Deriving Protein Backbone Using Traces Extracted from Density Maps at Medium Resolutions

Cryo-electron microscopy (Cryo-EM) has become central to the study of large-scale molecular interactions and has produced three-dimensional (3D) density maps at various resolutions. Secondary structure element (SSE) identification from volumetric protein density maps is critical for de novo backbone structure derivation in cryo-EM. Multiple methods have been developed to detect helix and β-sheet from density maps at medium resolutions (~5-10A). β-barrel as a special β-sheet structure has been found in many proteins. However, currently there are no methods aimed to automatically and accurately extract the β-barrel as a complete chunk of density from cryo-EM map of β-barrel protein. We present an effective approach, BarrelMiner, to automatically extract the β-barrel region from cryo-EM density maps based on the conspicuous cylindrical shape of β-barrel. BarrelMiner has been tested using ten simulated density maps at 10A resolution and seven experimental cryo-EM maps between 5.5A and 8.25A resolution. The result suggests that BarrelMiner can be used for the detection of β-barrel from medium resolution cryo-EM density maps of β-barrel proteins.

Proteins are the molecules carry out the vital functions and make more than the half of dry weight in every cell. Protein in nature folds into a unique and energetically favorable 3-Dimensional (3-D) structure which is critical and unique to its biological function. In contrast to other methods for protein structure determination, Electron Cryo-rricroscopy (CryoEM) is able to produce volumetric maps of proteins that are poorly soluble, large and hard to crystallize. Furthermore, it studies the proteins in their native environment. Unfortunately, the volumetric maps generated by current advances in CryoEM technique produces protein maps at medium resolution about (~5 to 10A) in which it is hard to determine the atomic-structure of the protein. However, the resolution of the volumetric maps is improving steadily, and recent works could obtain atomic models at higher resolutions (~3A). De novo protein modeling is the process of building the structure of the protein using its CryoEM volumetric map. Thereupon, the volumetric maps at medium resolution generated by CryoEM technique proposed a new challenge. At the medium resolution, the location and orientation of secondary structure elements (SSE) can be visually and computationally identified. However, the order and direction (called protein topology) of the SSEs detected from the CryoEM volumetric map are not visible. In order to determine the protein structure, the topology of the SSEs has to be figured out and then the backbone can be built. Consequently, the topology problem has become a bottle neck for protein modeling using CryoEM In this dissertation, we focus to establish an effective computational framework to derive the atomic structure of a protein from the medium resolution CryoEM volumetric maps. This framework includes a topology graph component to rank effectively the topologies of the SSEs and a model building component. In order to generate the small subset of candidate topologies, the problem is translated into a layered graph representation. We developed a dynamic programming algorithm (TopoDP) for the new representation to overcome the problem of large search space. Our approach shows the improved accuracy, speed and memory use when compared with existing methods. However, the generating of such set was infeasible using a brute force method. Therefore, the topology graph component effectively reduces the topological space using the geometrical features of the secondary structures through a constrained K-shortest paths method in our layered graph. The model building component involves the bending of a helix and the loop construction using skeleton of the volumetric map. The forward-backward CCD is applied to bend the helices and model the loops.

The pyruvate dehydrogenase multienzyme complexes are among the largest multifunctional catalytic machines in cells, catalyzing the production of acetyl CoA from pyruvate. We have previously reported the molecular architecture of an 11-MDa subcomplex comprising the 60-mer icosahedral dihydrolipoyl acetyltransferase (E2) decorated with 60 copies of the heterotetrameric (alpha(2)beta(2)) 153-kDa pyruvate decarboxylase (E1) from Bacillus stearothermophilus (Milne, J. L. S., Shi, D., Rosenthal, P. B., Sunshine, J. S., Domingo, G. J., Wu, X., Brooks, B. R., Perham, R. N., Henderson, R., and Subramaniam, S. (2002) EMBO J. 21, 5587-5598). An annular gap of approximately 90 A separates the acetyltransferase catalytic domains of the E2 from an outer shell formed of E1 tetramers. Using cryoelectron microscopy, we present here a three-dimensional reconstruction of the E2 core decorated with 60 copies of the homodimeric 100-kDa dihydrolipoyl dehydrogenase (E3). The E2E3 complex has a similar annular gap of approximately 75 A between the inner icosahedral assembly of acetyltransferase domains and the outer shell of E3 homodimers. Automated fitting of the E3 coordinates into the map suggests excellent correspondence between the density of the outer shell map and the positions of the two best fitting orientations of E3. As in the case of E1 in the E1E2 complex, the central 2-fold axis of the E3 homodimer is roughly oriented along the periphery of the shell, making the active sites of the enzyme accessible from the annular gap between the E2 core and the outer shell. The similarities in architecture of the E1E2 and E2E3 complexes indicate fundamental similarities in the mechanism of active site coupling involved in the two key stages requiring motion of the swinging lipoyl domain across the annular gap, namely the synthesis of acetyl CoA and regeneration of the dithiolane ring of the lipoyl domain.

BackgroundDe novo protein modeling approaches utilize 3-dimensional (3D) images derived from electron cryomicroscopy (CryoEM) experiments. The skeleton connecting two secondary structures such as α-helices represent the loop in the 3D image. The accuracy of the skeleton and of the detected secondary structures are critical in De novo modeling. It is important to measure the length along the skeleton accurately since the length can be used as a constraint in modeling the protein.ResultsWe have developed a novel computational geometric approach to derive a simplified curve in order to estimate the loop length along the skeleton. The method was tested using fifty simulated density images of helix-loop-helix segments of atomic structures and eighteen experimentally derived density data from Electron Microscopy Data Bank (EMDB). The test using simulated density maps shows that it is possible to estimate within 0.5Å of the expected length for 48 of the 50 cases. The experiments, involving eighteen experimentally derived CryoEM images, show that twelve cases have error within 2Å.ConclusionsThe tests using both simulated and experimentally derived images show that it is possible for our proposed method to estimate the loop length along the skeleton if the secondary structure elements, such as α-helices, can be detected accurately, and there is a continuous skeleton linking the α-helices.

The cryo-EM structure of ETB-Gi complex, obtained using the Fusion-G system, provides insights into the distinct activation mechanism and G-protein promiscuity of ETB, aiding in the design of ETB agonists for therapeutic applications.

The cyclic coordinate descent (CCD) method is a popular loop closure method in protein structure modeling. It is a robotics algorithm originally developed for inverse kinematic applications. We demonstrate an effective method of building the backbone of protein structure models using the principle of CCD and a guiding trace. For medium-resolution 3-dimensional (3D) images derived using cryo-electron microscopy (cryo-EM), it is possible to obtain guiding traces of secondary structures and their skeleton connections. Our new method, constrained cyclic coordinate descent (CCCD), builds α-helices, β-strands, and loops quickly and fairly accurately along predefined traces. We show that it is possible to build the entire backbone of a protein fairly accurately when the guiding traces are accurate. In a test of 10 proteins, the models constructed using CCCD show an average of 3.91Å of backbone root mean square deviation (RMSD). When the CCCD method is incorporated in a simulated annealing framework to sample possible shift, translation, and rotation freedom, the models built with the true topology were ranked high on the list, with an average backbone RMSD100 of 3.76Å. CCCD is an effective method for modeling atomic structures after secondary structure traces and skeletons are extracted from 3D cryo-EM images.

Protein secondary structures such as α-helices and β-strands are major structural components in most proteins. The position of secondary structures provides important constraints in computing the tertiary structure of a protein. Electron cryomicroscopy is a biophysical technique that produces 3-dimensional images of large molecular complexes. For images at medium resolutions, such as 5–10 A, major secondary structures may be computationally detected. This paper summarizes our recent work in detection of secondary structures using SSETracer, SSELearner, StrandTwister and StrandRoller. The detection of helices and β-strands is illustrated using SSETracer and StrandTwister with a small dataset.

AlphaFold 3 (AF3) has recently been shown to offer improved accuracy in predicting the structures of protein multimers. Improved models may lead to new opportunities for fitting them to cryo-electron microscopy (cryo-EM) maps with medium resolution (5-10 Å). Deriving atomic models from such cryo-EM maps is still challenging due to the lack of high-resolution features. Our case study involving four AF3 multimer models and corresponding cryo-EM maps with 7-8 Å resolution showed that the predicted multimer models were partially correct. The predicted models contained fairly accurate domains, secondary structures, and individual chains, since 9 of the 17 chains exhibit TM-scores higher than 0.8 and 16 chains had TM-scores above 0.5 compared with the official atomic structures that were deposited with the cryo-EM maps. However, some cases exhibited incorrect relative positions of individual chains or domains. We observed that the order of cross-correlation (CC) scores between the multimers and their corresponding cryo-EM maps aligned with the order of the TM-scores. This shows that if regions are masked correctly, CC scores are sensitive enough to distinguish among the multimer models. A masking of monomeric chains may not always be attainable, so we also explored the level of accuracy in secondary structure segmentation for one of the cases in greater detail. Although molecular details are not fully visible in cryo-EM maps at medium resolution, the location of major secondary structures, such as α-helices and β-sheets, were detectable using our DeepSSETracer tool. Our analysis illustrates the potential for improvements in the accuracy of AF3-predicted multimer models by combining the density map-model similarity (CC scores) and the secondary structure map-model similarity in a future approach.

Cryo-EM structure and functional analyses show that human iron exporter ferroportin (Fpn) can bind and transport calcium ions and the transport activity is reduced when the concentration of ferrous ions is high.

Experimental structure analysis of biological molecules (e.g, proteins) or macromolecular complexes (e.g, viruses) can be used to generate three-dimensional density maps of these entities. Such a density map can be viewed as a three-dimensional gray-scale image where space is subdivided in voxels of a given size. The focus of this paper is the analysis of virus density maps. The hull of a virus consists of many copies of one or several different proteins. An important tool for the study of viruses is cryo-electron microscopy (cryo-EM), a technique with insufficient resolution to directly determine the arrangement of the proteins in the virus. We therefore created a tool that locates proteins in the three-dimensional density map of a virus. The goal is to fully determine the locations and orientations of the protein(s) in the virus given the virus' three-dimensional density map and a database of density maps of one or more protein candidates.

With the breakthroughs in protein structure prediction technology, constructing atomic structures from cryo-electron microscopy (cryo-EM) density maps through structural fitting has become increasingly critical. However, the accuracy of the constructed models heavily relies on the precision of the structure-to-map fitting. In this study, we introduce DEMO-EMfit, a progressive method that integrates deep learning-based backbone map extraction with a global-local structural pose search to fit atomic structures into density maps. DEMO-EMfit was extensively evaluated on a benchmark data set comprising both cryo-electron tomography (cryo-ET) and cryo-EM maps of protein and nucleic acid complexes. The results demonstrate that DEMO-EMfit outperforms state-of-the-art approaches, offering an efficient and accurate tool for fitting atomic structures into density maps.

Segmentation of icosahedral virus density map from cryo-electron microscope (CryoEM) is a challenging task because virus structure is complex and density map is at low resolution. Fast marching method is widely used in segmentation, in which seed selection is essential for correct segmentation results. However, the selection of an appropriate seed is difficult. In this paper, we present the method of selecting the seed in fast marching algorithm by making use of the shape symmetry to improve the fast marching method for icosahedral virus segmentation. Based on the feature of icosahedron, we compute and get its symmetry axes inside the density map. With these symmetry axes, we specify the initial seeds with the local maxima value along symmetry axes. Further, the new data structures are presented, which can effectively reduce the memory cost when implement the fast marching algorithm. Experimental results show that the approach can obtain segmentation results of the density maps fast and accurately.

More and more deep learning approaches have been proposed to segment secondary structures from cryo-electron density maps at medium resolution range (5-10Å). Although the deep learning approaches show great potential, only a few small experimental data sets have been used to test the approaches. There is limited understanding about potential factors, in data, that affect the performance of segmentation. We propose an approach to generate data sets with desired specifications in three potential factors - the protein sequence identity, structural contents, and data quality. The approach was implemented and has generated a test set and various training sets to study the effect of secondary structure content and data quality on the performance of DeepSSETracer, a deep learning method that segments regions of protein secondary structures from cryo-EM map components. Results show that various content levels in the secondary structure and data quality influence the performance of segmentation for DeepSSETracer.

Dense crowd counting aims to predict thousands of human instances from an image, by calculating integrals of a density map over image pixels. Existing approaches mainly suffer from the extreme density variances. Such density pattern shift poses challenges even for multi-scale model ensembling. In this paper, we propose a simple yet effective approach to tackle this problem. First, a patch-level density map is extracted by a density estimation model and further grouped into several density levels which are determined over full datasets. Second, each patch density map is automatically normalized by an online center learning strategy with a multipolar center loss. Such a design can significantly condense the density distribution into several clusters, and enable that the density variance can be learned by a single model. Extensive experiments demonstrate the superiority of the proposed method. Our work outperforms the state-of-the-art by 4.2%, 14.3%, 27.1% and 20.1% in MAE, on ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50 and UCF-QNRF datasets, respectively.

Cryo-electron microscopy (Cryo-EM) is widely used in molecular structure determination and drug discovery. Experimental cryo-EM images suffer from the noises introduced by electron beam dose and sample preparation. Although many approaches have been proposed to improve the signal-to-noise ratio (SNR) for cryo-EM image denoising, the noises are still presented after 3D reconstruction and can obstruct the analysis and visualization of the 3D density map. Here we present DeepTracer-Denoising, a method for 3D electron density map denoising. We employ a 3D Neural Network to learn the pattern of noises and the biological structure from density maps. Our method is designed to work on medium to high-resolution maps ranging from 2.5 A to 10.0A. It is configurated with two modes to tackle both background noise and structural noise in a 3D density map. Our method can correctly identify 97.70% background noise while preserving 96.46% density of the native structure. For the maps that contain structural noise, DeepTracer-Denoising achieves an overall accuracy of 98.95%.