1
|
Kashani-Amin E, Tabatabaei-Malazy O, Sakhteman A, Larijani B, Ebrahim-Habibi A. A Systematic Review on Popularity, Application and Characteristics of Protein Secondary Structure Prediction Tools. Curr Drug Discov Technol 2020; 16:159-172. [PMID: 29493456 DOI: 10.2174/1570163815666180227162157] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Revised: 02/15/2018] [Accepted: 02/22/2018] [Indexed: 01/22/2023]
Abstract
BACKGROUND Prediction of proteins' secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts. OBJECTIVE A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools. METHODS Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data. RESULTS Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields. CONCLUSION This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.
Collapse
Affiliation(s)
- Elaheh Kashani-Amin
- Biosensor Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Ozra Tabatabaei-Malazy
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.,Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Amirhossein Sakhteman
- Department of Medicinal Chemistry, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran.,Medicinal Chemistry and Natural Products Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Bagher Larijani
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Azadeh Ebrahim-Habibi
- Biosensor Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
2
|
Rosenfeld R, Alcalay R, Mechaly A, Lapidoth G, Epstein E, Kronman C, J Fleishman S, Mazor O. Improved antibody-based ricin neutralization by affinity maturation is correlated with slower off-rate values. Protein Eng Des Sel 2017; 30:611-617. [PMID: 28472478 DOI: 10.1093/protein/gzx028] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Accepted: 04/18/2017] [Indexed: 01/03/2023] Open
Abstract
While potent monoclonal antibodies against ricin were introduced over the years, the question whether increasing antibody affinity enables better toxin neutralization was not fully addressed yet. The aim of this study was to characterize the contribution of antibody affinity to the ricin neutralization potential of the antibody. cHD23 monoclonal antibody that targets the toxin B-subunit and interferes with its binding to membranal receptors, was isolated. In order to create antibody clones with improved affinity toward ricin, a scFv-phage display library containing mutated versions of the variable regions of cHD23 was constructed and clones with improved binding of ricin were isolated. Structural modeling of these mutants suggests that the inserted mutations may increase the antibody conformational flexibility thus improving its ability to bind ricin. While it was found that the selected clones exhibited improved neutralization of ricin, the correlation between the KD values and potency was only minor (r = 0.55). However, a positive correlation (r = 0.84) exist between the off-rate values (koff) of the affinity matured clones and their ability to neutralize ricin. As cell membranes display inordinately large amounts of potential surface binding sites for ricin, it is suggested that antibodies with improved off-rate values block the ability of the toxin to bind to target receptors, in a highly efficient manner. Currently, antibody-based therapy is the most effective treatment for ricin intoxication and it is anticipated that the findings of this study will provide useful information and a possible strategy to design an improved antibody-based therapy for the toxin.
Collapse
Affiliation(s)
- Ronit Rosenfeld
- Department of Biochemistry and Molecular Genetics, Israel Institute for Biological Research, Lerrer St., Ness-Ziona 74100, Israel
| | - Ron Alcalay
- Department of Biochemistry and Molecular Genetics, Israel Institute for Biological Research, Lerrer St., Ness-Ziona 74100, Israel
| | - Adva Mechaly
- Department of Infectious Diseases, Israel Institute for Biological Research, Lerrer St., Ness-Ziona 74100, Israel
| | - Gideon Lapidoth
- Department of Biomolecular Sciences, Weizmann Institute of Science, 234 Herzel St., Rehovot 7610001, Israel
| | - Eyal Epstein
- Department of Biotechnology, Israel Institute for Biological Research, Lerrer St., Ness-Ziona 74100, Israel
| | - Chanoch Kronman
- Department of Biochemistry and Molecular Genetics, Israel Institute for Biological Research, Lerrer St., Ness-Ziona 74100, Israel
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, 234 Herzel St., Rehovot 7610001, Israel
| | - Ohad Mazor
- Department of Biochemistry and Molecular Genetics, Israel Institute for Biological Research, Lerrer St., Ness-Ziona 74100, Israel
| |
Collapse
|
3
|
Pushing the size limit of de novo structure ensemble prediction guided by sparse SDSL-EPR restraints to 200 residues: The monomeric and homodimeric forms of BAX. J Struct Biol 2016; 195:62-71. [PMID: 27129417 DOI: 10.1016/j.jsb.2016.04.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2015] [Revised: 04/25/2016] [Accepted: 04/26/2016] [Indexed: 01/24/2023]
Abstract
Structure determination remains a challenge for many biologically important proteins. In particular, proteins that adopt multiple conformations often evade crystallization in all biologically relevant states. Although computational de novo protein folding approaches often sample biologically relevant conformations, the selection of the most accurate model for different functional states remains a formidable challenge, in particular, for proteins with more than about 150 residues. Electron paramagnetic resonance (EPR) spectroscopy can obtain limited structural information for proteins in well-defined biological states and thereby assist in selecting biologically relevant conformations. The present study demonstrates that de novo folding methods are able to accurately sample the folds of 192-residue long soluble monomeric Bcl-2-associated X protein (BAX). The tertiary structures of the monomeric and homodimeric forms of BAX were predicted using the primary structure as well as 25 and 11 EPR distance restraints, respectively. The predicted models were subsequently compared to respective NMR/X-ray structures of BAX. EPR restraints improve the protein-size normalized root-mean-square-deviation (RMSD100) of the most accurate models with respect to the NMR/crystal structure from 5.9Å to 3.9Å and from 5.7Å to 3.3Å, respectively. Additionally, the model discrimination is improved, which is demonstrated by an improvement of the enrichment from 5% to 15% and from 13% to 21%, respectively.
Collapse
|
4
|
Fischer AW, Heinze S, Putnam DK, Li B, Pino JC, Xia Y, Lopez CF, Meiler J. CASP11--An Evaluation of a Modular BCL::Fold-Based Protein Structure Prediction Pipeline. PLoS One 2016; 11:e0152517. [PMID: 27046050 PMCID: PMC4821492 DOI: 10.1371/journal.pone.0152517] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2015] [Accepted: 03/15/2016] [Indexed: 11/18/2022] Open
Abstract
In silico prediction of a protein's tertiary structure remains an unsolved problem. The community-wide Critical Assessment of Protein Structure Prediction (CASP) experiment provides a double-blind study to evaluate improvements in protein structure prediction algorithms. We developed a protein structure prediction pipeline employing a three-stage approach, consisting of low-resolution topology search, high-resolution refinement, and molecular dynamics simulation to predict the tertiary structure of proteins from the primary structure alone or including distance restraints either from predicted residue-residue contacts, nuclear magnetic resonance (NMR) nuclear overhauser effect (NOE) experiments, or mass spectroscopy (MS) cross-linking (XL) data. The protein structure prediction pipeline was evaluated in the CASP11 experiment on twenty regular protein targets as well as thirty-three 'assisted' protein targets, which also had distance restraints available. Although the low-resolution topology search module was able to sample models with a global distance test total score (GDT_TS) value greater than 30% for twelve out of twenty proteins, frequently it was not possible to select the most accurate models for refinement, resulting in a general decay of model quality over the course of the prediction pipeline. In this study, we provide a detailed overall analysis, study one target protein in more detail as it travels through the protein structure prediction pipeline, and evaluate the impact of limited experimental data.
Collapse
Affiliation(s)
- Axel W. Fischer
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37232, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Sten Heinze
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37232, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Daniel K. Putnam
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Bian Li
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37232, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - James C. Pino
- Chemical and Physical Biology Graduate Program, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Yan Xia
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37232, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Carlos F. Lopez
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
- Department of Cancer Biology and Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37232, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
| |
Collapse
|
5
|
Putnam DK, Weiner BE, Woetzel N, Lowe EW, Meiler J. BCL::SAXS: GPU accelerated Debye method for computation of small angle X-ray scattering profiles. Proteins 2015; 83:1500-12. [PMID: 26018949 PMCID: PMC4797635 DOI: 10.1002/prot.24838] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2015] [Revised: 05/08/2015] [Accepted: 05/19/2015] [Indexed: 12/25/2022]
Abstract
Small angle X-ray scattering (SAXS) is an experimental technique used for structural characterization of macromolecules in solution. Here, we introduce BCL::SAXS--an algorithm designed to replicate SAXS profiles from rigid protein models at different levels of detail. We first show our derivation of BCL::SAXS and compare our results with the experimental scattering profile of hen egg white lysozyme. Using this protein we show how to generate SAXS profiles representing: (1) complete models, (2) models with approximated side chain coordinates, and (3) models with approximated side chain and loop region coordinates. We evaluated the ability of SAXS profiles to identify a correct protein topology from a non-redundant benchmark set of proteins. We find that complete SAXS profiles can be used to identify the correct protein by receiver operating characteristic (ROC) analysis with an area under the curve (AUC) > 99%. We show how our approximation of loop coordinates between secondary structure elements improves protein recognition by SAχS for protein models without loop regions and side chains. Agreement with SAXS data is a necessary but not sufficient condition for structure determination. We conclude that experimental SAXS data can be used as a filter to exclude protein models with large structural differences from the native.
Collapse
Affiliation(s)
- Daniel K. Putnam
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37235, USA
| | - Brian E. Weiner
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA
| | - Nils Woetzel
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA
| | - Edward W. Lowe
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37235, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
| |
Collapse
|