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.

scholarly journals Analytical tools in protein structure determination

2021 ◽  
Vol 8 (3) ◽  
pp. 103-111
Author(s):  
Krishna R Gupta ◽  
Uttam Patle ◽  
Uma Kabra ◽  
P. Mishra ◽  
Milind J Umekar
Keyword(s):  
Protein Structure ◽  
Structure Determination ◽  
Structure Prediction ◽  
Protein Structures ◽  
Three Dimensional ◽  
Protein Structure Determination ◽  
Computational Techniques ◽  
Determination Process ◽  
Structure Databases ◽  
Analytical Tools

Three-dimensional protein structure prediction from amino acid sequence has been a thought-provoking task for decades, but it of pivotal importance as it provides a better understanding of its function. In recent years, the methods for prediction of protein structures have advanced considerably. Computational techniques and increase in protein sequence and structure databases have influence the laborious protein structure determination process. Still there is no single method which can predict all the protein structures. In this review, we describe the four stages of protein structure determination. We have also explored the currenttechniques used to uncover the protein structure and highpoint best suitable method for a given protein.

APL: An angle probability list to improve knowledge-based metaheuristics for the three-dimensional protein structure prediction

2015 ◽  
Vol 59 ◽  
pp. 142-157 ◽  
Author(s):  
Bruno Borguesan ◽  
Mariel Barbachan e Silva ◽  
Bruno Grisci ◽  
Mario Inostroza-Ponta ◽  
Márcio Dorn
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Three Dimensional ◽  
Knowledge Based

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.

Prediction of Structural and Functional Aspects of Protein

2014 ◽  
pp. 317-333
Author(s):  
Arun G. Ingale
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Protein Structures ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Sequence Information ◽  
Predict Protein Structure ◽  
Basic Ideas

To predict the structure of protein from a primary amino acid sequence is computationally difficult. An investigation of the methods and algorithms used to predict protein structure and a thorough knowledge of the function and structure of proteins are critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this chapter sheds light on the methods used for protein structure prediction. This chapter covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, it presents an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction, giving unique insight into the future applications of the modeled protein structures. In this chapter, current protein structure prediction methods are reviewed for a milieu on structure prediction, the prediction of structural fundamentals, tertiary structure prediction, and functional imminent. The basic ideas and advances of these directions are discussed in detail.

scholarly journals Protein Structure Determination in Living Cells

2019 ◽  
Vol 20 (10) ◽  
pp. 2442 ◽  
Author(s):  
Teppei Ikeya ◽  
Peter Güntert ◽  
Yutaka Ito
Keyword(s):  
Protein Structure ◽  
Structure Determination ◽  
Structure Prediction ◽  
Structural Information ◽  
Nuclear Overhauser Effect ◽  
Protein Structures ◽  
Three Dimensional ◽  
Structural Data ◽  
Sample Tube ◽  
In Cells

To date, in-cell NMR has elucidated various aspects of protein behaviour by associating structures in physiological conditions. Meanwhile, current studies of this method mostly have deduced protein states in cells exclusively based on ‘indirect’ structural information from peak patterns and chemical shift changes but not ‘direct’ data explicitly including interatomic distances and angles. To fully understand the functions and physical properties of proteins inside cells, it is indispensable to obtain explicit structural data or determine three-dimensional (3D) structures of proteins in cells. Whilst the short lifetime of cells in a sample tube, low sample concentrations, and massive background signals make it difficult to observe NMR signals from proteins inside cells, several methodological advances help to overcome the problems. Paramagnetic effects have an outstanding potential for in-cell structural analysis. The combination of a limited amount of experimental in-cell data with software for ab initio protein structure prediction opens an avenue to visualise 3D protein structures inside cells. Conventional nuclear Overhauser effect spectroscopy (NOESY)-based structure determination is advantageous to elucidate the conformations of side-chain atoms of proteins as well as global structures. In this article, we review current progress for the structure analysis of proteins in living systems and discuss the feasibility of its future works.

scholarly journals EM-Fold: De Novo Atomic-Detail Protein Structure Determination from Medium-Resolution Density Maps

Structure ◽  
2012 ◽  
Vol 20 (3) ◽  
pp. 464-478 ◽  
Author(s):  
Steffen Lindert ◽  
Nathan Alexander ◽  
Nils Wötzel ◽  
Mert Karakaş ◽  
Phoebe L. Stewart ◽  
...  
Keyword(s):  
Protein Structure ◽  
Structure Determination ◽  
De Novo ◽  
Protein Structure Determination ◽  
Density Maps ◽  
Medium Resolution

Three-dimensional protein structure prediction: Methods and computational strategies

2014 ◽  
Vol 53 ◽  
pp. 251-276 ◽  
Author(s):  
Márcio Dorn ◽  
Mariel Barbachan e Silva ◽  
Luciana S. Buriol ◽  
Luis C. Lamb
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Three Dimensional ◽  
Prediction Methods

Algorithmic Aspects of Protein Threading

2011 ◽  
pp. 605-619
Author(s):  
Tatsuya Akutsu
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Three Dimensional ◽  
Structure Alignment ◽  
Protein Structure Alignment ◽  
Amino Acid Residues ◽  
Optimal Solutions ◽  
Score Functions ◽  
Protein Threading

This chapter provides an overview of computational problems and techniques for protein threading. Protein threading is one of the most powerful approaches to protein structure prediction, where protein structure prediction is to infer three-dimensional (3-D) protein structure for a given protein sequence. Protein threading can be modeled as an optimization problem. Optimal solutions can be obtained in polynomial time using simple dynamic programming algorithms if profile type score functions are employed. However, this problem is computationally hard (NP-hard) if score functions include pairwise interaction preferences between amino acid residues. Therefore, various algorithms have been developed for finding optimal or near-optimal solutions. This chapter explains the ideas employed in these algorithms. This chapter also gives brief explanations of related problems: protein threading with constraints, comparison of RNA secondary structures and protein structure alignment.

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