The recent improvements in cryo-electron microscopy (cryo-EM) in the past few years are now allowing to observe molecular complexes at atomic resolution. As a consequence, numerous structures derived from cryo-EM are now available in the Protein Data Bank. However, if for some complexes atomic resolution is reached, this is not true for all. This is also the case in cryo-electron tomography where the achievable resolution is still limited. Furthermore the resolution in a cryo-EM map is not a constant, with often outer regions being of lower resolution, possibly linked to conformational variability. Although those low to medium resolution EM maps (or regions thereof) cannot directly provide atomic structure of large molecular complexes, they provide valuable information to model the individual components and their assembly into them. Most approaches for this kind of modelling are performing rigid fitting of the individual components into the EM density map. While this would appear an obvious option, they ignore key aspects of molecular recognition, the energetics and flexibility of the interfaces. Moreover, these often restricts the modelling to a unique source of data, the EM density map. In this chapter, we describe a protocol where an EM map is used as restraint in HADDOCK to guide the modelling process.
Recent development of high-resolution mass spectrometry (MS) instruments enables chemical cross-linking (XL) to become a high-throughput method for obtaining structural information about proteins. Restraints derived from XL-MS experiments have been used successfully for structure refinement and protein-protein docking. However, one formidable question is under which circumstances XL-MS data might be sufficient to determine a protein's tertiary structure de novo? Answering this question will not only include understanding the impact of XL-MS data on sampling and scoring within a de novo protein…
CryoNet.Refine: A One-step Diffusion Model for Rapid Refinement of Structural Models with Cryo-EM Density Map Restraints
State Key Laboratory of Membrane Biology, Beijing Frontier Research Center of Biological Structures, Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, China（膜生物学国家重点实验室，北京生物结构前沿研究中心，清华大学-北京大学联合生命科学中心，生命科学学院，清华大学，北京，中国）；State Key Laboratory of Membrane Biology–Membrane Structure and Artificial Intelligence Biology Branch, Hangzhou, China（膜生物学国家重点实验室——生物结构与人工智能生物学分支，杭州，中国）；State Key Laboratory of Membrane Biology, Beijing Tsinghua Institute for Frontier Interdisciplinary Innovation, Beijing, China（膜生物学国家重点实验室，北京清华大学前沿交叉创新研究院，北京，中国）
For many membrane proteins the determination of their topology remains a challenge for methods like X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. Electron paramagnetic resonance (EPR) spectroscopy has evolved as an alternative technique to study structure and dynamics of membrane proteins. The present study demonstrates the feasibility of membrane protein topology determination using limited EPR distance and accessibility measurements. The BCL::MP-Fold (BioChemical Library membrane protein fold) algorithm assembles secondary structure elements (SSEs) in the membrane …
Toyota Technological Institute at Chicago（芝加哥丰田工业大学）
Motivation. Protein contact map describes the pairwise spatial and functional relationship of residues in a protein and contains key information for protein 3D structure prediction. Although studied extensively, it remains very challenging to predict contact map using only sequence information. Most existing methods predict the contact map matrix element-by-element, ignoring correlation among contacts and physical feasibility of the whole contact map. A couple of recent methods predict contact map based upon residue co-evolution, taking into consideration contact correlation and enforcing a sp…
Background. All-atom crystallographic refinement of proteins is a laborious manually driven procedure, as a result of which, alternative and multiconformer interpretations are not routinely investigated. Results. We describe efficient loop sampling procedures in Rappertk and demonstrate that single loops in proteins can be automatically and accurately modelled with few positional restraints. Loops constructed with a composite CNS/Rappertk protocol consistently have better Rfree than those with CNS alone. This approach is extended to a more realistic scenario where there are often large positio…
Cryo-EM reconstruction algorithms seek to determine a molecule's 3D density map from a series of noisy, unlabeled 2D projection images captured with an electron microscope. Although reconstruction algorithms typically model the 3D volume as a generic function parameterized as a voxel array or neural network, the underlying atomic structure of the protein of interest places well-defined physical constraints on the reconstructed structure. In this work, we exploit prior information provided by an atomic model to reconstruct distributions of 3D structures from a cryo-EM dataset. We propose Cryofo…
Achieving a comprehensive understanding of the behaviour of proteins is greatly facilitated by the knowledge of their structures, thermodynamics and dynamics. All this information can be provided in an effective manner in terms of structural ensembles. A structural ensemble can be obtained by determining the structures, populations and interconversion rates for all the main states that a protein can occupy. To achieve this goal, integrative methods that combine experimental and computational approaches provide powerful tools. Here we focus on cryo-electron microscopy, which has become over rec…
Resolving the structural variability of proteins is often key to understanding the structure-function relationship of those macromolecular machines. Single particle analysis using Cryogenic electron microscopy (CryoEM), combined with machine learning algorithms, provides a way to reveal the dynamics within the protein system from noisy micrographs. Here, we introduce an improved computational method that uses Gaussian mixture models for protein structure representation and deep neural networks for conformation space embedding. By integrating information from molecular models into the heterogen…
Electron cryo-microscopy (cryo-EM) produces three-dimensional (3D) maps of the electrostatic potential of biological macromolecules, including proteins. Along with knowledge about the imaged molecules, cryo-EM maps allow de novo atomic modelling, which is typically done through a laborious manual process. Taking inspiration from recent advances in machine learning applications to protein structure prediction, we propose a graph neural network (GNN) approach for automated model building of proteins in cryo-EM maps. The GNN acts on a graph with nodes assigned to individual amino acids and edges …
Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）
Recently exciting progress has been made on protein contact prediction, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual networks. This deep neural network allows us to model very complex sequence-contact relationship as well as long-range inter-contact correlation. Our method gr…
The Protein Data Bank (PDB) contains the atomic structures of over 105 biomolecules with better than 2.8A resolution. The listing of the identities and coordinates of the atoms comprising each macromolecule permits an analysis of the slow-time vibrational response of these large systems to minor perturbations. 3D video animations of individual modes of oscillation demonstrate how regions interdigitate to create cohesive collective motions, providing a comprehensive framework for and familiarity with the overall 3D architecture. Furthermore, the isolation and representation of the softest, slow…
Small-angle X-ray scattering (SAXS) experiments are increasingly used to probe RNA structure. A number of \emph{forward models} that relate measured SAXS intensities and structural features, and that are suitable to model either explicit-solvent effects or solute dynamics, have been proposed in the past years. Here we introduce an approach that integrates atomistic molecular dynamics simulations and SAXS experiments to reconstruct RNA structural ensembles while simultaneously accounting for both RNA conformational dynamics and explicit-solvent effects. Our protocol exploits SAXS pure-solute fo…
Stanford University（斯坦福大学）；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）；Atomwise, Inc.（Atomwise公司）
Single particle, cryogenic electron microscopy (cryo-EM) experiments now routinely produce high-resolution data for large proteins and their complexes. Building an atomic model into a cryo-EM density map is challenging, particularly when no structure for the target protein is known a priori. Existing protocols for this type of task often rely on significant human intervention and can take hours to many days to produce an output. Here, we present a fully automated, template-free model building approach that is based entirely on neural networks. We use a graph convolutional network (GCN) to gene…
Single particle electron cryomicroscopy (cryo-EM) allows for structures of proteins and protein complexes to be determined from images of non-crystalline specimens. Cryo-EM data analysis requires electron microscope images of randomly oriented ice-embedded protein particles to be rotated and translated to allow for coherent averaging when calculating three-dimensional (3D) structures. Rotation of 2D images is usually done with the assumption that the magnification of the electron microscope is the same in all directions. However, due to electron optical aberrations, this condition is not met w…
Fold'EM: Direct atomic structure inference from Cryo-EM particles
Institute of Science and Technology Austria（奥地利科学技术研究所）；University of Zurich（苏黎世大学）；Frankfurt Institute for Advanced Studies（法兰克福高等研究院）；Princeton University（普林斯顿大学）；Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学博德研究所）
Protein function and activity are a consequence of its three-dimensional structure. Single particle analysis of cryogenic electron micrographs has radically changed structural biology allowing atomic reconstruction of almost any type of proteins. While such an approach provides snapshots of three-dimensional structural information that can be correlated with function, the new frontier of protein structural biology is in the fourth dimension, time. Here we propose the use of liquid phase electron microscopy to expand structural biology into dynamic studies. We apply here single particle analysi…
Cryo-SWAN: the Multi-Scale Wavelet-decomposition-inspired Autoencoder Network for molecular density representation of molecular volumes
Center for Advanced Systems Understanding (CASUS)（先进系统理解中心（CASUS））；Helmholtz-Zentrum Dresden-Rossendorf e. V. (HZDR)（德累斯顿-罗斯托克亥姆霍尔茨研究中心（HZDR））；In situ Structural Biology（原位结构生物学）；Department of Physics（物理系）；Institute of Medical Physics and Biophysics（医学物理与生物物理研究所）；Institute of Computer Science（计算机科学研究所）；Cluster of Excellence Physics of Life（生命物理卓越中心）
University of Washington Bothell（华盛顿大学博塞尔分校）；University of Washington Seattle（华盛顿大学西雅图分校）；Saint Louis University（圣路易斯大学）；Pacific Lutheran University（太平洋路德大学）；University of Chicago（芝加哥大学）
Cryo-electron microscopy (cryo-EM) has become a major experimental technique to determine the structures of large protein complexes and molecular assemblies, as evidenced by the 2017 Nobel Prize. Although cryo-EM has been drastically improved to generate high-resolution three-dimensional (3D) maps that contain detailed structural information about macromolecules, the computational methods for using the data to automatically build structure models are lagging far behind. The traditional cryo-EM model building approach is template-based homology modeling. Manual de novo modeling is very time-con…
Duke University（杜克大学）；Duke University School of Medicine（杜克大学医学院）
Single-particle cryo-electron microscopy (cryo-EM) is an emerging imaging modality capable of visualizing proteins and macro-molecular complexes at near-atomic resolution. The low electron-doses used to prevent sample radiation damage, result in images where the power of the noise is 100 times greater than the power of the signal. To overcome the low-SNRs, hundreds of thousands of particle projections acquired over several days of data collection are averaged in 3D to determine the structure of interest. Meanwhile, recent image super-resolution (SR) techniques based on neural networks have sho…
PERC: a suite of software tools for the curation of cryoEM data with application to simulation, modelling and machine learning
The Alan Turing Institute（阿兰·图灵研究所）；Science & Technology Facilities Council（科学与技术设施委员会）；Rosalind Franklin Institute（罗莎琳德·富兰克林研究所）；University of Oxford（牛津大学）；Diamond Light Source（钻石光源）
ARCHER: Amortized cross-specimen pose estimation for cryo-electron microscopy
Pritzker School of Molecular Engineering, University of Chicago（芝加哥大学普利兹克分子工程学院）；Department of Computer Science, Rensselaer Polytechnic Institute（伦斯勒理工学院计算机科学系）
Changes in lattice structure across sub-regions of protein crystals are challenging to assess when relying on whole crystal measurements. Because of this difficulty, macromolecular structure determination from protein micro and nano crystals requires assumptions of bulk crystallinity and domain block substructure. To evaluate the fidelity of these assumptions in protein nanocrystals we map lattice structure across micron size areas of cryogenically preserved three-dimensional peptide crystals using a nano-focused electron beam. This approach produces diffraction from as few as 1,500 molecules …
