151
|
Wang C, Wei Y, Zhang H, Kong L, Sun S, Zheng WM, Bu D. Constructing effective energy functions for protein structure prediction through broadening attraction-basin and reverse Monte Carlo sampling. BMC Bioinformatics 2019; 20:135. [PMID: 30925867 PMCID: PMC6439974 DOI: 10.1186/s12859-019-2652-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The ab initio approaches to protein structure prediction usually employ the Monte Carlo technique to search the structural conformation that has the lowest energy. However, the widely-used energy functions are usually ineffective for conformation search. How to construct an effective energy function remains a challenging task. RESULTS Here, we present a framework to construct effective energy functions for protein structure prediction. Unlike existing energy functions only requiring the native structure to be the lowest one, we attempt to maximize the attraction-basin where the native structure lies in the energy landscape. The underlying rationale is that each energy function determines a specific energy landscape together with a native attraction-basin, and the larger the attraction-basin is, the more likely for the Monte Carlo search procedure to find the native structure. Following this rationale, we constructed effective energy functions as follows: i) To explore the native attraction-basin determined by a certain energy function, we performed reverse Monte Carlo sampling starting from the native structure, identifying the structural conformations on the edge of attraction-basin. ii) To broaden the native attraction-basin, we smoothened the edge points of attraction-basin through tuning weights of energy terms, thus acquiring an improved energy function. Our framework alternates the broadening attraction-basin and reverse sampling steps (thus called BARS) until the native attraction-basin is sufficiently large. We present extensive experimental results to show that using the BARS framework, the constructed energy functions could greatly facilitate protein structure prediction in improving the quality of predicted structures and speeding up conformation search. CONCLUSION Using the BARS framework, we constructed effective energy functions for protein structure prediction, which could improve the quality of predicted structures and speed up conformation search as well.
Collapse
Affiliation(s)
- Chao Wang
- Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, 6, Kexueyuan South Road, Zhongguancun, Beijing, 100190 China
- University of Chinese Academy of Sciences, 19-1, Yuquan Road, Shijingshan, Beijing, 100049 China
| | - Yi Wei
- Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, 6, Kexueyuan South Road, Zhongguancun, Beijing, 100190 China
- University of Chinese Academy of Sciences, 19-1, Yuquan Road, Shijingshan, Beijing, 100049 China
| | - Haicang Zhang
- Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, 6, Kexueyuan South Road, Zhongguancun, Beijing, 100190 China
- University of Chinese Academy of Sciences, 19-1, Yuquan Road, Shijingshan, Beijing, 100049 China
| | - Lupeng Kong
- Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, 6, Kexueyuan South Road, Zhongguancun, Beijing, 100190 China
- University of Chinese Academy of Sciences, 19-1, Yuquan Road, Shijingshan, Beijing, 100049 China
| | - Shiwei Sun
- Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, 6, Kexueyuan South Road, Zhongguancun, Beijing, 100190 China
- University of Chinese Academy of Sciences, 19-1, Yuquan Road, Shijingshan, Beijing, 100049 China
| | - Wei-Mou Zheng
- University of Chinese Academy of Sciences, 19-1, Yuquan Road, Shijingshan, Beijing, 100049 China
- Institute of Theoretical Physics, Chinese Academy of Sciences, 55, Zhongguancun East Road, Beijing, 100190 China
| | - Dongbo Bu
- Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, 6, Kexueyuan South Road, Zhongguancun, Beijing, 100190 China
- University of Chinese Academy of Sciences, 19-1, Yuquan Road, Shijingshan, Beijing, 100049 China
| |
Collapse
|
152
|
Tubiana J, Cocco S, Monasson R. Learning protein constitutive motifs from sequence data. eLife 2019; 8:e39397. [PMID: 30857591 PMCID: PMC6436896 DOI: 10.7554/elife.39397] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Accepted: 02/24/2019] [Indexed: 12/11/2022] Open
Abstract
Statistical analysis of evolutionary-related protein sequences provides information about their structure, function, and history. We show that Restricted Boltzmann Machines (RBM), designed to learn complex high-dimensional data and their statistical features, can efficiently model protein families from sequence information. We here apply RBM to 20 protein families, and present detailed results for two short protein domains (Kunitz and WW), one long chaperone protein (Hsp70), and synthetic lattice proteins for benchmarking. The features inferred by the RBM are biologically interpretable: they are related to structure (residue-residue tertiary contacts, extended secondary motifs (α-helixes and β-sheets) and intrinsically disordered regions), to function (activity and ligand specificity), or to phylogenetic identity. In addition, we use RBM to design new protein sequences with putative properties by composing and 'turning up' or 'turning down' the different modes at will. Our work therefore shows that RBM are versatile and practical tools that can be used to unveil and exploit the genotype-phenotype relationship for protein families.
Collapse
Affiliation(s)
- Jérôme Tubiana
- Laboratory of Physics of the Ecole Normale SupérieureCNRS UMR 8023 & PSL ResearchParisFrance
| | - Simona Cocco
- Laboratory of Physics of the Ecole Normale SupérieureCNRS UMR 8023 & PSL ResearchParisFrance
| | - Rémi Monasson
- Laboratory of Physics of the Ecole Normale SupérieureCNRS UMR 8023 & PSL ResearchParisFrance
| |
Collapse
|
153
|
Braitbard M, Schneidman-Duhovny D, Kalisman N. Integrative Structure Modeling: Overview and Assessment. Annu Rev Biochem 2019; 88:113-135. [PMID: 30830798 DOI: 10.1146/annurev-biochem-013118-111429] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Integrative structure modeling computationally combines data from multiple sources of information with the aim of obtaining structural insights that are not revealed by any single approach alone. In the first part of this review, we survey the commonly used sources of structural information and the computational aspects of model building. Throughout the past decade, integrative modeling was applied to various biological systems, with a focus on large protein complexes. Recent progress in the field of cryo-electron microscopy (cryo-EM) has resolved many of these complexes to near-atomic resolution. In the second part of this review, we compare a range of published integrative models with their higher-resolution counterparts with the aim of critically assessing their accuracy. This comparison gives a favorable view of integrative modeling and demonstrates its ability to yield accurate and informative results. We discuss possible roles of integrative modeling in the new era of cryo-EM and highlight future challenges and directions.
Collapse
Affiliation(s)
- Merav Braitbard
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel;
| | - Dina Schneidman-Duhovny
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel; .,School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel;
| | - Nir Kalisman
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel;
| |
Collapse
|
154
|
Molecular simulation of peptides coming of age: Accurate prediction of folding, dynamics and structures. Arch Biochem Biophys 2019; 664:76-88. [DOI: 10.1016/j.abb.2019.01.033] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 01/23/2019] [Accepted: 01/28/2019] [Indexed: 12/24/2022]
|
155
|
Kundert K, Kortemme T. Computational design of structured loops for new protein functions. Biol Chem 2019; 400:275-288. [PMID: 30676995 PMCID: PMC6530579 DOI: 10.1515/hsz-2018-0348] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 12/18/2018] [Indexed: 12/20/2022]
Abstract
The ability to engineer the precise geometries, fine-tuned energetics and subtle dynamics that are characteristic of functional proteins is a major unsolved challenge in the field of computational protein design. In natural proteins, functional sites exhibiting these properties often feature structured loops. However, unlike the elements of secondary structures that comprise idealized protein folds, structured loops have been difficult to design computationally. Addressing this shortcoming in a general way is a necessary first step towards the routine design of protein function. In this perspective, we will describe the progress that has been made on this problem and discuss how recent advances in the field of loop structure prediction can be harnessed and applied to the inverse problem of computational loop design.
Collapse
Affiliation(s)
- Kale Kundert
- Graduate Group in Biophysics, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Tanja Kortemme
- Graduate Group in Biophysics, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA
- Chan Zuckerberg Biohub, 499 Illinois St, San Francisco, CA 94158, USA
| |
Collapse
|
156
|
Evans ED, Pentelute BL. Studies on a landscape of perfluoroaromatic-reactive peptides. Org Biomol Chem 2019; 17:1862-1868. [PMID: 30065992 DOI: 10.1039/c8ob01678j] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
We investigated 26 midsized peptides (∼30 amino acids in length) selected using mRNA display to perform a nucleophilic aromatic substitution reaction (SNAr). Analysis suggested a diverse set of reactive sequences with significant differences in primary sequence, secondary structure and even predicted tertiary structural features. Several of the sequences displayed rapid kinetics allowing for near complete labeling in under one hour. Rosetta ab initio structure prediction of these sequences suggested a landscape of structural features, ranging from beta-sheet-based sequences to those possessing more alpha-helical-like character. Circular dichroism spectroscopy confirmed elements of the structure predictions for the majority of peptides. This analysis additionally uncovered that several peptides underwent secondary structure alterations upon reaction. These results suggest a broad sequence and structural landscape of SNAr active peptides along with a potentially important feature of these biopolymers.
Collapse
Affiliation(s)
- Ethan D Evans
- Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.
| | | |
Collapse
|
157
|
Pei J, Zheng Z, Merz KM. Random Forest Refinement of the KECSA2 Knowledge-Based Scoring Function for Protein Decoy Detection. J Chem Inf Model 2019; 59:1919-1929. [DOI: 10.1021/acs.jcim.8b00734] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jun Pei
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Zheng Zheng
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M. Merz
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
- Institute for Cyber Enabled Research, Michigan State University, 567 Wilson Road, East Lansing, Michigan 48824, United States
| |
Collapse
|
158
|
Evans ED, Gates ZP, Sun ZYJ, Mijalis AJ, Pentelute BL. Conformational Stabilization and Rapid Labeling of a 29-Residue Peptide by a Small Molecule Reaction Partner. Biochemistry 2019; 58:1343-1353. [DOI: 10.1021/acs.biochem.8b00940] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ethan D. Evans
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Zachary P. Gates
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Zhen-Yu J. Sun
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 240 Longwood Avenue, Boston, Massachusetts 02115, United States
| | - Alexander J. Mijalis
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Bradley L. Pentelute
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| |
Collapse
|
159
|
Kendall LV, Owiny JR, Dohm ED, Knapek KJ, Lee ES, Kopanke JH, Fink M, Hansen SA, Ayers JD. Replacement, Refinement, and Reduction in Animal Studies With Biohazardous Agents. ILAR J 2019; 59:177-194. [DOI: 10.1093/ilar/ily021] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Revised: 06/11/2018] [Indexed: 12/17/2022] Open
Abstract
Abstract
Animal models are critical to the advancement of our knowledge of infectious disease pathogenesis, diagnostics, therapeutics, and prevention strategies. The use of animal models requires thoughtful consideration for their well-being, as infections can significantly impact the general health of an animal and impair their welfare. Application of the 3Rs—replacement, refinement, and reduction—to animal models using biohazardous agents can improve the scientific merit and animal welfare. Replacement of animal models can use in vitro techniques such as cell culture systems, mathematical models, and engineered tissues or invertebrate animal hosts such as amoeba, worms, fruit flies, and cockroaches. Refinements can use a variety of techniques to more closely monitor the course of disease. These include the use of biomarkers, body temperature, behavioral observations, and clinical scoring systems. Reduction is possible using advanced technologies such as in vivo telemetry and imaging, allowing longitudinal assessment of animals during the course of disease. While there is no single method to universally replace, refine, or reduce animal models, the alternatives and techniques discussed are broadly applicable and they should be considered when infectious disease animal models are developed.
Collapse
Affiliation(s)
- Lon V Kendall
- Department of Microbiology, Immunology and Pathology, and Laboratory Animal Resources, Colorado State University, Fort Collins, Colorado
| | - James R Owiny
- Laboratory Animal Resources, Colorado State University, Fort Collins, Colorado
| | - Erik D Dohm
- Animal Resources Program, University of Alabama, Birmingham, Alabama
| | - Katie J Knapek
- Comparative Medicine Training Program, Colorado State University, Fort Collins, Colorado
| | - Erin S Lee
- Animal Resource Center, University of Texas Medical Branch, Galveston, Texas
| | - Jennifer H Kopanke
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado
| | - Michael Fink
- Department of Veterinary Pathobiology, University of Missouri, Columbia, Missouri
| | - Sarah A Hansen
- Office of Animal Resources, University of Iowa, Iowa City, Iowa
| | - Jessica D Ayers
- Laboratory Animal Resources, Colorado State University, Fort Collins, Colorado
| |
Collapse
|
160
|
Hauri S, Khakzad H, Happonen L, Teleman J, Malmström J, Malmström L. Rapid determination of quaternary protein structures in complex biological samples. Nat Commun 2019; 10:192. [PMID: 30643114 PMCID: PMC6331586 DOI: 10.1038/s41467-018-07986-1] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Accepted: 12/07/2018] [Indexed: 12/26/2022] Open
Abstract
The understanding of complex biological systems is still hampered by limited knowledge of biologically relevant quaternary protein structures. Here, we demonstrate quaternary structure determination in biological samples using a combination of chemical cross-linking, high-resolution mass spectrometry and high-accuracy protein structure modeling. This approach, termed targeted cross-linking mass spectrometry (TX-MS), relies on computational structural models to score sets of targeted cross-linked peptide signals acquired using a combination of mass spectrometry acquisition techniques. We demonstrate the utility of TX-MS by creating a high-resolution quaternary model of a 1.8 MDa protein complex composed of a pathogen surface protein and ten human plasma proteins. The model is based on a dense network of cross-link distance constraints obtained directly in a mixture of human plasma and live bacteria. These results demonstrate that TX-MS can increase the applicability of flexible backbone docking algorithms to large protein complexes by providing rich cross-link distance information from complex biological samples. Protein structure determination in complex biological samples is still challenging. Here, the authors develop a computational modeling-guided cross-linking mass spectrometry method, obtaining a high-resolution model of a 1.8 MDa protein assembly from cross-links detected in a mixture of human plasma and bacteria.
Collapse
Affiliation(s)
- Simon Hauri
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Klinikgatan 32, SE-22184, Lund, Sweden
| | - Hamed Khakzad
- S3IT, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.,Institute for Computational Science, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland
| | - Lotta Happonen
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Klinikgatan 32, SE-22184, Lund, Sweden
| | - Johan Teleman
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Klinikgatan 32, SE-22184, Lund, Sweden
| | - Johan Malmström
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Klinikgatan 32, SE-22184, Lund, Sweden.
| | - Lars Malmström
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Klinikgatan 32, SE-22184, Lund, Sweden. .,S3IT, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland. .,Institute for Computational Science, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.
| |
Collapse
|
161
|
Ferrari AJR, Gozzo FC, Martínez L. Statistical force-field for structural modeling using chemical cross-linking/mass spectrometry distance constraints. Bioinformatics 2019; 35:3005-3012. [DOI: 10.1093/bioinformatics/btz013] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 12/03/2018] [Accepted: 01/04/2019] [Indexed: 12/22/2022] Open
Abstract
Abstract
Motivation
Chemical cross-linking/mass spectrometry (XLMS) is an experimental method to obtain distance constraints between amino acid residues which can be applied to structural modeling of tertiary and quaternary biomolecular structures. These constraints provide, in principle, only upper limits to the distance between amino acid residues along the surface of the biomolecule. In practice, attempts to use of XLMS constraints for tertiary protein structure determination have not been widely successful. This indicates the need of specifically designed strategies for the representation of these constraints within modeling algorithms.
Results
A force-field designed to represent XLMS-derived constraints is proposed. The potential energy functions are obtained by computing, in the database of known protein structures, the probability of satisfaction of a topological cross-linking distance as a function of the Euclidean distance between amino acid residues. First, the strategy suggests that XL constraints should be set to shorter distances than usually assumed. Second, the complete statistical force-field improves the models obtained and can be easily incorporated into current modeling methods and software. The force-field was implemented and is distributed to be used within the Rosetta ab initio relax protocol.
Availability and implementation
Force-field parameters and usage instructions are freely available online (http://m3g.iqm.unicamp.br/topolink/xlff).
Supplementary information
Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Allan J R Ferrari
- Institute of Chemistry, University of Campinas, Campinas, SP, Brazil
| | - Fabio C Gozzo
- Institute of Chemistry, University of Campinas, Campinas, SP, Brazil
| | - Leandro Martínez
- Institute of Chemistry, University of Campinas, Campinas, SP, Brazil
- Center for Computing in Engineering & Sciences, University of Campinas, Campinas, SP, Brazil
| |
Collapse
|
162
|
MacCarthy E, Perry D, Kc DB. Advances in Protein Super-Secondary Structure Prediction and Application to Protein Structure Prediction. Methods Mol Biol 2019; 1958:15-45. [PMID: 30945212 DOI: 10.1007/978-1-4939-9161-7_2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Due to the advancement in various sequencing technologies, the gap between the number of protein sequences and the number of experimental protein structures is ever increasing. Community-wide initiatives like CASP have resulted in considerable efforts in the development of computational methods to accurately model protein structures from sequences. Sequence-based prediction of super-secondary structure has direct application in protein structure prediction, and there have been significant efforts in the prediction of super-secondary structure in the last decade. In this chapter, we first introduce the protein structure prediction problem and highlight some of the important progress in the field of protein structure prediction. Next, we discuss recent methods for the prediction of super-secondary structures. Finally, we discuss applications of super-secondary structure prediction in structure prediction/analysis of proteins. We also discuss prediction of protein structures that are composed of simple super-secondary structure repeats and protein structures that are composed of complex super-secondary structure repeats. Finally, we also discuss the recent trends in the field.
Collapse
Affiliation(s)
- Elijah MacCarthy
- Department of Computational Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA
| | - Derrick Perry
- Department of Computational Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA
| | - Dukka B Kc
- Department of Computational Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA.
| |
Collapse
|
163
|
Physics-Based Modeling of Side Chain—Side Chain Interactions in the UNRES Force Field. SPRINGER SERIES ON BIO- AND NEUROSYSTEMS 2019. [DOI: 10.1007/978-3-319-95843-9_4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
|
164
|
Studer G, Tauriello G, Bienert S, Waterhouse AM, Bertoni M, Bordoli L, Schwede T, Lepore R. Modeling of Protein Tertiary and Quaternary Structures Based on Evolutionary Information. Methods Mol Biol 2019; 1851:301-316. [PMID: 30298405 DOI: 10.1007/978-1-4939-8736-8_17] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Proteins are subject to evolutionary forces that shape their three-dimensional structure to meet specific functional demands. The knowledge of the structure of a protein is therefore instrumental to gain information about the molecular basis of its function. However, experimental structure determination is inherently time consuming and expensive, making it impossible to follow the explosion of sequence data deriving from genome-scale projects. As a consequence, computational structural modeling techniques have received much attention and established themselves as a valuable complement to experimental structural biology efforts. Among these, comparative modeling remains the method of choice to model the three-dimensional structure of a protein when homology to a protein of known structure can be detected.The general strategy consists of using experimentally determined structures of proteins as templates for the generation of three-dimensional models of related family members (targets) of which the structure is unknown. This chapter provides a description of the individual steps needed to obtain a comparative model using SWISS-MODEL, one of the most widely used automated servers for protein structure homology modeling.
Collapse
Affiliation(s)
- Gabriel Studer
- Biozentrum, University of Basel and SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Gerardo Tauriello
- Biozentrum, University of Basel and SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Stefan Bienert
- Biozentrum, University of Basel and SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Andrew Mark Waterhouse
- Biozentrum, University of Basel and SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Martino Bertoni
- Biozentrum, University of Basel and SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Lorenza Bordoli
- Biozentrum, University of Basel and SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Torsten Schwede
- Biozentrum, University of Basel and SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Rosalba Lepore
- Biozentrum, University of Basel and SIB Swiss Institute of Bioinformatics, Basel, Switzerland.
| |
Collapse
|
165
|
Roushar FJ, Gruenhagen TC, Penn WD, Li B, Meiler J, Jastrzebska B, Schlebach JP. Contribution of Cotranslational Folding Defects to Membrane Protein Homeostasis. J Am Chem Soc 2018; 141:204-215. [PMID: 30537820 DOI: 10.1021/jacs.8b08243] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Membrane proteins are prone to misfolding and degradation within the cell, yet the nature of the conformational defects involved in this process remain poorly understood. The earliest stages of membrane protein folding are mediated by the Sec61 translocon, a molecular machine that facilitates the lateral partitioning of the polypeptide into the membrane. Proper membrane integration is an essential prerequisite for folding of the nascent chain. However, the marginal energetic drivers of this reaction suggest the translocon may operate with modest fidelity. In this work, we employed biophysical modeling in conjunction with quantitative biochemical measurements in order to evaluate the extent to which cotranslational folding defects influence membrane protein homeostasis. Protein engineering was employed to selectively perturb the topological energetics of human rhodopsin, and the expression and cellular trafficking of engineered variants were quantitatively compared. Our results reveal clear relationships between topological energetics and the efficiency of rhodopsin biogenesis, which appears to be limited by the propensity of a polar transmembrane domain to achieve its correct topological orientation. Though the polarity of this segment is functionally constrained, we find that its topology can be stabilized in a manner that enhances biogenesis without compromising the functional properties of rhodopsin. Furthermore, sequence alignments reveal this topological instability has been conserved throughout the course of evolution. These results suggest that topological defects significantly contribute to the inefficiency of membrane protein folding in the cell. Additionally, our findings suggest that the marginal stability of rhodopsin may represent an evolved trait.
Collapse
Affiliation(s)
- Francis J Roushar
- Department of Chemistry , Indiana University , Bloomington , Indiana 47405 , United States
| | - Timothy C Gruenhagen
- Department of Chemistry , Indiana University , Bloomington , Indiana 47405 , United States
| | - Wesley D Penn
- Department of Chemistry , Indiana University , Bloomington , Indiana 47405 , United States
| | - Bian Li
- Department of Chemistry , Vanderbilt University , Nashville , Tennessee 37235 , United States
| | - Jens Meiler
- Department of Chemistry , Vanderbilt University , Nashville , Tennessee 37235 , United States
| | - Beata Jastrzebska
- Department of Pharmacology , Case Western Reserve University , Cleveland , Ohio 44106 , United States
| | - Jonathan P Schlebach
- Department of Chemistry , Indiana University , Bloomington , Indiana 47405 , United States
| |
Collapse
|
166
|
Kappel K, Das R. Sampling Native-like Structures of RNA-Protein Complexes through Rosetta Folding and Docking. Structure 2018; 27:140-151.e5. [PMID: 30416038 DOI: 10.1016/j.str.2018.10.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 08/27/2018] [Accepted: 10/05/2018] [Indexed: 10/27/2022]
Abstract
RNA-protein complexes underlie numerous cellular processes including translation, splicing, and posttranscriptional regulation of gene expression. The structures of these complexes are crucial to their functions but often elude high-resolution structure determination. Computational methods are needed that can integrate low-resolution data for RNA-protein complexes while modeling de novo the large conformational changes of RNA components upon complex formation. To address this challenge, we describe RNP-denovo, a Rosetta method to simultaneously fold-and-dock RNA to a protein surface. On a benchmark set of diverse RNA-protein complexes not solvable with prior strategies, RNP-denovo consistently sampled native-like structures with better than nucleotide resolution. We revisited three past blind modeling challenges involving the spliceosome, telomerase, and a methyltransferase-ribosomal RNA complex in which previous methods gave poor results. When coupled with the same sparse FRET, crosslinking, and functional data used previously, RNP-denovo gave models with significantly improved accuracy. These results open a route to modeling global folds of RNA-protein complexes from low-resolution data.
Collapse
Affiliation(s)
- Kalli Kappel
- Biophysics Program, Stanford University, Stanford, CA 94305, USA
| | - Rhiju Das
- Biophysics Program, Stanford University, Stanford, CA 94305, USA; Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Physics, Stanford University, Stanford, CA 94305, USA.
| |
Collapse
|
167
|
Rubenstein AB, Blacklock K, Nguyen H, Case DA, Khare SD. Systematic Comparison of Amber and Rosetta Energy Functions for Protein Structure Evaluation. J Chem Theory Comput 2018; 14:6015-6025. [PMID: 30240210 DOI: 10.1021/acs.jctc.8b00303] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
An accurate energy function is an essential component of biomolecular structural modeling and design. The comparison of differently derived energy functions enables analysis of the strengths and weaknesses of each energy function and provides independent benchmarks for evaluating improvements within a given energy function. We compared the molecular mechanics Amber empirical energy function to two versions of the Rosetta energy function (talaris2014 and REF2015) in decoy discrimination and loop modeling tests. In decoy discrimination tests, both Rosetta and Amber (ff14SBonlySC) energy functions performed well in scoring the native state as the lowest energy conformation in many cases, but several false minima were found in with both talaris2014 and Amber ff14SBonlySC scoring functions. The current default version of the Rosetta energy function, REF2015, which is parametrized on both small molecule and macromolecular benchmark sets to improve decoy discrimination, performs significantly better than talaris2014, highlighting the improvements made to the Rosetta scoring approach. There are no cases in Rosetta REF2015, and 8/140 cases in Amber, where a false minimum is found that is absent in the alternative landscape. In loop modeling tests, Amber ff14SBonlySC and REF2015 perform equivalently, although false minima are detected in several cases for both. The balance between dihedral, electrostatic, solvation and hydrogen bonding scores contribute to the existence of false minima. To take advantage of the semi-orthogonal nature of the Rosetta and Amber energy functions, we developed a technique that combines Amber and Rosetta conformational rankings to predict the most near-native model for a given protein. This algorithm improves upon predictions from either energy function in isolation and should aid in model selection for structure evaluation and loop modeling tasks.
Collapse
|
168
|
Zheng Z, Pei J, Bansal N, Liu H, Song LF, Merz KM. Generation of Pairwise Potentials Using Multidimensional Data Mining. J Chem Theory Comput 2018; 14:5045-5067. [PMID: 30183299 DOI: 10.1021/acs.jctc.8b00516] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The rapid development of molecular structural databases provides the chemistry community access to an enormous array of experimental data that can be used to build and validate computational models. Using radial distribution functions collected from experimentally available X-ray and NMR structures, a number of so-called statistical potentials have been developed over the years using the structural data mining strategy. These potentials have been developed within the context of the two-particle Kirkwood equation by extending its original use for isotropic monatomic systems to anisotropic biomolecular systems. However, the accuracy and the unclear physical meaning of statistical potentials have long formed the central arguments against such methods. In this work, we present a new approach to generate molecular energy functions using structural data mining. Instead of employing the Kirkwood equation and introducing the "reference state" approximation, we model the multidimensional probability distributions of the molecular system using graphical models and generate the target pairwise Boltzmann probabilities using the Bayesian field theory. Different from the current statistical potentials that mimic the "knowledge-based" PMF based on the 2-particle Kirkwood equation, the graphical-model-based structure-derived potential developed in this study focuses on the generation of lower-dimensional Boltzmann distributions of atoms through reduction of dimensionality. We have named this new scoring function GARF, and in this work we focus on the mathematical derivation of our novel approach followed by validation studies on its ability to predict protein-ligand interactions.
Collapse
Affiliation(s)
- Zheng Zheng
- Department of Chemistry , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Jun Pei
- Department of Chemistry , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Nupur Bansal
- Department of Chemistry , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Hao Liu
- Department of Chemistry , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Lin Frank Song
- Department of Chemistry , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Kenneth M Merz
- Department of Chemistry , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| |
Collapse
|
169
|
Kandathil SM, Garza-Fabre M, Handl J, Lovell SC. Improved fragment-based protein structure prediction by redesign of search heuristics. Sci Rep 2018; 8:13694. [PMID: 30209258 PMCID: PMC6135816 DOI: 10.1038/s41598-018-31891-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 08/22/2018] [Indexed: 11/09/2022] Open
Abstract
Difficulty in sampling large and complex conformational spaces remains a key limitation in fragment-based de novo prediction of protein structure. Our previous work has shown that even for small-to-medium-sized proteins, some current methods inadequately sample alternative structures. We have developed two new conformational sampling techniques, one employing a bilevel optimisation framework and the other employing iterated local search. We combine strategies of forced structural perturbation (where some fragment insertions are accepted regardless of their impact on scores) and greedy local optimisation, allowing greater exploration of the available conformational space. Comparisons against the Rosetta Abinitio method indicate that our protocols more frequently generate native-like predictions for many targets, even following the low-resolution phase, using a given set of fragment libraries. By contrasting results across two different fragment sets, we show that our methods are able to better take advantage of high-quality fragments. These improvements can also translate into more reliable identification of near-native structures in a simple clustering-based model selection procedure. We show that when fragment libraries are sufficiently well-constructed, improved breadth of exploration within runs improves prediction accuracy. Our results also suggest that in benchmarking scenarios, a total exclusion of fragments drawn from homologous templates can make performance differences between methods appear less pronounced.
Collapse
Affiliation(s)
- Shaun M Kandathil
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PL, United Kingdom. .,Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom.
| | - Mario Garza-Fabre
- Decision and Cognitive Sciences Research Centre, University of Manchester, Manchester, M13 9PL, United Kingdom.,Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), Km. 5.5 Carretera Cd. Victoria-Soto La Marina, Cd. Victoria, Tamaulipas, 87130, Mexico
| | - Julia Handl
- Decision and Cognitive Sciences Research Centre, University of Manchester, Manchester, M13 9PL, United Kingdom
| | - Simon C Lovell
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PL, United Kingdom
| |
Collapse
|
170
|
Abstract
Chemical Shift-Rosetta (CS-Rosetta) is an automated method that employs NMR chemical shifts to model protein structures de novo. In this chapter, we introduce the terminology and central concepts of CS-Rosetta. We describe the architecture and functionality of automatic NOESY assignment (AutoNOE) and structure determination protocols (Abrelax and RASREC) within the CS-Rosetta framework. We further demonstrate how CS-Rosetta can discriminate near-native structures against a large conformational search space using restraints obtained from NMR data, and/or sequence and structure homology. We highlight how CS-Rosetta can be combined with alternative automated approaches to (i) model oligomeric systems and (ii) create NMR-based structure determination pipeline. To show its practical applicability, we emphasize on the computational requirements and performance of CS-Rosetta for protein targets of varying molecular weight and complexity. Finally, we discuss the current Python interface, which enables easy execution of protocols for rapid and accurate high-resolution structure determination.
Collapse
Affiliation(s)
- Santrupti Nerli
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, United States; Department of Computer Science, University of California Santa Cruz, Santa Cruz, CA, United States
| | - Nikolaos G Sgourakis
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, United States.
| |
Collapse
|
171
|
Kc DB. Recent advances in sequence-based protein structure prediction. Brief Bioinform 2018; 18:1021-1032. [PMID: 27562963 DOI: 10.1093/bib/bbw070] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2016] [Indexed: 11/13/2022] Open
Abstract
The most accurate characterizations of the structure of proteins are provided by structural biology experiments. However, because of the high cost and labor-intensive nature of the structural experiments, the gap between the number of protein sequences and solved structures is widening rapidly. Development of computational methods to accurately model protein structures from sequences is becoming increasingly important to the biological community. In this article, we highlight some important progress in the field of protein structure prediction, especially those related to free modeling (FM) methods that generate structure models without using homologous templates. We also provide a short synopsis of some of the recent advances in FM approaches as demonstrated in the recent Computational Assessment of Structure Prediction competition as well as recent trends and outlook for FM approaches in protein structure prediction.
Collapse
|
172
|
Anishchenko I, Kundrotas PJ, Vakser IA. Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model. Biophys J 2018; 115:809-821. [PMID: 30122295 DOI: 10.1016/j.bpj.2018.07.035] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 07/16/2018] [Accepted: 07/31/2018] [Indexed: 12/18/2022] Open
Abstract
The energy function is the key component of protein modeling methodology. This work presents a semianalytical approach to the development of contact potentials for protein structure modeling. Residue-residue and atom-atom contact energies were derived by maximizing the probability of observing native sequences in a nonredundant set of protein structures. The optimization task was formulated as an inverse statistical mechanics problem applied to the Potts model. Its solution by pseudolikelihood maximization provides consistent estimates of coupling constants at atomic and residue levels. The best performance was achieved when interacting atoms were grouped according to their physicochemical properties. For individual protein structures, the performance of the contact potentials in distinguishing near-native structures from the decoys is similar to the top-performing scoring functions. The potentials also yielded significant improvement in the protein docking success rates. The potentials recapitulated experimentally determined protein stability changes upon point mutations and protein-protein binding affinities. The approach offers a different perspective on knowledge-based potentials and may serve as the basis for their further development.
Collapse
Affiliation(s)
- Ivan Anishchenko
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas
| | - Petras J Kundrotas
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas.
| | - Ilya A Vakser
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas.
| |
Collapse
|
173
|
Deng H, Jia Y, Zhang Y. Protein structure prediction. INTERNATIONAL JOURNAL OF MODERN PHYSICS. B 2018; 32:1840009. [PMID: 30853739 PMCID: PMC6407873 DOI: 10.1142/s021797921840009x] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Predicting 3D structure of protein from its amino acid sequence is one of the most important unsolved problems in biophysics and computational biology. This paper attempts to give a comprehensive introduction of the most recent effort and progress on protein structure prediction. Following the general flowchart of structure prediction, related concepts and methods are presented and discussed. Moreover, brief introductions are made to several widely-used prediction methods and the community-wide critical assessment of protein structure prediction (CASP) experiments.
Collapse
Affiliation(s)
- Haiyou Deng
- College of Science, Huazhong Agricultural University, Wuhan 4R0070, P. R. China
| | - Ya Jia
- College of Physical Science and Technology, Central China Normal University, Wuhan 430079, P. R. China
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 45108, USA
| |
Collapse
|
174
|
Porter KA, Xia B, Beglov D, Bohnuud T, Alam N, Schueler-Furman O, Kozakov D. ClusPro PeptiDock: efficient global docking of peptide recognition motifs using FFT. Bioinformatics 2018; 33:3299-3301. [PMID: 28430871 PMCID: PMC5860028 DOI: 10.1093/bioinformatics/btx216] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Accepted: 04/14/2017] [Indexed: 11/14/2022] Open
Abstract
Summary We present an approach for the efficient docking of peptide motifs to their free receptor structures. Using a motif based search, we can retrieve structural fragments from the Protein Data Bank (PDB) that are very similar to the peptide’s final, bound conformation. We use a Fast Fourier Transform (FFT) based docking method to quickly perform global rigid body docking of these fragments to the receptor. According to CAPRI peptide docking criteria, an acceptable conformation can often be found among the top-ranking predictions. Availability and Implementation The method is available as part of the protein-protein docking server ClusPro at https://peptidock.cluspro.org/nousername.php. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Kathryn A Porter
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Bing Xia
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Tanggis Bohnuud
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Nawsad Alam
- Department of Microbiology, Hebrew University, Jerusalem 91120, Israel
| | | | - Dima Kozakov
- Department of Applied Mathematics and Statistics.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| |
Collapse
|
175
|
Chen X, Yang B, Lin Z. A random forest learning assisted "divide and conquer" approach for peptide conformation search. Sci Rep 2018; 8:8796. [PMID: 29891960 PMCID: PMC5995823 DOI: 10.1038/s41598-018-27167-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 05/17/2018] [Indexed: 11/09/2022] Open
Abstract
Computational determination of peptide conformations is challenging as it is a problem of finding minima in a high-dimensional space. The "divide and conquer" approach is promising for reliably reducing the search space size. A random forest learning model is proposed here to expand the scope of applicability of the "divide and conquer" approach. A random forest classification algorithm is used to characterize the distributions of the backbone φ-ψ units ("words"). A random forest supervised learning model is developed to analyze the combinations of the φ-ψ units ("grammar"). It is found that amino acid residues may be grouped as equivalent "words", while the φ-ψ combinations in low-energy peptide conformations follow a distinct "grammar". The finding of equivalent words empowers the "divide and conquer" method with the flexibility of fragment substitution. The learnt grammar is used to improve the efficiency of the "divide and conquer" method by removing unfavorable φ-ψ combinations without the need of dedicated human effort. The machine learning assisted search method is illustrated by efficiently searching the conformations of GGG/AAA/GGGG/AAAA/GGGGG through assembling the structures of GFG/GFGG. Moreover, the computational cost of the new method is shown to increase rather slowly with the peptide length.
Collapse
Affiliation(s)
- Xin Chen
- Hefei National Laboratory for Physical Sciences at Microscales & CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics, Department of Physics, University of Science and Technology of China, Hefei, 230026, China
| | - Bing Yang
- Hefei National Laboratory for Physical Sciences at Microscales & CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics, Department of Physics, University of Science and Technology of China, Hefei, 230026, China
| | - Zijing Lin
- Hefei National Laboratory for Physical Sciences at Microscales & CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics, Department of Physics, University of Science and Technology of China, Hefei, 230026, China.
| |
Collapse
|
176
|
Shirvanizadeh N, Vriend G, Arab SS. Loop modelling 1.0. J Mol Graph Model 2018; 84:64-68. [PMID: 29920424 DOI: 10.1016/j.jmgm.2018.06.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 05/28/2018] [Accepted: 06/01/2018] [Indexed: 10/14/2022]
Abstract
Engineering surface loops is a sub-topic of protein engineering that is used routinely in many research fields in academia and industry alike. We provide some tools that search in the PDB for loops satisfying a wide variety of constraints. We illustrate the usefulness of these tools by applying them to a series of recently published studies that included loop engineering or loop modelling. LoopFinder finds loops that fit between two anchor stretches of typically 2, 3, or 4 amino acids each. ProDA find loops of a given length with predefined secondary structure, residue types, hydrophobicity, etc. WHAT IF has gotten a series of new options to scan the whole PDB for loops combining the LoopFinder and ProDA techniques. The open nature of these tools will allow bioinformaticians in this field to easily design their own loop modelling software around our tools. AVAILABILITY AND IMPLEMENTATION LoopFinder is a stand-alone Fortran program that is likely to compile and run on every computer. The LoopFinder source code, data files, and documentation are freely available from swift.cmbi.ru.nl/gv/loops/. ProDA is free to all users. There is no login requirement. It is available at: http://bioinf.modares.ac.ir/software/linda/. WHAT IF is shareware that is available from https://swift.cmbi.ru.nl/whatif/.
Collapse
Affiliation(s)
- Niloofar Shirvanizadeh
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, 14115-175, Tehran, Iran
| | - Gert Vriend
- CMBI, Radboudumc, 260 Nijmegen, the Netherlands
| | - Seyed Shahriar Arab
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, 14115-175, Tehran, Iran.
| |
Collapse
|
177
|
Aprahamian ML, Chea EE, Jones LM, Lindert S. Rosetta Protein Structure Prediction from Hydroxyl Radical Protein Footprinting Mass Spectrometry Data. Anal Chem 2018; 90:7721-7729. [PMID: 29874044 DOI: 10.1021/acs.analchem.8b01624] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In recent years mass spectrometry-based covalent labeling techniques such as hydroxyl radical footprinting (HRF) have emerged as valuable structural biology techniques, yielding information on protein tertiary structure. These data, however, are not sufficient to predict protein structure unambiguously, as they provide information only on the relative solvent exposure of certain residues. Despite some recent advances, no software currently exists that can utilize covalent labeling mass spectrometry data to predict protein tertiary structure. We have developed the first such tool, which incorporates mass spectrometry derived protection factors from HRF labeling as a new centroid score term for the Rosetta scoring function to improve the prediction of protein tertiary structures. We tested our method on a set of four soluble benchmark proteins with known crystal structures and either published HRF experimental results or internally acquired data. Using the HRF labeling data, we rescored large decoy sets of structures predicted with Rosetta for each of the four benchmark proteins. As a result, the model quality improved for all benchmark proteins as compared to when scored with Rosetta alone. For two of the four proteins we were even able to identify atomic resolution models with the addition of HRF data.
Collapse
Affiliation(s)
- Melanie L Aprahamian
- Department of Chemistry and Biochemistry , Ohio State University , Columbus , Ohio 43210 , United States
| | - Emily E Chea
- Department of Pharmaceutical Sciences , University of Maryland , Baltimore , Maryland 21201 , United States
| | - Lisa M Jones
- Department of Pharmaceutical Sciences , University of Maryland , Baltimore , Maryland 21201 , United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry , Ohio State University , Columbus , Ohio 43210 , United States
| |
Collapse
|
178
|
Nerli S, McShan AC, Sgourakis NG. Chemical shift-based methods in NMR structure determination. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2018; 106-107:1-25. [PMID: 31047599 PMCID: PMC6788782 DOI: 10.1016/j.pnmrs.2018.03.002] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 03/09/2018] [Accepted: 03/09/2018] [Indexed: 05/08/2023]
Abstract
Chemical shifts are highly sensitive probes harnessed by NMR spectroscopists and structural biologists as conformational parameters to characterize a range of biological molecules. Traditionally, assignment of chemical shifts has been a labor-intensive process requiring numerous samples and a suite of multidimensional experiments. Over the past two decades, the development of complementary computational approaches has bolstered the analysis, interpretation and utilization of chemical shifts for elucidation of high resolution protein and nucleic acid structures. Here, we review the development and application of chemical shift-based methods for structure determination with a focus on ab initio fragment assembly, comparative modeling, oligomeric systems, and automated assignment methods. Throughout our discussion, we point out practical uses, as well as advantages and caveats, of using chemical shifts in structure modeling. We additionally highlight (i) hybrid methods that employ chemical shifts with other types of NMR restraints (residual dipolar couplings, paramagnetic relaxation enhancements and pseudocontact shifts) that allow for improved accuracy and resolution of generated 3D structures, (ii) the utilization of chemical shifts to model the structures of sparsely populated excited states, and (iii) modeling of sidechain conformations. Finally, we briefly discuss the advantages of contemporary methods that employ sparse NMR data recorded using site-specific isotope labeling schemes for chemical shift-driven structure determination of larger molecules. With this review, we aim to emphasize the accessibility and versatility of chemical shifts for structure determination of challenging biological systems, and to point out emerging areas of development that lead us towards the next generation of tools.
Collapse
Affiliation(s)
- Santrupti Nerli
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA 95064, United States; Department of Computer Science, University of California Santa Cruz, Santa Cruz, CA 95064, United States
| | - Andrew C McShan
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA 95064, United States
| | - Nikolaos G Sgourakis
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA 95064, United States.
| |
Collapse
|
179
|
Postic G, Hamelryck T, Chomilier J, Stratmann D. MyPMFs: a simple tool for creating statistical potentials to assess protein structural models. Biochimie 2018; 151:37-41. [PMID: 29857183 DOI: 10.1016/j.biochi.2018.05.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2018] [Accepted: 05/25/2018] [Indexed: 01/18/2023]
Abstract
Evaluating the model quality of protein structures that evolve in environments with particular physicochemical properties requires scoring functions that are adapted to their specific residue compositions and/or structural characteristics. Thus, computational methods developed for structures from the cytosol cannot work properly on membrane or secreted proteins. Here, we present MyPMFs, an easy-to-use tool that allows users to train statistical potentials of mean force (PMFs) on the protein structures of their choice, with all parameters being adjustable. We demonstrate its use by creating an accurate statistical potential for transmembrane protein domains. We also show its usefulness to study the influence of the physical environment on residue interactions within protein structures. Our open-source software is freely available for download at https://github.com/bibip-impmc/mypmfs.
Collapse
Affiliation(s)
- Guillaume Postic
- Sorbonne Université, UMR 7590 CNRS, MNHN, IRD, Institut de Minéralogie de Physique des Matériaux et de Cosmochimie (IMPMC), Paris, France.
| | - Thomas Hamelryck
- Bioinformatics Centre, Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Copenhagen, Denmark; Image Section, Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Jacques Chomilier
- Sorbonne Université, UMR 7590 CNRS, MNHN, IRD, Institut de Minéralogie de Physique des Matériaux et de Cosmochimie (IMPMC), Paris, France
| | - Dirk Stratmann
- Sorbonne Université, UMR 7590 CNRS, MNHN, IRD, Institut de Minéralogie de Physique des Matériaux et de Cosmochimie (IMPMC), Paris, France
| |
Collapse
|
180
|
Abstract
During the last two decades, the pharmaceutical industry has progressed from detecting small molecules to designing biologic-based therapeutics. Amino acid-based drugs are a group of biologic-based therapeutics that can effectively combat the diseases caused by drug resistance or molecular deficiency. Computational techniques play a key role to design and develop the amino acid-based therapeutics such as proteins, peptides and peptidomimetics. In this study, it was attempted to discuss the various elements for computational design of amino acid-based therapeutics. Protein design seeks to identify the properties of amino acid sequences that fold to predetermined structures with desirable structural and functional characteristics. Peptide drugs occupy a middle space between proteins and small molecules and it is hoped that they can target "undruggable" intracellular protein-protein interactions. Peptidomimetics, the compounds that mimic the biologic characteristics of peptides, present refined pharmacokinetic properties compared to the original peptides. Here, the elaborated techniques that are developed to characterize the amino acid sequences consistent with a specific structure and allow protein design are discussed. Moreover, the key principles and recent advances in currently introduced computational techniques for rational peptide design are spotlighted. The most advanced computational techniques developed to design novel peptidomimetics are also summarized.
Collapse
Affiliation(s)
- Tayebeh Farhadi
- Chronic Respiratory Diseases Research Center (CRDRC), National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed MohammadReza Hashemian
- Chronic Respiratory Diseases Research Center (CRDRC), National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Clinical Tuberculosis and Epidemiology Research Center, National Research Institute of Tuberculosis and Lung Disease, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| |
Collapse
|
181
|
Seo JH, Min WK, Lee SG, Yun H, Kim BG. To the Final Goal: Can We Predict and Suggest Mutations for Protein to Develop Desired Phenotype? BIOTECHNOL BIOPROC E 2018. [DOI: 10.1007/s12257-018-0064-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
|
182
|
Correa L, Borguesan B, Farfan C, Inostroza-Ponta M, Dorn M. A Memetic Algorithm for 3-D Protein Structure Prediction Problem. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:690-704. [PMID: 27925594 DOI: 10.1109/tcbb.2016.2635143] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Memetic Algorithms are population-based metaheuristics intrinsically concerned with exploiting all available knowledge about the problem under study. The incorporation of problem domain knowledge is not an optional mechanism, but a fundamental feature of the Memetic Algorithms. In this paper, we present a Memetic Algorithm to tackle the three-dimensional protein structure prediction problem. The method uses a structured population and incorporates a Simulated Annealing algorithm as a local search strategy, as well as ad-hoc crossover and mutation operators to deal with the problem. It takes advantage of structural knowledge stored in the Protein Data Bank, by using an Angle Probability List that helps to reduce the search space and to guide the search strategy. The proposed algorithm was tested on nineteen protein sequences of amino acid residues, and the results show the ability of the algorithm to find native-like protein structures. Experimental results have revealed that the proposed algorithm can find good solutions regarding root-mean-square deviation and global distance total score test in comparison with the experimental protein structures. We also show that our results are comparable in terms of folding organization with state-of-the-art prediction methods, corroborating the effectiveness of our proposal.
Collapse
|
183
|
A Novel Method for Drug Screen to Regulate G Protein-Coupled Receptors in the Metabolic Network of Alzheimer's Disease. BIOMED RESEARCH INTERNATIONAL 2018; 2018:5486403. [PMID: 29675426 PMCID: PMC5838471 DOI: 10.1155/2018/5486403] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Revised: 12/02/2017] [Accepted: 12/17/2017] [Indexed: 12/02/2022]
Abstract
Alzheimer's disease (AD) is a chronic and progressive neurodegenerative disorder and the pathogenesis of AD is poorly understood. G protein-coupled receptors (GPCRs) are involved in numerous key AD pathways and play a key role in the pathology of AD. To fully understand the pathogenesis of AD and design novel drug therapeutics, analyzing the connection between AD and GPCRs is of great importance. In this paper, we firstly build and analyze the AD-related pathway by consulting the KEGG pathway of AD and a mass of literature and collect 25 AD-related GPCRs for drug discovery. Then the ILbind and AutoDock Vina tools are integrated to find out potential drugs related to AD. According to the analysis of DUD-E dataset, we select five drugs, that is, Acarbose (ACR), Carvedilol (CVD), Digoxin (DGX), NADH (NAI), and Telmisartan (TLS), by sorting the ILbind scores (≥0.73). Then depending on their AutoDock Vina scores and pocket position information, the binding patterns of these five drugs are obtained. We analyze the regulation function of GPCRs in the metabolic network of AD based on the drug screen results, which may be helpful for the study of the off-target effect and the side effect of drugs.
Collapse
|
184
|
Guiding exploration in conformational feature space with Lipschitz underestimation for ab-initio protein structure prediction. Comput Biol Chem 2018; 73:105-119. [PMID: 29475175 DOI: 10.1016/j.compbiolchem.2018.02.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2017] [Revised: 01/25/2018] [Accepted: 02/03/2018] [Indexed: 01/04/2023]
Abstract
Computing conformations which are essential to associate structural and functional information with gene sequences, is challenging due to the high dimensionality and rugged energy surface of the protein conformational space. Consequently, the dimension of the protein conformational space should be reduced to a proper level, and an effective exploring algorithm should be proposed. In this paper, a plug-in method for guiding exploration in conformational feature space with Lipschitz underestimation (LUE) for ab-initio protein structure prediction is proposed. The conformational space is converted into ultrafast shape recognition (USR) feature space firstly. Based on the USR feature space, the conformational space can be further converted into Underestimation space according to Lipschitz estimation theory for guiding exploration. As a consequence of the use of underestimation model, the tight lower bound estimate information can be used for exploration guidance, the invalid sampling areas can be eliminated in advance, and the number of energy function evaluations can be reduced. The proposed method provides a novel technique to solve the exploring problem of protein conformational space. LUE is applied to differential evolution (DE) algorithm, and metropolis Monte Carlo(MMC) algorithm which is available in the Rosetta; When LUE is applied to DE and MMC, it will be screened by the underestimation method prior to energy calculation and selection. Further, LUE is compared with DE and MMC by testing on 15 small-to-medium structurally diverse proteins. Test results show that near-native protein structures with higher accuracy can be obtained more rapidly and efficiently with the use of LUE.
Collapse
|
185
|
Kim SS, Seffernick JT, Lindert S. Accurately Predicting Disordered Regions of Proteins Using Rosetta ResidueDisorder Application. J Phys Chem B 2018; 122:3920-3930. [PMID: 29595057 DOI: 10.1021/acs.jpcb.8b01763] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Although many proteins necessitate well-folded structures to properly instigate their biological functions, a large fraction of functioning proteins contain regions-known as intrinsically disordered protein regions-where stable structures are not likely to form. Notable functional roles of intrinsically disordered proteins are in transcriptional regulation, translation, and cellular signal transduction. Moreover, intrinsically disordered protein regions are highly abundant in many proteins associated with various human diseases, therefore these segments have become attractive drug targets for potential therapeutics. Over the past decades, numerous computational methods have been developed to accurately predict disordered regions of proteins. Here we introduce a user-friendly and reliable approach for the prediction of disordered protein regions using the structure prediction software Rosetta. Using 245 proteins from a benchmark data set (16 DisProt database proteins) and a test data set (229 proteins with NMR data), we use Rosetta to predict the global protein structures and then show that there is a statistically significant difference between Rosetta scores in disordered and ordered regions, with scores being less favorable in disordered regions. Furthermore, the difference in scores between ordered and disordered protein regions is sufficient to accurately identify disordered protein regions. As a result, our Rosetta ResidueDisorder method (benchmark data set prediction accuracy of 71.77% and independent test data set prediction accuracy of 65.37%) outperformed other established disorder prediction tools and did not exhibit a biased prediction toward either ordered or disordered regions. To facilitate usage, a Rosetta application has been developed for the Rosetta ResidueDisorder method.
Collapse
Affiliation(s)
- Stephanie S Kim
- Department of Chemistry and Biochemistry , Ohio State University , Columbus , Ohio 43210 , United States
| | - Justin T Seffernick
- Department of Chemistry and Biochemistry , Ohio State University , Columbus , Ohio 43210 , United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry , Ohio State University , Columbus , Ohio 43210 , United States
| |
Collapse
|
186
|
Park H, Ovchinnikov S, Kim DE, DiMaio F, Baker D. Protein homology model refinement by large-scale energy optimization. Proc Natl Acad Sci U S A 2018; 115:3054-3059. [PMID: 29507254 PMCID: PMC5866580 DOI: 10.1073/pnas.1719115115] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Proteins fold to their lowest free-energy structures, and hence the most straightforward way to increase the accuracy of a partially incorrect protein structure model is to search for the lowest-energy nearby structure. This direct approach has met with little success for two reasons: first, energy function inaccuracies can lead to false energy minima, resulting in model degradation rather than improvement; and second, even with an accurate energy function, the search problem is formidable because the energy only drops considerably in the immediate vicinity of the global minimum, and there are a very large number of degrees of freedom. Here we describe a large-scale energy optimization-based refinement method that incorporates advances in both search and energy function accuracy that can substantially improve the accuracy of low-resolution homology models. The method refined low-resolution homology models into correct folds for 50 of 84 diverse protein families and generated improved models in recent blind structure prediction experiments. Analyses of the basis for these improvements reveal contributions from both the improvements in conformational sampling techniques and the energy function.
Collapse
Affiliation(s)
- Hahnbeom Park
- Department of Biochemistry, University of Washington, Seattle, WA 98105
- Institute for Protein Design, University of Washington, Seattle, WA 98105
| | - Sergey Ovchinnikov
- Department of Biochemistry, University of Washington, Seattle, WA 98105
- Institute for Protein Design, University of Washington, Seattle, WA 98105
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA 98105
| | - David E Kim
- Institute for Protein Design, University of Washington, Seattle, WA 98105
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA 98105
- Institute for Protein Design, University of Washington, Seattle, WA 98105
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98105;
- Institute for Protein Design, University of Washington, Seattle, WA 98105
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105
| |
Collapse
|
187
|
Sekiguchi T, Tamura M, Oba H, Çarçarbal P, Lozada-Garcia RR, Zehnacker-Rentien A, Grégoire G, Ishiuchi SI, Fujii M. Molecular Recognition by a Short Partial Peptide of the Adrenergic Receptor: A Bottom-Up Approach. Angew Chem Int Ed Engl 2018; 57:5626-5629. [DOI: 10.1002/anie.201712976] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2017] [Revised: 02/13/2018] [Indexed: 12/26/2022]
Affiliation(s)
- Tsubasa Sekiguchi
- Laboratory for Chemistry and Life Science; Institute of Innovative Research; Tokyo Institute of Technology; 4259, Nagatsuta-cho, Midori-ku Yokohama 226-8503 Japan
- School of Life Science and Technology; Tokyo Institute of Technology; 4259, Nagatsuta-cho, Midori-ku Yokohama 226-8503 Japan
| | - Masato Tamura
- Laboratory for Chemistry and Life Science; Institute of Innovative Research; Tokyo Institute of Technology; 4259, Nagatsuta-cho, Midori-ku Yokohama 226-8503 Japan
- School of Life Science and Technology; Tokyo Institute of Technology; 4259, Nagatsuta-cho, Midori-ku Yokohama 226-8503 Japan
| | - Hikari Oba
- Laboratory for Chemistry and Life Science; Institute of Innovative Research; Tokyo Institute of Technology; 4259, Nagatsuta-cho, Midori-ku Yokohama 226-8503 Japan
| | - Pierre Çarçarbal
- Institut des Sciences Moléculaires d'Orsay (ISMO); CNRS, Univ. Paris-Sud, Université Paris-Saclay; F-91405 Orsay France
| | - Rolando Rafael Lozada-Garcia
- Institut des Sciences Moléculaires d'Orsay (ISMO); CNRS, Univ. Paris-Sud, Université Paris-Saclay; F-91405 Orsay France
| | - Anne Zehnacker-Rentien
- Institut des Sciences Moléculaires d'Orsay (ISMO); CNRS, Univ. Paris-Sud, Université Paris-Saclay; F-91405 Orsay France
- Centre Laser de l'Université Paris-Sud (CLUPS/LUMAT); Univ. Paris-Sud, CNRS, IOGS, Université Paris-Saclay; F-91405 Orsay France
| | - Gilles Grégoire
- Institut des Sciences Moléculaires d'Orsay (ISMO); CNRS, Univ. Paris-Sud, Université Paris-Saclay; F-91405 Orsay France
- Centre Laser de l'Université Paris-Sud (CLUPS/LUMAT); Univ. Paris-Sud, CNRS, IOGS, Université Paris-Saclay; F-91405 Orsay France
| | - Shun-ichi Ishiuchi
- Laboratory for Chemistry and Life Science; Institute of Innovative Research; Tokyo Institute of Technology; 4259, Nagatsuta-cho, Midori-ku Yokohama 226-8503 Japan
| | - Masaaki Fujii
- Laboratory for Chemistry and Life Science; Institute of Innovative Research; Tokyo Institute of Technology; 4259, Nagatsuta-cho, Midori-ku Yokohama 226-8503 Japan
| |
Collapse
|
188
|
Sekiguchi T, Tamura M, Oba H, Çarçarbal P, Lozada-Garcia RR, Zehnacker-Rentien A, Grégoire G, Ishiuchi SI, Fujii M. Molecular Recognition by a Short Partial Peptide of the Adrenergic Receptor: A Bottom-Up Approach. Angew Chem Int Ed Engl 2018. [DOI: 10.1002/ange.201712976] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Tsubasa Sekiguchi
- Laboratory for Chemistry and Life Science; Institute of Innovative Research; Tokyo Institute of Technology; 4259, Nagatsuta-cho, Midori-ku Yokohama 226-8503 Japan
- School of Life Science and Technology; Tokyo Institute of Technology; 4259, Nagatsuta-cho, Midori-ku Yokohama 226-8503 Japan
| | - Masato Tamura
- Laboratory for Chemistry and Life Science; Institute of Innovative Research; Tokyo Institute of Technology; 4259, Nagatsuta-cho, Midori-ku Yokohama 226-8503 Japan
- School of Life Science and Technology; Tokyo Institute of Technology; 4259, Nagatsuta-cho, Midori-ku Yokohama 226-8503 Japan
| | - Hikari Oba
- Laboratory for Chemistry and Life Science; Institute of Innovative Research; Tokyo Institute of Technology; 4259, Nagatsuta-cho, Midori-ku Yokohama 226-8503 Japan
| | - Pierre Çarçarbal
- Institut des Sciences Moléculaires d'Orsay (ISMO); CNRS, Univ. Paris-Sud, Université Paris-Saclay; F-91405 Orsay France
| | - Rolando Rafael Lozada-Garcia
- Institut des Sciences Moléculaires d'Orsay (ISMO); CNRS, Univ. Paris-Sud, Université Paris-Saclay; F-91405 Orsay France
| | - Anne Zehnacker-Rentien
- Institut des Sciences Moléculaires d'Orsay (ISMO); CNRS, Univ. Paris-Sud, Université Paris-Saclay; F-91405 Orsay France
- Centre Laser de l'Université Paris-Sud (CLUPS/LUMAT); Univ. Paris-Sud, CNRS, IOGS, Université Paris-Saclay; F-91405 Orsay France
| | - Gilles Grégoire
- Institut des Sciences Moléculaires d'Orsay (ISMO); CNRS, Univ. Paris-Sud, Université Paris-Saclay; F-91405 Orsay France
- Centre Laser de l'Université Paris-Sud (CLUPS/LUMAT); Univ. Paris-Sud, CNRS, IOGS, Université Paris-Saclay; F-91405 Orsay France
| | - Shun-ichi Ishiuchi
- Laboratory for Chemistry and Life Science; Institute of Innovative Research; Tokyo Institute of Technology; 4259, Nagatsuta-cho, Midori-ku Yokohama 226-8503 Japan
| | - Masaaki Fujii
- Laboratory for Chemistry and Life Science; Institute of Innovative Research; Tokyo Institute of Technology; 4259, Nagatsuta-cho, Midori-ku Yokohama 226-8503 Japan
| |
Collapse
|
189
|
Prezioso SM, Xue K, Leung N, Gray-Owen SD, Christendat D. Shikimate Induced Transcriptional Activation of Protocatechuate Biosynthesis Genes by QuiR, a LysR-Type Transcriptional Regulator, in Listeria monocytogenes. J Mol Biol 2018. [PMID: 29530613 DOI: 10.1016/j.jmb.2018.03.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Listeria monocytogenes is a common foodborne bacterial pathogen that contaminates plant and animal consumable products. The persistent nature of L. monocytogenes is associated with millions of dollars in food recalls annually. Here, we describe the role of shikimate in directly modulating the expression of genes encoding enzymes for the conversion of quinate and shikimate metabolites to protocatechuate. In L. monocytogenes, these genes are found within two operons, named qui1 and qui2. In addition, a gene named quiR, encoding a LysR-Type Transcriptional Regulator (QuiR), is located immediately upstream of the qui1 operon. Transcriptional lacZ-promoter fusion experiments show that QuiR induces gene expression of both qui1 and qui2 operons in the presence of shikimate. Furthermore, co-crystallization of the QuiR effector binding domain in complex with shikimate provides insights into the mechanism of activation of this regulator. Together these data show that upon shikimate accumulation, QuiR activates the transcription of genes encoding enzymes involved in shikimate and quinate utilization for the production of protocatechuate. Furthermore, the accumulation of protocatechuate leads to the inhibition of Listeria growth. Since protocatechuate is not known to be utilized by Listeria, its role is distinct from those established in other bacteria.
Collapse
Affiliation(s)
- Stephanie M Prezioso
- Department of Cell and Systems Biology, University of Toronto, 25 Willcocks Street, Toronto, Ontario, Canada M5S 3B2
| | - Kevin Xue
- Department of Cell and Systems Biology, University of Toronto, 25 Willcocks Street, Toronto, Ontario, Canada M5S 3B2
| | - Nelly Leung
- Department of Molecular Genetics, University of Toronto, 1 King's College Cir, Toronto, Ontario, Canada M5S 1A8
| | - Scott D Gray-Owen
- Department of Molecular Genetics, University of Toronto, 1 King's College Cir, Toronto, Ontario, Canada M5S 1A8
| | - Dinesh Christendat
- Department of Cell and Systems Biology, University of Toronto, 25 Willcocks Street, Toronto, Ontario, Canada M5S 3B2; Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, Ontario, Canada M5S 3B2.
| |
Collapse
|
190
|
Álvarez Ó, Fernández-Martínez JL, Fernández-Brillet C, Cernea A, Fernández-Muñiz Z, Kloczkowski A. Principal component analysis in protein tertiary structure prediction. J Bioinform Comput Biol 2018; 16:1850005. [PMID: 29566640 DOI: 10.1142/s0219720018500051] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
We discuss applicability of principal component analysis (PCA) for protein tertiary structure prediction from amino acid sequence. The algorithm presented in this paper belongs to the category of protein refinement models and involves establishing a low-dimensional space where the sampling (and optimization) is carried out via particle swarm optimizer (PSO). The reduced space is found via PCA performed for a set of low-energy protein models previously found using different optimization techniques. A high frequency term is added into this expansion by projecting the best decoy into the PCA basis set and calculating the residual model. This term is aimed at providing high frequency details in the energy optimization. The goal of this research is to analyze how the dimensionality reduction affects the prediction capability of the PSO procedure. For that purpose, different proteins from the Critical Assessment of Techniques for Protein Structure Prediction experiments were modeled. In all the cases, both the energy of the best decoy and the distance to the native structure have decreased. Our analysis also shows how the predicted backbone structure of native conformation and of alternative low energy states varies with respect to the PCA dimensionality. Generally speaking, the reconstruction can be successfully achieved with 10 principal components and the high frequency term. We also provide a computational analysis of protein energy landscape for the inverse problem of reconstructing structure from the reduced number of principal components, showing that the dimensionality reduction alleviates the ill-posed character of this high-dimensional energy optimization problem. The procedure explained in this paper is very fast and allows testing different PCA expansions. Our results show that PSO improves the energy of the best decoy used in the PCA when the adequate number of PCA terms is considered.
Collapse
Affiliation(s)
- Óscar Álvarez
- * Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C. Federico García Lorca, 18, 33007 Oviedo, Spain
| | - Juan Luis Fernández-Martínez
- * Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C. Federico García Lorca, 18, 33007 Oviedo, Spain
| | - Celia Fernández-Brillet
- * Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C. Federico García Lorca, 18, 33007 Oviedo, Spain
| | - Ana Cernea
- * Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C. Federico García Lorca, 18, 33007 Oviedo, Spain
| | - Zulima Fernández-Muñiz
- * Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C. Federico García Lorca, 18, 33007 Oviedo, Spain
| | - Andrzej Kloczkowski
- † Batelle Center for Mathematical Medicine, Nationwide Children's Hospital, Columbus, OH, USA.,‡ Department of Pediatrics, The Ohio State University, Columbus, OH, USA
| |
Collapse
|
191
|
Wang X, Zhang D, Huang SY. New Knowledge-Based Scoring Function with Inclusion of Backbone Conformational Entropies from Protein Structures. J Chem Inf Model 2018; 58:724-732. [DOI: 10.1021/acs.jcim.7b00601] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Xinxiang Wang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Di Zhang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| |
Collapse
|
192
|
Mirzaie M. Hydrophobic residues can identify native protein structures. Proteins 2018; 86:467-474. [DOI: 10.1002/prot.25466] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 12/28/2017] [Accepted: 01/23/2018] [Indexed: 11/06/2022]
Affiliation(s)
- Mehdi Mirzaie
- Department of Applied Mathematics, Faculty of Mathematical Sciences; Tarbiat Modares University, Jalal Ale Ahmad Highway; Tehran Iran
- School of Biological Sciences; Institute for Research in Fundamental Sciences (IPM); Tehran Iran
| |
Collapse
|
193
|
Li B, Fooksa M, Heinze S, Meiler J. Finding the needle in the haystack: towards solving the protein-folding problem computationally. Crit Rev Biochem Mol Biol 2018; 53:1-28. [PMID: 28976219 PMCID: PMC6790072 DOI: 10.1080/10409238.2017.1380596] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Revised: 08/22/2017] [Accepted: 09/13/2017] [Indexed: 12/22/2022]
Abstract
Prediction of protein tertiary structures from amino acid sequence and understanding the mechanisms of how proteins fold, collectively known as "the protein folding problem," has been a grand challenge in molecular biology for over half a century. Theories have been developed that provide us with an unprecedented understanding of protein folding mechanisms. However, computational simulation of protein folding is still difficult, and prediction of protein tertiary structure from amino acid sequence is an unsolved problem. Progress toward a satisfying solution has been slow due to challenges in sampling the vast conformational space and deriving sufficiently accurate energy functions. Nevertheless, several techniques and algorithms have been adopted to overcome these challenges, and the last two decades have seen exciting advances in enhanced sampling algorithms, computational power and tertiary structure prediction methodologies. This review aims at summarizing these computational techniques, specifically conformational sampling algorithms and energy approximations that have been frequently used to study protein-folding mechanisms or to de novo predict protein tertiary structures. We hope that this review can serve as an overview on how the protein-folding problem can be studied computationally and, in cases where experimental approaches are prohibitive, help the researcher choose the most relevant computational approach for the problem at hand. We conclude with a summary of current challenges faced and an outlook on potential future directions.
Collapse
Affiliation(s)
- Bian Li
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Michaela Fooksa
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
- Chemical and Physical Biology Graduate Program, Vanderbilt University, Nashville, TN, USA
| | - Sten Heinze
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| |
Collapse
|
194
|
Joo K, Heo S, Joung I, Hong SH, Lee SJ, Lee J. Data-assisted protein structure modeling by global optimization in CASP12. Proteins 2018; 86 Suppl 1:240-246. [PMID: 29341255 DOI: 10.1002/prot.25457] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Revised: 12/29/2017] [Accepted: 01/08/2018] [Indexed: 12/26/2022]
Abstract
In CASP12, 2 types of data-assisted protein structure modeling were experimented. Either SAXS experimental data or cross-linking experimental data was provided for a selected number of CASP12 targets that the CASP12 predictor could utilize for better protein structure modeling. We devised 2 separate energy terms for SAXS data and cross-linking data to drive the model structures into more native-like structures that satisfied the given experimental data as much as possible. In CASP11, we successfully performed protein structure modeling using simulated sparse and ambiguously assigned NOE data and/or correct residue-residue contact information, where the only energy term that folded the protein into its native structure was the term which was originated from the given experimental data. However, the 2 types of experimental data provided in CASP12 were far from being sufficient enough to fold the target protein into its native structure because SAXS data provides only the overall shape of the molecule and the cross-linking contact information provides only very low-resolution distance information. For this reason, we combined the SAXS or cross-linking energy term with our regular modeling energy function that includes both the template energy term and the de novo energy terms. By optimizing the newly formulated energy function, we obtained protein models that fit better with provided SAXS data than the X-ray structure of the target. However, the improvement of the model relative to the 1 modeled without the SAXS data, was not significant. Consistent structural improvement was achieved by incorporating cross-linking data into the protein structure modeling.
Collapse
Affiliation(s)
- Keehyoung Joo
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, 02455, South Korea.,Center for Advanced Computation, Korea Institute for Advanced Study, Seoul, 02455, South Korea
| | - Seungryong Heo
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul, 02455, South Korea
| | - InSuk Joung
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, 02455, South Korea
| | - Seung Hwan Hong
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul, 02455, South Korea
| | - Sung Jong Lee
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, 02455, South Korea.,The Research Institute for Basic Sciences, Changwon National University, Changwon-Si, Gyeongsangnam-do, South Korea
| | - Jooyoung Lee
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, 02455, South Korea.,Center for Advanced Computation, Korea Institute for Advanced Study, Seoul, 02455, South Korea.,School of Computational Sciences, Korea Institute for Advanced Study, Seoul, 02455, South Korea
| |
Collapse
|
195
|
Protein Tertiary Structure by Crosslinking/Mass Spectrometry. Trends Biochem Sci 2018; 43:157-169. [PMID: 29395654 PMCID: PMC5854373 DOI: 10.1016/j.tibs.2017.12.006] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2017] [Revised: 12/19/2017] [Accepted: 12/21/2017] [Indexed: 12/21/2022]
Abstract
Observing the structures of proteins within the cell and tracking structural changes under different cellular conditions are the ultimate challenges for structural biology. This, however, requires an experimental technique that can generate sufficient data for structure determination and is applicable in the native environment of proteins. Crosslinking/mass spectrometry (CLMS) and protein structure determination have recently advanced to meet these requirements and crosslinking-driven de novo structure determination in native environments is now possible. In this opinion article, we highlight recent successes in the field of CLMS with protein structure modeling and challenges it still holds. The earliest structural studies on proteins using crosslinking/mass spectrometry aimed to elucidate their tertiary three-dimensional structure. Tertiary structure modeling using crosslinking fell out of favor for almost two decades because crosslink data were not informative to aid structure modeling. Two game-changing trends emerged: using short-range crosslinkers that capture relevant modeling information and high-density crosslinking. High-density crosslinking uses unspecific crosslinkers to dramatically increase crosslink numbers. In addition, computational structure modeling methods made significant progress in exploiting CLMS data. The combination of high-density crosslinking and computational structure modeling enables the elucidation of tertiary protein structure in native environments. This sidesteps the key limitation of today’s structure determination methods, which are unable (except for a few, specialized methods) to probe the structure of proteins in cell lysates or even intact cells.
Collapse
|
196
|
Pan BB, Yang F, Ye Y, Wu Q, Li C, Huber T, Su XC. 3D structure determination of a protein in living cells using paramagnetic NMR spectroscopy. Chem Commun (Camb) 2018; 52:10237-40. [PMID: 27470136 DOI: 10.1039/c6cc05490k] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Determining the three-dimensional structure of a protein in living cells remains particularly challenging. We demonstrated that the integration of site-specific tagging proteins and GPS-Rosetta calculations provides a fast and effective way of determining the structures of proteins in living cells, and in principle the interactions and dynamics of protein-ligand complexes.
Collapse
Affiliation(s)
- Bin-Bin Pan
- State Key Laboratory and Research Institute of Elemento-Organic Chemistry, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Nankai University, Tianjin 300071, China.
| | - Feng Yang
- State Key Laboratory and Research Institute of Elemento-Organic Chemistry, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Nankai University, Tianjin 300071, China.
| | - Yansheng Ye
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, China.
| | - Qiong Wu
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, China.
| | - Conggang Li
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, China.
| | - Thomas Huber
- Research School of Chemistry, Australian National University, Canberra, ACT 0200, Australia.
| | - Xun-Cheng Su
- State Key Laboratory and Research Institute of Elemento-Organic Chemistry, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Nankai University, Tianjin 300071, China.
| |
Collapse
|
197
|
Al Nasr K, Yousef F, Jebril R, Jones C. Analytical Approaches to Improve Accuracy in Solving the Protein Topology Problem. Molecules 2018; 23:E28. [PMID: 29360779 PMCID: PMC6017786 DOI: 10.3390/molecules23020028] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Revised: 01/19/2018] [Accepted: 01/19/2018] [Indexed: 11/17/2022] Open
Abstract
To take advantage of recent advances in genomics and proteomics it is critical that the three-dimensional physical structure of biological macromolecules be determined. Cryo-Electron Microscopy (cryo-EM) is a promising and improving method for obtaining this data, however resolution is often not sufficient to directly determine the atomic scale structure. Despite this, information for secondary structure locations is detectable. De novo modeling is a computational approach to modeling these macromolecular structures based on cryo-EM derived data. During de novo modeling a mapping between detected secondary structures and the underlying amino acid sequence must be identified. DP-TOSS (Dynamic Programming for determining the Topology Of Secondary Structures) is one tool that attempts to automate the creation of this mapping. By treating the correspondence between the detected structures and the structures predicted from sequence data as a constraint graph problem DP-TOSS achieved good accuracy in its original iteration. In this paper, we propose modifications to the scoring methodology of DP-TOSS to improve its accuracy. Three scoring schemes were applied to DP-TOSS and tested: (i) a skeleton-based scoring function; (ii) a geometry-based analytical function; and (iii) a multi-well potential energy-based function. A test of 25 proteins shows that a combination of these schemes can improve the performance of DP-TOSS to solve the topology determination problem for macromolecule proteins.
Collapse
Affiliation(s)
- Kamal Al Nasr
- Department of Computer Science, Tennessee State University, Nashville, TN 37209, USA.
| | - Feras Yousef
- Department of Mathematics, The University of Jordan, Amman 11942, Jordan.
| | - Ruba Jebril
- Department of Computer Science, Tennessee State University, Nashville, TN 37209, USA.
| | - Christopher Jones
- Department of Computer Science, Tennessee State University, Nashville, TN 37209, USA.
| |
Collapse
|
198
|
Diamond JS, Zhang Y. THE-DB: a threading model database for comparative protein structure analysis of the E. coli K12 and human proteomes. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2018; 2018:5094579. [PMID: 30239678 PMCID: PMC6146127 DOI: 10.1093/database/bay090] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Accepted: 08/08/2018] [Indexed: 11/14/2022]
Abstract
New methodology must be developed to improve the ability to characterize the growing number of amino acid sequences, which vastly exceeds the number of experimentally determined protein structures. Homologous proteins can be used as structural templates for modeling proteins that do not have experimentally determined structures. However, in many cases, there are no homologous proteins (typically <30% sequence identity) with determined structures from which a query sequence can be reliably modeled. The aim of protein threading is to use features, such as secondary structure, solvent accessibility and torsional angles, in addition to sequence patterns to identify structural templates from the protein databank to assist for full-length atomic-level structural modeling. However, there are still numerous protein sequences for which correct templates cannot be recognized. This raises the question as to what attributes allow query sequences to be matched to the correct but distantly homologous templates. To aid the investigation into this question and to provide genome-score protein structure for the biological community, a database called THE-DB (threading hard and easy protein database) has been developed in which it becomes possible to analyze over 15 000 query sequences from the Escherichia coli (E. coli) K12 and human proteomes, as well as to find their three-dimensional templates derived from the state-of-the-art threading algorithms which is not feasible with existing protein template databases. The E. coli K12 and human data can be downloaded in bulk from the THE-DB page.
Collapse
Affiliation(s)
- Justin S Diamond
- Department of Computational Medicine and Bioinformatics, University of Michigan, Washtenaw Avenue, Ann Arbor, MI, USA.,Department of Bioinformatics, Boston University, Cummington Mall, Boston, MA, USA
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Washtenaw Avenue, Ann Arbor, MI, USA
| |
Collapse
|
199
|
Alam N, Goldstein O, Xia B, Porter KA, Kozakov D, Schueler-Furman O. High-resolution global peptide-protein docking using fragments-based PIPER-FlexPepDock. PLoS Comput Biol 2017; 13:e1005905. [PMID: 29281622 PMCID: PMC5760072 DOI: 10.1371/journal.pcbi.1005905] [Citation(s) in RCA: 82] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 01/09/2018] [Accepted: 11/29/2017] [Indexed: 11/24/2022] Open
Abstract
Peptide-protein interactions contribute a significant fraction of the protein-protein interactome. Accurate modeling of these interactions is challenging due to the vast conformational space associated with interactions of highly flexible peptides with large receptor surfaces. To address this challenge we developed a fragment based high-resolution peptide-protein docking protocol. By streamlining the Rosetta fragment picker for accurate peptide fragment ensemble generation, the PIPER docking algorithm for exhaustive fragment-receptor rigid-body docking and Rosetta FlexPepDock for flexible full-atom refinement of PIPER docked models, we successfully addressed the challenge of accurate and efficient global peptide-protein docking at high-resolution with remarkable accuracy, as validated on a small but representative set of peptide-protein complex structures well resolved by X-ray crystallography. Our approach opens up the way to high-resolution modeling of many more peptide-protein interactions and to the detailed study of peptide-protein association in general. PIPER-FlexPepDock is freely available to the academic community as a server at http://piperfpd.furmanlab.cs.huji.ac.il. Peptide-protein interactions are crucial components of various important biological processes in living cells. High-resolution structural information of such interactions provides insight about the underlying biophysical principles governing the interactions, and a starting point for their targeted manipulations. Accurate docking algorithms can help fill the gap between the vast number of these interactions and the small number of experimentally solved structures. However, the accuracies of the existing protocols have been limited, in particular for ab initio docking when no information about the peptide beyond its sequence is available. Here we introduce PIPER-FlexPepDock, a fragment-based global docking protocol for high-resolution modeling of peptide-protein interactions. Integration of accurate and efficient representation of the peptide using fragment ensembles, their fast and exhaustive rigid-body docking, and their subsequent accurate flexible refinement, enables peptide-protein docking of remarkable accuracy. The validation on a representative benchmark set of crystallographically solved high-resolution peptide-protein complexes demonstrates significantly improved performance over all existing docking protocols. This opens up the way to the modeling of many more peptide-protein interactions, and to a more detailed study of peptide-protein association in general.
Collapse
Affiliation(s)
- Nawsad Alam
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Oriel Goldstein
- School of Computer Sciences and Engineering, The Hebrew University, Jerusalem, Israel
| | - Bing Xia
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
| | - Kathryn A. Porter
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, United States of America
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, United States of America
- Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, New York, United States of America
- * E-mail: (OSF); (DK)
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
- * E-mail: (OSF); (DK)
| |
Collapse
|
200
|
Leong SW, Lim TS, Ismail A, Choong YS. Integration of molecular dynamics simulation and hotspot residues grafting for de novo scFv design against Salmonella Typhi TolC protein. J Mol Recognit 2017; 31:e2695. [PMID: 29230887 DOI: 10.1002/jmr.2695] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 11/13/2017] [Accepted: 11/19/2017] [Indexed: 01/10/2023]
Abstract
With the development of de novo binders for protein targets from non-related scaffolds, many possibilities for therapeutics and diagnostics have been created. In this study, we described the use of de novo design approach to create single-chain fragment variable (scFv) for Salmonella enterica subspecies enterica serovar Typhi TolC protein. Typhoid fever is a global health concern in developing and underdeveloped countries. Rapid typhoid diagnostics will improve disease management and therapy. In this work, molecular dynamics simulation was first performed on a homology model of TolC protein in POPE membrane bilayer to obtain the central structure that was subsequently used as the target for scFv design. Potential hotspot residues capable of anchoring the binders to the target were identified by docking "disembodied" amino acid residues against TolC surface. Next, scFv scaffolds were selected from Protein Data Bank to harbor the computed hotspot residues. The hotspot residues were then incorporated into the scFv scaffold complementarity determining regions. The designs recapitulated binding energy, shape complementarity, and interface surface area of natural protein-antibody interfaces. This approach has yielded 5 designs with high binding affinity against TolC that may be beneficial for the future development of antigen-based detection agents for typhoid diagnostics.
Collapse
Affiliation(s)
- Siew Wen Leong
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Minden, Penang, Malaysia
| | - Theam Soon Lim
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Minden, Penang, Malaysia
| | - Asma Ismail
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Health Campus, Kubang Kerian, Kelantan, Malaysia
| | - Yee Siew Choong
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Minden, Penang, Malaysia
| |
Collapse
|