抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

/pdf/highly-accurate-protein-structure-prediction-with-alphafold-2xjbzfwu3n.pdf

Highly accurate protein structure prediction with AlphaFold

https://www.biorxiv.org/content/biorxiv/early/2020/02/20/2020.02.19.956581.full.pdf

Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Glycoprotein.

MotionCor2 software corrects for beam-induced sample motion, improving the resolution of cryo-EM reconstructions.

/pdf/motioncor2-anisotropic-correction-of-beam-induced-motion-for-7sigifk3hj.pdf

MotionCor2: Anisotropic Correction of Beam-Induced Motion for Improved Cryo-Electron Microscopy

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

RELION: implementation of a Bayesian approach to cryo-EM structure determination.

Here, we describe the third major release of RELION. CPU-based vector acceleration has been added in addition to GPU support, which provides flexibility in use of resources and avoids memory limitations. Reference-free autopicking with Laplacian-of-Gaussian filtering and execution of jobs from python allows non-interactive processing during acquisition, including 2D-classification, de novo model generation and 3D-classification. Per-particle refinement of CTF parameters and correction of estimated beam tilt provides higher resolution reconstructions when particles are at different heights in the ice, and/or coma-free alignment has not been optimal. Ewald sphere curvature correction improves resolution for large particles. We illustrate these developments with publicly available data sets: together with a Bayesian approach to beam-induced motion correction it leads to resolution improvements of 0.2-0.7 A compared to previous RELION versions.

/pdf/new-tools-for-automated-high-resolution-cryo-em-structure-d3sztfqg71.pdf

New tools for automated high-resolution cryo-EM structure determination in RELION-3.

Alzheimer’s disease is the most common neurodegenerative disease, and there are no mechanism-based therapies. The disease is defined by the presence of abundant neurofibrillary lesions and neuritic plaques in the cerebral cortex. Neurofibrillary lesions comprise paired helical and straight tau filaments, whereas tau filaments with different morphologies characterize other neurodegenerative diseases. No high-resolution structures of tau filaments are available. Here we present cryo-electron microscopy (cryo-EM) maps at 3.4–3.5 A resolution and corresponding atomic models of paired helical and straight filaments from the brain of an individual with Alzheimer’s disease. Filament cores are made of two identical protofilaments comprising residues 306–378 of tau protein, which adopt a combined cross-β/β-helix structure and define the seed for tau aggregation. Paired helical and straight filaments differ in their inter-protofilament packing, showing that they are ultrastructural polymorphs. These findings demonstrate that cryo-EM allows atomic characterization of amyloid filaments from patient-derived material, and pave the way for investigation of a range of neurodegenerative diseases. High-resolution structures of tau filaments shed light on the ultrastructure of neurofibrillary lesions in Alzheimer’s disease. Alzheimer's disease is defined by the presence of abundant neurofibrillary lesions and neuritic plaques in the cerebral cortex. The lesions are made of paired helical and straight tau filaments (PHFs and SFs, respectively). Different tau filaments characterize other neurodegenerative diseases, suggesting that molecular conformers of aggregated tau underlie human tauopathies. No high-resolution structures of tau filaments are currently available. Here, Sjors Scheres and colleagues present cryo-electron microscopy (cryo-EM) maps at 3.5 A resolution and corresponding atomic models of PHFs and SFs from the brain of an individual with Alzheimer's disease. Their results show that cryo-EM enables atomic characterization of amyloid filaments from patient-derived material and could be used to study a range of neurodegenerative diseases.

/pdf/cryo-em-structures-of-tau-filaments-from-alzheimer-s-disease-1pf284ip14.pdf

Cryo-EM structures of tau filaments from Alzheimer’s disease

In the field of single-particle analysis of electron cryo-microscopy (cryo-EM) data, a growing concern that some resolution claims might not be substantiated by the data has been one of the instigators of community-wide efforts to develop new validation tools1. A known issue with commonly used cryo-EM structure determination procedures is their liability to overfit the data. Most procedures counter overfitting by low-pass filtering, but the effective frequencies for these filters are often based on suboptimal Fourier Shell Correlation2 (FSC) procedures. In the suboptimal procedure, FSC curves are calculated between reconstructions from two halves of the data, while a single model is used to determine the relative orientations of all particles. It is well known that bias towards noise in the single model may inflate the resulting resolution estimates. To illustrate this, we applied the suboptimal procedure to a simulated cryo-EM data set of 20,212 GroEL particles. Whereas the reported resolution was 4.6 A, the true resolution of the map was only 7.8 A. Also the presence of expected density features in the map does not necessarily provide sufficient evidence for a resolution claim: we could make convincingly looking figures of apparent side-chain density that in reality corresponded to overfitted noise (Supplementary Figure 1). Consequently, overfitting may remain undetected and interpretation of cryo-EM maps may be subject to errors.

The dangers of overfitting have been recognized, and refinement procedures with resolution-dependent weighting schemes to reduce overfitting have been proposed3,4. However, two known solutions to prevent it are not in common use. By refining two models independently (one for each half of the data), so-called gold-standard1 FSC curves may be calculated that are free from spurious correlations. Alternatively, the data used for the orientation determination may be limited to a user-specified frequency, so that model bias beyond that frequency may be avoided. However, the argument that withholding part of the data from the refinement would substantially deteriorate the orientations and thereby the quality of the structure has prevented the wide-spread use of either of these solutions. In what follows, we prove this thesis to be false.

Analysis of simulated data with realistic signal-to-noise ratios (SNRs) indicates that the accuracy of the orientation determination is not affected by the exclusion of high-frequency terms, nor by the use of a model that is reconstructed from only half of the particles (Supplementary Figure 2). These simulations illustrate that only the low-medium frequency terms in the individual particles contain sufficiently high SNRs to contribute significantly to the orientation determination, which is in good agreement with experimental evidence that cryo-EM particles may be aligned accurately using only low-frequency data5. Because in most cryo-EM studies the low-medium frequencies of reconstructions from half of the particles are not expected to be significantly worse than those of reconstructions from all particles, we hypothesize that overfitting may be prevented without a notable loss of resolution using either frequency-limited refinement or refinement based on gold-standard FSCs. Since the former involves a decision by the user, i.e. choosing the frequency at which to limit the refinement, we favour gold-standard FSCs and implemented a procedure to independently refine two models as a script on top of the conventional projection matching protocol in the XMIPP package6 (Supplementary Figure 3 & Supplementary Software).

We tested our hypothesis using three cryo-EM data sets: 5,053 GroEL particles that are distributed by the National Center for Macromolecular Imaging; an in-house collected data set of 50,330 β-galactosidase particles (Supplementary Methods); and 5,403 hepatitis B capsid particles from a previously published study7. High-resolution crystal structures are available for all three data sets, and these were used to assess the “true” resolution obtained using refinements based on either gold-standard or conventional FSC procedures (Figure 1). For all three cases, the conventional procedure reported apparently better FSC curves than the gold-standard procedure, but in no case did the gold-standard procedure actually result in a lower resolution map compared to the crystal structure. On the contrary, for the β-galactosidase data the gold-standard procedure yielded a structure that correlated up to higher frequencies with the crystal structure than the conventional procedure, which suffered from severe overfitting and gave rise to strong artefacts in the map. We also note that, in the absence of overfitting, the frequency at which the gold-standard FSC drops below 0.143 is a good indicator of the true resolution of the map (Supplementary Table 1), which is as expected from theory8. Finally, in the limit of very small data sets, division of the data into two halves might affect resolution. However, calculations with subsets of the GroEL particles suggest that this only becomes an issue for data sets that are much smaller than those typically used in cryo-EM reconstructions (Supplementary Figure 4).



Figure 1

The prevention of overfitting



The principal conclusion is therefore that overfitting of noise using suboptimal FSCs causes worse orientations and leads to a worse structure. In contrast, the use of gold-standard FSCs provides a realistic estimate of the true signal, which ultimately leads to a better map. The procedures proposed here are straightforward to implement in existing programs, and their application will eradicate the hazards of overfitting from cryo-EM structure determination procedures.

/pdf/prevention-of-overfitting-in-cryo-em-structure-determination-5f6hf672e1.pdf

Sjors H.W. Scheres

Papers

RELION: implementation of a Bayesian approach to cryo-EM structure determination.

New tools for automated high-resolution cryo-EM structure determination in RELION-3.

Cryo-EM structures of tau filaments from Alzheimer’s disease

Prevention of overfitting in cryo-EM structure determination

Author response: New tools for automated high-resolution cryo-EM structure determination in RELION-3