A New Protocol for Atomic-Level Protein Structure Modeling and Refinement Using Low-to-Medium Resolution Cryo-EM Density Maps

Application of conformational space annealing to the protein structure modeling using cryo-EM maps

Conformational space annealing (CSA), a global optimization method, has been applied to various protein structure modeling tasks. In this paper, we applied CSA to the cryo-EM structure modeling task by combining the python subroutine of CSA (PyCSA) and the fast relax (FastRelax) protocol of PyRosetta. Refinement of initial structures generated from two methods, rigid fitting of predicted structures to the Cryo-EM map and de novo protein modeling by tracing the Cryo-EM map, was performed by CSA. In the refinement of the rigid-fitted structures, the final models showed that CSA can generate reliable atomic structures of proteins, even when large movements of protein domains were required. In the de novo modeling case, although the overall structural qualities of the final models were rather dependent on the initial models, the final models generated by CSA showed improved MolProbity scores and cross-correlation coefficients to the maps. These results suggest that CSA can accomplish flexible fitting and refinement together by sampling diverse conformations effectively and thus can be utilized for cryo-EM structure modeling.

Machine learning models based on residue interaction network for ABCG2 transportable compounds recognition

As the one of the most important protein of placental transport of environmental substances, the identification of ABCG2 transport molecules is the key step for assessing the risk of placental exposure to environmental chemicals. Here, residue interaction network (RIN) was used to explore the difference of ABCG2 binding conformations between transportable and non-transportable compounds. The RIN were treated as a kind of special quantitative data of protein conformation, which not only reflected the changes of single amino acid conformation in protein, but also indicated the changes of distance and action type between amino acids. Based on the quantitative RIN, four machine learning algorithms were applied to establish the classification and recognition model for 1100 compounds with transported by ABCG2 potential. The random forest (RF) models constructed with RIN presented the best and satisfied predictive ability with an accuracy of training set of 0.97 and the test set of 0.96 respectively. In conclusion, the construction of residue interaction network provided a new perspective for the quantitative characterization of protein conformation and the establishment of prediction models for transporter molecular recognition. The ABCG2 transport molecular recognition model based on residue interaction network provides a possible way for screening environmental chemistry transported through placenta.

Novel Artificial Intelligence-Based Approaches for Ab Initio Structure Determination and Atomic Model Building for Cryo-Electron Microscopy

Single particle cryo-electron microscopy (cryo-EM) has emerged as the prevailing method for near-atomic structure determination, shedding light on the important molecular mechanisms of biological macromolecules. However, the inherent dynamics and structural variability of biological complexes coupled with the large number of experimental images generated by a cryo-EM experiment make data processing nontrivial. In particular, ab initio reconstruction and atomic model building remain major bottlenecks that demand substantial computational resources and manual intervention. Approaches utilizing recent innovations in artificial intelligence (AI) technology, particularly deep learning, have the potential to overcome the limitations that cannot be adequately addressed by traditional image processing approaches. Here, we review newly proposed AI-based methods for ab initio volume generation, heterogeneous 3D reconstruction, and atomic model building. We highlight the advancements made by the implementation of AI methods, as well as discuss remaining limitations and areas for future development.

Ribavirin inhibits the replication of infectious bursal disease virus predominantly through depletion of cellular guanosine pool

Introduction

The antiviral activity of different mutagens against single-stranded RNA viruses is well documented; however, their activity on the replication of double-stranded RNA viruses remains unexplored. This study aims to investigate the effect of different antivirals on the replication of a chicken embryo fibroblast-adapted Infectious Bursal Disease virus, FVSKG2. This study further explores the antiviral mechanism utilized by the most effective anti-IBDV agent.

Methods

The cytotoxicity and anti-FVSKG2 activity of different antiviral agents (ribavirin, 5-fluorouracil, 5-azacytidine, and amiloride) were evaluated. The virus was serially passaged in chicken embryo fibroblasts 11 times at sub-cytotoxic concentrations of ribavirin, 5-fluorouracil or amiloride. Further, the possible mutagenic and non-mutagenic mechanisms utilized by the most effective anti-FVSKG2 agent were explored.

Results and Discussion

Ribavirin was the least cytotoxic on chicken embryo fibroblasts, followed by 5-fluorouracil, amiloride and 5-azacytidine. Ribavirin inhibited the replication of FVSKG2 in chicken embryo fibroblasts significantly at concentrations as low as 0.05 mM. The extinction of FVSKG2 was achieved during serial passage of the virus in chicken embryo fibroblasts at ≥0.05 mM ribavirin; however, the emergence of a mutagen-resistant virus was not observed until the eleventh passage. Further, no mutation was observed in 1,898 nucleotides of the FVSKG2 following its five passages in chicken embryo fibroblasts in the presence of 0.025 mM ribavirin. Ribavarin inhibited the FVSKG2 replication in chicken embryo fibroblasts primarily through IMPDH-mediated depletion of the Guanosine Triphosphate pool of cells. However, other mechanisms like ribavirin-mediated cytokine induction or possible inhibition of viral RNA-dependent RNA polymerase through its interaction with the enzyme's active sites enhance the anti-IBDV effect. Ribavirin inhibits ds- RNA viruses, likely through IMPDH inhibition and not mutagenesis. The inhibitory effect may, however, be augmented by other non-mutagenic mechanisms, like induction of antiviral cytokines in chicken embryo fibroblasts or interaction of ribavirin with the active sites of RNA-dependent RNA polymerase of the virus.

Antibacterial Activity and Mechanism of Peptide PV-Q5 against Vibrio parahaemolyticus and Escherichia coli, Derived from Salt-Fermented Penaeus vannamei

The increasing threat posed by antibiotic-resistant pathogens has prompted a shift to the use of naturally-derived antimicrobial peptides (AMPs) in place of chemical preservatives in controlling foodborne pathogens. In this study, ten peptides were identified from salt-fermented shrimps (Penaeus vannamei) using ultra-performance liquid chromatography-mass spectrometry. One of the peptides, designated PV-Q5 (QVRNFPRGSAASPSALASPR), with most features of an AMP, was further explored and found to possess strong antibacterial activity against Vibrio parahaemolyticus and Escherichia coli, with a minimum inhibitory concentration of 31.25 μg/mL. Moreover, PV-Q5 increased bacterial cell membrane permeability and ruptured bacteria cell membranes, as revealed by transmission electron microscopy. Circular dichroism analysis showed that the conformation of PV-Q5 was a random coil in phosphate-buffered saline and α-helical in sodium dodecyl sulfate, which is conducive for interaction with bacteria cell membranes. These findings indicated that PV-Q5 could find potential use in food preservation to control foodborne pathogenic bacteria.

Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly

Advances in microscopy instruments and image processing algorithms have led to an increasing number of cryo-electron microscopy (cryo-EM) maps. However, building accurate models into intermediate-resolution EM maps remains challenging and labor-intensive. Here, we propose an automatic model building method of multi-chain protein complexes from intermediate-resolution cryo-EM maps, named EMBuild, by integrating AlphaFold structure prediction, FFT-based global fitting, domain-based semi-flexible refinement, and graph-based iterative assembling on the main-chain probability map predicted by a deep convolutional network. EMBuild is extensively evaluated on diverse test sets of 47 single-particle EM maps at 4.0-8.0 Å resolution and 16 subtomogram averaging maps of cryo-ET data at 3.7-9.3 Å resolution, and compared with state-of-the-art approaches. We demonstrate that EMBuild is able to build high-quality complex structures that are comparably accurate to the manually built PDB structures from the cryo-EM maps. These results demonstrate the accuracy and reliability of EMBuild in automatic model building.

Recent advances in RNA structurome

RNA structures are essential to support RNA functions and regulation in various biological processes. Recently, a range of novel technologies have been developed to decode genome-wide RNA structures and novel modes of functionality across a wide range of species. In this review, we summarize key strategies for probing the RNA structurome and discuss the pros and cons of representative technologies. In particular, these new technologies have been applied to dissect the structural landscape of the SARS-CoV-2 RNA genome. We also summarize the functionalities of RNA structures discovered in different regulatory layers-including RNA processing, transport, localization, and mRNA translation-across viruses, bacteria, animals, and plants. We review many versatile RNA structural elements in the context of different physiological and pathological processes (e.g., cell differentiation, stress response, and viral replication). Finally, we discuss future prospects for RNA structural studies to map the RNA structurome at higher resolution and at the single-molecule and single-cell level, and to decipher novel modes of RNA structures and functions for innovative applications.

Progressive assembly of multi-domain protein structures from cryo-EM density maps

Progress in cryo-electron microscopy has provided the potential for large-size protein structure determination. However, the success rate for solving multi-domain proteins remains low because of the difficulty in modelling inter-domain orientations. Here we developed domain enhanced modeling using cryo-electron microscopy (DEMO-EM), an automatic method to assemble multi-domain structures from cryo-electron microscopy maps through a progressive structural refinement procedure combining rigid-body domain fitting and flexible assembly simulations with deep-neural-network inter-domain distance profiles. The method was tested on a large-scale benchmark set of proteins containing up to 12 continuous and discontinuous domains with medium- to low-resolution density maps, where DEMO-EM produced models with correct inter-domain orientations (template modeling score (TM-score) >0.5) for 97% of cases and outperformed state-of-the-art methods. DEMO-EM was applied to the severe acute respiratory syndrome coronavirus 2 genome and generated models with average TM-score and root-mean-square deviation of 0.97 and 1.3 Å, respectively, with respect to the deposited structures. These results demonstrate an efficient pipeline that enables automated and reliable large-scale multi-domain protein structure modelling from cryo-electron microscopy maps.

Accurate flexible refinement for atomic-level protein structure using cryo-EM density maps and deep learning

With the rapid progress of deep learning in cryo-electron microscopy and protein structure prediction, improving the accuracy of the protein structure model by using a density map and predicted contact/distance map through deep learning has become an urgent need for robust methods. Thus, designing an effective protein structure optimization strategy based on the density map and predicted contact/distance map is critical to improving the accuracy of structure refinement. In this article, a protein structure optimization method based on the density map and predicted contact/distance map by deep-learning technology was proposed in accordance with the result of matching between the density map and the initial model. Physics- and knowledge-based energy functions, integrated with Cryo-EM density map data and deep-learning data, were used to optimize the protein structure in the simulation. The dynamic confidence score was introduced to the iterative process for choosing whether it is a density map or a contact/distance map to dominate the movement in the simulation to improve the accuracy of refinement. The protocol was tested on a large set of 224 non-homologous membrane proteins and generated 214 structural models with correct folds, where 4.5% of structural models were generated from structural models with incorrect folds. Compared with other state-of-the-art methods, the major advantage of the proposed methods lies in the skills for using density map and contact/distance map in the simulation, as well as the new energy function in the re-assembly simulations. Overall, the results demonstrated that this strategy is a valuable approach and ready to use for atomic-level structure refinement using cryo-EM density map and predicted contact/distance map.

CR-I-TASSER: assemble protein structures from cryo-EM density maps using deep convolutional neural networks

Cryo-electron microscopy (cryo-EM) has become a leading approach for protein structure determination, but it remains challenging to accurately model atomic structures with cryo-EM density maps. We propose a hybrid method, CR-I-TASSER, which integrates deep neural-network learning with I-TASSER assembly simulations for automated cryo-EM structure determination. The method is benchmarked on 778 proteins with simulated and experimental density maps, where CR-I-TASSER constructs models with a correct fold (TM-score>0.5) for 643 targets that is 64% higher than the best of other de novo and refinement-based approaches on high-resolution data samples. Detailed data analyses showed that the major advantage of CR-I-TASSER lies in the deep-learning based Cα position prediction, which significantly improves the threading template quality and therefore boosts the accuracy of final models through optimized fragment assembly simulations. These results demonstrate a new avenue to determine cryo-EM protein structures with high accuracy and robustness covering various target types and density-map resolutions.

Three-Dimensional Graph Matching to Identify Secondary Structure Correspondence of Medium-Resolution Cryo-EM Density Maps

Cryo-electron microscopy (cryo-EM) is a structural technique that has played a significant role in protein structure determination in recent years. Compared to the traditional methods of X-ray crystallography and NMR spectroscopy, cryo-EM is capable of producing images of much larger protein complexes. However, cryo-EM reconstructions are limited to medium-resolution (~4-10 Å) for some cases. At this resolution range, a cryo-EM density map can hardly be used to directly determine the structure of proteins at atomic level resolutions, or even at their amino acid residue backbones. At such a resolution, only the position and orientation of secondary structure elements (SSEs) such as α-helices and β-sheets are observable. Consequently, finding the mapping of the secondary structures of the modeled structure (SSEs-A) to the cryo-EM map (SSEs-C) is one of the primary concerns in cryo-EM modeling. To address this issue, this study proposes a novel automatic computational method to identify SSEs correspondence in three-dimensional (3D) space. Initially, through a modeling of the target sequence with the aid of extracting highly reliable features from a generated 3D model and map, the SSEs matching problem is formulated as a 3D vector matching problem. Afterward, the 3D vector matching problem is transformed into a 3D graph matching problem. Finally, a similarity-based voting algorithm combined with the principle of least conflict (PLC) concept is developed to obtain the SSEs correspondence. To evaluate the accuracy of the method, a testing set of 25 experimental and simulated maps with a maximum of 65 SSEs is selected. Comparative studies are also conducted to demonstrate the superiority of the proposed method over some state-of-the-art techniques. The results demonstrate that the method is efficient, robust, and works well in the presence of errors in the predicted secondary structures of the cryo-EM images.

EMNUSS: a deep learning framework for secondary structure annotation in cryo-EM maps

Cryo-electron microscopy (cryo-EM) has become one of important experimental methods in structure determination. However, despite the rapid growth in the number of deposited cryo-EM maps motivated by advances in microscopy instruments and image processing algorithms, building accurate structure models for cryo-EM maps remains a challenge. Protein secondary structure information, which can be extracted from EM maps, is beneficial for cryo-EM structure modeling. Here, we present a novel secondary structure annotation framework for cryo-EM maps at both intermediate and high resolutions, named EMNUSS. EMNUSS adopts a three-dimensional (3D) nested U-net architecture to assign secondary structures for EM maps. Tested on three diverse datasets including simulated maps, middle resolution experimental maps, and high-resolution experimental maps, EMNUSS demonstrated its accuracy and robustness in identifying the secondary structures for cyro-EM maps of various resolutions. The EMNUSS program is freely available at http://huanglab.phys.hust.edu.cn/EMNUSS.

Efficient Flexible Fitting Refinement with Automatic Error Fixing for De Novo Structure Modeling from Cryo-EM Density Maps

Structural modeling of proteins from cryo-electron microscopy (cryo-EM) density maps is one of the challenging issues in structural biology. De novo modeling combined with flexible fitting refinement (FFR) has been widely used to build a structure of new proteins. In de novo prediction, artificial conformations containing local structural errors such as chirality errors, cis peptide bonds, and ring penetrations are frequently generated and cannot be easily removed in the subsequent FFR. Moreover, refinement can be significantly suppressed due to the low mobility of atoms inside the protein. To overcome these problems, we propose an efficient scheme for FFR, in which the local structural errors are fixed first, followed by FFR using an iterative simulated annealing (SA) molecular dynamics protocol with the united atom (UA) model in an implicit solvent model; we call this scheme "SAUA-FFR". The best model is selected from multiple flexible fitting runs with various biasing force constants to reduce overfitting. We apply our scheme to the decoys obtained from MAINMAST and demonstrate an improvement of the best model of eight selected proteins in terms of the root-mean-square deviation, MolProbity score, and RWplus score compared to the original scheme of MAINMAST. Fixing the local structural errors can enhance the formation of secondary structures, and the UA model enables progressive refinement compared to the all-atom model owing to its high mobility in the implicit solvent. The SAUA-FFR scheme realizes efficient and accurate protein structure modeling from medium-resolution maps with less overfitting.

Deep Learning-Based Advances in Protein Structure Prediction

Obtaining an accurate description of protein structure is a fundamental step toward understanding the underpinning of biology. Although recent advances in experimental approaches have greatly enhanced our capabilities to experimentally determine protein structures, the gap between the number of protein sequences and known protein structures is ever increasing. Computational protein structure prediction is one of the ways to fill this gap. Recently, the protein structure prediction field has witnessed a lot of advances due to Deep Learning (DL)-based approaches as evidenced by the success of AlphaFold2 in the most recent Critical Assessment of protein Structure Prediction (CASP14). In this article, we highlight important milestones and progresses in the field of protein structure prediction due to DL-based methods as observed in CASP experiments. We describe advances in various steps of protein structure prediction pipeline viz. protein contact map prediction, protein distogram prediction, protein real-valued distance prediction, and Quality Assessment/refinement. We also highlight some end-to-end DL-based approaches for protein structure prediction approaches. Additionally, as there have been some recent DL-based advances in protein structure determination using Cryo-Electron (Cryo-EM) microscopy based, we also highlight some of the important progress in the field. Finally, we provide an outlook and possible future research directions for DL-based approaches in the protein structure prediction arena.

Full-length de novo protein structure determination from cryo-EM maps using deep learning

MOTIVATION

Advances in microscopy instruments and image processing algorithms have led to an increasing number of cryo-EM maps. However, building accurate models for the EM maps at 3-5 Å resolution remains a challenging and time-consuming process. With the rapid growth of deposited EM maps, there is an increasing gap between the maps and reconstructed/modeled 3-dimensional (3D) structures. Therefore, automatic reconstruction of atomic-accuracy full-atomstructures fromEMmaps is pressingly needed.

RESULTS

We present a semi-automatic de novo structure determination method using a deep learningbased framework, named as DeepMM, which builds atomic-accuracy all-atom models from cryo-EM maps at near-atomic resolution. In our method, the main-chain and Cα positions as well as their amino acid and secondary structure types are predicted in the EM map using Densely Connected Convolutional Networks. DeepMM was extensively validated on 40 simulated maps at 5 Å resolution and 30 experimental maps at 2.6-4.8 Å resolution as well as an EMDB-wide data set of 2931 experimental maps at 2.6-4.9 Å resolution, and compared with state-of-the-art algorithms including RosettaES, MAINMAST, and Phenix. Overall, our DeepMM algorithm obtained a significant improvement over existing methods in terms of both accuracy and coverage in building full-length protein structures on all test sets, demonstrating the efficacy and general applicability of DeepMM.

AVAILABILITY

http://huanglab.phys.hust.edu.cn/DeepMM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Fitting Low-Resolution Protein Structures into Cryo-EM Density Maps by Multiobjective Optimization of Global and Local Correlations

The rigid-body fitting of predicted structural models into cryo-electron microscopy (cryo-EM) density maps is a necessary procedure for density map-guided protein structure determination and prediction. We proposed a novel multiobjective optimization protocol, MOFIT, which performs a rigid-body density-map fitting based on particle swarm optimization (PSO). MOFIT was tested on a large set of 292 nonhomologous single-domain proteins. Starting from structural models predicted by I-TASSER, MOFIT achieved an average coordinate root-mean-square deviation of 2.46 Å, which was 1.57, 2.79, and 3.95 Å lower than three leading single-objective function-based methods, where the differences were statistically significant with p-values of 1.65 × 10^-6, 6.36 × 10^-8, and 6.44 × 10^-11 calculated using two-tail Student's t tests. Detailed analyses showed that the major advantages of MOFIT lie in the multiobjective protocol and the extensive PSO search simulations guided by the composite objective functions, which integrates complementary correlation coefficients from the global structure, local fragments, and individual residues with the cryo-EM density maps.

Progressive and accurate assembly of multi-domain protein structures from cryo-EM density maps

Progress in cryo-electron microscopy (cryo-EM) has provided the potential for large-size protein structure determination. However, the solution rate for multi-domain proteins remains low due to the difficulty in modeling inter-domain orientations. We developed DEMO-EM, an automatic method to assemble multi-domain structures from cryo-EM maps through a progressive structural refinement procedure combining rigid-body domain fitting and flexible assembly simulations with deep neural network inter-domain distance profiles. The method was tested on a large-scale benchmark set of proteins containing up to twelve continuous and discontinuous domains with medium-to-low-resolution density maps, where DEMO-EM produced models with correct inter-domain orientations (TM-score >0.5) for 98% of cases and significantly outperformed the state-of-the-art methods. DEMO-EM was applied to SARS-Cov-2 coronavirus genome and generated models with average TM-score/RMSD of 0.97/1.4Å to the deposited structures. These results demonstrated an efficient pipeline that enables automated and reliable large-scale multi-domain protein structure modeling with atomic-level accuracy from cryo-EM maps.