Putting structure into context: fitting of atomic models into electron microscopic and electron tomographic reconstructions

Few-shot learning for classification of novel macromolecular structures in cryo-electron tomograms

Cryo-electron tomography (cryo-ET) provides 3D visualization of subcellular components in the near-native state and at sub-molecular resolutions in single cells, demonstrating an increasingly important role in structural biology in situ. However, systematic recognition and recovery of macromolecular structures in cryo-ET data remain challenging as a result of low signal-to-noise ratio (SNR), small sizes of macromolecules, and high complexity of the cellular environment. Subtomogram structural classification is an essential step for such task. Although acquisition of large amounts of subtomograms is no longer an obstacle due to advances in automation of data collection, obtaining the same number of structural labels is both computation and labor intensive. On the other hand, existing deep learning based supervised classification approaches are highly demanding on labeled data and have limited ability to learn about new structures rapidly from data containing very few labels of such new structures. In this work, we propose a novel approach for subtomogram classification based on few-shot learning. With our approach, classification of unseen structures in the training data can be conducted given few labeled samples in test data through instance embedding. Experiments were performed on both simulated and real datasets. Our experimental results show that we can make inference on new structures given only five labeled samples for each class with a competitive accuracy (> 0.86 on the simulated dataset with SNR = 0.1), or even one sample with an accuracy of 0.7644. The results on real datasets are also promising with accuracy > 0.9 on both conditions and even up to 1 on one of the real datasets. Our approach achieves significant improvement compared with the baseline method and has strong capabilities of generalizing to other cellular components.

Piecing it together: Unraveling the elusive structure-function relationship in single-pass membrane receptors

The challenge of crystallizing single-pass plasma membrane receptors has remained an obstacle to understanding the structural mechanisms that connect extracellular ligand binding to cytosolic activation. For example, the complex interplay between receptor oligomerization and conformational dynamics has been, historically, only inferred from static structures of isolated receptor domains. A fundamental challenge in the field of membrane receptor biology, then, has been to integrate experimentally observable dynamics of full-length receptors (e.g. diffusion and conformational flexibility) into static structural models of the disparate domains. In certain receptor families, e.g. the ErbB receptors, structures have led somewhat linearly to a putative model of activation. In other families, e.g. the tumor necrosis factor (TNF) receptors, structures have produced divergent hypothetical mechanisms of activation and transduction. Here, we discuss in detail these and other related receptors, with the goal of illuminating the current challenges and opportunities in building comprehensive models of single-pass receptor activation. The deepening understanding of these receptors has recently been accelerated by new experimental and computational tools that offer orthogonal perspectives on both structure and dynamics. As such, this review aims to contextualize those technological developments as we highlight the elegant and complex conformational communication between receptor domains. This article is part of a Special Issue entitled: Interactions between membrane receptors in cellular membranes edited by Kalina Hristova.

Efficient Extraction of Macromolecular Complexes from Electron Tomograms Based on Reduced Representation Templates

Electron tomography is the most widely applicable method for obtaining 3D information by electron microscopy. In the field of biology it has been realized that electron tomography is capable of providing a complete, molecular resolution three-dimensional mapping of entire proteoms. However, to realize this goal, information needs to be extracted efficiently from these tomograms. Owing to extremely low signal-to-noise ratios, this task is mostly carried out manually. Standard template matching approaches tend to generate large amounts of false positives. We developed an alternative method for feature extraction in biological electron tomography based on reduced representation templates, approximating the search model by a small number of anchor points used to calculate the scoring function. Using this approach we see a reduction of about 50% false positives with matched-filter approaches to below 5%. At the same time, false negatives stay below 5%, thus essentially matching the performance one would expect from human operators.

LipidWrapper: an algorithm for generating large-scale membrane models of arbitrary geometry

As ever larger and more complex biological systems are modeled in silico, approximating physiological lipid bilayers with simple planar models becomes increasingly unrealistic. In order to build accurate large-scale models of subcellular environments, models of lipid membranes with carefully considered, biologically relevant curvature will be essential. In the current work, we present a multi-scale utility called LipidWrapper capable of creating curved membrane models with geometries derived from various sources, both experimental and theoretical. To demonstrate its utility, we use LipidWrapper to examine an important mechanism of influenza virulence. A copy of the program can be downloaded free of charge under the terms of the open-source FreeBSD License from http://nbcr.ucsd.edu/lipidwrapper. LipidWrapper has been tested on all major computer operating systems.

PCASSO: a fast and efficient Cα-based method for accurately assigning protein secondary structure elements

Proteins are often characterized in terms of their primary, secondary, tertiary, and quaternary structure. Algorithms such as define secondary structure of proteins (DSSP) can automatically assign protein secondary structure based on the backbone hydrogen-bonding pattern. However, the assignment of secondary structure elements (SSEs) becomes a challenge when only the Cα coordinates are available. In this work, we present protein C-alpha secondary structure output (PCASSO), a fast and accurate program for assigning protein SSEs using only the Cα positions. PCASSO achieves ∼95% accuracy with respect to DSSP and takes ∼0.1 s using a single processor to analyze a 1000 residue system with multiple chains. Our approach was compared with current state-of-the-art Cα-based methods and was found to outperform all of them in both speed and accuracy. A practical application is also presented and discussed.

Assembly of macromolecular complexes by satisfaction of spatial restraints from electron microscopy images

To obtain a structural model of a macromolecular assembly by single-particle EM, a large number of particle images need to be collected, aligned, clustered, averaged, and finally assembled via reconstruction into a 3D density map. This process is limited by the number and quality of the particle images, the accuracy of the initial model, and the compositional and conformational heterogeneity. Here, we describe a structure determination method that avoids the reconstruction procedure. The atomic structures of the individual complex components are assembled by optimizing a match against 2D EM class-average images, an excluded volume criterion, geometric complementarity, and optional restraints from proteomics and chemical cross-linking experiments. The optimization relies on a simulated annealing Monte Carlo search and a divide-and-conquer message-passing algorithm. Using simulated and experimentally determined EM class averages for 12 and 4 protein assemblies, respectively, we show that a few class averages can indeed result in accurate models for complexes of as many as five subunits. Thus, integrative structural biology can now benefit from the relative ease with which the EM class averages are determined.

Electron crystallography--the waking beauty of structural biology

Since its debut in the mid 1970s, electron crystallography has been a valuable alternative in the structure determination of biological macromolecules. Its reliance on single-layered or double-layered two-dimensionally ordered arrays and the ability to obtain structural information from small and disordered crystals make this approach particularly useful for the study of membrane proteins in a lipid bilayer environment. Despite its unique advantages, technological hurdles have kept electron crystallography from reaching its full potential. Addressing the issues, recent initiatives developed high-throughput pipelines for crystallization and screening. Adding progress in automating data collection, image analysis and phase extension methods, electron crystallography is poised to raise its profile and may lead the way in exploring the structural biology of macromolecular complexes.