences 3–10 is 3%. Figure 4—figure supplement 1 displays the mean Φ values and SDs for the 10 sequences, showing a clear gap between inserters (sequences 1 and 2) and non-inserters (sequences 3–10). Therefore, PPM can unequivocally separate inserters from non-inserters, just like all-atom MD simulations. Indeed, the mean percentage of inserting conformations in PPM (Table 1) shows a very strong correlation with both the percentage of inserting frames in MD simulations (Figure 2E) and the percentage of MD simulations labeled as membrane-inserting (Figure 4D). In both cases, the correlation coefficients are very close to 1. This strong correlation firmly supports our use of PPM for fast classification of aromatic-centered motifs into membrane inserters or non-inserters.

To complete the PPM-based workflow, we had to choose a threshold Φ value, Φ_th, for separating each peptide sequence into inserters and non-inserters. We took into consideration the following factors. First, PPM did not account for lipid composition (Lomize et al., 2011). Cell membranes contain various levels of acidic lipids, which tend to attract basic side chains for membrane association (Qin et al., 2022; Mancinelli et al., 2024), thereby facilitating membrane insertion of adjacent aromatic residues (Stautz et al., 2025; Zhang et al., 2003), as also demonstrated in our all-atom MD simulations (Figure 3A and B). We accounted for the contributions of flanking basic side chains by lowering Φ_th. Second, Φ_th should fall in the gap between known inserters and non-inserters (Figure 4—figure supplement 1). The end result for Φ_th is 40–15×R – 10×K (or 9 if that value is <9), where R and K are the number of Arg and Lys residues, respectively, in the eight flanking positions of the aromatic-centered motif. As a minimum test, applying this threshold to the PPM results (Table 1), sequences 1 and 2 are correctly classified as inserters and sequences 3–10 are correctly classified as non-inserters.

We next applied the PPM-based workflow to an exhaustive library of sequences where the central position was filled with F, W, or Y and each of the four flanking positions on either side was filled with a reduced amino-acid alphabet comprising L, R, G, N, and E (Figure 5A). This alphabet was selected to represent the chemical diversity of the 20 natural amino acids: L (Leu) as an aliphatic residue, R (Arg) as a basic residue, G (Gly) standing for the backbone, N (Asn) as a polar residue, and E (Glu) as an acidic residue. The total size of the library is 3×5⁸ = 1.2×10⁶ sequences.

Applying the PPM-based workflow to this exhaustive library, F-centered sequences are found to have the highest membrane insertion propensities, with 48.6% of all the sequences as inserters, followed by W-centered sequences, with 42.4% inserters (Figure 5B). In contrast, Y-centered motifs show very low membrane insertion propensities, with only 1.3% inserters. This contrast between F/W-centered sequences and Y-centered sequences is consistent with the outcome of the initial set of 10 sequences.

With the large library, we can now examine the distributions of amino acids in flanking positions. Within either the membrane inserters or non-inserters, we calculated the frequencies at which each flanking position was occupied by the five types of amino acids. If the distributions of amino acids in the two subsets were totally random, the occupation frequency of any amino acid at any position would be 0.2. An occupation frequency higher than 0.2 in the inserter subset means that that amino acid promotes membrane insertion. Figure 5C displays the occupation frequencies in the inserter subset. The frequencies have the order L>R > G>N > E. For each amino acid, the occupation frequency is relatively uniform across all eight positions. For L and R, the frequencies in the inserter subset are >0.2, indicating that these amino acids promote membrane insertion. An aliphatic side chain like L is favored in the acyl chain region, thereby stabilizing the inserted state (Figure 3). While burying a charge in the acyl chain region is highly unfavorable, R can snorkel to form both hydrophobic interactions via its aliphatic portion and salt bridges via its terminal guanidinium group (Figure 3A and B).

The order in occupation frequency is reversed in the non-inserter subset (Figure 5D), where E, N, and G have values >0.2, meaning that they suppress membrane insertion. In contrast to R, an acidic side chain like E is electrostatically repelled by lipid phosphates; its suppression of membrane insertion is thus expected. The suppressive effects of N and G can be attributed to the fact that it is unfavorable to position polar groups, either on the side chain or the backbone, in the acyl chain region.

Given the much lower membrane insertion propensities of Y-centered sequences relative to F- and W-centered sequences, we report the distributions of amino acids in Figure 5—figure supplement 1 for each aromatic center. The results for F- and W-centered sequences (Figure 5—figure supplement 1A–D) are similar to the aggregated results shown in Figure 5C and D. However, for Y-centered sequences, the inserter subset is highly enriched in the insertion promoter L, with corresponding depletion of insertion suppressors E, N, and G (Figure 5—figure supplement 1E). Apparently, Y-centered sequences require substantial help from insertion promoters in flanking positions for them to become membrane inserters. In the non-inserter subset of Y-centered sequences, occupation frequencies have only very small differences from the random value of 0.2 (Figure 5—figure supplement 1F), reflecting the fact that the non-inserter subset differs from the full set only by a small percentage (i.e. 1.3%) of sequences that belong to the inserter subset.

The PPM results on the exhaustive library clearly demonstrate two simple rules: (1) flanking residues play key roles in determining whether an aromatic-centered motif is a membrane inserter; (2) aliphatic and basic residues promote membrane insertion, whereas polar and acidic residues suppress it. These rules motivated us to test whether a sequence-based method can predict the membrane insertion status. Based on previous models of sequence-dependent properties of IDPs, including membrane-association propensities and drug-interacting residues (Li et al., 2020; Qin and Zhou, 2024; Qin et al., 2022; MacAinsh et al., 2025b), we devised a membrane insertion score by multiplying the contributions, , of all flanking residues:

(1)

depends on the amino-acid type of the residue at position i (the central aromatic residue is at i=0). A q value >1 corresponds to promotion of membrane insertion whereas a value <1 corresponds to suppression of membrane insertion. Note that has the flavor of a partition function. We then converted this score into a membrane-insertion propensity that ranges from 0 to 1:

(2)

For a given aromatic residue (F, W, or Y), we optimized the five q parameters to minimize the difference between and the pre-defined label (0 for non-inserter and 1 for membrane inserter according to PPM) of each sequence. We call this sequence-dependent method AroMIP.

As the training size increases from 1000 to the maximum value 5⁸=390625, the q parameters quickly converge (Figure 5—figure supplement 2). Figure 5E displays the final q parameters for F-, W-, and Y-centered sequences. For all three aromatic centers, the q values follow the order L>R > G>N > E, as expected from the amino-acid distribution results in the inserter subsets. These values are very similar for F and W centers, >1 for L and R but <1 for G, N, and E. The corresponding values for the Y center are lower for all five flanking amino acids. In particular, the q value for R dips below 1, making it a weak suppressor of membrane insertion for Y-centered motifs.

As the first test of the sequence-dependent approach, we assessed how well the trained AroMIP model reproduced the PPM-based classification of the 5-amino-acid library, setting a threshold P at 0.5 for inserters (corresponding to Q=1). The retrospective accuracy is 87.5, 90.1, and 98.8% for F-, W-, and Y-centered motifs, respectively (Figure 5B). Such high accuracy levels demonstrate that AroMIP is a promising method.

We illustrate the utility of AroMIP by applying it to all the IDRs in the human proteome (Piovesan et al., 2023). From these IDRs, we collected 9-residue motifs centered on an aromatic residue, resulting in a dataset of 24,308 motifs (Figure 6A). We applied the PPM-based workflow to generate labels (membrane inserters or non-inserters) for all these motifs and randomly separated them into a training set (80% of motifs) and a test set (20% of motifs).

We then optimized the AroMIP model (Equations 1 and 2) on the training set, now with 20 q parameters for the full amino-acid alphabet. Figure 6B displays the resulting q parameters for F-, W-, and Y-centered motifs. For F- and W-centered motifs, 12 of the 20 amino acids have q>1 and hence are insertion promoters at flanking positions. This group includes three aromatic amino acids (F, W, and Y), six aliphatic amino acids (L, I, M, V, P, and A), two basic amino acids (R and K), and a single polar amino acid (T). The q values of F and W are much higher than those of the other amino acids, indicating unique membrane-insertion propensities. Between F- and W-centered motifs, the q values of F and W are higher in the latter; for the remaining 18 amino acids, q values are similar between F- and W-centered motifs. In comparison, all 20 q values are reduced for Y-centered motifs. Consequently, 5 of the 12 amino acids are no longer in the insertion-promoting group: M, P, T, K, and A. Comparing the q values of L, R, G, N, and E here from training on a 20-amino-acid dataset (Figure 6B inset) against those from a 5-amino-acid dataset (Figure 5E), we see concordance in several respects, including the same rank order L>R > G>N > E, similarity between F- and W-centered motifs and contrast with Y-centered motifs, and same groupings of insertion promoters and suppressors, except for R in Y-centered motifs, which switches from a weak suppressor in the 5-amino-acid dataset to a weak promoter in the 20-amino-acid dataset. Quantitatively, for these five amino acids, the range of q values obtained from the 20-amino-acid dataset is widened somewhat, at both the higher (for L) and lower (for E) ends.

Now let us look at the performance of the trained AroMIP model. In the training set, the percentages of membrane inserters are 59.7%, 51.2%, and 0.7% for F-, W-, and Y-centered motifs, respectively (Figure 6C). The retrospective accuracy is 91.6, 91.4, and 99.4%, respectively. In the test set, the percentages of membrane inserters are 60.1, 55.0, and 0.5% for F-, W-, and Y-centered motifs, respectively, which are, as expected, comparable to the counterparts in the training set. The prospective accuracy is 91.2, 92.0, and 99.7%, respectively. The latter are essentially identical to the counterparts on the training set, indicating the robustness of the AroMIP model.

We list the top-scoring aromatic-centered motifs in Table 2. The F- and W-centered motifs reach the full value of 1 in membrane-insertion propensity and are typically flanked by two other F and W residues and a combination of aliphatic and basic residues. In contrast, the Y-centered motifs are limited to 0.78 in P and are flanked by one F or W but a high number of aliphatic residues. Some of these motifs may actually be functionally important. In particular, the transmembrane protein anoctamin-8 resides in the endoplasmic reticulum and tethers the plasma membrane via its C-terminal IDR to assemble Ca²⁺-signaling complexes (Jha et al., 2019). The A₁₀₂₃FLSFKFLK₁₀₃₁ motif in this IDR may insert into the plasma membrane to facilitate this function. Similarly, GARRE1 localizes to the plasma membrane and intracellular vesicles, likely via a truncated BAR domain, to mediate ciliogenesis (Serres et al., 2023). Membrane insertion by R₈₈₅TWPFPEFF₈₉₃ in the C-terminal IDR may fine-tune this function. Sec16B, with a key role in the biogenesis of peroxisomes, tightly associates with endoplasmic reticulum membranes via a central conserved domain but with help from its C-terminal IDR (Yonekawa et al., 2011). The latter may involve membrane insertion by the S₉₁₀GFGWFSWF₉₁₈ motif.

Insertion of an aromatic side chain into the acyl chain region requires membrane binding of the parent sequence. We asked how well our insertion score ( ) is modeled by the membrane binding free energy of the 9-residue motif. As shown in Figure 6—figure supplement 1A–C, insertion scores (expressed as ) show moderate-to-strong correlations with membrane binding free energies ( ), calculated using the experimentally determined scale for the transfer from water to octanol by Wimley et al., 1996. For F-, W-, and Y-centered motifs in the human proteome, the correlation coefficients range from 0.751 to 0.556. Interestingly, the correlations of our insertion scores with the octanol scale are moderately stronger than with the counterparts with the membrane interfacial scale (Wimley and White, 1996; Figure 6—figure supplement 1D). This observation can be rationalized because octanol is a mimic of the hydrophobic core instead of the interfacial region (White and Wimley, 1999), and hence more in line with our insertion score.

We have implemented AroMIP as a web server, accessible at https://zhougroup-uic.github.io/AroMIP/, and now report results on additional test cases. First, we go back to our initial set of 10 sequences. The Q and P values for these sequences are listed in Table 1, which result in correct predictions of sequences 1 and 2 as membrane inserters and the other 8 as non-inserters. Moreover, the raw P values are highly correlated with both the percentages of membrane-inserting MD simulations (Figure 6—figure supplement 2A) and the percentages of membrane-inserting conformations in PPM runs (Figure 6—figure supplement 2B). This correlation suggests that AroMIP can be used not only for binary classification but also for detecting fine differences between motifs.

Next, we report the performance of AroMIP on aromatic-centered motifs in 12 other IDPs or IDRs for which information regarding membrane insertion is available (Table 3). Using solid-state NMR, Zhang et al., 2003 observed strong cross peaks between acyl chains and five phenol rings from a 25-residue IDR (residues 152–176) of MARCKS. AroMIP predicts very high membrane-insertion propensities for four F residues and high P value for the fifth F residue, consistent with the solid-state NMR results. Similarly, solution NMR found cross peaks between the indole ring of W11 in the 86-residue IDP Tat and acyl chains (Ghanam et al., 2023). AroMIP correctly predicts W11 as a membrane inserter with a high P (=0.73). In comparison, four other aromatic residues are predicted with lower P values, with one (F38) passing the 0.5 threshold.

Attenuation of NMR signals indicated strong membrane binding by Y511 and weaker binding by Y483 in the C-terminal IDR of Tir (Vieira et al., 2024). Whether these Y side chains were inserted into the acyl chain region was unclear. AroMIP predicts both residues as non-inserters, but with a higher membrane-insertion propensity for Y511 (P=0.14) than for Y483 (P=0.01). A slightly higher propensity is predicted for F489 (P=0.35). Interestingly, these P values correlate with the extents of NMR signal attenuation upon binding DMPG:DHPC membranes. A similar correlation is found for three aromatic residues in the cytoplasmic IDR of mGluR3, with high NMR signal attenuation (Mancinelli et al., 2024) and high membrane-insertion propensity (P=0.72) for F844 and low signal attenuation and low P values (i.e. 0.002 and 0.04) for Y853 and Y861. Moreover, the Y853F mutation increases both the extent of NMR signal attenuation and the membrane-insertion propensity (from 0.002 to 0.33). AroMIP predicts Y853 as a non-inserter, which is consistent with all-atom MD simulations showing that this residue only makes excursions into the acyl chain region (Mancinelli et al., 2024).

All-atom MD simulations showed that the aromatic motif W₄₄₀FFGW₄₄₄ in the 57-residue IDR of Bap1 is stably inserted into the acyl chain region (Huang et al., 2026). AroMIP predicts extremely high propensities (close to 1) for these four aromatic residues, due to the presence of flanking F or W (see Figure 6B). Three other W residues (at positions 420, 432, and 456) are also predicted as inserters (P values range from 0.78 to 0.86); MD simulations did not find membrane insertion by these residues. For KtrB, all-atom simulations showed membrane insertion by F10 in the N-terminal IDR (residues 1–24), but not the adjacent Y11 (Stautz et al., 2025). In complete agreement, AroMIP predicts a high membrane-insertion propensity (P=0.75) for F10 but a very low P value (0.07) for Y11. All-atom simulations also found membrane insertion by W21, W25, and W29 in the N-terminal IDR (residues 1–33) of ABHD5, with a greater depth for W25 than for W21 and W29 (Kumar et al., 2025). AroMIP predicts a perfect P value of 1 for W25, due to the presence of the other two W residues in flanking positions, and somewhat lower values for W21 and W29. Coarse-grained simulations showed membrane insertion by the I₂₆₇FPPV₂₇₁ motif in the membrane-proximal IDR (residues 260–280) of the prolactin receptor (Araya-Secchi et al., 2023); AroMIP predicts a very high membrane-insertion propensity (P=0.99) for F268.

Cryo-EM structures of OPA1 place W775 and W778 in the membrane-inserting loop deeply into the acyl chain region of cardiolipin-containing membranes (Nyenhuis et al., 2023; von der Malsburg et al., 2023). Likewise, cryo-EM structures suggest that aromatic residues at the tip of the β1–β2 loop in gasdermin D (W₄₈FW₅₀) and gasdermin B (F₄₆F₄₇) insert into the acyl chain region as an anchor (Xia et al., 2021; Wang et al., 2023). This scenario was demonstrated in all-atom MD simulations (Schaefer and Hummer, 2022). AroMIP predicts very high membrane-insertion propensities for all these aromatic residues. In contrast, a medium propensity (P=0.55) is predicted for W41 in a loop of PLAT1, consistent with its transient insertion in all-atom MD simulations (Kulke et al., 2024).

Each of the 10 nine-residue peptides from the initial dataset in a disordered conformation generated through TraDES (Feldman and Hogue, 2000) was placed near a membrane, with the central aromatic residue just above the phosphate plane. The peptides were N-terminally capped with an acetyl group and C-terminally capped with an amide group to mimic the state of the motifs within the full-length protein. The upper leaflet of the membrane was composed of 70 POPC, 25 POPS, and 5 PIP₂ lipids; the lower leaflet was composed of 97–99 POPC lipids, with matching surface areas between the two leaflets. The system was then solvated in a rectangular box (dimensions listed in Table 4) with water and NaCl (neutralizing ions plus 150 mM) using the CHARMM-GUI web server (Jo et al., 2008). The force fields were CHARMM36 (Klauda et al., 2010) for proteins and TIP3P (Jorgensen et al., 1983) for water. After energy minimization for 10000 steps, each system was equilibrated in six steps in NAMD 3.0 (Phillips et al., 2005). The first two were at constant temperature and volume, while the remaining four were at constant temperature and pressure. The timesteps were 1 fs in the first three and increased to 2 fs in the last three steps. The simulation times were 125 ps in the first three and 500 ps for the last three steps. During these six steps, restraints on the lipid phosphorus atoms were gradually reduced with the force constant changing from 5 kcal/mol/Ų to 0; similarly, the force constant on peptide heavy atoms was reduced from 10 kcal/mol/Ų to 0. Production runs, with 20 replicates for each peptide, were carried out at constant temperature and pressure without restraints and a 2 fs timestep using pmemd.cuda (Salomon-Ferrer et al., 2013) in AMBER 22 (Case et al., 2022), for a simulation time of 1 µs.

Bond lengths containing hydrogen were constrained using the SHAKE algorithm (Ryckaert et al., 1977). Long-range electrostatic interactions were treated using the particle mesh Ewald method (Essmann et al., 1995) with a nonbonded cutoff of 12 Å. The temperature was maintained at 300 K by the Langevin thermostat (Pastor et al., 1988) with a damping constant of 1 ps^–1; the pressure was maintained at 1 bar by the Berendsen barostat (Berendsen et al., 1984). Snapshots were saved every 100 ps for analysis.

Z_tip distances were calculated for the last 100 ns of simulations using a custom Python code by measuring the difference between the Z coordinate of the tip carbon atom of an aromatic side chain and the average Z coordinate of phosphorus atoms in the upper leaflet. Frames in which the peptide moved to the image membrane (Z_tip beyond the midpoint between the upper leaflet and its periodic image) were excluded from analysis. Distributions were calculated using only the remaining frames. The tilt angle of the aromatic side chain was the polar angle of a vector connecting two diagonal atoms on the six-membered ring (Cγ-Cζ in F and Y and Cδ₂-Cη₂ in W; Figure 2—figure supplement 2A). A peptide was considered membrane-interacting when any of its heavy atoms was within 5 Å of any heavy atom of the membrane. Cation-π interaction between a lipid headgroup and an aromatic side chain was defined when the distances between a choline nitrogen and carbon atoms on the six-membered ring in the aromatic side chain were less than 7 Å and these distances differed by less than 1.5 Å (https://github.com/weberdak/flexible-interaction-tool/blob/master/flirt.tcl; Weber, 2019; Grauffel et al., 2013). Cα chemical shifts were calculated every 1 ns over the last 100 ns of simulations using SHIFTX2 (Han et al., 2011). After averaging the 100 frames in a simulation and then over three selected simulations, the corresponding random-coil values were subtracted to yield Cα secondary chemical shifts.

PPM runs used the source code from https://console.cloud.google.com/storage/browser/opm-assets/ppm3_code (Lomize et al., 2011), with a batch of 1000 or 100 conformations generated by TraDES as input for each peptide. The output for each peptide conformation was the membrane interaction free energy (ΔG) and a coordinate file representing the peptide’s pose relative to the membrane. Here Z_tip was defined using the most buried ring atom on the central aromatic residue.

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

1. Preprint posted: March 13, 2026
2. Sent for peer review: April 13, 2026
3. Reviewed Preprint version 1: July 2, 2026
4. Reviewed Preprint version 2: August 5, 2026
5. Version of Record published: August 26, 2026

© 2026, Kunnath Muhammedkutty and Zhou

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