Sampling alternative conformational states of transporters and receptors with AlphaFold2

1Dynamic interconversion between multiple conformations underpins the functions of integral membrane proteins in all domains of life. For example, the vectorial translocation of substrates by transporters is mediated by movements that open and close extra- and intracellular gates. For G-protein-coupled receptors (GPCRs), ligand binding on the extracellular side triggers structural rearrangements on the intracellular side that initiate downstream signaling. Traditional computational prediction pipelines reliant on inter-residue distance restraints calculated from deep multiple sequence alignments (MSAs) have historically struggled to accurately predict the structures of these proteins and their movements. The resulting models are unnaturally compact and frequently distorted, preventing critical questions about ligand and/or drug binding modes from being addressed.

2A performance breakthrough was unveiled during CASP14 by AlphaFold2 (AF2), which achieved remarkably accurate de novo structure prediction. Upon examining the list of CASP14 targets and corresponding models, we found that AF2 modeled the multidrug transporter LmrP (target T1024) in multiple conformations, two of which were individually consistent with published experimental data. This observation stimulated the question of whether such performance can be duplicated for other membrane proteins. At its essence, this question centers on whether AF2 can sample the conformational landscape in the minimum energy basin. Here, we investigate this hypothesis using a benchmark set of topologically diverse transporters and GPCRs. Our results demonstrate that reducing the depth of the input MSAs is often conducive to the generation of accurate models in multiple conformations by AF2, suggesting that the algorithm’s outstanding predictive performance can be extended to sample alternative structures of the same target. For most proteins considered, we report a striking correlation between the breadth of structures predicted by AF2 and the corresponding cryo-EM and/or X-ray crystal structures. Finally, we propose a modeling pipeline for researchers interested in sampling alternative conformations of specific membrane proteins, which we apply to the structurally unknown GPR114/ADGRG5 adhesion GPCR as an example.

3The default three-stage AF2 pipeline consists of (1) querying of sequence databases and generation of an MSA, (2) inference of structure via a neural network using a randomly resampled subset of this MSA containing up to 5120 sequences, which is repeated three times (a process termed ‘recycling’), and (3) resolution of steric clashes and bond geometry using a constrained all-atom molecular dynamics simulation. The neural networks used for prediction were trained on all structures deposited in the Protein Data Bank (PDB) on or before April 30, 2018. Therefore, by necessity, this study is restricted to proteins whose structures were absent from the PDB before this date and have since been determined at atomic resolution in two or more conformations. We selected five transporters that not only met these criteria but also reflected a range of transport mechanisms characterized in the literature, including rocking-bundle (LAT1,;; ZnT8, ), rocker-switch (MCT1,; STP10, ), and elevator (ASCT2,; ). We also included three GPCRs, which were distributed across classes A (CGRPR,; ), B1 (PTH1R,; ), and F (FZD7,; to serve as points of comparison, we used the active conformation of FZD7 and the inactive conformation of the nearly identical FZD4, ).

4The sequences of all targets were truncated at the N- and C-termini to remove large soluble and/or intrinsically disordered regions which represent a challenge for AF2 (see Methods). The structures were then predicted using the default AF2 structure prediction pipeline in the absence of templates. However, the resulting models were largely identical to one another and failed to shed light on the target protein’s conformational space. To diversify the models generated by AF2, we reduced the number of recycles to one and restricted the depth of the randomly subsampled MSAs to contain as few as 16 sequences. To sample the conformational landscape more exhaustively, we generated 50 models of each protein for each MSA size, while skipping the final MD simulation to reduce the pipeline’s total computational cost. For the targets, each model’s similarity to the experimental structures was quantified using template modeling (TM) score, a metric ranging from 0 to 1, which indicates how well the two backbone (Cα atoms) structures superimpose over one another (higher values corresponding to greater similarity; Figure 1A).

5Accurate models of all eight protein targets were obtained for at least one conformation (TM score ≥0.9), consistent with published performance statistics (Figure 1B). MSAs with hundreds or thousands of sequences were generally observed to engender tighter clustering in conformations specific to each protein. Decreasing the depth of the subsampled MSAs, by contrast, appeared to promote the generation of alternative conformations in most proteins. The increased diversity coincided with the generation of misfolded or outlier models. However, unlike the models of interest that resembled experimentally determined structures, misfolded models virtually never coclustered and could thus be identified and excluded from further analysis (example shown in Figure 1—figure supplement 1). Increasing the depth of subsampled MSAs had the desirable effect of eliminating these models, while also limiting the extent to which alternative conformations were sampled. Thus, our results revealed a delicate balance that must be achieved to generate models that are both diverse and natively folded. No general pattern was readily apparent regarding the ideal MSA depth required to achieve this balance, even when accounting for sequence length of the target (Figure 1—figure supplement 2).

6One target, MCT1, was exclusively modeled by AF2 in either inward-facing (IF) or fully occluded conformations; over 99% of the models had TM scores of ≥0.9 and <0.9 to the IF and outward-facing (OF) structures, respectively, regardless of MSA depth. Therefore, we investigated the effect of providing templates of homologs in exclusively OF conformations alongside MSAs of various sizes (see Methods for details on template selection). Accurate OF models were obtained only with MSAs containing 16–32 sequences and constituted a minor population in an ensemble dominated by IF models. Thus, the generation of large numbers of models appeared to be necessary to yield intermediate conformations of interest. Similar results were observed when we modeled PTH1R using either inactive or active templates, as well as LAT1 using either OF or IF templates (Figure 1—figure supplement 3), further indicating that the information content provided by the templates diminishes as the depth of the MSA increases.

7Overall, these results demonstrate that both conformations of all eight protein targets could be predicted with AF2 to high accuracy (TM score ≥0.9) by using MSAs that are far shallower than the default. However, because the optimal MSA depth and choice of templates varied for each protein, these parameters need to be explored for conformational sampling of a particular target.

8To further investigate the structural heterogeneity predicted by these models, we calculated each residue’s Cα atom distance between the two superimposed experimental structures, as well as each residue’s root mean square fluctuation (RMSF) among all 50 models following structure-based alignment (Figure 2). Correlation between these two measures was observed in most cases and was notable for ASCT2, LAT1, CGRPR, and MCT1 with templates (R2 ≥ 0.75). The exception was MCT1 without templates, which was likely due to a lack of conformational diversity among the sampled models. The inclusion of templates restored this correlation in MCT1 but contributed negligibly to those of PTH1R and LAT1 (Figure 2—figure supplement 1). The correlation demonstrates that predicted flexibility by AF2 is related to the protein’s dynamics inferred from the experimental structures. In contrast with a recent preprint, the predicted flexibility values failed to correlate with their pLDDT values, which reflect the confidence of the AF2 prediction of each residue’s local environment.

9Visual examination suggested that many of the predicted models fall ‘in between’ the two experimentally determined conformations (example shown in Figure 3A). Furthermore, certain structural features expected to be conformationally heterogeneous, such as long loops, appeared to be nearly identical across these models. Both observations raised questions about the relationship between the diversity of the predicted models and the breadth of the conformational ensembles bracketed by the experimental structures. To quantitatively place the predicted conformational variance in the context of the experimentally determined structures, we used principal component analysis (PCA), which reduces the multidimensional space to a smaller space representative of the main conformational motions. In our benchmark set, the first principal component (PC1) captured 64.9% ± 16.1% of the structural variations among the models generated using MSAs with 32 or more sequences (Figure 3B), while comparison of PC1/PC2 values suggested that the predicted dynamics deviate from simple interpolation of two end states (Figure 3—figure supplement 1). The experimental structures virtually always occupied well-defined extreme positions. In every case, a correlation was evident between each model’s PC1 values and their TM scores. Indeed, the models with the most extreme PC1 values were also among the most accurate: average TM scores were 0.94 for the top 1, top 3, and top 10 PC1 models, and Pearson’s correlation coefficients between PC1 and TM scores of the ensemble of models exceeded 0.8 for all transporters in this dataset. Moreover, the experimental structures virtually always flanked the AF2 models along PC1. The exception, PTH1R, was determined in a partially inactive and active conformation, suggesting that models extending beyond the former state along PC1 may represent the fully inactive conformation. Therefore, these results indicate that accurate representative models of conformations of interest can be selected from the extreme positions along PC1.

10A follow-up question centers on whether this strategy can yield similar results for proteins with one conformation present in the AF2 training set. We investigated this question using four membrane proteins with two experimentally determined conformations, at least one of which was included in the AF2 training set: the class A GPCR CCR5, the serotonin transporter SERT, the multidrug transporter PfMATE, and the lipid flippase MurJ. Using the template-free prediction pipeline outlined above, we determined the resultant models’ similarity to the structures included in and absent from the training set. Unlike the results presented above, the majority of the transporter models generated this way were more similar to the conformation present in the training set than the conformation absent from the training set (i.e., their TM scores were greater; Figure 1—figure supplement 4). The conformational diversity of these models, including those generated using shallow MSAs, was far more limited than what was generally observed for the proteins discussed above, with the exception of MCT1 (Figure 1—figure supplement 2). Although conformational diversity was demonstrated to a limited extent by the generation of occluded models of MurJ and PfMATE, none of the models observed adopted the alternative conformer. By contrast, while models of CCR5 were less biased toward the training set conformation, deep MSAs reduced conformational diversity. This divergence in performance may stem from the composition of the AF2 training set, which featured the structures of many active GPCRs but no structures, for example, of IF MATEs.

11Our results indicate that the state-of-the-art de novo structural modeling algorithm AF2 can be manipulated to accurately model alternative conformations of transporters and GPCRs whose structures were not available in the training set. The use of shallow MSAs was instrumental to obtaining structurally diverse models in most proteins, and in one case (MCT1) accurate modeling of alternative conformations also required the manual curation of template structures. Thus, while the results presented here provide a blueprint for obtaining AF2 models of alternative conformations, they also argue against an optimal one-size-fits-all approach for sampling the conformational space of every protein with high accuracy. Indeed, whereas the DeepMind team reportedly required templates to obtain models of LmrP in an OF conformation, we found that this procedure was usually unnecessary. Accurate representatives of distinct conformers were generally obtainable with exhaustive sampling and could be identified by performing PCA and selecting models at the extreme positions of PC1. Nevertheless, prediction pipelines will likely require a combination of iterative fine-tuning specific to each target of interest followed by experimental verification to identify proposed conformers. Moreover, this approach showed limited success when applied to transporters whose structures were used to train AF2, hinting at the possibility that traditional methods may still be required to capture alternative conformers.

12As a final verification of this proposed pipeline, we tested it on GPR114/ADGRG5, a class B2 adhesion GPCR whose structure has not been experimentally determined. The structural model deposited in the AF2 database, which likely depicts an active conformation that diverges from the structure of the homolog GPR97, could be recapitulated by using deep MSAs. The use of shallow MSAs (≤64 sequences), by contrast, yielded a range of intermediate conformations distributed across three well-separated clusters (Figure 3—figure supplement 2). One of these clusters contains models with an orientation of TM6 and TM7 that fully occludes the orthosteric site and partially blocks the cytosolic pocket where G proteins bind. The physiological relevance of these proposed structural movements nonetheless requires experimental validation.

13While these results reinforce the notion that AF2 can provide models to guide biophysical studies of conformationally heterogeneous membrane proteins, they represent a methodological ‘hack’, rather than an explicit objective built into the algorithm’s architecture. Several preprints have provided evidence that AF2, despite its accuracy, likely does not learn the energy landscapes underpinning protein folding and function. Moreover, AF2 does not directly account for the lipid environment, which has been experimentally shown to bias the conformational equilibria of membrane proteins. As our results show, the exploration of the conformational space is in part a byproduct of low sequence information provided for inference. Ultimately, they highlight the need for further development of artificial intelligence methods capable of learning the conformational flexibility intrinsic to protein structures.

14Prediction runs were executed using AlphaFold v2.0.1 and a modified version of ColabFold that is available at https://github.com/delalamo/af2_conformations, ( copy archived at swh:1:rev:d60db86886186e80622deaa91045caccaf4103d3). The pipeline used in this study differs from the default AF2 pipeline in several aspects. First, all MSAs were obtained using the MMSeqs2 server, rather than the default databases. Second, template search was disabled, except when explicitly performed with specific templates of interest (see below). Third, the number of recycles was set to one, rather than three by default. Finally, models were not refined following their prediction. This study utilized all 5 neural networks when predicting structures without templates, with 10 predictions per neural network per MSA size. The following residues were omitted from modeling: 1–131 and 401–461 of CGRPR, 1–247 of FZD7, 1–175 and 492–593 of PTH1R, and 1–49 of LAT1.

15MSA subsampling was carried out randomly by AF2, and depth values were controlled by modifying ‘max_msa_clusters’ and ‘max_extra_msa’ parameters prior to execution. The former parameter determines the number of randomly chosen sequence clusters provided to the AF2 neural network. The latter parameter determines the number of extra sequences used to compute additional summary statistics. Throughout this manuscript, we refer to the latter when describing the depth of the MSA used for prediction and set the former to half this value in all cases except when 5120 sequences were used, in which case we set the former to 512. No manual intervention was carried out to fine-tune the composition of these alignments.

16Templates were fetched using the MMSeqs2 server used by ColabFold. All templates were manually inspected, and those with structures similar to the desired conformation of interest were retained. The following templates were used: OF MCT1 with FucB (PDB 3O7Pa and 3O7Qa, 18% sequence identity calculated using Needleman Wunsch; ); OF LAT1 with AdiC (3OB6a and 5J4Ib, 22%); IF LAT1 with b(0,+)AT1 (6LI9, 45%), KCC3 (6Y5Ra, 12%), GkApcT (6F34a, 24%), NKCC1 (6NPLa, 6PZTa, and 6NPHa, 12%), BasC (6F2Ga and 6F2Wa, 29%), and GadC (4DJIa and 4DJKb, 21%); active PTH1R with GCGR (6LMLr and 6VCBr, 30%) PAC1 (6M1I, 34%), and CRF1 (6P9X, 34%); inactive PTH1R with GLP1 (6LN2, 30%) and GCGR (5YQZ, 5XF1, and 5EE7, 28%). Template processing then proceeded as previously described, except that the parameter ‘subsample_templates’ was set to True and the template similarity cutoff was reduced from 10% to 1%. Additionally, as only 2 of the 5 AF2 neural networks were parametrized to use templates, each of these 2 neural networks generated 25 models in order to arrive at 50 total models per MSA depth.

17TM scores were calculated using TM align. PCA and RMSF calculations were carried out in CPPTRAJ. Loop residues were omitted from PCA.