findMySequence: a neural-network-based approach for identification of unknown proteins in X-ray crystallography and cryo-EM

Although experimental protein-structure determination usually targets known proteins, chains of unknown sequence are often encountered. They can be purified from natural sources, appear as an unexpected fragment of a well characterized protein or appear as a contaminant. Regardless of the source of the problem, the unknown protein always requires characterization. Here, an automated pipeline is presented for the identification of protein sequences from cryo-EM reconstructions and crystallographic data. The method's application to characterize the crystal structure of an unknown protein purified from a snake venom is presented. It is also shown that the approach can be successfully applied to the identification of protein sequences and validation of sequence assignments in cryo-EM protein structures.

Three-Dimensional Graph Matching to Identify Secondary Structure Correspondence of Medium-Resolution Cryo-EM Density Maps

Cryo-electron microscopy (cryo-EM) is a structural technique that has played a significant role in protein structure determination in recent years. Compared to the traditional methods of X-ray crystallography and NMR spectroscopy, cryo-EM is capable of producing images of much larger protein complexes. However, cryo-EM reconstructions are limited to medium-resolution (~4–10 Å) for some cases. At this resolution range, a cryo-EM density map can hardly be used to directly determine the structure of proteins at atomic level resolutions, or even at their amino acid residue backbones. At such a resolution, only the position and orientation of secondary structure elements (SSEs) such as α-helices and β-sheets are observable. Consequently, finding the mapping of the secondary structures of the modeled structure (SSEs-A) to the cryo-EM map (SSEs-C) is one of the primary concerns in cryo-EM modeling. To address this issue, this study proposes a novel automatic computational method to identify SSEs correspondence in three-dimensional (3D) space. Initially, through a modeling of the target sequence with the aid of extracting highly reliable features from a generated 3D model and map, the SSEs matching problem is formulated as a 3D vector matching problem. Afterward, the 3D vector matching problem is transformed into a 3D graph matching problem. Finally, a similarity-based voting algorithm combined with the principle of least conflict (PLC) concept is developed to obtain the SSEs correspondence. To evaluate the accuracy of the method, a testing set of 25 experimental and simulated maps with a maximum of 65 SSEs is selected. Comparative studies are also conducted to demonstrate the superiority of the proposed method over some state-of-the-art techniques. The results demonstrate that the method is efficient, robust, and works well in the presence of errors in the predicted secondary structures of the cryo-EM images.

Nanocrystalline protein domains via salting-out

Protein salting-out is a well established phenomenon that in many cases leads to amorphous structures and protein gels, which are usually not considered to be useful for protein structure determination. Here, microstructural measurements of several different salted-out protein dense phases are reported, including of lysozyme, ribonuclease A and an IgG1, showing that salted-out protein gels unexpectedly contain highly ordered protein nanostructures that assemble hierarchically to create the gel. The nanocrystalline domains are approximately 10–100 nm in size, are shown to have structures commensurate with those of bulk crystals and grow on time scales in the order of an hour to a day. Beyond revealing the rich, hierarchical nanoscale to mesoscale structure of protein gels, the nanocrystals that these phases contain are candidates for structural biology on next-generation X-ray free-electron lasers, which may enable the study of biological macromolecules that are difficult or impossible to crystallize in bulk.

Analytical tools in protein structure determination

Three-dimensional protein structure prediction from amino acid sequence has been a thought-provoking task for decades, but it of pivotal importance as it provides a better understanding of its function. In recent years, the methods for prediction of protein structures have advanced considerably. Computational techniques and increase in protein sequence and structure databases have influence the laborious protein structure determination process. Still there is no single method which can predict all the protein structures. In this review, we describe the four stages of protein structure determination. We have also explored the currenttechniques used to uncover the protein structure and highpoint best suitable method for a given protein.

Rotating-Frame Overhauser Transfer via Long-Lived Coherences

Solution-state distance restraints for protein structure determination with Ångström-level resolution rely on through-space transfer of magnetization between nuclear spins. Such magnetization transfers, named Overhauser effects, occur via dipolar magnetic couplings. We demonstrate improvements in magnetization transfer using long-lived coherences (LLCs)—singlet-triplet superpositions that are antisymmetric with respect to spin-permutation within pairs of coupled magnetic nuclei—as the magnetization source. Magnetization transfers in the presence of radio-frequency irradiation, known as ‘rotating-frame’ Overhauser effects (ROEs), are predicted by theory to improve by the use of LLCs; calculations are matched by preliminary experiments herein. The LLC-ROE transfers were compared to the transmission of magnetization via classical transverse routes. Long-lived coherences accumulate magnetization on an external third proton, K, with transfer rates that depended on the tumbling regime. I,S →K transfers in the LLC configuration for (I,S) are anticipated to match, and then overcome, the same transfer rates in the classical configuration as the molecular rotational correlation times increase. Experimentally, we measured the LLC-ROE transfer in dipeptide AlaGly between aliphatic protons in different residues K = Ala − Hα and (I,S) = Gly − Hα1,2 over a distance dK,I,S = 2.3 Å. Based on spin dynamics calculations, we anticipate that, for such distances, a superior transfer of magnetization occurs using LLC-ROE compared to classical ROE at correlation times above τC=10 ns. The LLC-ROE effect shows potential for improving structural studies of large proteins and offering constraints of increased precision for high-affinity protein-ligand complexes in slow tumbling in the liquid state.

Tandem domain structure determination based on a systematic enumeration of conformations

Protein structure determination is undergoing a change of perspective due to the larger importance taken in biology by the disordered regions of biomolecules. In such cases, the convergence criterion is more difficult to set up and the size of the conformational space is a obstacle to exhaustive exploration. A pipeline is proposed here to exhaustively sample protein conformations using backbone angle limits obtained by nuclear magnetic resonance (NMR), and then to determine the populations of conformations. The pipeline is applied to a tandem domain of the protein whirlin. An original approach, derived from a reformulation of the Distance Geometry Problem is used to enumerate the conformations of the linker connecting the two domains. Specifically designed procedure then permit to assemble the domains to the linker conformations and to optimize the tandem domain conformations with respect to two sets of NMR measurements: residual dipolar couplings and paramagnetic resonance enhancements. The relative populations of optimized conformations are finally determined by fitting small angle X-ray scattering (SAXS) data. The most populated conformation of the tandem domain is a semi-closed one, fully closed and more extended conformations being in minority, in agreement with previous observations. The SAXS and NMR data show different influences on the determination of populations.

Abstract P-5: Neural Network Approaches to Classify 3D Protein Structures from the Data of X-ray Laser Radiation Diffraction from Single Particles

Background: Protein structure determination using X-ray free-electron laser (XFEL) includes analysis and merging a large number of snapshot diffraction patterns. Convolutional neural networks are widely used to solve numerous computer vision problems, e.g. image classification, and can be used for diffraction pattern analysis. But the task of protein structure determination with the use of CNNs only is not yet solved. Methods: We simulated the diffraction patterns using the Condor software library and obtained more than 1000 diffraction patterns for each structure with simulation parameters resembling real ones. To classify diffraction patterns, we tried two approaches, which are widely known in the area of image classification: a classic VGG network and residual networks. Results: 1. Recognition of a protein class (GPCRs vs globins). Globins and GPCR-like proteins are typical α-helical proteins. Each of these protein families has a large number of representatives (including those with known structure) but we used only 8 structures from every family. 12,000 of diffraction patterns were used for training and 4,000 patterns for testing. Results indicate that all considered networks are able to recognize the protein family type with high accuracy. 2. Recognition of the number of protein molecules in the liposome. We considered the usage of lyposomes as carriers of membrane or globular proteins for sample delivery in XFEL experiments in order to improve the X-ray beam hit rate. Three sets of diffractograms for liposomes of various radius were calculated, including diffractograms for empty liposomes, liposomes loaded with 5 bacteriorhodopsin molecules, and liposomes loaded with 10 bacteriorhodopsin molecules. The training set consisted of 23625 diffraction patterns, and test set of 7875 patterns. We found that all networks used in our study were able to identify the number of protein molecules in liposomes independent of the liposome radius. Our findings make this approach rather promising for the usage of liposomes as protein carriers in XFEL experiments. Conclusion: Thus, the performed numerical experiments show that the use of neural network algorithms for the recognition of diffraction images from single macromolecular particles makes it possible to determine changes in the structure at the angstrom scale.