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

Biomolecules ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1773
Author(s):  
Bahareh Behkamal ◽  
Mahmoud Naghibzadeh ◽  
Mohammad Reza Saberi ◽  
Zeinab Amiri Tehranizadeh ◽  
Andrea Pagnani ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Graph Matching ◽  
Protein Complexes ◽  
Three Dimensional ◽  
Protein Structure Determination ◽  
Secondary Structures ◽  
Computational Method ◽  
Target Sequence ◽  
Matching Problem ◽  
Medium Resolution

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.

scholarly journals Structural relation matching: an algorithm to identify structural patterns into RNAs and their interactions

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Michela Quadrini
Keyword(s):  
Hydrogen Bonding ◽  
Secondary Structure ◽  
Nucleotide Sequence ◽  
Thermus Thermophilus ◽  
Three Dimensional ◽  
Secondary Structures ◽  
Structural Effect ◽  
Biological Processes ◽  
Structural Pattern ◽  
Rna Molecules

Abstract RNA molecules play crucial roles in various biological processes. Their three-dimensional configurations determine the functions and, in turn, influences the interaction with other molecules. RNAs and their interaction structures, the so-called RNA–RNA interactions, can be abstracted in terms of secondary structures, i.e., a list of the nucleotide bases paired by hydrogen bonding within its nucleotide sequence. Each secondary structure, in turn, can be abstracted into cores and shadows. Both are determined by collapsing nucleotides and arcs properly. We formalize all of these abstractions as arc diagrams, whose arcs determine loops. A secondary structure, represented by an arc diagram, is pseudoknot-free if its arc diagram does not present any crossing among arcs otherwise, it is said pseudoknotted. In this study, we face the problem of identifying a given structural pattern into secondary structures or the associated cores or shadow of both RNAs and RNA–RNA interactions, characterized by arbitrary pseudoknots. These abstractions are mapped into a matrix, whose elements represent the relations among loops. Therefore, we face the problem of taking advantage of matrices and submatrices. The algorithms, implemented in Python, work in polynomial time. We test our approach on a set of 16S ribosomal RNAs with inhibitors of Thermus thermophilus, and we quantify the structural effect of the inhibitors.

scholarly journals Analysis of Three-Dimensional Protein Images

1997 ◽  
Vol 7 ◽  
pp. 125-159 ◽  
Author(s):  
L. Leherte ◽  
J. Glasgow ◽  
K. Baxter ◽  
E. Steeg ◽  
S. Fortier
Keyword(s):  
Protein Structure ◽  
Structure Prediction ◽  
Three Dimensional ◽  
Protein Structure Determination ◽  
Beta Sheets ◽  
Scene Analysis ◽  
Certainty Factor ◽  
Alpha Helices ◽  
Medium Resolution ◽  
Fundamental Goal

A fundamental goal of research in molecular biology is to understand protein structure. Protein crystallography is currently the most successful method for determining the three-dimensional (3D) conformation of a protein, yet it remains labor intensive and relies on an expert's ability to derive and evaluate a protein scene model. In this paper, the problem of protein structure determination is formulated as an exercise in scene analysis. A computational methodology is presented in which a 3D image of a protein is segmented into a graph of critical points. Bayesian and certainty factor approaches are described and used to analyze critical point graphs and identify meaningful substructures, such as alpha-helices and beta-sheets. Results of applying the methodologies to protein images at low and medium resolution are reported. The research is related to approaches to representation, segmentation and classification in vision, as well as to top-down approaches to protein structure prediction.

FTIR Analysis of Conformational Changes in the Secondary Structure of Ovalbumin: Effect of pH and Cosolvent Sugar-free Natura

2020 ◽  
Vol 8 (1) ◽  
pp. 78-83
Author(s):  
P. Agalya ◽  
◽  
V. Velusamy
Keyword(s):  
Secondary Structure ◽  
Conformational Changes ◽  
Three Dimensional ◽  
Protein Secondary Structure ◽  
Secondary Structures ◽  
Structural Elements ◽  
Random Coils ◽  
Protein Secondary Structures ◽  
Derivative Analysis ◽  
Influence Of Ph

a-helix, þ-sheet, þ-turns, and random coils are the three-dimensional local segments that constitute a protein secondary structure. Molecular vibrations of proteins are sensitive to structural organizations of peptide chains hence Fourier Transform infrared (FTIR) spectroscopy is one of the recognized techniques for the identification of protein secondary structures. However, the lower frequency region of FTIR especially the amide VI bands (in the region 590-490cm-1) is little studied for proteins. Further, the effect of sugar-free natura on ovalbumin stability is not yet studied to our knowledge. The present study examines the conformational changes in the secondary structure of ovalbumin (OVA) protein under the influence of pH variations (2, 5, 7, 9, and 12) and also cosolvent sugar-free Natura (SFN) inclusion. From the primary absorption spectra of the amide VI bands, the second derivative analysis is furnished to quantify the secondary structural elements of protein thereby conformational changes are analyzed. From obtained results, it is found that conformational changes occur between two major secondary structures of a-helix and þ-sheet of OVA due to variation of pH and inclusion of cosolvent. Also, the results confirm that the denaturation of OVA in the presence of SFN irrespective of pH.

scholarly journals TMPSS: A Deep Learning-Based Predictor for Secondary Structure and Topology Structure Prediction of Alpha-Helical Transmembrane Proteins

2021 ◽  
Vol 8 ◽  
Author(s):  
Zhe Liu ◽  
Yingli Gong ◽  
Yihang Bao ◽  
Yuanzhao Guo ◽  
Han Wang ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Transmembrane Proteins ◽  
Secondary Structures ◽  
Computational Method ◽  
Transmembrane Region ◽  
Long Distance ◽  
Topology Structure ◽  
And Topology

Alpha transmembrane proteins (αTMPs) profoundly affect many critical biological processes and are major drug targets due to their pivotal protein functions. At present, even though the non-transmembrane secondary structures are highly relevant to the biological functions of αTMPs along with their transmembrane structures, they have not been unified to be studied yet. In this study, we present a novel computational method, TMPSS, to predict the secondary structures in non-transmembrane parts and the topology structures in transmembrane parts of αTMPs. TMPSS applied a Convolutional Neural Network (CNN), combined with an attention-enhanced Bidirectional Long Short-Term Memory (BiLSTM) network, to extract the local contexts and long-distance interdependencies from primary sequences. In addition, a multi-task learning strategy was used to predict the secondary structures and the transmembrane helixes. TMPSS was thoroughly trained and tested against a non-redundant independent dataset, where the Q3 secondary structure prediction accuracy achieved 78% in the non-transmembrane region, and the accuracy of the transmembrane region prediction achieved 90%. In sum, our method showcased a unified model for predicting the secondary structure and topology structure of αTMPs by only utilizing features generated from primary sequences and provided a steady and fast prediction, which promisingly improves the structural studies on αTMPs.

scholarly journals Combining Cryo-EM Density Map and Residue Contact for Protein Secondary Structure Topologies

Molecules ◽  
2021 ◽  
Vol 26 (22) ◽  
pp. 7049
Author(s):  
Maytha Alshammari ◽  
Jing He
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Dimensional Space ◽  
Protein A ◽  
Three Dimensional ◽  
Protein Secondary Structure ◽  
Secondary Structures ◽  
Sources Of Information ◽  
Density Map

Although atomic structures have been determined directly from cryo-EM density maps with high resolutions, current structure determination methods for medium resolution (5 to 10 Å) cryo-EM maps are limited by the availability of structure templates. Secondary structure traces are lines detected from a cryo-EM density map for α-helices and β-strands of a protein. A topology of secondary structures defines the mapping between a set of sequence segments and a set of traces of secondary structures in three-dimensional space. In order to enhance accuracy in ranking secondary structure topologies, we explored a method that combines three sources of information: a set of sequence segments in 1D, a set of amino acid contact pairs in 2D, and a set of traces in 3D at the secondary structure level. A test of fourteen cases shows that the accuracy of predicted secondary structures is critical for deriving topologies. The use of significant long-range contact pairs is most effective at enriching the rank of the maximum-match topology for proteins with a large number of secondary structures, if the secondary structure prediction is fairly accurate. It was observed that the enrichment depends on the quality of initial topology candidates in this approach. We provide detailed analysis in various cases to show the potential and challenge when combining three sources of information.

scholarly journals Improving integrative 3D modeling into low- to medium- resolution EM structures with evolutionary couplings

2021 ◽  
Author(s):  
Caitlyn L. McCafferty ◽  
David W. Taylor ◽  
Edward M. Marcotte
Keyword(s):  
Conformational Changes ◽  
Spatial Data ◽  
Sequence Data ◽  
Protein Complexes ◽  
Three Dimensional ◽  
Distance Restraint ◽  
Evolutionary Information ◽  
Additional Information ◽  
Integrative Modeling ◽  
Medium Resolution

AbstractElectron microscopy (EM) continues to provide near-atomic resolution structures for well-behaved proteins and protein complexes. Unfortunately, structures of some complexes are limited to low- to medium-resolution due to biochemical or conformational heterogeneity. Thus, the application of unbiased systematic methods for fitting individual structures into EM maps is important. A method that employs co-evolutionary information obtained solely from sequence data could prove invaluable for quick, confident localization of subunits within these structures. Here, we incorporate the co-evolution of intermolecular amino acids as a new type of distance restraint in the Integrative Modeling Platform (IMP) in order to build three-dimensional models of atomic structures into EM maps ranging from 10-14 Å in resolution. We validate this method using four complexes of known structure, where we highlight the conservation of intermolecular couplings despite dynamic conformational changes using the BAM complex. Finally, we use this method to assemble the subunits of the bacterial holo-translocon into a model that agrees with previous biochemical data. The use of evolutionary couplings in integrative modeling improves systematic, unbiased fitting of atomic models into medium- to low-resolution EM maps, providing additional information to integrative models lacking in spatial data.

Searching techniques for databases of protein secondary structures

1989 ◽  
Vol 15 (4-5) ◽  
pp. 287-298 ◽  
Author(s):  
Peter J. Artymiuk ◽  
David W. Rice ◽  
Eleanor M. Mitchell ◽  
Peter Willett
Keyword(s):  
Secondary Structure ◽  
Protein Data Bank ◽  
Protein Structures ◽  
Three Dimensional ◽  
Data Bank ◽  
Secondary Structures ◽  
British Library ◽  
Geometric Information ◽  
Protein Secondary Structures ◽  
Funded Research Project

This paper summarizes the findings of a recent, British Library-funded research project into computer techniques for searching the three-dimensional protein structures that occur in the Protein Data Bank. The work focuses on the secondary structures of proteins and utilizes both angular and distance geometric information. Algorithms are presented for the auto matic identification of secondary structure elements, of sec ondary structure motifs and of proteins with similar secondary structures.

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