Backbone Traces Research Articles

DiffModeler: large macromolecular structure modeling for cryo-EM maps using a diffusion model.

Cryogenic electron microscopy (cryo-EM) has now been widely used for determining multichain protein complexes. However, modeling a large complex structure, such as those with more than ten chains, is challenging, particularly when the map resolution decreases. Here we present DiffModeler, a fully automated method for modeling large protein complex structures. DiffModeler employs a diffusion model for backbone tracing and integrates AlphaFold2-predicted single-chain structures for structure fitting. DiffModeler showed an average template modeling score of 0.88 and 0.91 for two datasets of cryo-EM maps of 0-5 Å resolution and 0.92 for intermediate resolution maps (5-10 Å), substantially outperforming existing methodologies. Further benchmarking at low resolutions (10-20 Å) confirms its versatility, demonstrating plausible performance.

All-atom RNA structure determination from cryo-EM maps.

Many methods exist for determining protein structures from cryogenic electron microscopy maps, but this remains challenging for RNA structures. Here we developed EMRNA, a method for accurate, automated determination of full-length all-atom RNA structures from cryogenic electron microscopy maps. EMRNA integrates deep learning-based detection of nucleotides, three-dimensional backbone tracing and scoring with consideration of sequence and secondary structure information, and full-atom construction of the RNA structure. We validated EMRNA on 140 diverse RNA maps ranging from 37 to 423 nt at 2.0-6.0 Å resolutions, and compared EMRNA with auto-DRRAFTER, phenix.map_to_model and CryoREAD on a set of 71 cases. EMRNA achieves a median accuracy of 2.36 Å root mean square deviation and 0.86 TM-score for full-length RNA structures, compared with 6.66 Å and 0.58 for auto-DRRAFTER. EMRNA also obtains a high residue coverage and sequence match of 93.30% and 95.30% in the built models, compared with 58.20% and 42.20% for phenix.map_to_model and 56.45% and 52.3% for CryoREAD. EMRNA is fast and can build an RNA structure of 100 nt within 3 min.

Structural and Biochemical Studies of the Novel Hexameric Endoribonuclease YicC.

The decay of mRNA is an essential process to bacteria. The newly identified E. coli protein YicC is a founding member of the UPF0701 family, and biochemical studies indicated that it is an RNase involved in mRNA degradation. However, its biochemical properties and catalytic mechanism are poorly understood. Here, we report the crystal structure of YicC, which shows an extended shape consisting of modular domains. While the backbone trace of the monomer forms a unique, nearly closed loop, the three monomers present in the asymmetric unit make a "shoulder-by-shoulder" trimer. In vitro RNA cleavage assays indicated that this endoribonuclease mainly recognizes the consensus GUG motif, with a preference for an extended CGUG sequence. Additionally, the active enzyme exists as a hexamer in solution and assumes a funnel shape. Structural analysis indicated that the hexamer interface is mainly formed by the hexamerization domain consisting of D71-D124 and that the disruption of the oligomeric form greatly diminished the enzymatic activity. By studying the surface charge potential and the sequence conservation, we identified a series of residues that play critical functional roles, which helps to reveal the catalytic mechanism of this divalent metal-ion-dependent RNase. Last but not least, we discovered that the catalytic domain of YicC did not share similarity with any known nuclease fold, suggesting that the enzyme adopts a novel fold to perform its catalysis and in vivo functions. In summary, our investigations into YicC provide an in-depth understanding of the functions of the UPF0701 protein family and the DUF1732 domain in general.

Buccaneer model building with neural network fragment selection.

Tracing the backbone is a critical step in protein model building, as incorrect tracing leads to poor protein models. Here, a neural network trained to identify unfavourable fragments and remove them from the model-building process in order to improve backbone tracing is presented. Moreover, a decision tree was trained to select an optimal threshold to eliminate unfavourable fragments. The neural network was tested on experimental phasing data sets from the Joint Center for Structural Genomics (JCSG), recently deposited experimental phasing data sets (from 2015 to 2021) and molecular-replacement data sets. The experimental results show that using the neural network in the Buccaneer protein-model-building software can produce significantly more complete protein models than those built using Buccaneer alone. In particular, Buccaneer with the neural network built protein models with a completeness that was at least 5% higher for 25% and 50% of the original and truncated resolution JCSG experimental phasing data sets, respectively, for 28% of the recently collected experimental phasing data sets and for 43% of the molecular-replacement data sets.

Building Protein Atomic Models from Cryo-EM Density Maps and Residue Co-Evolution.

Electron cryo-microscopy (cryo-EM) has emerged as a powerful method by which to obtain three-dimensional (3D) structures of macromolecular complexes at atomic or near-atomic resolution. However, de novo building of atomic models from near-atomic resolution (3–5 Å) cryo-EM density maps is a challenging task, in particular because poorly resolved side-chain densities hamper sequence assignment by automatic procedures at a lower resolution. Furthermore, segmentation of EM density maps into individual subunits remains a difficult problem when the structure of the subunits is not known, or when significant conformational rearrangement occurs between the isolated and associated form of the subunits. To tackle these issues, we have developed a graph-based method to thread most of the C- trace of the protein backbone into the EM density map. The EM density is described as a weighted graph such that the resulting minimum spanning tree encompasses the high-density regions of the map. A pruning algorithm cleans the tree and finds the most probable positions of the C- atoms, by using side-chain density when available, as a collection of C- trace fragments. By complementing experimental EM maps with contact predictions from sequence co-evolutionary information, we demonstrate that this approach can correctly segment EM maps into individual subunits and assign amino acid sequences to backbone traces to generate atomic models.

Computational models in the service of X-ray and cryo-electron microscopy structure determination.

Critical assessment of structure prediction (CASP) conducts community experiments to determine the state of the art in computing protein structure from amino acid sequence. The process relies on the experimental community providing information about not yet public or about to be solved structures, for use as targets. For some targets, the experimental structure is not solved in time for use in CASP. Calculated structure accuracy improved dramatically in this round, implying that models should now be much more useful for resolving many sorts of experimental difficulties. To test this, selected models for seven unsolved targets were provided to the experimental groups. These models were from the AlphaFold2 group, who overall submitted the most accurate predictions in CASP14. Four targets were solved with the aid of the models, and, additionally, the structure of an already solved target was improved. An a posteriori analysis showed that, in some cases, models from other groups would also be effective. This paper provides accounts of the successful application of models to structure determination, including molecular replacement for X-ray crystallography, backbone tracing and sequence positioning in a cryo-electron microscopy structure, and correction of local features. The results suggest that, in future, there will be greatly increased synergy between computational and experimental approaches to structure determination.

Cryo-EM Map-Based Model Validation Using the False Discovery Rate Approach.

Significant technological developments and increasing scientific interest in cryogenic electron microscopy (cryo-EM) has resulted in a rapid increase in the amount of data generated by these experiments and the derived atomic models. Robust measures for the validation of 3D reconstructions and atomic models are essential for appropriate interpretation of the data. The resolution of data and availability of software tools that work across a range of resolutions often limit the quality of derived models. Hence, the final atomic model is often incomplete or contains regions where atomic positions are less reliable or incorrectly built. Extensive manual pruning and local adjustments or rebuilding are usually required to address these issues. The presented research introduces a software tool for the validation of the backbone trace of atomic models built in the cryo-EM density maps. In this study, we use the false discovery rate analysis, which can be used to segregate molecular signals from the background. Each atomic position in the model can be associated with an FDR backbone validation score, which can be used to identify potential mistraced residues. We demonstrate that the proposed validation score is complementary to existing validation metrics and is useful especially in cases where the model is built in the maps having varying local resolution. We also discuss the application of the score for automated pruning of atomic models built ab-initio during the iterative model building process in Buccaneer. We have implemented this score in the CCP-EM software suite.

Acquired Functional Capsid Structures in Metazoan Totivirus-like dsRNA Virus

Non-enveloped icosahedral double-stranded RNA (dsRNA) viruses possess multifunctional capsids required for their proliferation. Whereas protozoan/fungal dsRNA viruses have a relatively simple capsid structure, which suffices for the intracellular phase in their life cycle, metazoan dsRNA viruses have acquired additional structural features as an adaptation for extracellular cell-to-cell transmission in multicellular hosts. Here, we present the first atomic model of a metazoan dsRNA totivirus-like virus and the structure reveals three unique structural traits: a C-terminal interlocking arm, surface projecting loops, and an obstruction at the pore on the 5-fold symmetry axis. These traits are keys to understanding the capsid functions of metazoan dsRNA viruses, such as particle stability and formation, cell entry, and endogenous intraparticle transcription of mRNA. On the basis of molecular dynamics simulations of the obstructed pore, we propose a possible mechanism of intraparticle transcription in totivirus-like viruses, which dynamically switches between open and closed states of the pore(s).

Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps

Cryo-electron microscopy (cryo-EM) has become a leading technology for determining protein structures. Recent advances in this field have allowed for atomic resolution. However, predicting the backbone trace of a protein has remained a challenge on all but the most pristine density maps (<2.5 Å resolution). Here we introduce a deep learning model that uses a set of cascaded convolutional neural networks (CNNs) to predict Cα atoms along a protein’s backbone structure. The cascaded-CNN (C-CNN) is a novel deep learning architecture comprised of multiple CNNs, each predicting a specific aspect of a protein’s structure. This model predicts secondary structure elements (SSEs), backbone structure, and Cα atoms, combining the results of each to produce a complete prediction map. The cascaded-CNN is a semantic segmentation image classifier and was trained using thousands of simulated density maps. This method is largely automatic and only requires a recommended threshold value for each protein density map. A specialized tabu-search path walking algorithm was used to produce an initial backbone trace with Cα placements. A helix-refinement algorithm made further improvements to the α-helix SSEs of the backbone trace. Finally, a novel quality assessment-based combinatorial algorithm was used to effectively map protein sequences onto Cα traces to obtain full-atom protein structures. This method was tested on 50 experimental maps between 2.6 Å and 4.4 Å resolution. It outperformed several state-of-the-art prediction methods including Rosetta de-novo, MAINMAST, and a Phenix based method by producing the most complete predicted protein structures, as measured by percentage of found Cα atoms. This method accurately predicted 88.9% (mean) of the Cα atoms within 3 Å of a protein’s backbone structure surpassing the 66.8% mark achieved by the leading alternate method (Phenix based fully automatic method) on the same set of density maps. The C-CNN also achieved an average root-mean-square deviation (RMSD) of 1.24 Å on a set of 50 experimental density maps which was tested by the Phenix based fully automatic method. The source code and demo of this research has been published at https://github.com/DrDongSi/Ca-Backbone-Prediction.

Structural and biophysical properties of RIG-I bound to dsRNA with G-U wobble base pairs

ABSTRACTRetinoic acid-inducible gene I (RIG-I) is responsible for innate immunity via the recognition of short double-stranded RNAs in the cytosol. With the clue that G-U wobble base pairs in the influenza A virus’s RNA promoter region are responsible for RIG-I activation, we determined the complex structure of RIG-I ΔCARD and a short hairpin RNA with G-U wobble base pairs by X-ray crystallography. Interestingly, the overall helical backbone trace was not affected by the presence of the wobble base pairs; however, the base pair inclination and helical axis angle changed upon RIG-I binding. NMR spectroscopy revealed that RIG-I binding renders the flexible base pair of the influenza A virus’s RNA promoter region between the two G-U wobble base pairs even more flexible. Binding to RNA with wobble base pairs resulted in a more flexible RIG-I complex. This flexible complex formation correlates with the entropy-favoured binding of RIG-I and RNA, which results in tighter binding affinity and RIG-I activation. This study suggests that the structure and dynamics of RIG-I are tailored to the binding of specific RNA sequences with different flexibility.

MAINMAST-MELD-MDFF: Denovo Structure-Determination with Data-Guided Molecular Dynamics

The past three decades of hybrid modeling has seen molecular dynamics (MD) simulations leverage NMR, X-ray crystallography and cryo EM data for the determination of medium- to high-resolution structures atomic structures. However, all these approaches, including our popular Molecular Dynamics Flexible Fitting (MDFF), and its various extensions work under the conventional molecular replacement paradigm, whereby any initial search model is morphed to satiate the data-imposed constraints. As a natural consequence, quality of the determined model remains heavily biased by choices of the initial model. Here, we deliver a novel modeling pipeline that iteratively combines minimum spanning tree-based backbone tracing tool (MAINMAST), Bayesian-likelihood based protein-folding methodology (MELD), and a resolution exchange-based fitting protocol (ReMDFF). Starting from only sequence information, the algorithm places C-alpha atoms into the density, fits a random coil to this C-alpha trace, generates protein secondary structures on-the-fly, and exhaustively samples the backbone and side chain geometries to deliver a refined model. Overcoming limitations of traditional approaches, the need for an initial model or homology information is completely subsided, and de novo modeling is now made feasible even at low resolutions. Available on cloud computing resources, the method is equally applicable for determining globular and transmembrane protein structures, as demonstrated for ubiquitin, TRPV1 and Magnesium-channel CorA (all in the 3-5 Å resolution range), and the de novo atomic structure of a lipoprotein flp3.

Flow Caching in Protocol Oblivious Forwarding Switches

Protocol Oblivious Forwarding (POF) supports the arbitrary protocol processing, enhancing the programmability of Software Defined Networking (SDN). In order to improve the forwarding performance, a flow caching method is proposed. To parse the packet in advance, absolute positions of matching fields are obtained by identifying the dependency of matching and actions. To guarantee the acceleration effect of flow caching, flow tables are selected according to their matching types and number of entries. In addition, the single-flow table cache and multi-flow table cache are compared and an adaptive switching strategy is proposed based on the actual situation of network traffic. The POFSwitch is extended to implement the proposed method and it is validated under the real rules and backbone traces. The switch packet forwarding rate is increased by 220% after applying flow caching. Flow caching can provide higher forwarding performance for programmable data planes.

Analysis of Optical Backbone Fiber and Trace Report of Break Fiber by Using Optical Time Domain Reflectometer

Abstract Now a day all the telecommunication industries are using fibers for faster data communication and long distance communication. Fibers use light as the information and it is carried over long distances. Optical fiber has large advantages over a copper and co-axial cable because of its lower attenuation and interference. Optical time domain reflectometer (OTDR) is also one of the important devices using in telecommunication industries to characterize an optical fiber. This article deals with the study of long distance backbone single mode fiber and their features are discussed with the use of an OTDR. The results of this article focused on the losses and breaks of the fiber with the events. The Single mode OTDR is used to find the events, losses and breaks of the backbone fiber between the two locations. This work will give a way to study the nature of optical fibers and understand the practical application of optical fiber communication.

Internet traffic characterisation: Third-order statistics & higher-order spectra for precise traffic modelling

Undoubtedly, the characterisation of network traffic flows is vitally important in understanding the dynamics of Internet traffic and in appropriately dimensioning network resources for network and systems management. The vast majority of modelling techniques developed for volume-based traffic profiling (based on packet and/byte counts) imply the statistical assumptions of stationarity, Gaussianity and linearity, which are often taken for granted without being explicitly validated. In this paper, we demonstrate that such properties are often not applicable due to the high fluctuations in Internet traffic, and should therefore be validated first before they are assumed. We employ Time-Frequency (TF) representations and the Hinich algorithms for validating these three modelling assumptions on real backbone and edge network traces. We show by conducting a passive, offline statistical analysis on real operational network traffic traces from both backbone and edge links that link traffic is extremely dynamic irrespective of the level of aggregation and that model characteristics vary. Subsequently, we propose the use of a representative of higher order spectra, the bispectrum, to act as a particularly suitable method for volume-based traffic profiling due to its ability to adapt to different underlying statistical assumptions, as opposed to ARIMA timeseries models that have been typically used in the literature. We demonstrate that the bispectrum, a signal processing tool that has so far been used in the area of image processing and acoustic signals, can be exploited to accurately characterise traffic volumes per transport protocol, and can therefore contribute to fine-grained network operations tasks such as application classification and anomaly detection.

Online Adaptive Anomaly Detection for Augmented Network Flows

Traditional network anomaly detection involves developing models that rely on packet inspection. However, increasing network speeds and use of encrypted protocols make per-packet inspection unsuited for today’s networks. One method of overcoming this obstacle is aggregating packet header information and performing flow-based analysis where data flow patterns are examined rather than deep packet inspection. Many existing approaches are special purpose limited to detecting specific behavior. Also, the data reduction inherent in identifying anomalous flows hinders alert correlation. In this article, we propose and develop a dynamic anomaly detection approach for augmented network flows. We sketch network state during flow creation, enabling general-purpose threat detection. We describe an efficient flow augmentation approach based on the count-min sketch that provides per-flow-, per-node-, and per-network-level statistics parallel to flow record generation. We design and develop a support vector machine-based adaptive anomaly detection and correlation mechanism, which is capable of aggregating alerts without a priori alert classification and evolving models online. We further develop a lightweight evolving alert aggregation method and combine it with a confidence forwarding mechanism identifying a small percentage predictions for additional processing. We show effectiveness of our methods on both enterprise and backbone traces. Experimental results demonstrate its ability to maintain high accuracy without the need for offline training.

De Novo modeling in cryo-EM density maps with Pathwalking.

As electron cryo-microscopy (cryo-EM) can now frequently achieve near atomic resolution, accurate interpretation of these density maps in terms of atomistic detail has become paramount in deciphering macromolecular structure and function. However, there are few software tools for modeling protein structure from cryo-EM density maps in this resolution range. Here, we present an extension of our original Pathwalking protocol, which can automatically trace a protein backbone directly from a near-atomic resolution (3-6Å) density map. The original Pathwalking approach utilized a Traveling Salesman Problem solver for backbone tracing, but manual adjustment was still required during modeling. In the new version, human intervention is minimized and we provide a more robust approach for backbone modeling. This includes iterative secondary structure identification, termini detection and the ability to model multiple subunits without prior segmentation. Overall, the new Pathwalking procedure provides a more complete and robust tool for annotating protein structure function in near-atomic resolution density maps.

Maximizing the Throughput of Hash Tables in Network Devices with Combined SRAM/DRAM Memory

Hash tables form a core component of many algorithms as well as network devices. Because of their large size, they often require a combined memory model, in which some of the elements are stored in a fast memory (for example, cache or on-chip SRAM) while others are stored in much slower memory (namely, the main memory or off-chip DRAM). This makes the implementation of real-life hash tables particularly delicate, as a suboptimal choice of the hashing scheme parameters may result in a higher average query time, and therefore in a lower throughput. In this paper, we focus on multiple-choice hash tables. Given the number of choices, we study the tradeoff between the load of a hash table and its average lookup time. The problem is solved by analyzing an equivalent problem: the expected maximum matching size of a random bipartite graph with a fixed left-side vertex degree. Given two choices, we provide exact results for any finite system, and also deduce asymptotic results as the fast memory size increases. In addition, we further consider other variants of this problem and model the impact of several parameters. Finally, we evaluate the performance of our models on Internet backbone traces, and illustrate the impact of the memories speed difference on the choice of parameters. In particular, we show that the common intuition of entirely avoiding slow memory accesses by using highly efficient schemes (namely, with many fast-memory choices) is not always optimal.

Identifying elephant flows in internet backbone traffic with bloom filters and LRU

Traffic measurements provide critical input for network security, traffic engineering and accounting. Restrained capabilities of computing and storage have motivated recent researches on partial flow maintenance like identifying elephant flows. Considering high false negative probability of traditional algorithms, a novel scheme called BF–LRU (Bloom filters and least recent used) is presented. Our BF–LRU scheme adopts LRU replacement to evict mice flows and Bloom filters representation to conserve heavy hitters. Based on Pareto and hypergeometric distribution, expressions of upper-bound error probability are analyzed in detail. Experiments are conducted based on real Internet traffic data. Simulation results indicate that BF–LRU can not only achieve lower error probability, but also scale up to OC-768 backbone trace without losing any space efficiency.

Protruding knob-like proteins violate local symmetries in an icosahedral marine virus.

Marine viruses play crucial roles in shaping the dynamics of oceanic microbial communities and in the carbon cycle on Earth. Here we report a 4.7-Å structure of a cyanobacterial virus, Syn5, by electron cryo-microscopy and modelling. A Cα backbone trace of the major capsid protein (gp39) reveals a classic phage protein fold. In addition, two knob-like proteins protruding from the capsid surface are also observed. Using bioinformatics and structure analysis tools, these proteins are identified to correspond to gp55 and gp58 (each with two copies per asymmetric unit). The non 1:1 stoichiometric distribution of gp55/58 to gp39 breaks all expected local symmetries and leads to non-quasi-equivalence of the capsid subunits, suggesting a role in capsid stabilization. Such a structural arrangement has not yet been observed in any known virus structures.

Protein structure modeling for CASP10 by multiple layers of global optimization

In the template-based modeling (TBM) category of CASP10 experiment, we introduced a new protocol called protein modeling system (PMS) to generate accurate protein structures in terms of side-chains as well as backbone trace. In the new protocol, a global optimization algorithm, called conformational space annealing (CSA), is applied to the three layers of TBM procedure: multiple sequence-structure alignment, 3D chain building, and side-chain re-modeling. For 3D chain building, we developed a new energy function which includes new distance restraint terms of Lorentzian type (derived from multiple templates), and new energy terms that combine (physical) energy terms such as dynamic fragment assembly (DFA) energy, DFIRE statistical potential energy, hydrogen bonding term, etc. These physical energy terms are expected to guide the structure modeling especially for loop regions where no template structures are available. In addition, we developed a new quality assessment method based on random forest machine learning algorithm to screen templates, multiple alignments, and final models. For TBM targets of CASP10, we find that, due to the combination of three stages of CSA global optimizations and quality assessment, the modeling accuracy of PMS improves at each additional stage of the protocol. It is especially noteworthy that the side-chains of the final PMS models are far more accurate than the models in the intermediate steps.