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Tych K, Žoldák G. Stable Substructures in Proteins and How to Find Them Using Single-Molecule Force Spectroscopy. Methods Mol Biol 2019; 1958:263-282. [PMID: 30945223 DOI: 10.1007/978-1-4939-9161-7_13] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Three-dimensional structures of proteins are a source of fascination for scientists, due to the beauty of their sequence-encoded architectures and their highly diverse range of functions. These functions include acting as powerful catalysts, signal receptors, and versatile molecular motors as well as being building blocks for macroscopic structures, thus defining the shape of multicellular organisms. How protein structure is organized and assembled at the sub-nanometer scale is of great current interest. Specifically, the discovery of stable substructures and supersecondary structures has inspired research into their potential use in rationally engineered proteins with tailor-made properties. Here, we show how the search for stable substructures in large proteins can benefit from recent advances in single-molecule force spectroscopy using highly sensitive dual-beam optical tweezers. Our chapter provides a step-by-step description of the experimental workflow for (1) preparing proteins for mechanical interrogation, (2) interpreting the data, and (3) avoiding the most commonly occurring mistakes.
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Affiliation(s)
- Katarzyna Tych
- Physics Department E22, Technical University of Munich, Garching, Germany
| | - Gabriel Žoldák
- Center for Interdisciplinary Biosciences, Technology and Innovation Park, P.J. Šafárik University, Košice, Slovakia.
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2
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Computational screening of potential non-immunoglobulin scaffolds using overlapped conserved residues (OCR)-based fingerprints. KOREAN J CHEM ENG 2018. [DOI: 10.1007/s11814-017-0350-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Edwardraja S, Munussami G, Goyal A, Lee SG. Generation of efficient fingerprint for GFP-like fold and computational identification of potential GFP-like homologs. BIOTECHNOL BIOPROC E 2017. [DOI: 10.1007/s12257-016-0362-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Banach M, Prudhomme N, Carpentier M, Duprat E, Papandreou N, Kalinowska B, Chomilier J, Roterman I. Contribution to the prediction of the fold code: application to immunoglobulin and flavodoxin cases. PLoS One 2015; 10:e0125098. [PMID: 25915049 PMCID: PMC4411048 DOI: 10.1371/journal.pone.0125098] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2014] [Accepted: 03/20/2015] [Indexed: 12/19/2022] Open
Abstract
Background Folding nucleus of globular proteins formation starts by the mutual interaction of a group of hydrophobic amino acids whose close contacts allow subsequent formation and stability of the 3D structure. These early steps can be predicted by simulation of the folding process through a Monte Carlo (MC) coarse grain model in a discrete space. We previously defined MIRs (Most Interacting Residues), as the set of residues presenting a large number of non-covalent neighbour interactions during such simulation. MIRs are good candidates to define the minimal number of residues giving rise to a given fold instead of another one, although their proportion is rather high, typically [15-20]% of the sequences. Having in mind experiments with two sequences of very high levels of sequence identity (up to 90%) but different folds, we combined the MIR method, which takes sequence as single input, with the “fuzzy oil drop” (FOD) model that requires a 3D structure, in order to estimate the residues coding for the fold. FOD assumes that a globular protein follows an idealised 3D Gaussian distribution of hydrophobicity density, with the maximum in the centre and minima at the surface of the “drop”. If the actual local density of hydrophobicity around a given amino acid is as high as the ideal one, then this amino acid is assigned to the core of the globular protein, and it is assumed to follow the FOD model. Therefore one obtains a distribution of the amino acids of a protein according to their agreement or rejection with the FOD model. Results We compared and combined MIR and FOD methods to define the minimal nucleus, or keystone, of two populated folds: immunoglobulin-like (Ig) and flavodoxins (Flav). The combination of these two approaches defines some positions both predicted as a MIR and assigned as accordant with the FOD model. It is shown here that for these two folds, the intersection of the predicted sets of residues significantly differs from random selection. It reduces the number of selected residues by each individual method and allows a reasonable agreement with experimentally determined key residues coding for the particular fold. In addition, the intersection of the two methods significantly increases the specificity of the prediction, providing a robust set of residues that constitute the folding nucleus.
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Affiliation(s)
- Mateusz Banach
- Department of Bioinformatics and Telemedicine, Medical College, Jagiellonian University, Krakow, Poland
| | - Nicolas Prudhomme
- Protein Structure Prediction group, IMPMC, UPMC & CNRS, Paris, France
| | - Mathilde Carpentier
- Protein Structure Prediction group, IMPMC, UPMC & CNRS, Paris, France
- RPBS, 35 rue Hélène Brion, 75013, Paris, France
| | - Elodie Duprat
- Protein Structure Prediction group, IMPMC, UPMC & CNRS, Paris, France
- RPBS, 35 rue Hélène Brion, 75013, Paris, France
| | - Nikolaos Papandreou
- Genetics Department, Agricultural University of Athens, Iera Odos 75, Athens, Greece
| | - Barbara Kalinowska
- Department of Bioinformatics and Telemedicine, Medical College, Jagiellonian University, Krakow, Poland
| | - Jacques Chomilier
- Protein Structure Prediction group, IMPMC, UPMC & CNRS, Paris, France
- RPBS, 35 rue Hélène Brion, 75013, Paris, France
- * E-mail: (JC); (IR)
| | - Irena Roterman
- Department of Bioinformatics and Telemedicine, Medical College, Jagiellonian University, Krakow, Poland
- * E-mail: (JC); (IR)
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Kister A. Amino acid distribution rules predict protein fold: protein grammar for beta-strand sandwich-like structures. Biomolecules 2015; 5:41-59. [PMID: 25625198 PMCID: PMC4384110 DOI: 10.3390/biom5010041] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2014] [Accepted: 12/31/2014] [Indexed: 11/16/2022] Open
Abstract
We present an alternative approach to protein 3D folding prediction based on determination of rules that specify distribution of "favorable" residues, that are mainly responsible for a given fold formation, and "unfavorable" residues, that are incompatible with that fold, in polypeptide sequences. The process of determining favorable and unfavorable residues is iterative. The starting assumptions are based on the general principles of protein structure formation as well as structural features peculiar to a protein fold under investigation. The initial assumptions are tested one-by-one for a set of all known proteins with a given structure. The assumption is accepted as a "rule of amino acid distribution" for the protein fold if it holds true for all, or near all, structures. If the assumption is not accepted as a rule, it can be modified to better fit the data and then tested again in the next step of the iterative search algorithm, or rejected. We determined the set of amino acid distribution rules for a large group of beta sandwich-like proteins characterized by a specific arrangement of strands in two beta sheets. It was shown that this set of rules is highly sensitive (~90%) and very specific (~99%) for identifying sequences of proteins with specified beta sandwich fold structure. The advantage of the proposed approach is that it does not require that query proteins have a high degree of homology to proteins with known structure. So long as the query protein satisfies residue distribution rules, it can be confidently assigned to its respective protein fold. Another advantage of our approach is that it allows for a better understanding of which residues play an essential role in protein fold formation. It may, therefore, facilitate rational protein engineering design.
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Affiliation(s)
- Alexander Kister
- Department of Mathematics, Rutgers University, Piscataway, NJ 08854, USA.
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Identification of an ideal-like fingerprint for a protein fold using overlapped conserved residues based approach. Sci Rep 2014; 4:5643. [PMID: 25008052 PMCID: PMC4090624 DOI: 10.1038/srep05643] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2014] [Accepted: 06/19/2014] [Indexed: 02/04/2023] Open
Abstract
Design of an efficient fingerprint that detects homologous proteins at distant sequence identity has been a great challenge. This paper proposes a strategy to extract an ideal-like fingerprint with high specificity and sensitivity from a group of sequences related to a fold. The approach is devised based on the assumptions that the critical residues for a protein fold may be conserved in three aspects, i.e. sequence, structure, and intramolecular interaction, and embedded in secondary structures. We hypothesized that the residues satisfying such conditions simultaneously may work as an efficient fingerprint. This idea was tested on protein folds of various classes, such as beta-strand rich, alpha + beta proteins and alpha/beta proteins with discrete sequence similarities. The fingerprint for each fold was generated by selecting the overlapped conserved residues (OCR) from the conserved residues obtained using independent three alignment methods, i.e. multiple sequence alignment, structure-based alignment, and alignment based on the interstrand hydrogen-bonds. The OCR fingerprints showed more than 90% detection efficiency for all the folds tested and were identified to be almost the minimal fingerprints composed of only critical residues. This study is expected to provide an important conceptual improvement in the identification or design of ideal fingerprints for a protein fold.
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Kubrycht J, Sigler K, Souček P, Hudeček J. Structures composing protein domains. Biochimie 2013; 95:1511-24. [DOI: 10.1016/j.biochi.2013.04.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 04/02/2013] [Indexed: 12/21/2022]
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Crivelli S, Max N. Creating supersecondary structures with BuildBeta. Methods Mol Biol 2013; 932:115-140. [PMID: 22987350 DOI: 10.1007/978-1-62703-065-6_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
BuildBeta is a feature of the ProteinShop software designed to thoroughly sample a protein conformational space given the protein's sequence of amino acids and secondary structure predictions. It targets proteins with beta sheets because they are particularly challenging to predict due to the complexity of sampling long-range strand pairings. Here we discuss some of the most difficult targets in the recent 9th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP9) and show how BuildBeta can leverage some of the most successful methods in the category "template-free modeling" by augmenting their sampling capabilities. We also discuss ongoing efforts to improve the quality of the supersecondary structures it generates.
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Affiliation(s)
- Silvia Crivelli
- Department of Computer Science, University of California, Davis, CA, USA.
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