51
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Meng F, Uversky VN, Kurgan L. Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions. Cell Mol Life Sci 2017; 74:3069-3090. [PMID: 28589442 PMCID: PMC11107660 DOI: 10.1007/s00018-017-2555-4] [Citation(s) in RCA: 130] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Accepted: 06/01/2017] [Indexed: 12/19/2022]
Abstract
Computational prediction of intrinsic disorder in protein sequences dates back to late 1970 and has flourished in the last two decades. We provide a brief historical overview, and we review over 30 recent predictors of disorder. We are the first to also cover predictors of molecular functions of disorder, including 13 methods that focus on disordered linkers and disordered protein-protein, protein-RNA, and protein-DNA binding regions. We overview their predictive models, usability, and predictive performance. We highlight newest methods and predictors that offer strong predictive performance measured based on recent comparative assessments. We conclude that the modern predictors are relatively accurate, enjoy widespread use, and many of them are fast. Their predictions are conveniently accessible to the end users, via web servers and databases that store pre-computed predictions for millions of proteins. However, research into methods that predict many not yet addressed functions of intrinsic disorder remains an outstanding challenge.
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Affiliation(s)
- Fanchi Meng
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
| | - Vladimir N Uversky
- Department of Molecular Medicine, USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
- Institute for Biological Instrumentation, Russian Academy of Sciences, Pushchino, Moscow Region, Russian Federation
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, USA.
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52
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Kosciolek T, Buchan DWA, Jones DT. Predictions of Backbone Dynamics in Intrinsically Disordered Proteins Using De Novo Fragment-Based Protein Structure Predictions. Sci Rep 2017; 7:6999. [PMID: 28765603 PMCID: PMC5539115 DOI: 10.1038/s41598-017-07156-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Accepted: 06/23/2017] [Indexed: 11/08/2022] Open
Abstract
Intrinsically disordaered proteins (IDPs) are a prevalent phenomenon with over 30% of human proteins estimated to have long disordered regions. Computational methods are widely used to study IDPs, however, nearly all treat disorder in a binary fashion, not accounting for the structural heterogeneity present in disordered regions. Here, we present a new de novo method, FRAGFOLD-IDP, which addresses this problem. Using 200 protein structural ensembles derived from NMR, we show that FRAGFOLD-IDP achieves superior results compared to methods which can predict related data (NMR order parameter, or crystallographic B-factor). FRAGFOLD-IDP produces very good predictions for 33.5% of cases and helps to get a better insight into the dynamics of the disordered ensembles. The results also show it is not necessary to predict the correct fold of the protein to reliably predict per-residue fluctuations. It implies that disorder is a local property and it does not depend on the fold. Our results are orthogonal to DynaMine, the only other method significantly better than the naïve prediction. We therefore combine these two using a neural network. FRAGFOLD-IDP enables better insight into backbone dynamics in IDPs and opens exciting possibilities for the design of disordered ensembles, disorder-to-order transitions, or design for protein dynamics.
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Affiliation(s)
- Tomasz Kosciolek
- Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Daniel W A Buchan
- Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom
| | - David T Jones
- Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom.
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53
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Meng F, Kurgan L. DFLpred: High-throughput prediction of disordered flexible linker regions in protein sequences. Bioinformatics 2017; 32:i341-i350. [PMID: 27307636 PMCID: PMC4908364 DOI: 10.1093/bioinformatics/btw280] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Motivation: Disordered flexible linkers (DFLs) are disordered regions that serve as flexible linkers/spacers in multi-domain proteins or between structured constituents in domains. They are different from flexible linkers/residues because they are disordered and longer. Availability of experimentally annotated DFLs provides an opportunity to build high-throughput computational predictors of these regions from protein sequences. To date, there are no computational methods that directly predict DFLs and they can be found only indirectly by filtering predicted flexible residues with predictions of disorder. Results: We conceptualized, developed and empirically assessed a first-of-its-kind sequence-based predictor of DFLs, DFLpred. This method outputs propensity to form DFLs for each residue in the input sequence. DFLpred uses a small set of empirically selected features that quantify propensities to form certain secondary structures, disordered regions and structured regions, which are processed by a fast linear model. Our high-throughput predictor can be used on the whole-proteome scale; it needs <1 h to predict entire proteome on a single CPU. When assessed on an independent test dataset with low sequence-identity proteins, it secures area under the receiver operating characteristic curve equal 0.715 and outperforms existing alternatives that include methods for the prediction of flexible linkers, flexible residues, intrinsically disordered residues and various combinations of these methods. Prediction on the complete human proteome reveals that about 10% of proteins have a large content of over 30% DFL residues. We also estimate that about 6000 DFL regions are long with ≥30 consecutive residues. Availability and implementation:http://biomine.ece.ualberta.ca/DFLpred/. Contact:lkurgan@vcu.edu Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Fanchi Meng
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6G 2V4, Canada
| | - Lukasz Kurgan
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6G 2V4, Canada Department of Computer Science, Virginia Commonwealth University, Richmond, 23284, U.S.A
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54
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Reinstein E, Drasinover V, Lotan R, Gal-Tanamy M, Bolocan Nachman I, Eyal E, Jaber L, Magal N, Shohat M. Mutations in ERGIC1 cause Arthrogryposis multiplex congenita, neuropathic type. Clin Genet 2017; 93:160-163. [PMID: 28317099 DOI: 10.1111/cge.13018] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2017] [Revised: 03/13/2017] [Accepted: 03/15/2017] [Indexed: 11/29/2022]
Abstract
Arthrogryposis multiplex congenita (AMC) is heterogeneous group of disorders characterized by non-progressive joint contractures from birth that involve more than 1 part of the body. There are various etiologies for AMC including genetic and environmental depends on the specific type, however, for most types, the cause is not fully understood. We previously reported large Israeli Arab kindred consisting of 16 patients affected with AMC neuropathic type, and mapped the locus to a 5.5 cM interval on chromosome 5qter. Using whole exome sequencing, we have now identified homozygous pathogenic variant in the ERGIC1 gene within the previously defined linked region. ERGIC1 encodes a cycling membrane protein which has a possible role in transport between endoplasmic reticulum and Golgi. We further show that this mutation was absent in more than 200 samples of healthy unrelated individuals of the Israeli Arab population. Thus, our findings expand the spectrum of hereditary AMC and suggest that abnormalities in protein trafficking may underlie AMC-related disorders.
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Affiliation(s)
- E Reinstein
- Medical Genetics Institute, Meir Medical Center, Kfar-Saba, Israel.,Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - V Drasinover
- Medical Genetics Institute, Rabin Medical Center, Petah Tikva, Israel
| | - R Lotan
- Medical Genetics Institute, Rabin Medical Center, Petah Tikva, Israel
| | - M Gal-Tanamy
- Medical Genetics Institute, Rabin Medical Center, Petah Tikva, Israel
| | - I Bolocan Nachman
- Medical Genetics Institute, Rabin Medical Center, Petah Tikva, Israel
| | - E Eyal
- Cancer Research Center, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - L Jaber
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.,Medical Genetics Institute, Rabin Medical Center, Petah Tikva, Israel
| | - N Magal
- Medical Genetics Institute, Rabin Medical Center, Petah Tikva, Israel
| | - M Shohat
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.,Cancer Research Center, Chaim Sheba Medical Center, Tel Hashomer, Israel.,Medical Genetics institute, Maccabi HMO, Rechovot, Israel
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55
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Chakravorty D, Khan MF, Patra S. Multifactorial level of extremostability of proteins: can they be exploited for protein engineering? Extremophiles 2017; 21:419-444. [PMID: 28283770 DOI: 10.1007/s00792-016-0908-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Accepted: 12/19/2016] [Indexed: 12/20/2022]
Abstract
Research on extremostable proteins has seen immense growth in the past decade owing to their industrial importance. Basic research of attributes related to extreme-stability requires further exploration. Modern mechanistic approaches to engineer such proteins in vitro will have more impact in industrial biotechnology economy. Developing a priori knowledge about the mechanism behind extreme-stability will nurture better understanding of pathways leading to protein molecular evolution and folding. This review is a vivid compilation about all classes of extremostable proteins and the attributes that lead to myriad of adaptations divulged after an extensive study of 6495 articles belonging to extremostable proteins. Along with detailing on the rationale behind extreme-stability of proteins, emphasis has been put on modern approaches that have been utilized to render proteins extremostable by protein engineering. It was understood that each protein shows different approaches to extreme-stability governed by minute differences in their biophysical properties and the milieu in which they exist. Any general rule has not yet been drawn regarding adaptive mechanisms in extreme environments. This review was further instrumental to understand the drawback of the available 14 stabilizing mutation prediction algorithms. Thus, this review lays the foundation to further explore the biophysical pleiotropy of extreme-stable proteins to deduce a global prediction model for predicting the effect of mutations on protein stability.
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Affiliation(s)
- Debamitra Chakravorty
- Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, 781039, Assam, India
| | - Mohd Faheem Khan
- Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, 781039, Assam, India
| | - Sanjukta Patra
- Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, 781039, Assam, India.
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Peterson L, Jamroz M, Kolinski A, Kihara D. Predicting Real-Valued Protein Residue Fluctuation Using FlexPred. Methods Mol Biol 2017; 1484:175-186. [PMID: 27787827 DOI: 10.1007/978-1-4939-6406-2_13] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The conventional view of a protein structure as static provides only a limited picture. There is increasing evidence that protein dynamics are often vital to protein function including interaction with partners such as other proteins, nucleic acids, and small molecules. Considering flexibility is also important in applications such as computational protein docking and protein design. While residue flexibility is partially indicated by experimental measures such as the B-factor from X-ray crystallography and ensemble fluctuation from nuclear magnetic resonance (NMR) spectroscopy as well as computational molecular dynamics (MD) simulation, these techniques are resource-intensive. In this chapter, we describe the web server and stand-alone version of FlexPred, which rapidly predicts absolute per-residue fluctuation from a three-dimensional protein structure. On a set of 592 nonredundant structures, comparing the fluctuations predicted by FlexPred to the observed fluctuations in MD simulations showed an average correlation coefficient of 0.669 and an average root mean square error of 1.07 Å. FlexPred is available at http://kiharalab.org/flexPred/ .
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Affiliation(s)
- Lenna Peterson
- Department of Biological Sciences, College of Science, Purdue University, 915 W. State Street, West Lafayette, IN, 47907-2054, USA
| | - Michal Jamroz
- Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Pasteura 1, Warszawa, 02-093, Poland
| | - Andrzej Kolinski
- Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Pasteura 1, Warszawa, 02-093, Poland
| | - Daisuke Kihara
- Department of Biological Sciences, College of Science, Purdue University, 915 W. State Street, West Lafayette, IN, 47907-2054, USA. .,Department of Computer Science, College of Science, Purdue University, 305 N. University Street, West Lafayette, IN, 47907-2107, USA.
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57
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Sharma R, Kumar S, Tsunoda T, Patil A, Sharma A. Predicting MoRFs in protein sequences using HMM profiles. BMC Bioinformatics 2016; 17:504. [PMID: 28155710 PMCID: PMC5259822 DOI: 10.1186/s12859-016-1375-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Intrinsically Disordered Proteins (IDPs) lack an ordered three-dimensional structure and are enriched in various biological processes. The Molecular Recognition Features (MoRFs) are functional regions within IDPs that undergo a disorder-to-order transition on binding to a partner protein. Identifying MoRFs in IDPs using computational methods is a challenging task. METHODS In this study, we introduce hidden Markov model (HMM) profiles to accurately identify the location of MoRFs in disordered protein sequences. Using windowing technique, HMM profiles are utilised to extract features from protein sequences and support vector machines (SVM) are used to calculate a propensity score for each residue. Two different SVM kernels with high noise tolerance are evaluated with a varying window size and the scores of the SVM models are combined to generate the final propensity score to predict MoRF residues. The SVM models are designed to extract maximal information between MoRF residues, its neighboring regions (Flanks) and the remainder of the sequence (Others). RESULTS To evaluate the proposed method, its performance was compared to that of other MoRF predictors; MoRFpred and ANCHOR. The results show that the proposed method outperforms these two predictors. CONCLUSIONS Using HMM profile as a source of feature extraction, the proposed method indicates improvement in predicting MoRFs in disordered protein sequences.
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Affiliation(s)
- Ronesh Sharma
- School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji.,School of Engineering and Physics, The University of the South Pacific, Suva, Fiji
| | - Shiu Kumar
- School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji.,School of Engineering and Physics, The University of the South Pacific, Suva, Fiji
| | - Tatsuhiko Tsunoda
- CREST, JST, Yokohama, 230-0045, Japan.,RIKEN Center for Integrative Medical Science, Yokohama, 230-0045, Japan.,Medical Research Institute, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan
| | - Ashwini Patil
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
| | - Alok Sharma
- School of Engineering and Physics, The University of the South Pacific, Suva, Fiji. .,CREST, JST, Yokohama, 230-0045, Japan. .,RIKEN Center for Integrative Medical Science, Yokohama, 230-0045, Japan. .,Medical Research Institute, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan.
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58
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Lieutaud P, Ferron F, Uversky AV, Kurgan L, Uversky VN, Longhi S. How disordered is my protein and what is its disorder for? A guide through the "dark side" of the protein universe. INTRINSICALLY DISORDERED PROTEINS 2016; 4:e1259708. [PMID: 28232901 DOI: 10.1080/21690707.2016.1259708] [Citation(s) in RCA: 80] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Revised: 11/03/2016] [Accepted: 11/04/2016] [Indexed: 12/18/2022]
Abstract
In the last 2 decades it has become increasingly evident that a large number of proteins are either fully or partially disordered. Intrinsically disordered proteins lack a stable 3D structure, are ubiquitous and fulfill essential biological functions. Their conformational heterogeneity is encoded in their amino acid sequences, thereby allowing intrinsically disordered proteins or regions to be recognized based on properties of these sequences. The identification of disordered regions facilitates the functional annotation of proteins and is instrumental for delineating boundaries of protein domains amenable to structural determination with X-ray crystallization. This article discusses a comprehensive selection of databases and methods currently employed to disseminate experimental and putative annotations of disorder, predict disorder and identify regions involved in induced folding. It also provides a set of detailed instructions that should be followed to perform computational analysis of disorder.
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Affiliation(s)
- Philippe Lieutaud
- Aix-Marseille Université, AFMB UMR, Marseille, France; CNRS, AFMB UMR, Marseille, France
| | - François Ferron
- Aix-Marseille Université, AFMB UMR, Marseille, France; CNRS, AFMB UMR, Marseille, France
| | - Alexey V Uversky
- Center for Data Analytics and Biomedical Informatics, Department of Computer and Information Sciences, College of Science and Technology, Temple University , Philadelphia, PA, USA
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University , Richmond, VA, USA
| | - Vladimir N Uversky
- Department of Molecular Medicine and USF Health Byrd Alzheimer Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA; Laboratory of Structural Dynamics, Stability and Folding of Proteins, Institute of Cytology, Russian Academy of Sciences, St. Petersburg, Russia
| | - Sonia Longhi
- Aix-Marseille Université, AFMB UMR, Marseille, France; CNRS, AFMB UMR, Marseille, France
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59
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EL-Manzalawy Y, Abbas M, Malluhi Q, Honavar V. FastRNABindR: Fast and Accurate Prediction of Protein-RNA Interface Residues. PLoS One 2016; 11:e0158445. [PMID: 27383535 PMCID: PMC4934694 DOI: 10.1371/journal.pone.0158445] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Accepted: 06/16/2016] [Indexed: 11/24/2022] Open
Abstract
A wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses are mediated by RNA-protein interactions. However, experimental determination of the structures of protein-RNA complexes is expensive and technically challenging. Hence, a number of computational tools have been developed for predicting protein-RNA interfaces. Some of the state-of-the-art protein-RNA interface predictors rely on position-specific scoring matrix (PSSM)-based encoding of the protein sequences. The computational efforts needed for generating PSSMs severely limits the practical utility of protein-RNA interface prediction servers. In this work, we experiment with two approaches, random sampling and sequence similarity reduction, for extracting a representative reference database of protein sequences from more than 50 million protein sequences in UniRef100. Our results suggest that random sampled databases produce better PSSM profiles (in terms of the number of hits used to generate the profile and the distance of the generated profile to the corresponding profile generated using the entire UniRef100 data as well as the accuracy of the machine learning classifier trained using these profiles). Based on our results, we developed FastRNABindR, an improved version of RNABindR for predicting protein-RNA interface residues using PSSM profiles generated using 1% of the UniRef100 sequences sampled uniformly at random. To the best of our knowledge, FastRNABindR is the only protein-RNA interface residue prediction online server that requires generation of PSSM profiles for query sequences and accepts hundreds of protein sequences per submission. Our approach for determining the optimal BLAST database for a protein-RNA interface residue classification task has the potential of substantially speeding up, and hence increasing the practical utility of, other amino acid sequence based predictors of protein-protein and protein-DNA interfaces.
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Affiliation(s)
- Yasser EL-Manzalawy
- College of Information Sciences and Technology, Pennsylvania State University, University Park, PA, United States of America
- Systems and Computer Engineering, Al-Azhar University, Cairo, Egypt
- * E-mail:
| | - Mostafa Abbas
- KINDI Center for Computing Research, College of Engineering, Qatar University, Duha, Qatar
| | - Qutaibah Malluhi
- KINDI Center for Computing Research, College of Engineering, Qatar University, Duha, Qatar
| | - Vasant Honavar
- College of Information Sciences and Technology, Pennsylvania State University, University Park, PA, United States of America
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60
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Taghizadeh M, Goliaei B, Madadkar-Sobhani A. Variability of the Cyclin-Dependent Kinase 2 Flexibility Without Significant Change in the Initial Conformation of the Protein or Its Environment; a Computational Study. IRANIAN JOURNAL OF BIOTECHNOLOGY 2016; 14:1-12. [PMID: 28959320 DOI: 10.15171/ijb.1419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
BACKGROUND Protein flexibility, which has been referred as a dynamic behavior has various roles in proteins' functions. Furthermore, for some developed tools in bioinformatics, such as protein-protein docking software, considering the protein flexibility, causes a higher degree of accuracy. Through undertaking the present work, we have accomplished the quantification plus analysis of the variations in the human Cyclin Dependent Kinase 2 (hCDK2) protein flexibility without affecting a significant change in its initial environment or the protein per se. OBJECTIVES The main goal of the present research was to calculate variations in the flexibility for each residue of the hCDK2, analysis of their flexibility variations through clustering, and to investigate the functional aspects of the residues with high flexibility variations. MATERIALS AND METHODS Using Gromacs package (version 4.5.4), three independent molecular dynamics (MD) simulations of the hCDK2 protein (PDB ID: 1HCL) was accomplished with no significant changes in their initial environments, structures, or conformations, followed by Root Mean Square Fluctuations (RMSF) calculation of these MD trajectories. The amount of variations in these three curves of RMSF was calculated using two formulas. RESULTS More than 50% of the variation in the flexibility (the distance between the maximum and the minimum amount of the RMSF) was found at the region of Val-154. As well, there are other major flexibility fluctuations in other residues. These residues were mostly positioned in the vicinity of the functional residues. The subsequent works were done, as followed by clustering all hCDK2 residues into four groups considering the amount of their variability with respect to flexibility and their position in the RMSF curves. CONCLUSIONS This work has introduced a new class of flexibility aspect of the proteins' residues. It could also help designing and engineering proteins, with introducing a new dynamic aspect of hCDK2, and accordingly, for the other similar globular proteins. In addition, it could provide a better computational calculation of the protein flexibility, which is, especially important in the comparative studies of the proteins' flexibility.
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Affiliation(s)
- Mohammad Taghizadeh
- Laboratory of Biophysics and Molecular Biology, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Bahram Goliaei
- Laboratory of Biophysics and Molecular Biology, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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61
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Neuhaus K, Landstorfer R, Fellner L, Simon S, Schafferhans A, Goldberg T, Marx H, Ozoline ON, Rost B, Kuster B, Keim DA, Scherer S. Translatomics combined with transcriptomics and proteomics reveals novel functional, recently evolved orphan genes in Escherichia coli O157:H7 (EHEC). BMC Genomics 2016; 17:133. [PMID: 26911138 PMCID: PMC4765031 DOI: 10.1186/s12864-016-2456-1] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2015] [Accepted: 02/09/2016] [Indexed: 12/30/2022] Open
Abstract
Background Genomes of E. coli, including that of the human pathogen Escherichia coli O157:H7 (EHEC) EDL933, still harbor undetected protein-coding genes which, apparently, have escaped annotation due to their small size and non-essential function. To find such genes, global gene expression of EHEC EDL933 was examined, using strand-specific RNAseq (transcriptome), ribosomal footprinting (translatome) and mass spectrometry (proteome). Results Using the above methods, 72 short, non-annotated protein-coding genes were detected. All of these showed signals in the ribosomal footprinting assay indicating mRNA translation. Seven were verified by mass spectrometry. Fifty-seven genes are annotated in other enterobacteriaceae, mainly as hypothetical genes; the remaining 15 genes constitute novel discoveries. In addition, protein structure and function were predicted computationally and compared between EHEC-encoded proteins and 100-times randomly shuffled proteins. Based on this comparison, 61 of the 72 novel proteins exhibit predicted structural and functional features similar to those of annotated proteins. Many of the novel genes show differential transcription when grown under eleven diverse growth conditions suggesting environmental regulation. Three genes were found to confer a phenotype in previous studies, e.g., decreased cattle colonization. Conclusions These findings demonstrate that ribosomal footprinting can be used to detect novel protein coding genes, contributing to the growing body of evidence that hypothetical genes are not annotation artifacts and opening an additional way to study their functionality. All 72 genes are taxonomically restricted and, therefore, appear to have evolved relatively recently de novo. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2456-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Klaus Neuhaus
- Lehrstuhl für Mikrobielle Ökologie, Zentralinstitut für Ernährungs- und Lebensmittelforschung, Wissenschaftszentrum Weihenstephan, Technische Universität München, Weihenstephaner Berg 3, 85354, Freising, Germany.
| | - Richard Landstorfer
- Lehrstuhl für Mikrobielle Ökologie, Zentralinstitut für Ernährungs- und Lebensmittelforschung, Wissenschaftszentrum Weihenstephan, Technische Universität München, Weihenstephaner Berg 3, 85354, Freising, Germany.
| | - Lea Fellner
- Lehrstuhl für Mikrobielle Ökologie, Zentralinstitut für Ernährungs- und Lebensmittelforschung, Wissenschaftszentrum Weihenstephan, Technische Universität München, Weihenstephaner Berg 3, 85354, Freising, Germany.
| | - Svenja Simon
- Lehrstuhl für Datenanalyse und Visualisierung, Fachbereich Informatik und Informationswissenschaft, Universität Konstanz, Box 78, 78457, Konstanz, Germany.
| | - Andrea Schafferhans
- Department of Informatics - Bioinformatics & TUM-IAS, Technische Universität München, Boltzmannstraße 3, 85748, Garching, Germany.
| | - Tatyana Goldberg
- Department of Informatics - Bioinformatics & TUM-IAS, Technische Universität München, Boltzmannstraße 3, 85748, Garching, Germany.
| | - Harald Marx
- Chair of Proteomics and Bioanalytics, Wissenschaftszentrum Weihenstephan, Technische Universität München, Emil-Erlenmeyer-Forum 5, 85354, Freising, Germany.
| | - Olga N Ozoline
- Institute of Cell Biophysics, Russian Academy of Sciences, Moscow Region, 142290, Pushchino, Russia.
| | - Burkhard Rost
- Department of Informatics - Bioinformatics & TUM-IAS, Technische Universität München, Boltzmannstraße 3, 85748, Garching, Germany.
| | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Wissenschaftszentrum Weihenstephan, Technische Universität München, Emil-Erlenmeyer-Forum 5, 85354, Freising, Germany. .,Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS), Technische Universität München, Gregor-Mendel-Str. 4, 85354, Freising, Germany.
| | - Daniel A Keim
- Lehrstuhl für Datenanalyse und Visualisierung, Fachbereich Informatik und Informationswissenschaft, Universität Konstanz, Box 78, 78457, Konstanz, Germany.
| | - Siegfried Scherer
- Lehrstuhl für Mikrobielle Ökologie, Zentralinstitut für Ernährungs- und Lebensmittelforschung, Wissenschaftszentrum Weihenstephan, Technische Universität München, Weihenstephaner Berg 3, 85354, Freising, Germany.
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Abstract
In the last two decades, it has become increasingly evident that a large number of proteins are either fully or partially disordered. Intrinsically disordered proteins are ubiquitous proteins that fulfill essential biological functions while lacking a stable 3D structure. Their conformational heterogeneity is encoded at the amino acid sequence level, thereby allowing intrinsically disordered proteins or regions to be recognized based on their sequence properties. The identification of disordered regions facilitates the functional annotation of proteins and is instrumental for delineating boundaries of protein domains amenable to crystallization. This chapter focuses on the methods currently employed for predicting disorder and identifying regions involved in induced folding.
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Affiliation(s)
- Philippe Lieutaud
- AFMB UMR 7257, Aix-Marseille Université, 163, avenue de Luminy, Case 932, 13288, Marseille Cedex 09, France
- AFMB UMR 7257, CNRS, 163, avenue de Luminy, Case 932, 13288, Marseille Cedex 09, France
| | - François Ferron
- AFMB UMR 7257, Aix-Marseille Université, 163, avenue de Luminy, Case 932, 13288, Marseille Cedex 09, France
- AFMB UMR 7257, CNRS, 163, avenue de Luminy, Case 932, 13288, Marseille Cedex 09, France
| | - Sonia Longhi
- AFMB UMR 7257, Aix-Marseille Université, 163, avenue de Luminy, Case 932, 13288, Marseille Cedex 09, France.
- AFMB UMR 7257, CNRS, 163, avenue de Luminy, Case 932, 13288, Marseille Cedex 09, France.
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63
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Abstract
Using structure and sequence based analysis we can engineer proteins to increase their thermal stability.
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Affiliation(s)
- H. Pezeshgi Modarres
- Molecular Cell Biomechanics Laboratory
- Departments of Bioengineering and Mechanical Engineering
- University of California Berkeley
- Berkeley
- USA
| | - M. R. Mofrad
- Molecular Cell Biomechanics Laboratory
- Departments of Bioengineering and Mechanical Engineering
- University of California Berkeley
- Berkeley
- USA
| | - A. Sanati-Nezhad
- BioMEMS and Bioinspired Microfluidic Laboratory
- Department of Mechanical and Manufacturing Engineering
- University of Calgary
- Calgary
- Canada
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64
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DisPredict: A Predictor of Disordered Protein Using Optimized RBF Kernel. PLoS One 2015; 10:e0141551. [PMID: 26517719 PMCID: PMC4627842 DOI: 10.1371/journal.pone.0141551] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2015] [Accepted: 10/09/2015] [Indexed: 12/02/2022] Open
Abstract
Intrinsically disordered proteins or, regions perform important biological functions through their dynamic conformations during binding. Thus accurate identification of these disordered regions have significant implications in proper annotation of function, induced fold prediction and drug design to combat critical diseases. We introduce DisPredict, a disorder predictor that employs a single support vector machine with RBF kernel and novel features for reliable characterization of protein structure. DisPredict yields effective performance. In addition to 10-fold cross validation, training and testing of DisPredict was conducted with independent test datasets. The results were consistent with both the training and test error minimal. The use of multiple data sources, makes the predictor generic. The datasets used in developing the model include disordered regions of various length which are categorized as short and long having different compositions, different types of disorder, ranging from fully to partially disordered regions as well as completely ordered regions. Through comparison with other state of the art approaches and case studies, DisPredict is found to be a useful tool with competitive performance. DisPredict is available at https://github.com/tamjidul/DisPredict_v1.0.
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65
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Yang J, Wang Y, Zhang Y. ResQ: An Approach to Unified Estimation of B-Factor and Residue-Specific Error in Protein Structure Prediction. J Mol Biol 2015; 428:693-701. [PMID: 26437129 DOI: 10.1016/j.jmb.2015.09.024] [Citation(s) in RCA: 88] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2015] [Revised: 08/23/2015] [Accepted: 09/28/2015] [Indexed: 11/15/2022]
Abstract
Computer-based structure prediction becomes a major tool to provide large-scale structure models for annotating biological function of proteins. Information of residue-level accuracy and thermal mobility (or B-factor), which is critical to decide how biologists utilize the predicted models, is however missed in most structure prediction pipelines. We developed ResQ for unified residue-level model quality and B-factor estimations by combining local structure assembly variations with sequence-based and structure-based profiling. ResQ was tested on 635 non-redundant proteins with structure models generated by I-TASSER, where the average difference between estimated and observed distance errors is 1.4Å for the confidently modeled proteins. ResQ was further tested on structure decoys from CASP9-11 experiments, where the error of local structure quality prediction is consistently lower than or comparable to other state-of-the-art predictors. Finally, ResQ B-factor profile was used to assist molecular replacement, which resulted in successful solutions on several proteins that could not be solved from constant B-factor settings.
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Affiliation(s)
- Jianyi Yang
- School of Mathematical Sciences, Nankai University, Tianjin 300071, China; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yan Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA.
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66
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Meng F, Badierah RA, Almehdar HA, Redwan EM, Kurgan L, Uversky VN. Unstructural biology of the dengue virus proteins. FEBS J 2015; 282:3368-94. [DOI: 10.1111/febs.13349] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2015] [Revised: 06/01/2015] [Accepted: 06/15/2015] [Indexed: 01/02/2023]
Affiliation(s)
- Fanchi Meng
- Department of Electrical and Computer Engineering; University of Alberta; Edmonton Alberta Canada
| | - Reaid A. Badierah
- Biological Department; Faculty of Science; King Abdulaziz University; Jeddah Saudi Arabia
| | - Hussein A. Almehdar
- Biological Department; Faculty of Science; King Abdulaziz University; Jeddah Saudi Arabia
| | - Elrashdy M. Redwan
- Biological Department; Faculty of Science; King Abdulaziz University; Jeddah Saudi Arabia
- Therapeutic and Protective Proteins Laboratory; Protein Research Department; Genetic Engineering and Biotechnology Research Institute; City for Scientific Research and Technology Applications; New Borg El-Arab Alexandria Egypt
| | - Lukasz Kurgan
- Department of Electrical and Computer Engineering; University of Alberta; Edmonton Alberta Canada
| | - Vladimir N. Uversky
- Biological Department; Faculty of Science; King Abdulaziz University; Jeddah Saudi Arabia
- Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute; Morsani College of Medicine; University of South Florida; Tampa FL USA
- Laboratory of Structural Dynamics, Stability and Folding of Proteins; Institute of Cytology; Russian Academy of Sciences; St Petersburg Russia
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67
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Abstract
The hydrodynamic parameters measured in an AUC experiment, s(20,w) and D(t)(20,w)(0), can be used to gain information on the solution structure of (bio)macromolecules and their assemblies. This entails comparing the measured parameters with those that can be computed from usually "dry" structures by "hydrodynamic modeling." In this chapter, we will first briefly put hydrodynamic modeling in perspective and present the basic physics behind it as implemented in the most commonly used methods. The important "hydration" issue is also touched upon, and the distinction between rigid bodies versus those for which flexibility must be considered in the modeling process is then made. The available hydrodynamic modeling/computation programs, HYDROPRO, BEST, SoMo, AtoB, and Zeno, the latter four all implemented within the US-SOMO suite, are described and their performance evaluated. Finally, some literature examples are presented to illustrate the potential applications of hydrodynamics in the expanding field of multiresolution modeling.
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Affiliation(s)
- Mattia Rocco
- Biopolimeri e Proteomica, IRCCS AOU San Martino-IST, Istituto Nazionale per la Ricerca sul Cancro, Genova, Italy.
| | - Olwyn Byron
- School of Life Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, Scotland, United Kingdom.
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68
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Abstract
Elucidating the effects of naturally occurring genetic variation is one of the major challenges for personalized health and personalized medicine. Here, we introduce SNAP2, a novel neural network based classifier that improves over the state-of-the-art in distinguishing between effect and neutral variants. Our method's improved performance results from screening many potentially relevant protein features and from refining our development data sets. Cross-validated on >100k experimentally annotated variants, SNAP2 significantly outperformed other methods, attaining a two-state accuracy (effect/neutral) of 83%. SNAP2 also outperformed combinations of other methods. Performance increased for human variants but much more so for other organisms. Our method's carefully calibrated reliability index informs selection of variants for experimental follow up, with the most strongly predicted half of all effect variants predicted at over 96% accuracy. As expected, the evolutionary information from automatically generated multiple sequence alignments gave the strongest signal for the prediction. However, we also optimized our new method to perform surprisingly well even without alignments. This feature reduces prediction runtime by over two orders of magnitude, enables cross-genome comparisons, and renders our new method as the best solution for the 10-20% of sequence orphans. SNAP2 is available at: https://rostlab.org/services/snap2web
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69
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Craveur P, Joseph AP, Esque J, Narwani TJ, Noël F, Shinada N, Goguet M, Leonard S, Poulain P, Bertrand O, Faure G, Rebehmed J, Ghozlane A, Swapna LS, Bhaskara RM, Barnoud J, Téletchéa S, Jallu V, Cerny J, Schneider B, Etchebest C, Srinivasan N, Gelly JC, de Brevern AG. Protein flexibility in the light of structural alphabets. Front Mol Biosci 2015; 2:20. [PMID: 26075209 PMCID: PMC4445325 DOI: 10.3389/fmolb.2015.00020] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2015] [Accepted: 04/30/2015] [Indexed: 01/01/2023] Open
Abstract
Protein structures are valuable tools to understand protein function. Nonetheless, proteins are often considered as rigid macromolecules while their structures exhibit specific flexibility, which is essential to complete their functions. Analyses of protein structures and dynamics are often performed with a simplified three-state description, i.e., the classical secondary structures. More precise and complete description of protein backbone conformation can be obtained using libraries of small protein fragments that are able to approximate every part of protein structures. These libraries, called structural alphabets (SAs), have been widely used in structure analysis field, from definition of ligand binding sites to superimposition of protein structures. SAs are also well suited to analyze the dynamics of protein structures. Here, we review innovative approaches that investigate protein flexibility based on SAs description. Coupled to various sources of experimental data (e.g., B-factor) and computational methodology (e.g., Molecular Dynamic simulation), SAs turn out to be powerful tools to analyze protein dynamics, e.g., to examine allosteric mechanisms in large set of structures in complexes, to identify order/disorder transition. SAs were also shown to be quite efficient to predict protein flexibility from amino-acid sequence. Finally, in this review, we exemplify the interest of SAs for studying flexibility with different cases of proteins implicated in pathologies and diseases.
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Affiliation(s)
- Pierrick Craveur
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Agnel P Joseph
- Rutherford Appleton Laboratory, Science and Technology Facilities Council Didcot, UK
| | - Jeremy Esque
- Institut National de la Santé et de la Recherche Médicale U964,7 UMR Centre National de la Recherche Scientifique 7104, IGBMC, Université de Strasbourg Illkirch, France
| | - Tarun J Narwani
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Floriane Noël
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Nicolas Shinada
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Matthieu Goguet
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Sylvain Leonard
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Pierre Poulain
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France ; Ets Poulain Pointe-Noire, Congo
| | - Olivier Bertrand
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Guilhem Faure
- National Library of Medicine, National Center for Biotechnology Information, National Institutes of Health Bethesda, MD, USA
| | - Joseph Rebehmed
- Centre National de la Recherche Scientifique UMR7590, Sorbonne Universités, Université Pierre et Marie Curie - MNHN - IRD - IUC Paris, France
| | | | - Lakshmipuram S Swapna
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore Bangalore, India ; Hospital for Sick Children, and Departments of Biochemistry and Molecular Genetics, University of Toronto Toronto, ON, Canada
| | - Ramachandra M Bhaskara
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore Bangalore, India ; Department of Theoretical Biophysics, Max Planck Institute of Biophysics Frankfurt, Germany
| | - Jonathan Barnoud
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France ; Laboratoire de Physique, École Normale Supérieure de Lyon, Université de Lyon, Centre National de la Recherche Scientifique UMR 5672 Lyon, France
| | - Stéphane Téletchéa
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France ; Faculté des Sciences et Techniques, Université de Nantes, Unité Fonctionnalité et Ingénierie des Protéines, Centre National de la Recherche Scientifique UMR 6286, Université Nantes Nantes, France
| | - Vincent Jallu
- Platelet Unit, Institut National de la Transfusion Sanguine Paris, France
| | - Jiri Cerny
- Institute of Biotechnology, The Czech Academy of Sciences Prague, Czech Republic
| | - Bohdan Schneider
- Institute of Biotechnology, The Czech Academy of Sciences Prague, Czech Republic
| | - Catherine Etchebest
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | | | - Jean-Christophe Gelly
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Alexandre G de Brevern
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
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70
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Malhis N, Gsponer J. Computational identification of MoRFs in protein sequences. ACTA ACUST UNITED AC 2015; 31:1738-44. [PMID: 25637562 DOI: 10.1093/bioinformatics/btv060] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2014] [Accepted: 01/25/2015] [Indexed: 11/14/2022]
Abstract
MOTIVATION Intrinsically disordered regions of proteins play an essential role in the regulation of various biological processes. Key to their regulatory function is the binding of molecular recognition features (MoRFs) to globular protein domains in a process known as a disorder-to-order transition. Predicting the location of MoRFs in protein sequences with high accuracy remains an important computational challenge. METHOD In this study, we introduce MoRFCHiBi, a new computational approach for fast and accurate prediction of MoRFs in protein sequences. MoRFCHiBi combines the outcomes of two support vector machine (SVM) models that take advantage of two different kernels with high noise tolerance. The first, SVMS, is designed to extract maximal information from the general contrast in amino acid compositions between MoRFs, their surrounding regions (Flanks), and the remainders of the sequences. The second, SVMT, is used to identify similarities between regions in a query sequence and MoRFs of the training set. RESULTS We evaluated the performance of our predictor by comparing its results with those of two currently available MoRF predictors, MoRFpred and ANCHOR. Using three test sets that have previously been collected and used to evaluate MoRFpred and ANCHOR, we demonstrate that MoRFCHiBi outperforms the other predictors with respect to different evaluation metrics. In addition, MoRFCHiBi is downloadable and fast, which makes it useful as a component in other computational prediction tools. AVAILABILITY AND IMPLEMENTATION http://www.chibi.ubc.ca/morf/.
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Affiliation(s)
- Nawar Malhis
- Centre for High-Throughput Biology and Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Jörg Gsponer
- Centre for High-Throughput Biology and Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada Centre for High-Throughput Biology and Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
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71
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Touw WG, Baakman C, Black J, te Beek TAH, Krieger E, Joosten RP, Vriend G. A series of PDB-related databanks for everyday needs. Nucleic Acids Res 2014; 43:D364-8. [PMID: 25352545 PMCID: PMC4383885 DOI: 10.1093/nar/gku1028] [Citation(s) in RCA: 609] [Impact Index Per Article: 60.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
We present a series of databanks (http://swift.cmbi.ru.nl/gv/facilities/) that hold information that is computationally derived from Protein Data Bank (PDB) entries and that might augment macromolecular structure studies. These derived databanks run parallel to the PDB, i.e. they have one entry per PDB entry. Several of the well-established databanks such as HSSP, PDBREPORT and PDB_REDO have been updated and/or improved. The software that creates the DSSP databank, for example, has been rewritten to better cope with π-helices. A large number of databanks have been added to aid computational structural biology; some examples are lists of residues that make crystal contacts, lists of contacting residues using a series of contact definitions or lists of residue accessibilities. PDB files are not the optimal presentation of the underlying data for many studies. We therefore made a series of databanks that hold PDB files in an easier to use or more consistent representation. The BDB databank holds X-ray PDB files with consistently represented B-factors. We also added several visualization tools to aid the users of our databanks.
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Affiliation(s)
- Wouter G Touw
- Centre for Molecular and Biomolecular Informatics, CMBI, Radboud university medical center, Geert Grooteplein Zuid 26-28 6525 GA Nijmegen, The Netherlands
| | - Coos Baakman
- Centre for Molecular and Biomolecular Informatics, CMBI, Radboud university medical center, Geert Grooteplein Zuid 26-28 6525 GA Nijmegen, The Netherlands
| | - Jon Black
- Centre for Molecular and Biomolecular Informatics, CMBI, Radboud university medical center, Geert Grooteplein Zuid 26-28 6525 GA Nijmegen, The Netherlands
| | - Tim A H te Beek
- Bio-Prodict BV, Nieuwe Marktstraat 54E, 6511 AA Nijmegen, The Netherlands
| | - E Krieger
- Centre for Molecular and Biomolecular Informatics, CMBI, Radboud university medical center, Geert Grooteplein Zuid 26-28 6525 GA Nijmegen, The Netherlands
| | - Robbie P Joosten
- Centre for Molecular and Biomolecular Informatics, CMBI, Radboud university medical center, Geert Grooteplein Zuid 26-28 6525 GA Nijmegen, The Netherlands Department of Biochemistry, Netherlands Cancer Institute, Plesmanlaan 121, Amsterdam 1066 CX, The Netherlands
| | - Gert Vriend
- Centre for Molecular and Biomolecular Informatics, CMBI, Radboud university medical center, Geert Grooteplein Zuid 26-28 6525 GA Nijmegen, The Netherlands
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72
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Touw WG, Vriend G. BDB: Databank of PDB files with consistent B-factors. Protein Eng Des Sel 2014; 27:457-62. [DOI: 10.1093/protein/gzu044] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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73
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Roston RL, Wang K, Kuhn LA, Benning C. Structural determinants allowing transferase activity in SENSITIVE TO FREEZING 2, classified as a family I glycosyl hydrolase. J Biol Chem 2014; 289:26089-26106. [PMID: 25100720 DOI: 10.1074/jbc.m114.576694] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
SENSITIVE TO FREEZING 2 (SFR2) is classified as a family I glycosyl hydrolase but has recently been shown to have galactosyltransferase activity in Arabidopsis thaliana. Natural occurrences of apparent glycosyl hydrolases acting as transferases are interesting from a biocatalysis standpoint, and knowledge about the interconversion can assist in engineering SFR2 in crop plants to resist freezing. To understand how SFR2 evolved into a transferase, the relationship between its structure and function are investigated by activity assay, molecular modeling, and site-directed mutagenesis. SFR2 has no detectable hydrolase activity, although its catalytic site is highly conserved with that of family 1 glycosyl hydrolases. Three regions disparate from glycosyl hydrolases are identified as required for transferase activity as follows: a loop insertion, the C-terminal peptide, and a hydrophobic patch adjacent to the catalytic site. Rationales for the effects of these regions on the SFR2 mechanism are discussed.
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Affiliation(s)
- Rebecca L Roston
- Departments of Biochemistry and Molecular Biology and Michigan State University, East Lansing, Michigan 48824.
| | - Kun Wang
- Departments of Biochemistry and Molecular Biology and Michigan State University, East Lansing, Michigan 48824
| | - Leslie A Kuhn
- Departments of Biochemistry and Molecular Biology and Michigan State University, East Lansing, Michigan 48824; Departments of Computer Science and Engineering, Michigan State University, East Lansing, Michigan 48824
| | - Christoph Benning
- Departments of Biochemistry and Molecular Biology and Michigan State University, East Lansing, Michigan 48824
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74
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Yachdav G, Kloppmann E, Kajan L, Hecht M, Goldberg T, Hamp T, Hönigschmid P, Schafferhans A, Roos M, Bernhofer M, Richter L, Ashkenazy H, Punta M, Schlessinger A, Bromberg Y, Schneider R, Vriend G, Sander C, Ben-Tal N, Rost B. PredictProtein--an open resource for online prediction of protein structural and functional features. Nucleic Acids Res 2014; 42:W337-43. [PMID: 24799431 PMCID: PMC4086098 DOI: 10.1093/nar/gku366] [Citation(s) in RCA: 435] [Impact Index Per Article: 43.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
PredictProtein is a meta-service for sequence analysis that has been predicting
structural and functional features of proteins since 1992. Queried with a
protein sequence it returns: multiple sequence alignments, predicted aspects of
structure (secondary structure, solvent accessibility, transmembrane helices
(TMSEG) and strands, coiled-coil regions, disulfide bonds and disordered
regions) and function. The service incorporates analysis methods for the
identification of functional regions (ConSurf), homology-based inference of Gene
Ontology terms (metastudent), comprehensive subcellular localization prediction
(LocTree3), protein–protein binding sites (ISIS2),
protein–polynucleotide binding sites (SomeNA) and predictions of the
effect of point mutations (non-synonymous SNPs) on protein function (SNAP2). Our
goal has always been to develop a system optimized to meet the demands of
experimentalists not highly experienced in bioinformatics. To this end, the
PredictProtein results are presented as both text and a series of intuitive,
interactive and visually appealing figures. The web server and sources are
available at http://ppopen.rostlab.org.
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Affiliation(s)
- Guy Yachdav
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany Biosof LLC, New York, NY 10001, USA TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Edda Kloppmann
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany New York Consortium on Membrane Protein Structure (NYCOMPS), Columbia University, New York, NY 10032, USA
| | - Laszlo Kajan
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Maximilian Hecht
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Tatyana Goldberg
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Tobias Hamp
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Peter Hönigschmid
- Department of Genome Oriented Bioinformatics, Technische Universität München, Wissenschaftszentrum Weihenstephan, Freising 85354, Germany
| | - Andrea Schafferhans
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Manfred Roos
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Michael Bernhofer
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Lothar Richter
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Haim Ashkenazy
- The Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, 69978 Tel Aviv, Israel
| | - Marco Punta
- Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, UK Institute for Food and Plant Sciences WZW-Weihenstephan, Alte Akademie 8, Freising 85350, Germany
| | - Avner Schlessinger
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Yana Bromberg
- Biosof LLC, New York, NY 10001, USA Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | - Reinhard Schneider
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ 08901, USA
| | - Gerrit Vriend
- Luxembourg University & Luxembourg Centre for Systems Biomedicine, 4362 Belval, Luxembourg
| | - Chris Sander
- CMBI, NCMLS, Radboudumc Nijmegen Medical Centre, 6525 GA Nijmegen, The Netherlands
| | - Nir Ben-Tal
- Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, 10065 NY, USA
| | - Burkhard Rost
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany Biosof LLC, New York, NY 10001, USA New York Consortium on Membrane Protein Structure (NYCOMPS), Columbia University, New York, NY 10032, USA The Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, 69978 Tel Aviv, Israel Department of Biochemistry and Molecular Biophysics & New York Consortium on Membrane Protein Structure (NYCOMPS), Columbia University, New York, NY 10032, USA Institute for Advanced Study (TUM-IAS), Garching/Munich 85748, Germany
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75
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Joseph AP, de Brevern AG. From local structure to a global framework: recognition of protein folds. J R Soc Interface 2014; 11:20131147. [PMID: 24740960 DOI: 10.1098/rsif.2013.1147] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Protein folding has been a major area of research for many years. Nonetheless, the mechanisms leading to the formation of an active biological fold are still not fully apprehended. The huge amount of available sequence and structural information provides hints to identify the putative fold for a given sequence. Indeed, protein structures prefer a limited number of local backbone conformations, some being characterized by preferences for certain amino acids. These preferences largely depend on the local structural environment. The prediction of local backbone conformations has become an important factor to correctly identifying the global protein fold. Here, we review the developments in the field of local structure prediction and especially their implication in protein fold recognition.
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Affiliation(s)
- Agnel Praveen Joseph
- Science and Technology Facilities Council, Rutherford Appleton Laboratory, Harwell Oxford, , Didcot OX11 0QX, UK
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76
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Shen HB, Yi DL, Yao LX, Yang J, Chou KC. Knowledge-based computational intelligence development for predicting protein secondary structures from sequences. Expert Rev Proteomics 2014; 5:653-62. [DOI: 10.1586/14789450.5.5.653] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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77
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Yu H, Huang H. Engineering proteins for thermostability through rigidifying flexible sites. Biotechnol Adv 2013; 32:308-15. [PMID: 24211474 DOI: 10.1016/j.biotechadv.2013.10.012] [Citation(s) in RCA: 172] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2013] [Revised: 09/04/2013] [Accepted: 10/29/2013] [Indexed: 01/06/2023]
Abstract
Engineering proteins for thermostability is an exciting and challenging field since it is critical for broadening the industrial use of recombinant proteins. Thermostability of proteins arises from the simultaneous effect of several forces such as hydrophobic interactions, disulfide bonds, salt bridges and hydrogen bonds. All of these interactions lead to decreased flexibility of polypeptide chain. Structural studies of mesophilic and thermophilic proteins showed that the latter need more rigid structures to compensate for increased thermal fluctuations. Hence flexibility can be an indicator to pinpoint weak spots for enhancing thermostability of enzymes. A strategy has been proven effective in enhancing proteins' thermostability with two steps: predict flexible sites of proteins firstly and then rigidify these sites. We refer to this approach as rigidify flexible sites (RFS) and give an overview of such a method through summarizing the methods to predict flexibility of a protein, the methods to rigidify residues with high flexibility and successful cases regarding enhancing thermostability of proteins using RFS.
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Affiliation(s)
- Haoran Yu
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Key Laboratory of Systems Bioengineering, Ministry of Education, Tianjin University, Tianjin 300072, China
| | - He Huang
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Key Laboratory of Systems Bioengineering, Ministry of Education, Tianjin University, Tianjin 300072, China.
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78
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Peng Z, Mizianty MJ, Kurgan L. Genome-scale prediction of proteins with long intrinsically disordered regions. Proteins 2013; 82:145-58. [PMID: 23798504 DOI: 10.1002/prot.24348] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2013] [Accepted: 06/06/2013] [Indexed: 12/24/2022]
Abstract
Proteins with long disordered regions (LDRs), defined as having 30 or more consecutive disordered residues, are abundant in eukaryotes, and these regions are recognized as a distinct class of biologically functional domains. LDRs facilitate various cellular functions and are important for target selection in structural genomics. Motivated by the lack of methods that directly predict proteins with LDRs, we designed Super-fast predictor of proteins with Long Intrinsically DisordERed regions (SLIDER). SLIDER utilizes logistic regression that takes an empirically chosen set of numerical features, which consider selected physicochemical properties of amino acids, sequence complexity, and amino acid composition, as its inputs. Empirical tests show that SLIDER offers competitive predictive performance combined with low computational cost. It outperforms, by at least a modest margin, a comprehensive set of modern disorder predictors (that can indirectly predict LDRs) and is 16 times faster compared to the best currently available disorder predictor. Utilizing our time-efficient predictor, we characterized abundance and functional roles of proteins with LDRs over 110 eukaryotic proteomes. Similar to related studies, we found that eukaryotes have many (on average 30.3%) proteins with LDRs with majority of proteomes having between 25 and 40%, where higher abundance is characteristic to proteomes that have larger proteins. Our first-of-its-kind large-scale functional analysis shows that these proteins are enriched in a number of cellular functions and processes including certain binding events, regulation of catalytic activities, cellular component organization, biogenesis, biological regulation, and some metabolic and developmental processes. A webserver that implements SLIDER is available at http://biomine.ece.ualberta.ca/SLIDER/.
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Affiliation(s)
- Zhenling Peng
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
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79
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Light S, Sagit R, Sachenkova O, Ekman D, Elofsson A. Protein Expansion Is Primarily due to Indels in Intrinsically Disordered Regions. Mol Biol Evol 2013; 30:2645-53. [DOI: 10.1093/molbev/mst157] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
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80
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Voges MJ, Silver PA, Way JC, Mattozzi MD. Targeting a heterologous protein to multiple plant organelles via rationally designed 5' mRNA tags. J Biol Eng 2013; 7:20. [PMID: 24011257 PMCID: PMC3847293 DOI: 10.1186/1754-1611-7-20] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2013] [Accepted: 08/17/2013] [Indexed: 11/24/2022] Open
Abstract
Background Plant bioengineers require simple genetic devices for predictable localization of heterologous proteins to multiple subcellular compartments. Results We designed novel hybrid signal sequences for multiple-compartment localization and characterize their function when fused to GFP in Nicotiana benthamiana leaf tissue. TriTag-1 and TriTag-2 use alternative splicing to generate differentially localized GFP isoforms, localizing it to the chloroplasts, peroxisomes and cytosol. TriTag-1 shows a bias for targeting the chloroplast envelope while TriTag-2 preferentially targets the peroxisomes. TriTag-3 embeds a conserved peroxisomal targeting signal within a chloroplast transit peptide, directing GFP to the chloroplasts and peroxisomes. Conclusions Our novel signal sequences can reduce the number of cloning steps and the amount of genetic material required to target a heterologous protein to multiple locations in plant cells. This work harnesses alternative splicing and signal embedding for engineering plants to express multi-functional proteins from single genetic constructs.
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Affiliation(s)
- Mathias J Voges
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA.
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81
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Cloud prediction of protein structure and function with PredictProtein for Debian. BIOMED RESEARCH INTERNATIONAL 2013; 2013:398968. [PMID: 23971032 PMCID: PMC3732596 DOI: 10.1155/2013/398968] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2013] [Accepted: 07/05/2013] [Indexed: 11/18/2022]
Abstract
We report the release of PredictProtein for the Debian operating system and derivatives, such as Ubuntu, Bio-Linux, and Cloud BioLinux. The PredictProtein suite is available as a standard set of open source Debian packages. The release covers the most popular prediction methods from the Rost Lab, including methods for the prediction of secondary structure and solvent accessibility (profphd), nuclear localization signals (predictnls), and intrinsically disordered regions (norsnet). We also present two case studies that successfully utilize PredictProtein packages for high performance computing in the cloud: the first analyzes protein disorder for whole organisms, and the second analyzes the effect of all possible single sequence variants in protein coding regions of the human genome.
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82
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Yan J, Mizianty MJ, Filipow PL, Uversky VN, Kurgan L. RAPID: fast and accurate sequence-based prediction of intrinsic disorder content on proteomic scale. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2013; 1834:1671-80. [PMID: 23732563 DOI: 10.1016/j.bbapap.2013.05.022] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2013] [Accepted: 05/22/2013] [Indexed: 11/24/2022]
Abstract
Recent research in the protein intrinsic disorder was stimulated by the availability of accurate computational predictors. However, most of these methods are relatively slow, especially considering proteome-scale applications, and were shown to produce relatively large errors when estimating disorder at the protein- (in contrast to residue-) level, which is defined by the fraction/content of disordered residues. To this end, we propose a novel support vector Regression-based Accurate Predictor of Intrinsic Disorder (RAPID). Key advantages of RAPID are speed (prediction of an average-size eukaryotic proteome takes <1h on a modern desktop computer); sophisticated design (multiple, complementary information sources that are aggregated over an input chain are combined using feature selection); and high-quality and robust predictive performance. Empirical tests on two diverse benchmark datasets reveal that RAPID's predictive performance compares favorably to a comprehensive set of state-of-the-art disorder and disorder content predictors. Drawing on high speed and good predictive quality, RAPID was used to perform large-scale characterization of disorder in 200+ fully sequenced eukaryotic proteomes. Our analysis reveals interesting relations of disorder with structural coverage and chain length, and unusual distribution of fully disordered chains. We also performed a comprehensive (using 56000+ annotated chains, which doubles the scope of previous studies) investigation of cellular functions and localizations that are enriched in the disorder in the human proteome. RAPID, which allows for batch (proteome-wide) predictions, is available as a web server at http://biomine.ece.ualberta.ca/RAPID/.
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Affiliation(s)
- Jing Yan
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
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83
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Mizianty MJ, Peng Z, Kurgan L. MFDp2: Accurate predictor of disorder in proteins by fusion of disorder probabilities, content and profiles. INTRINSICALLY DISORDERED PROTEINS 2013; 1:e24428. [PMID: 28516009 PMCID: PMC5424793 DOI: 10.4161/idp.24428] [Citation(s) in RCA: 79] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2013] [Accepted: 03/23/2013] [Indexed: 11/28/2022]
Abstract
Intrinsically disordered proteins (IDPs) are either entirely disordered or contain disordered regions in their native state. IDPs were found to be abundant in complex organisms and implicated in numerous cellular processes. Experimental annotation of disorder lags behind the rapidly growing sizes of the protein databases, and thus computational methods are used to close this gap and to investigate the disorder. MFDp2 is a novel content-rich and user-friendly web server for sequence-based prediction of protein disorder that builds upon our residue-level disorder predictor MFDp and chain-level disorder content predictor DisCon. It applies novel post-processing filters and uses sequence alignment to improve predictive quality. Using a new benchmark data set, which has reduced sequence identity to corresponding training data sets, MFDp2 is shown to provide competitive predictive quality when compared with MFDp and a comprehensive set of 13 other state-of-the-art predictors, including publicly available versions of the top predictors from CASP9. Our server obtains the highest Mathews Correlation Coefficient (MCC) and the second best Area Under the receiver operating characteristic Curve (AUC). In addition to the disorder predictions, our server also outputs well-described sequence-derived information that allows profiling the predicted disorder. We conveniently visualize sequence conservation, predicted secondary structure, relative solvent accessibility and alignments to chains with annotated disorder. We allow predictions for multiple proteins at the same time and each prediction can be downloaded as text-based (parsable) file. The web server, which includes help pages and tutorial, is freely available at biomine.ece.ualberta.ca/MFDp2/.
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Affiliation(s)
- Marcin J Mizianty
- Department of Electrical and Computer Engineering; University of Alberta; Edmonton, AB Canada
| | - Zhenling Peng
- Department of Electrical and Computer Engineering; University of Alberta; Edmonton, AB Canada
| | - Lukasz Kurgan
- Department of Electrical and Computer Engineering; University of Alberta; Edmonton, AB Canada
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84
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Fan X, Kurgan L. Accurate prediction of disorder in protein chains with a comprehensive and empirically designed consensus. J Biomol Struct Dyn 2013; 32:448-64. [DOI: 10.1080/07391102.2013.775969] [Citation(s) in RCA: 113] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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85
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Wahid A, Helle F, Descamps V, Duverlie G, Penin F, Dubuisson J. Disulfide bonds in hepatitis C virus glycoprotein E1 control the assembly and entry functions of E2 glycoprotein. J Virol 2013; 87:1605-17. [PMID: 23175356 PMCID: PMC3554189 DOI: 10.1128/jvi.02659-12] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2012] [Accepted: 11/09/2012] [Indexed: 12/24/2022] Open
Abstract
Class II membrane fusion proteins have been described in viruses in which the envelope proteins are derived from a precursor polyprotein containing two transmembrane glycoproteins arranged in tandem. Although the second protein, which carries the membrane fusion function, is in general well characterized, the companion protein, which is a protein chaperone for the folding of the fusion protein, is less well characterized for some viruses, like hepatitis C virus (HCV). To investigate the role of the class II companion glycoprotein E1 of HCV, we chose to target conserved cysteine residues in the protein, and we systematically mutated them in a full-length infectious HCV clone by reverse genetics. All the mutants were infectious, albeit with lower titers than the wild-type virus. The reduced infectivity was in part due to a decrease in viral assembly, as revealed by measurement of intracellular infectivity and by quantification of core protein released from cells transfected with mutant genomes. Analyses of mutated proteins did not show any major defect in folding. However, the mutations reduced virus stability, and they could also affect the density of infectious viral particles. Mutant viruses also showed a defect in cell-to-cell transmission. Finally, our data indicate that HCV glycoprotein E1 can also affect the fusion protein E2 by modulating its recognition by the cellular coreceptor CD81. Therefore, in the context of HCV, our data identify an additional function of a class II companion protein as a molecule that can control the binding capacity of the fusion protein.
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Affiliation(s)
- Ahmed Wahid
- Center for Infection and Immunity of Lille (CIIL), INSERM U1019, CNRS UMR8204, Institut Pasteur de Lille, Université Lille Nord de France, Lille, France
- Department of Biochemistry, Faculty of Pharmacy, Minia, University, Minia, Egypt
| | - François Helle
- Laboratoire de Virologie EA4294, Centre Hospitalier Universitaire d'Amiens, Université de Picardie Jules Verne, Amiens, France
| | - Véronique Descamps
- Laboratoire de Virologie EA4294, Centre Hospitalier Universitaire d'Amiens, Université de Picardie Jules Verne, Amiens, France
| | - Gilles Duverlie
- Laboratoire de Virologie EA4294, Centre Hospitalier Universitaire d'Amiens, Université de Picardie Jules Verne, Amiens, France
| | - François Penin
- Institut de Biologie et Chimie des Protéines, Bases Moléculaires et Structurales des Systèmes Infectieux, UMR-5086-CNRS, Université de Lyon, Lyon, France
| | - Jean Dubuisson
- Center for Infection and Immunity of Lille (CIIL), INSERM U1019, CNRS UMR8204, Institut Pasteur de Lille, Université Lille Nord de France, Lille, France
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86
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Disfani FM, Hsu WL, Mizianty MJ, Oldfield CJ, Xue B, Dunker AK, Uversky VN, Kurgan L. MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins. ACTA ACUST UNITED AC 2013; 28:i75-83. [PMID: 22689782 PMCID: PMC3371841 DOI: 10.1093/bioinformatics/bts209] [Citation(s) in RCA: 268] [Impact Index Per Article: 24.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Motivation: Molecular recognition features (MoRFs) are short binding regions located within longer intrinsically disordered regions that bind to protein partners via disorder-to-order transitions. MoRFs are implicated in important processes including signaling and regulation. However, only a limited number of experimentally validated MoRFs is known, which motivates development of computational methods that predict MoRFs from protein chains. Results: We introduce a new MoRF predictor, MoRFpred, which identifies all MoRF types (α, β, coil and complex). We develop a comprehensive dataset of annotated MoRFs to build and empirically compare our method. MoRFpred utilizes a novel design in which annotations generated by sequence alignment are fused with predictions generated by a Support Vector Machine (SVM), which uses a custom designed set of sequence-derived features. The features provide information about evolutionary profiles, selected physiochemical properties of amino acids, and predicted disorder, solvent accessibility and B-factors. Empirical evaluation on several datasets shows that MoRFpred outperforms related methods: α-MoRF-Pred that predicts α-MoRFs and ANCHOR which finds disordered regions that become ordered when bound to a globular partner. We show that our predicted (new) MoRF regions have non-random sequence similarity with native MoRFs. We use this observation along with the fact that predictions with higher probability are more accurate to identify putative MoRF regions. We also identify a few sequence-derived hallmarks of MoRFs. They are characterized by dips in the disorder predictions and higher hydrophobicity and stability when compared to adjacent (in the chain) residues. Availability:http://biomine.ece.ualberta.ca/MoRFpred/; http://biomine.ece.ualberta.ca/MoRFpred/Supplement.pdf Contact:lkurgan@ece.ualberta.ca Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Fatemeh Miri Disfani
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, T6G 2V4, Canada
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87
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Prediction of deleterious nonsynonymous single-nucleotide polymorphism for human diseases. ScientificWorldJournal 2013; 2013:675851. [PMID: 23431257 PMCID: PMC3572689 DOI: 10.1155/2013/675851] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2012] [Accepted: 12/11/2012] [Indexed: 12/13/2022] Open
Abstract
The identification of genetic variants that are responsible for human inherited diseases is a fundamental problem in human and medical genetics. As a typical type of genetic variation, nonsynonymous single-nucleotide polymorphisms (nsSNPs) occurring in protein coding regions may alter the encoded amino acid, potentially affect protein structure and function, and further result in human inherited diseases. Therefore, it is of great importance to develop computational approaches to facilitate the discrimination of deleterious nsSNPs from neutral ones. In this paper, we review databases that collect nsSNPs and summarize computational methods for the identification of deleterious nsSNPs. We classify the existing methods for characterizing nsSNPs into three categories (sequence based, structure based, and annotation based), and we introduce machine learning models for the prediction of deleterious nsSNPs. We further discuss methods for identifying deleterious nsSNPs in noncoding variants and those for dealing with rare variants.
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88
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Caliandro R, Rossetti G, Carloni P. Local Fluctuations and Conformational Transitions in Proteins. J Chem Theory Comput 2012; 8:4775-85. [DOI: 10.1021/ct300610y] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Rocco Caliandro
- CNR—Institute of Crystallography,
via Amendola 122/o, I-70126, Bari, Italy
| | - Giulia Rossetti
- Institute for Research in Biomedicine
and Barcelona Supercomputing Center, Joint Research Program on Computational
Biology, Baldiri I Reixac 10, 08028, Barcelona, Spain
- Jülich Supercomputing Centre,
Institute for Advanced Simulation, Forschungszentrum Jülich,
D-52425 Jülich, Germany
- Computational Biophysics, German
Research School for Simulation Sciences 1, D-52425 Jülich,
Germany, and Institute for Advanced Simulation, Forschungszentrum
Jülich, D-52425 Jülich, Germany
| | - Paolo Carloni
- Computational Biophysics, German
Research School for Simulation Sciences 1, D-52425 Jülich,
Germany, and Institute for Advanced Simulation, Forschungszentrum
Jülich, D-52425 Jülich, Germany
- Statistical and Biological Physics
Sector, International School for Advanced Studies (SISSA), Trieste,
Italy
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89
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Schaefer C, Bromberg Y, Achten D, Rost B. Disease-related mutations predicted to impact protein function. BMC Genomics 2012; 13 Suppl 4:S11. [PMID: 22759649 PMCID: PMC3394413 DOI: 10.1186/1471-2164-13-s4-s11] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Background Non-synonymous single nucleotide polymorphisms (nsSNPs) alter the protein sequence and can cause disease. The impact has been described by reliable experiments for relatively few mutations. Here, we study predictions for functional impact of disease-annotated mutations from OMIM, PMD and Swiss-Prot and of variants not linked to disease. Results Most disease-causing mutations were predicted to impact protein function. More surprisingly, the raw predictions scores for disease-causing mutations were higher than the scores for the function-altering data set originally used for developing the prediction method (here SNAP). We might expect that diseases are caused by change-of-function mutations. However, it is surprising how well prediction methods developed for different purposes identify this link. Conversely, our predictions suggest that the set of nsSNPs not currently linked to diseases contains very few strong disease associations to be discovered. Conclusions Firstly, annotations of disease-causing nsSNPs are on average so reliable that they can be used as proxies for functional impact. Secondly, disease-causing nsSNPs can be identified very well by methods that predict the impact of mutations on protein function. This implies that the existing prediction methods provide a very good means of choosing a set of suspect SNPs relevant for disease.
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Affiliation(s)
- Christian Schaefer
- Bioinformatics-i12, Informatics, Technical University Munich, Boltzmannstrasse 3, Garching/Munich, Germany.
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90
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Abstract
Background Amino acid point mutations (nsSNPs) may change protein structure and function. However, no method directly predicts the impact of mutations on structure. Here, we compare pairs of pentamers (five consecutive residues) that locally change protein three-dimensional structure (3D, RMSD>0.4Å) to those that do not alter structure (RMSD<0.2Å). Mutations that alter structure locally can be distinguished from those that do not through a machine-learning (logistic regression) method. Results The method achieved a rather high overall performance (AUC>0.79, two-state accuracy >72%). This discriminative power was particularly unexpected given the enormous structural variability of pentamers. Mutants for which our method predicted a change of structure were also enriched in terms of disrupting stability and function. Although distinguishing change and no change in structure, the new method overall failed to distinguish between mutants with and without effect on stability or function. Conclusions Local structural change can be predicted. Future work will have to establish how useful this new perspective on predicting the effect of nsSNPs will be in combination with other methods.
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Affiliation(s)
- Christian Schaefer
- TUM, Bioinformatics-I12, Informatik, Boltzmannstrasse 3, Garching, Germany.
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91
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de Brevern AG, Bornot A, Craveur P, Etchebest C, Gelly JC. PredyFlexy: flexibility and local structure prediction from sequence. Nucleic Acids Res 2012; 40:W317-22. [PMID: 22689641 PMCID: PMC3394303 DOI: 10.1093/nar/gks482] [Citation(s) in RCA: 69] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Protein structures are necessary for understanding protein function at a molecular level. Dynamics and flexibility of protein structures are also key elements of protein function. So, we have proposed to look at protein flexibility using novel methods: (i) using a structural alphabet and (ii) combining classical X-ray B-factor data and molecular dynamics simulations. First, we established a library composed of structural prototypes (LSPs) to describe protein structure by a limited set of recurring local structures. We developed a prediction method that proposes structural candidates in terms of LSPs and predict protein flexibility along a given sequence. Second, we examine flexibility according to two different descriptors: X-ray B-factors considered as good indicators of flexibility and the root mean square fluctuations, based on molecular dynamics simulations. We then define three flexibility classes and propose a method based on the LSP prediction method for predicting flexibility along the sequence. This method does not resort to sophisticate learning of flexibility but predicts flexibility from average flexibility of predicted local structures. The method is implemented in PredyFlexy web server. Results are similar to those obtained with the most recent, cutting-edge methods based on direct learning of flexibility data conducted with sophisticated algorithms. PredyFlexy can be accessed at http://www.dsimb.inserm.fr/dsimb_tools/predyflexy/.
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92
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Andersen SO. Are structural proteins in insect cuticles dominated by intrinsically disordered regions? INSECT BIOCHEMISTRY AND MOLECULAR BIOLOGY 2011; 41:620-627. [PMID: 21477652 DOI: 10.1016/j.ibmb.2011.03.015] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2011] [Revised: 03/19/2011] [Accepted: 03/29/2011] [Indexed: 05/30/2023]
Abstract
Fifty years ago it was concluded that the highly elastic cuticular protein, resilin, is devoid of secondary structure and that the peptide chains are randomly coiled and easily and reversibly deformed. These properties indicate that resilin is an intrinsically disordered protein and suggest that also other cuticular proteins may contain disordered regions. Amino acid sequences are now available for cuticular proteins from many insect species, and several programs have been developed to predict the probability for a given protein to contain disordered regions. The present paper describes the results obtained when the predictors are applied to various types of cuticular proteins from several insects. The results suggest that most cuticular proteins contain shorter or longer disordered regions, and the possible functions for such regions are briefly discussed.
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Affiliation(s)
- Svend Olav Andersen
- The Collstrop Foundation, The Royal Danish Academy of Sciences and Letters, H.C. Andersens Boulevard 35, 1553 Copenhagen V, Denmark.
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Mizianty MJ, Zhang T, Xue B, Zhou Y, Dunker AK, Uversky VN, Kurgan L. In-silico prediction of disorder content using hybrid sequence representation. BMC Bioinformatics 2011; 12:245. [PMID: 21682902 PMCID: PMC3212983 DOI: 10.1186/1471-2105-12-245] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2010] [Accepted: 06/17/2011] [Indexed: 11/25/2022] Open
Abstract
Background Intrinsically disordered proteins play important roles in various cellular activities and their prevalence was implicated in a number of human diseases. The knowledge of the content of the intrinsic disorder in proteins is useful for a variety of studies including estimation of the abundance of disorder in protein families, classes, and complete proteomes, and for the analysis of disorder-related protein functions. The above investigations currently utilize the disorder content derived from the per-residue disorder predictions. We show that these predictions may over-or under-predict the overall amount of disorder, which motivates development of novel tools for direct and accurate sequence-based prediction of the disorder content. Results We hypothesize that sequence-level aggregation of input information may provide more accurate content prediction when compared with the content extracted from the local window-based residue-level disorder predictors. We propose a novel predictor, DisCon, that takes advantage of a small set of 29 custom-designed descriptors that aggregate and hybridize information concerning sequence, evolutionary profiles, and predicted secondary structure, solvent accessibility, flexibility, and annotation of globular domains. Using these descriptors and a ridge regression model, DisCon predicts the content with low, 0.05, mean squared error and high, 0.68, Pearson correlation. This is a statistically significant improvement over the content computed from outputs of ten modern disorder predictors on a test dataset with proteins that share low sequence identity with the training sequences. The proposed predictive model is analyzed to discuss factors related to the prediction of the disorder content. Conclusions DisCon is a high-quality alternative for high-throughput annotation of the disorder content. We also empirically demonstrate that the DisCon's predictions can be used to improve binary annotations of the disordered residues from the real-value disorder propensities generated by current residue-level disorder predictors. The web server that implements the DisCon is available at http://biomine.ece.ualberta.ca/DisCon/.
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Affiliation(s)
- Marcin J Mizianty
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta T6G 2V4, Canada
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94
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Falero A, Caballero A, Trigueros S, Pérez C, Campos J, Marrero K, Fando R. Characterization of the single-stranded DNA binding protein pV(VGJΦ) of VGJΦ phage from Vibrio cholerae. BIOCHIMICA ET BIOPHYSICA ACTA 2011; 1814:1107-12. [PMID: 21586349 DOI: 10.1016/j.bbapap.2011.04.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2010] [Revised: 03/22/2011] [Accepted: 04/21/2011] [Indexed: 01/01/2023]
Abstract
pV(VGJΦ), a single-stranded DNA binding protein of the vibriophage VGJΦ was subject to biochemical analysis. Here, we show that this protein has a general affinity for single-stranded DNA (ssDNA) as documented by Electrophoretic Mobility Shift Assay (EMSA). The apparent molecular weight of the monomer is about 12.7kDa as measured by HPLC-SEC. Moreover, isoelectrofocusing showed an isoelectric point for pV(VGJΦ) of 6.82 pH units. Size exclusion chromatography in 150mM NaCl, 50mM sodium phosphate buffer, pH 7.0 revealed a major protein species of 27.0kDa, suggesting homodimeric protein architecture. Furthermore, pV(VGJΦ) binds ssDNA at extreme temperatures and the complex was stable after extended incubation times. Upon frozen storage at -20°C for a year the protein retained its integrity, biological activity and oligomericity. On the other hand, bioinformatics analysis predicted that pV(VGJΦ) protein has a disordered C-terminal, which might be involved in its functional activity. All the aforementioned features make pV(VGJΦ) interesting for biotechnological applications.
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Affiliation(s)
- Alina Falero
- Department of Molecular Biology, National Center for Scientific Research, Havana, Cuba.
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95
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González-Díaz H, Muíño L, Anadón AM, Romaris F, Prado-Prado FJ, Munteanu CR, Dorado J, Sierra AP, Mezo M, González-Warleta M, Gárate T, Ubeira FM. MISS-Prot: web server for self/non-self discrimination of protein residue networks in parasites; theory and experiments in Fasciola peptides and Anisakis allergens. MOLECULAR BIOSYSTEMS 2011; 7:1938-55. [PMID: 21468430 DOI: 10.1039/c1mb05069a] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Infections caused by human parasites (HPs) affect the poorest 500 million people worldwide but chemotherapy has become expensive, toxic, and/or less effective due to drug resistance. On the other hand, many 3D structures in Protein Data Bank (PDB) remain without function annotation. We need theoretical models to quickly predict biologically relevant Parasite Self Proteins (PSP), which are expressed differentially in a given parasite and are dissimilar to proteins expressed in other parasites and have a high probability to become new vaccines (unique sequence) or drug targets (unique 3D structure). We present herein a model for PSPs in eight different HPs (Ascaris, Entamoeba, Fasciola, Giardia, Leishmania, Plasmodium, Trypanosoma, and Toxoplasma) with 90% accuracy for 15 341 training and validation cases. The model combines protein residue networks, Markov Chain Models (MCM) and Artificial Neural Networks (ANN). The input parameters are the spectral moments of the Markov transition matrix for electrostatic interactions associated with the protein residue complex network calculated with the MARCH-INSIDE software. We implemented this model in a new web-server called MISS-Prot (MARCH-INSIDE Scores for Self-Proteins). MISS-Prot was programmed using PHP/HTML/Python and MARCH-INSIDE routines and is freely available at: . This server is easy to use by non-experts in Bioinformatics who can carry out automatic online upload and prediction with 3D structures deposited at PDB (mode 1). We can also study outcomes of Peptide Mass Fingerprinting (PMFs) and MS/MS for query proteins with unknown 3D structures (mode 2). We illustrated the use of MISS-Prot in experimental and/or theoretical studies of peptides from Fasciola hepatica cathepsin proteases or present on 10 Anisakis simplex allergens (Ani s 1 to Ani s 10). In doing so, we combined electrophoresis (1DE), MALDI-TOF Mass Spectroscopy, and MASCOT to seek sequences, Molecular Mechanics + Molecular Dynamics (MM/MD) to generate 3D structures and MISS-Prot to predict PSP scores. MISS-Prot also allows the prediction of PSP proteins in 16 additional species including parasite hosts, fungi pathogens, disease transmission vectors, and biotechnologically relevant organisms.
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Affiliation(s)
- Humberto González-Díaz
- Department of Microbiology & Parasitology, Faculty of Pharmacy, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain.
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96
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Thusberg J, Olatubosun A, Vihinen M. Performance of mutation pathogenicity prediction methods on missense variants. Hum Mutat 2011; 32:358-68. [PMID: 21412949 DOI: 10.1002/humu.21445] [Citation(s) in RCA: 384] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2010] [Accepted: 12/07/2010] [Indexed: 11/10/2022]
Abstract
Single nucleotide polymorphisms (SNPs) are the most common form of genetic variation in humans. The number of SNPs identified in the human genome is growing rapidly, but attaining experimental knowledge about the possible disease association of variants is laborious and time-consuming. Several computational methods have been developed for the classification of SNPs according to their predicted pathogenicity. In this study, we have evaluated the performance of nine widely used pathogenicity prediction methods available on the Internet. The evaluated methods were MutPred, nsSNPAnalyzer, Panther, PhD-SNP, PolyPhen, PolyPhen2, SIFT, SNAP, and SNPs&GO. The methods were tested with a set of over 40,000 pathogenic and neutral variants. We also assessed whether the type of original or substituting amino acid residue, the structural class of the protein, or the structural environment of the amino acid substitution, had an effect on the prediction performance. The performances of the programs ranged from poor (MCC 0.19) to reasonably good (MCC 0.65), and the results from the programs correlated poorly. The overall best performing methods in this study were SNPs&GO and MutPred, with accuracies reaching 0.82 and 0.81, respectively.
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Affiliation(s)
- Janita Thusberg
- Institute of Biomedical Technology, F1-33014 University of Tampere, Finland
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97
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Zhang H, Zhang T, Chen K, Kedarisetti KD, Mizianty MJ, Bao Q, Stach W, Kurgan L. Critical assessment of high-throughput standalone methods for secondary structure prediction. Brief Bioinform 2011; 12:672-88. [PMID: 21252072 DOI: 10.1093/bib/bbq088] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Sequence-based prediction of protein secondary structure (SS) enjoys wide-spread and increasing use for the analysis and prediction of numerous structural and functional characteristics of proteins. The lack of a recent comprehensive and large-scale comparison of the numerous prediction methods results in an often arbitrary selection of a SS predictor. To address this void, we compare and analyze 12 popular, standalone and high-throughput predictors on a large set of 1975 proteins to provide in-depth, novel and practical insights. We show that there is no universally best predictor and thus detailed comparative studies are needed to support informed selection of SS predictors for a given application. Our study shows that the three-state accuracy (Q3) and segment overlap (SOV3) of the SS prediction currently reach 82% and 81%, respectively. We demonstrate that carefully designed consensus-based predictors improve the Q3 by additional 2% and that homology modeling-based methods are significantly better by 1.5% Q3 than ab initio approaches. Our empirical analysis reveals that solvent exposed and flexible coils are predicted with a higher quality than the buried and rigid coils, while inverse is true for the strands and helices. We also show that longer helices are easier to predict, which is in contrast to longer strands that are harder to find. The current methods confuse 1-6% of strand residues with helical residues and vice versa and they perform poorly for residues in the β- bridge and 3(10)-helix conformations. Finally, we compare predictions of the standalone implementations of four well-performing methods with their corresponding web servers.
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Affiliation(s)
- Hua Zhang
- Zhejiang Gongshang University, Hangzhou, Zhejiang, P.R. China
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98
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Gao J, Zhang T, Zhang H, Shen S, Ruan J, Kurgan L. Accurate prediction of protein folding rates from sequence and sequence-derived residue flexibility and solvent accessibility. Proteins 2010; 78:2114-30. [PMID: 20455267 DOI: 10.1002/prot.22727] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Protein folding rates vary by several orders of magnitude and they depend on the topology of the fold and the size and composition of the sequence. Although recent works show that the rates can be predicted from the sequence, allowing for high-throughput annotations, they consider only the sequence and its predicted secondary structure. We propose a novel sequence-based predictor, PFR-AF, which utilizes solvent accessibility and residue flexibility predicted from the sequence, to improve predictions and provide insights into the folding process. The predictor includes three linear regressions for proteins with two-state, multistate, and unknown (mixed-state) folding kinetics. PFR-AF on average outperforms current methods when tested on three datasets. The proposed approach provides high-quality predictions in the absence of similarity between the predicted and the training sequences. The PFR-AF's predictions are characterized by high (between 0.71 and 0.95, depending on the dataset) correlation and the lowest (between 0.75 and 0.9) mean absolute errors with respect to the experimental rates, as measured using out-of-sample tests. Our models reveal that for the two-state chains inclusion of solvent-exposed Ala may accelerate the folding, while increased content of Ile may reduce the folding speed. We also demonstrate that increased flexibility of coils facilitates faster folding and that proteins with larger content of solvent-exposed strands may fold at a slower pace. The increased flexibility of the solvent-exposed residues is shown to elongate folding, which also holds, with a lower correlation, for buried residues. Two case studies are included to support our findings.
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Affiliation(s)
- Jianzhao Gao
- College of Mathematics and LPMC, Nankai University, Tianjin, People's Republic of China
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99
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Mizianty MJ, Stach W, Chen K, Kedarisetti KD, Disfani FM, Kurgan L. Improved sequence-based prediction of disordered regions with multilayer fusion of multiple information sources. ACTA ACUST UNITED AC 2010; 26:i489-96. [PMID: 20823312 PMCID: PMC2935446 DOI: 10.1093/bioinformatics/btq373] [Citation(s) in RCA: 132] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Motivation: Intrinsically disordered proteins play a crucial role in numerous regulatory processes. Their abundance and ubiquity combined with a relatively low quantity of their annotations motivate research toward the development of computational models that predict disordered regions from protein sequences. Although the prediction quality of these methods continues to rise, novel and improved predictors are urgently needed. Results: We propose a novel method, named MFDp (Multilayered Fusion-based Disorder predictor), that aims to improve over the current disorder predictors. MFDp is as an ensemble of 3 Support Vector Machines specialized for the prediction of short, long and generic disordered regions. It combines three complementary disorder predictors, sequence, sequence profiles, predicted secondary structure, solvent accessibility, backbone dihedral torsion angles, residue flexibility and B-factors. Our method utilizes a custom-designed set of features that are based on raw predictions and aggregated raw values and recognizes various types of disorder. The MFDp is compared at the residue level on two datasets against eight recent disorder predictors and top-performing methods from the most recent CASP8 experiment. In spite of using training chains with ≤25% similarity to the test sequences, our method consistently and significantly outperforms the other methods based on the MCC index. The MFDp outperforms modern disorder predictors for the binary disorder assignment and provides competitive real-valued predictions. The MFDp's outputs are also shown to outperform the other methods in the identification of proteins with long disordered regions. Availability:http://biomine.ece.ualberta.ca/MFDp.html Supplementary information:Supplementary data are available at Bioinformatics online. Contact:lkurgan@ece.ualberta.ca
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Affiliation(s)
- Marcin J Mizianty
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
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100
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Hirose S, Yokota K, Kuroda Y, Wako H, Endo S, Kanai S, Noguchi T. Prediction of protein motions from amino acid sequence and its application to protein-protein interaction. BMC STRUCTURAL BIOLOGY 2010; 10:20. [PMID: 20626880 PMCID: PMC3245509 DOI: 10.1186/1472-6807-10-20] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2009] [Accepted: 07/13/2010] [Indexed: 11/10/2022]
Abstract
BACKGROUND Structural flexibility is an important characteristic of proteins because it is often associated with their function. The movement of a polypeptide segment in a protein can be broken down into two types of motions: internal and external ones. The former is deformation of the segment itself, but the latter involves only rotational and translational motions as a rigid body. Normal Model Analysis (NMA) can derive these two motions, but its application remains limited because it necessitates the gathering of complete structural information. RESULTS In this work, we present a novel method for predicting two kinds of protein motions in ordered structures. The prediction uses only information from the amino acid sequence. We prepared a dataset of the internal and external motions of segments in many proteins by application of NMA. Subsequently, we analyzed the relation between thermal motion assessed from X-ray crystallographic B-factor and internal/external motions calculated by NMA. Results show that attributes of amino acids related to the internal motion have different features from those related to the B-factors, although those related to the external motion are correlated strongly with the B-factors. Next, we developed a method to predict internal and external motions from amino acid sequences based on the Random Forest algorithm. The proposed method uses information associated with adjacent amino acid residues and secondary structures predicted from the amino acid sequence. The proposed method exhibited moderate correlation between predicted internal and external motions with those calculated by NMA. It has the highest prediction accuracy compared to a naïve model and three published predictors. CONCLUSIONS Finally, we applied the proposed method predicting the internal motion to a set of 20 proteins that undergo large conformational change upon protein-protein interaction. Results show significant overlaps between the predicted high internal motion regions and the observed conformational change regions.
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Affiliation(s)
- Shuichi Hirose
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST),2-42, Aomi, Koto-ku, Tokyo, 135-0064, Japan.
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