Intermediate-to-low resolution feature detection in cryoEM maps using cascaded neural networks

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Understanding the function of biomolecules is fundamental to comprehend how life is sustained and design specific therapeutics for diseases associated with their function. Proteins form the largest fraction of cell constituents and often assemble together, and also with other biomolecules, into large molecular machines that perform vital roles in many cellular processes. The three dimensional (3D) structure of a molecular machine forms the platform for its function and determining the 3D structure is crucial to understand the details of its activity. Cryogenic electron microscopy (cryo-EM) has had an immense impact on the structure determination of such large molecular assemblies in a near native state. These assemblies can either be studied in isolation (single particle analysis) or in the native cellular environment (electron tomography). Advances in technology and software for cryo-EM have helped to push the level of detail that can be discerned. Nonetheless, intrinsic properties of biological samples often make them less amenable to high resolution structure determination. 88% of cryo-EM structures deposited in the public repository EMDB are worse than 3.5Å resolution, and therefore don't contain atomic detail. The 3D structural details of a macromolecular assembly are obtained as a density map. Interpreting details of the map requires detection of structural features of the components of the assembly. Intermediate (between 3.5 Å to 6Å) and low (>6Å) resolution maps are extremely difficult to interpret using standard automated tools. Available methods usually detect structural features by six-dimensional search procedures that are computationally expensive and are associated with a large number of false-positives. Moreover, for most of these methods, it is required that the structural details of each component of the assembly is known. Related problems that go in hand are validation of features derived from low resolution map data and representation of these low resolution models themselves. The basic structural organization of protein structures and the process of 3D folding from a 1D sequence of amino acids have been studied over several decades. Proteins use a finite set of modular features like secondary structures and folds, and the functional form is formed of a unique arrangement of these features. An intermediate level of features is also observed where a few secondary structures organize into stable motifs or sub-folds. We plan to exploit the hierarchical feature organization of protein structures and using powerful deep learning approaches established for pattern recognition we aim to address the problem of feature recognition in intermediate-to-low resolution maps. We will use structural feature libraries of different sizes ranging from secondary structures and smaller motifs (e.g. turns of the protein chain) to sub-folds and folds. A specialized set of motifs or sub-folds covering the intermediate size features will be generated based on compactness (contacts). Deep neural network architectures will be designed to detect these 3D structural features in the map, with layers arranged to reflect the structural hierarchy. We also plan to use the developed networks for validation of existing structure models derived from low resolution data. In the future we would like to extend this work to potentially build structural models by assembling the features using additional sequence based information. The developed approach would help to extend structure interpretability at intermediate and low-resolutions and make better use of such data to get insights into the mechanisms of biological function. The proposed development will be implemented as a user-friendly tool and distributed to the scientific community. We anticipate that other scientific fields could potentially benefit from the machine learning architecture designed for such multi-label 3D segmentation from noisy data.