Resolving the conformational ensemble of a membrane protein by integrating small-angle scattering with AlphaFold

Abstract

The function of a protein is enabled by its conformational landscape. For non-rigid proteins, a complete characterization of this landscape requires understanding the protein's structure in all functional states, the stability of these states under target conditions, and the transition pathways between them. Several strategies have recently been developed to drive the machine learning algorithm AlphaFold2 (AF) to sample multiple conformations, but it is more challenging to a priori predict what states are stabilized in particular conditions and how the transition occurs. Here, we combine AF sampling with small-angle scattering curves to obtain a weighted conformational ensemble of functional states under target environmental conditions. We apply this to the pentameric ion channel GLIC using small-angle neutron scattering (SANS) curves, and identify apparent closed and open states. By comparing experimental SANS data under resting and activating conditions, we can quantify the subpopulation of closed channels that open upon activation, matching both experiments and extensive simulation sampling using Markov state models. The predicted closed and open states closely resemble crystal structures determined under resting and activating conditions respectively, and project to predicted basins in free energy landscapes calculated from the Markov state models. Further, without using any structural information, the AF sampling also correctly captures intermediate conformations and projects onto the transition pathway resolved in the extensive sampling. This combination of machine learning algorithms and low-dimensional experimental data appears to provide an efficient way to predict not only stable conformations but also accurately sample the transition pathways several orders of magnitude faster than simulation-based sampling.

Fig 1. A schematic depiction of the method for combining AlphaFold2 with small-angle scattering data to predict key conformational states of proteins.

An ensemble of conformational states (in green) is generated from AlphaFold2 by stochastically subsampling the multi-sequence alignment (MSA) depth. The theoretical small-angle scattering (SAS) intensity profile is calculated for each conformation, followed by Principal Component Analysis (PCA) to project these profiles into a lower-dimensional space. In the PC space, conformations are clustered and the highest confidence-scoring conformations from each cluster are chosen as predictions of the protein states.

Fig 2. Theoretical SANS curves of AF-sampled conformations separate into two distinct clusters.

(A, B) Principal component analysis (PCA) of the theoretical SANS profiles of all AF-generated conformations with an average pLDDT score above 75 (A) and 86.6 (B). Black circles in B indicate the conformations in each cluster with the highest pLDDT scores: 88.1 (left) and 87.5 (right). (C) Average SANS curve of the AF-generated conformations with pLDDT$\ge 86.6$ (in blue), as well as the curve when the first or second principal component from panel B is added to the average (in orange and green, respectively). The SANS curves of the PCs are scaled by the maximal value of the corresponding PC coordinates in B.

Fig 3. Change in experimental SANS profiles corresponds to the difference between generated states.

(A, B) Theoretical and experimental SANS curves at resting (pH 7.5) and activating (pH 3.0) conditions of the predicted GLIC conformational states for scattering vectors $q\in [0,0.45]$ Å⁻¹ (A) and $q\in [0.08,0.18]$ Å⁻¹ (B). Error-normalized residuals are shown below. (C) Experimental and theoretical SANS difference curves between activating and resting conditions as well as between the open and closed prediction, with the latter scaled to fit the former. An error-normalized residual is shown below. (D) Fit ${\chi }_{\Delta I}^2$ of the predicted versus experimental difference curves as a function of the population shift from the closed prediction to open prediction. The dashed line indicates the optimal fit, and corresponds to a 30% increase in the contribution of the predicted open versus closed states.

Fig 4. AlphaFold2-generated conformations overlap well with crystal structures of the open and closed state of GLIC.

(A, B) Overlay of experimentally determined crystal structures in (A) the closed state (PDB ID 4NPQ) and (B) the open state (PDB ID 4HFI) with the corresponding prediction (visualized using VMD [42]). The structures were aligned to minimize the RMSD of all C${ }_{\alpha }$ atoms in both the predicted and corresponding crystal structures. The pore hydration profiles for the predicted structures (calculated using HOLE [43]) are also shown, with cyan and orange signifying wide (radius > 2.3 Å) and narrow (radius < 2.3 Å) parts of the pore, respectively. (C) Distance between the centers of mass of the pore and that of the upper part of the pore lining M2 helix (M2 spread) for AF-generated conformations ranging from closed-like (in red) to open-like (in blue). (D) The corresponding values for the predictions and the crystal structures as well as the density of states for all AF-generated conformations with an average pLDDT score above 75. (E, F) Upper spread of the extracellular domain (ECD), depicted as in panels C and D, respectively.

Fig 5. The predicted states are consistent with free energy landscapes from MD simulations.

Projections of the AF-generated conformations with pLDDT $\ge 75$ onto the deprotonated (A) and protonated (B) energy landscape of GLIC, with predicted closed and open conformations marked in red and blue respectively. For the protonated energy landscape (B) the free energy well on the left (where tIC1$~-1$) corresponds to closed conformations of GLIC while the free energy well on the right (where tIC1$~1.5$) corresponds to open GLIC conformations [26].