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 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.

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 shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction

2021 ◽  
Author(s):  
SM Bargeen Alam Turzo ◽  
Justin Thomas Seffernick ◽  
Amber D Rolland ◽  
Micah T Donor ◽  
Sten Heinze ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Structure ◽  
Ion Mobility ◽  
Structure Determination ◽  
Tertiary Structure ◽  
De Novo ◽  
Structural Information ◽  
Collision Cross Section ◽  
Protein Structures ◽  
Protein Shape

Among a wide variety of mass spectrometry (MS) methodologies available for structural characterizations of proteins, ion mobility (IM) provides structural information about protein shape and size in the form of an orientationally averaged collision cross-section (CCS). While IM data have been predominantly employed for the structural assessment of protein complexes, CCS data from IM experiments have not yet been used to predict tertiary structure from sequence. Here, we are showing that IM data can significantly improve protein structure determination using the modeling suite Rosetta. The Rosetta Projection Approximation using Rough Circular Shapes (PARCS) algorithm was developed that allows for fast and accurate prediction of CCS from structure. Following successful rigorous testing for accuracy, speed, and convergence of PARCS, an integrative modelling approach was developed in Rosetta to use CCS data from IM experiments. Using this method, we predicted protein structures from sequence for a benchmark set of 23 proteins. When using IM data, the predicted structure improved or remained unchanged for all 23 proteins, compared to the predicted models in the absence of CCS data. For 15/23 proteins, the RMSD (root-mean-square deviation) of the predicted model was less than 5.50 Å, compared to only 10/23 without IM data. We also developed a confidence metric that successfully identified near-native models in the absence of a native structure. These results demonstrate the ability of IM data in de novo structure determination.

Is the growth rate of Protein Data Bank sufficient to solve the protein structure prediction problem using template-based modeling?

2015 ◽  
Vol 11 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Michal Brylinski
Keyword(s):  
Protein Structure ◽  
Protein Data Bank ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Structural Information ◽  
Three Dimensional ◽  
Data Bank ◽  
Prediction Problem ◽  
Three Dimensional Models ◽  
Protein Structure Prediction Problem

AbstractThe Protein Data Bank (PDB) undergoes an exponential expansion in terms of the number of macromolecular structures deposited every year. A pivotal question is how this rapid growth of structural information improves the quality of three-dimensional models constructed by contemporary bioinformatics approaches. To address this problem, we performed a retrospective analysis of the structural coverage of a representative set of proteins using remote homology detected by COMPASS and HHpred. We show that the number of proteins whose structures can be confidently predicted increased during a 9-year period between 2005 and 2014 on account of the PDB growth alone. Nevertheless, this encouraging trend slowed down noticeably around the year 2008 and has yielded insignificant improvements ever since. At the current pace, it is unlikely that the protein structure prediction problem will be solved in the near future using existing template-based modeling techniques. Therefore, further advances in experimental structure determination, qualitatively better approaches in fold recognition, and more accurate template-free structure prediction methods are desperately needed.

scholarly journals Protein Structure Prediction Based on Improved Genetic Algorithm

2020 ◽  
Vol 11 (9) ◽  
pp. 450-454
Author(s):  
Jiaxi Liu ◽  
Keyword(s):  
Genetic Algorithm ◽  
Protein Structure ◽  
Amino Acid ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Protein Structures ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Improved Genetic Algorithm ◽  
Research Areas

The prediction of protein three-dimensional structure from amino acid sequence has been a challenge problem in bioinformatics, owing to the many potential applications for robust protein structure prediction methods. Protein structure prediction is essential to bioscience, and its research results are important for other research areas. Methods for the prediction an才d design of protein structures have advanced dramatically. The prediction of protein structure based on average hydrophobic values is discussed and an improved genetic algorithm is proposed to solve the optimization problem of hydrophobic protein structure prediction. An adjustment operator is designed with the average hydrophobic value to prevent the overlapping of amino acid positions. Finally, some numerical experiments are conducted to verify the feasibility and effectiveness of the proposed algorithm by comparing with the traditional HNN algorithm.

scholarly journals PROBABILISTIC ENSEMBLES FOR IMPROVED INFERENCE IN PROTEIN-STRUCTURE DETERMINATION

2012 ◽  
Vol 10 (01) ◽  
pp. 1240009 ◽  
Author(s):  
AMEET SONI ◽  
JUDE SHAVLIK
Keyword(s):  
Protein Structure ◽  
Structure Determination ◽  
Protein Structures ◽  
Three Dimensional ◽  
Protein Structure Determination ◽  
Complex Problem ◽  
Approximate Inference ◽  
X Ray ◽  
X Ray Crystallography ◽  
Inference Methods

Protein X-ray crystallography — the most popular method for determining protein structures — remains a laborious process requiring a great deal of manual crystallographer effort to interpret low-quality protein images. Automating this process is critical in creating a high-throughput protein-structure determination pipeline. Previously, our group developed ACMI, a probabilistic framework for producing protein-structure models from electron-density maps produced via X-ray crystallography. ACMI uses a Markov Random Field to model the three-dimensional (3D) location of each non-hydrogen atom in a protein. Calculating the best structure in this model is intractable, so ACMI uses approximate inference methods to estimate the optimal structure. While previous results have shown ACMI to be the state-of-the-art method on this task, its approximate inference algorithm remains computationally expensive and susceptible to errors. In this work, we develop Probabilistic Ensembles in ACMI (PEA), a framework for leveraging multiple, independent runs of approximate inference to produce estimates of protein structures. Our results show statistically significant improvements in the accuracy of inference resulting in more complete and accurate protein structures. In addition, PEA provides a general framework for advanced approximate inference methods in complex problem domains.

scholarly journals Complementing sequence-derived features with structural information extracted from fragment libraries for protein structure prediction

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Siyuan Liu ◽  
Tong Wang ◽  
Qijiang Xu ◽  
Bin Shao ◽  
Jian Yin ◽  
...  
Keyword(s):  
Protein Folding ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Structural Information ◽  
Three Dimensional ◽  
Structural Features ◽  
Dimensional Structure ◽  
Fragment Assembly ◽  
Fragment Libraries

Abstract Background Fragment libraries play a key role in fragment-assembly based protein structure prediction, where protein fragments are assembled to form a complete three-dimensional structure. Rich and accurate structural information embedded in fragment libraries has not been systematically extracted and used beyond fragment assembly. Methods To better leverage the valuable structural information for protein structure prediction, we extracted seven types of structural information from fragment libraries. We broadened the usage of such structural information by transforming fragment libraries into protein-specific potentials for gradient-descent based protein folding and encoding fragment libraries as structural features for protein property prediction. Results Fragment libraires improved the accuracy of protein folding and outperformed state-of-the-art algorithms with respect to predicted properties, such as torsion angles and inter-residue distances. Conclusion Our work implies that the rich structural information extracted from fragment libraries can complement sequence-derived features to help protein structure prediction.

Multi-Agent Systems in Three-Dimensional Protein Structure Prediction

2017 ◽  
pp. 241-278 ◽  
Author(s):  
Leonardo de Lima Corrêa ◽  
Márcio Dorn
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Protein Structures ◽  
Three Dimensional ◽  
Multi Agent Systems ◽  
Agent Based ◽  
Agent Systems ◽  
Tertiary Protein Structure ◽  
Multi Agent

Tertiary protein structure prediction in silico is currently a challenging problem in Structural Bioinformatics and can be classified according to the computational complexity theory as an NP-hard problem. Determining the 3-D structure of a protein is both experimentally expensive, and time-consuming. The agent-based paradigm has been shown a useful technique for the applications that have repetitive and time-consuming activities, knowledge share and management, such as integration of different knowledge sources and modeling of complex systems, supporting a great variety of domains. This chapter provides an integrated view and insights about the protein structure prediction area concerned to the usage, application and implementation of multi-agent systems to predict the protein structures or to support and coordinate the existing predictors, as well as it is advantages, issues, needs, and demands. It is noteworthy that there is a great need for works related to multi-agent and agent-based paradigms applied to the problem due to their excellent suitability to the problem.

scholarly journals Large-scale determination of previously unsolved protein structures using evolutionary information

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Sergey Ovchinnikov ◽  
Lisa Kinch ◽  
Hahnbeom Park ◽  
Yuxing Liao ◽  
Jimin Pei ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Large Scale ◽  
De Novo ◽  
Structural Information ◽  
Sequence Similarity ◽  
Protein Structures ◽  
Three Dimensional ◽  
Public Access ◽  
Evolutionary Information ◽  
Blind Test

The prediction of the structures of proteins without detectable sequence similarity to any protein of known structure remains an outstanding scientific challenge. Here we report significant progress in this area. We first describe de novo blind structure predictions of unprecendented accuracy we made for two proteins in large families in the recent CASP11 blind test of protein structure prediction methods by incorporating residue–residue co-evolution information in the Rosetta structure prediction program. We then describe the use of this method to generate structure models for 58 of the 121 large protein families in prokaryotes for which three-dimensional structures are not available. These models, which are posted online for public access, provide structural information for the over 400,000 proteins belonging to the 58 families and suggest hypotheses about mechanism for the subset for which the function is known, and hypotheses about function for the remainder.

scholarly journals Prediction of Structural and Functional Aspects of Protein

2017 ◽  
pp. 551-568
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.

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