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Pradhan UK, Meher PK, Naha S, Sharma NK, Agarwal A, Gupta A, Parsad R. DBPMod: a supervised learning model for computational recognition of DNA-binding proteins in model organisms. Brief Funct Genomics 2024; 23:363-372. [PMID: 37651627 DOI: 10.1093/bfgp/elad039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 08/09/2023] [Accepted: 08/15/2023] [Indexed: 09/02/2023] Open
Abstract
DNA-binding proteins (DBPs) play critical roles in many biological processes, including gene expression, DNA replication, recombination and repair. Understanding the molecular mechanisms underlying these processes depends on the precise identification of DBPs. In recent times, several computational methods have been developed to identify DBPs. However, because of the generic nature of the models, these models are unable to identify species-specific DBPs with higher accuracy. Therefore, a species-specific computational model is needed to predict species-specific DBPs. In this paper, we introduce the computational DBPMod method, which makes use of a machine learning approach to identify species-specific DBPs. For prediction, both shallow learning algorithms and deep learning models were used, with shallow learning models achieving higher accuracy. Additionally, the evolutionary features outperformed sequence-derived features in terms of accuracy. Five model organisms, including Caenorhabditis elegans, Drosophila melanogaster, Escherichia coli, Homo sapiens and Mus musculus, were used to assess the performance of DBPMod. Five-fold cross-validation and independent test set analyses were used to evaluate the prediction accuracy in terms of area under receiver operating characteristic curve (auROC) and area under precision-recall curve (auPRC), which was found to be ~89-92% and ~89-95%, respectively. The comparative results demonstrate that the DBPMod outperforms 12 current state-of-the-art computational approaches in identifying the DBPs for all five model organisms. We further developed the web server of DBPMod to make it easier for researchers to detect DBPs and is publicly available at https://iasri-sg.icar.gov.in/dbpmod/. DBPMod is expected to be an invaluable tool for discovering DBPs, supplementing the current experimental and computational methods.
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Affiliation(s)
- Upendra K Pradhan
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
| | - Prabina K Meher
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
| | - Sanchita Naha
- Division of Computer Applications, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
| | - Nitesh K Sharma
- Titus Family Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, 1540 Alcazar Street, Los Angeles, CA 90033, USA
| | - Aarushi Agarwal
- Amity Institute of Biotechnology, Amity University, Noida, Uttar Pradesh 201313, India
| | - Ajit Gupta
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
| | - Rajender Parsad
- ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
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Carpentier M, Chomilier J. Protein multiple alignments: sequence-based versus structure-based programs. Bioinformatics 2020; 35:3970-3980. [PMID: 30942864 DOI: 10.1093/bioinformatics/btz236] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Revised: 03/05/2019] [Accepted: 04/02/2019] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Multiple sequence alignment programs have proved to be very useful and have already been evaluated in the literature yet not alignment programs based on structure or both sequence and structure. In the present article we wish to evaluate the added value provided through considering structures. RESULTS We compared the multiple alignments resulting from 25 programs either based on sequence, structure or both, to reference alignments deposited in five databases (BALIBASE 2 and 3, HOMSTRAD, OXBENCH and SISYPHUS). On the whole, the structure-based methods compute more reliable alignments than the sequence-based ones, and even than the sequence+structure-based programs whatever the databases. Two programs lead, MAMMOTH and MATRAS, nevertheless the performances of MUSTANG, MATT, 3DCOMB, TCOFFEE+TM_ALIGN and TCOFFEE+SAP are better for some alignments. The advantage of structure-based methods increases at low levels of sequence identity, or for residues in regular secondary structures or buried ones. Concerning gap management, sequence-based programs set less gaps than structure-based programs. Concerning the databases, the alignments of the manually built databases are more challenging for the programs. AVAILABILITY AND IMPLEMENTATION All data and results presented in this study are available at: http://wwwabi.snv.jussieu.fr/people/mathilde/download/AliMulComp/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mathilde Carpentier
- Institut Systématique Evolution Biodiversité (ISYEB), Sorbonne Université, MNHN, CNRS, EPHE, Paris, France
| | - Jacques Chomilier
- Sorbonne Université, MNHN, CNRS, IRD, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie (IMPMC), BiBiP, Paris, France
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Dong Z, Zhou H, Tao P. Combining protein sequence, structure, and dynamics: A novel approach for functional evolution analysis of PAS domain superfamily. Protein Sci 2017; 27:421-430. [PMID: 29052279 DOI: 10.1002/pro.3329] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Revised: 10/10/2017] [Accepted: 10/15/2017] [Indexed: 11/11/2022]
Abstract
PAS domains are widespread in archaea, bacteria, and eukaryota, and play important roles in various functions. In this study, we aim to explore functional evolutionary relationship among proteins in the PAS domain superfamily in view of the sequence-structure-dynamics-function relationship. We collected protein sequences and crystal structure data from RCSB Protein Data Bank of the PAS domain superfamily belonging to three biological functions (nucleotide binding, photoreceptor activity, and transferase activity). Protein sequences were aligned and then used to select sequence-conserved residues and build phylogenetic tree. Three-dimensional structure alignment was also applied to obtain structure-conserved residues. The protein dynamics were analyzed using elastic network model (ENM) and validated by molecular dynamics (MD) simulation. The result showed that the proteins with same function could be grouped by sequence similarity, and proteins in different functional groups displayed statistically significant difference in their vibrational patterns. Interestingly, in all three functional groups, conserved amino acid residues identified by sequence and structure conservation analysis generally have a lower fluctuation than other residues. In addition, the fluctuation of conserved residues in each biological function group was strongly correlated with the corresponding biological function. This research suggested a direct connection in which the protein sequences were related to various functions through structural dynamics. This is a new attempt to delineate functional evolution of proteins using the integrated information of sequence, structure, and dynamics.
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Affiliation(s)
- Zheng Dong
- Department of Chemistry, Center for Drug Discovery, Design, and Delivery (CD4), Center for Scientific Computation, Southern Methodist University, Dallas, Texas, 75275
| | - Hongyu Zhou
- Department of Chemistry, Center for Drug Discovery, Design, and Delivery (CD4), Center for Scientific Computation, Southern Methodist University, Dallas, Texas, 75275
| | - Peng Tao
- Department of Chemistry, Center for Drug Discovery, Design, and Delivery (CD4), Center for Scientific Computation, Southern Methodist University, Dallas, Texas, 75275
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Golestan Hashemi FS, Razi Ismail M, Rafii Yusop M, Golestan Hashemi MS, Nadimi Shahraki MH, Rastegari H, Miah G, Aslani F. Intelligent mining of large-scale bio-data: Bioinformatics applications. BIOTECHNOL BIOTEC EQ 2017. [DOI: 10.1080/13102818.2017.1364977] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Affiliation(s)
- Farahnaz Sadat Golestan Hashemi
- Plant Genetics, AgroBioChem Department, Gembloux Agro-Bio Tech, University of Liege, Liege, Belgium
- Laboratory of Food Crops, Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Mohd Razi Ismail
- Laboratory of Food Crops, Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
- Department of Crop Science, Faculty of Agriculture, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Mohd Rafii Yusop
- Laboratory of Food Crops, Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
- Department of Crop Science, Faculty of Agriculture, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Mahboobe Sadat Golestan Hashemi
- Department of Software Engineering, Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Isfahan,Iran
- Big Data Research Center, Najafabad Branch, Islamic Azad University, Isfahan, Iran
| | - Mohammad Hossein Nadimi Shahraki
- Department of Software Engineering, Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Isfahan,Iran
- Big Data Research Center, Najafabad Branch, Islamic Azad University, Isfahan, Iran
| | - Hamid Rastegari
- Department of Software Engineering, Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Isfahan,Iran
| | - Gous Miah
- Laboratory of Food Crops, Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Farzad Aslani
- Department of Crop Science, Faculty of Agriculture, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
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Pervez MT, Babar ME, Nadeem A, Aslam M, Awan AR, Aslam N, Hussain T, Naveed N, Qadri S, Waheed U, Shoaib M. Evaluating the accuracy and efficiency of multiple sequence alignment methods. Evol Bioinform Online 2014; 10:205-17. [PMID: 25574120 PMCID: PMC4267518 DOI: 10.4137/ebo.s19199] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2014] [Revised: 10/09/2014] [Accepted: 10/14/2014] [Indexed: 12/21/2022] Open
Abstract
A comparison of 10 most popular Multiple Sequence Alignment (MSA) tools, namely, MUSCLE, MAFFT(L-INS-i), MAFFT (FFT-NS-2), T-Coffee, ProbCons, SATe, Clustal Omega, Kalign, Multalin, and Dialign-TX is presented. We also focused on the significance of some implementations embedded in algorithm of each tool. Based on 10 simulated trees of different number of taxa generated by R, 400 known alignments and sequence files were constructed using indel-Seq-Gen. A total of 4000 test alignments were generated to study the effect of sequence length, indel size, deletion rate, and insertion rate. Results showed that alignment quality was highly dependent on the number of deletions and insertions in the sequences and that the sequence length and indel size had a weaker effect. Overall, ProbCons was consistently on the top of list of the evaluated MSA tools. SATe, being little less accurate, was 529.10% faster than ProbCons and 236.72% faster than MAFFT(L-INS-i). Among other tools, Kalign and MUSCLE achieved the highest sum of pairs. We also considered BALiBASE benchmark datasets and the results relative to BAliBASE- and indel-Seq-Gen-generated alignments were consistent in the most cases.
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Affiliation(s)
- Muhammad Tariq Pervez
- Department of Computer Science, Virtual University of Pakistan, Lahore, Pakistan. ; Institute of Biochemistry and Biotechnology, University of Veterinary and Animal Sciences, Lahore, Pakistan
| | | | - Asif Nadeem
- Institute of Biochemistry and Biotechnology, University of Veterinary and Animal Sciences, Lahore, Pakistan
| | - Muhammad Aslam
- Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, Pakistan
| | - Ali Raza Awan
- Institute of Biochemistry and Biotechnology, University of Veterinary and Animal Sciences, Lahore, Pakistan
| | - Naeem Aslam
- Institute of Biochemistry and Biotechnology, University of Veterinary and Animal Sciences, Lahore, Pakistan. ; Department of Computer Science, NFC Institute of Engineering and Technological Training, Multan, Pakistan
| | - Tanveer Hussain
- Institute of Biochemistry and Biotechnology, University of Veterinary and Animal Sciences, Lahore, Pakistan
| | | | - Salman Qadri
- Department of Computer Science, Islamia University of Bahawalpur, Pakistan
| | - Usman Waheed
- Department of Computer Science, Virtual University of Pakistan, Lahore, Pakistan
| | - Muhammad Shoaib
- Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, Pakistan
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Levasseur A, Lomascolo A, Chabrol O, Ruiz-Dueñas FJ, Boukhris-Uzan E, Piumi F, Kües U, Ram AFJ, Murat C, Haon M, Benoit I, Arfi Y, Chevret D, Drula E, Kwon MJ, Gouret P, Lesage-Meessen L, Lombard V, Mariette J, Noirot C, Park J, Patyshakuliyeva A, Sigoillot JC, Wiebenga A, Wösten HAB, Martin F, Coutinho PM, de Vries RP, Martínez AT, Klopp C, Pontarotti P, Henrissat B, Record E. The genome of the white-rot fungus Pycnoporus cinnabarinus: a basidiomycete model with a versatile arsenal for lignocellulosic biomass breakdown. BMC Genomics 2014; 15:486. [PMID: 24942338 PMCID: PMC4101180 DOI: 10.1186/1471-2164-15-486] [Citation(s) in RCA: 65] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2014] [Accepted: 05/19/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Saprophytic filamentous fungi are ubiquitous micro-organisms that play an essential role in photosynthetic carbon recycling. The wood-decayer Pycnoporus cinnabarinus is a model fungus for the study of plant cell wall decomposition and is used for a number of applications in green and white biotechnology. RESULTS The 33.6 megabase genome of P. cinnabarinus was sequenced and assembled, and the 10,442 predicted genes were functionally annotated using a phylogenomic procedure. In-depth analyses were carried out for the numerous enzyme families involved in lignocellulosic biomass breakdown, for protein secretion and glycosylation pathways, and for mating type. The P. cinnabarinus genome sequence revealed a consistent repertoire of genes shared with wood-decaying basidiomycetes. P. cinnabarinus is thus fully equipped with the classical families involved in cellulose and hemicellulose degradation, whereas its pectinolytic repertoire appears relatively limited. In addition, P. cinnabarinus possesses a complete versatile enzymatic arsenal for lignin breakdown. We identified several genes encoding members of the three ligninolytic peroxidase types, namely lignin peroxidase, manganese peroxidase and versatile peroxidase. Comparative genome analyses were performed in fungi displaying different nutritional strategies (white-rot and brown-rot modes of decay). P. cinnabarinus presents a typical distribution of all the specific families found in the white-rot life style. Growth profiling of P. cinnabarinus was performed on 35 carbon sources including simple and complex substrates to study substrate utilization and preferences. P. cinnabarinus grew faster on crude plant substrates than on pure, mono- or polysaccharide substrates. Finally, proteomic analyses were conducted from liquid and solid-state fermentation to analyze the composition of the secretomes corresponding to growth on different substrates. The distribution of lignocellulolytic enzymes in the secretomes was strongly dependent on growth conditions, especially for lytic polysaccharide mono-oxygenases. CONCLUSIONS With its available genome sequence, P. cinnabarinus is now an outstanding model system for the study of the enzyme machinery involved in the degradation or transformation of lignocellulosic biomass.
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Affiliation(s)
- Anthony Levasseur
- INRA, UMR1163 Biotechnologie des Champignons Filamenteux, Aix-Marseille Université, Polytech Marseille, 163 avenue de Luminy, CP 925, 13288 Marseille Cedex 09, France.
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Evolutionary systems biology: historical and philosophical perspectives on an emerging synthesis. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2012; 751:1-28. [PMID: 22821451 DOI: 10.1007/978-1-4614-3567-9_1] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Systems biology (SB) is at least a decade old now and maturing rapidly. A more recent field, evolutionary systems biology (ESB), is in the process of further developing system-level approaches through the expansion of their explanatory and potentially predictive scope. This chapter will outline the varieties of ESB existing today by tracing the diverse roots and fusions that make up this integrative project. My approach is philosophical and historical. As well as examining the recent origins of ESB, I will reflect on its central features and the different clusters of research it comprises. In its broadest interpretation, ESB consists of five overlapping approaches: comparative and correlational ESB; network architecture ESB; network property ESB; population genetics ESB; and finally, standard evolutionary questions answered with SB methods. After outlining each approach with examples, I will examine some strong general claims about ESB, particularly that it can be viewed as the next step toward a fuller modern synthesis of evolutionary biology (EB), and that it is also the way forward for evolutionary and systems medicine. I will conclude with a discussion of whether the emerging field of ESB has the capacity to combine an even broader scope of research aims and efforts than it presently does.
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Braun EL, Kimball RT, Han KL, Iuhasz-Velez NR, Bonilla AJ, Chojnowski JL, Smith JV, Bowie RCK, Braun MJ, Hackett SJ, Harshman J, Huddleston CJ, Marks BD, Miglia KJ, Moore WS, Reddy S, Sheldon FH, Witt CC, Yuri T. Homoplastic microinversions and the avian tree of life. BMC Evol Biol 2011; 11:141. [PMID: 21612607 PMCID: PMC3123225 DOI: 10.1186/1471-2148-11-141] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2010] [Accepted: 05/25/2011] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Microinversions are cytologically undetectable inversions of DNA sequences that accumulate slowly in genomes. Like many other rare genomic changes (RGCs), microinversions are thought to be virtually homoplasy-free evolutionary characters, suggesting that they may be very useful for difficult phylogenetic problems such as the avian tree of life. However, few detailed surveys of these genomic rearrangements have been conducted, making it difficult to assess this hypothesis or understand the impact of microinversions upon genome evolution. RESULTS We surveyed non-coding sequence data from a recent avian phylogenetic study and found substantially more microinversions than expected based upon prior information about vertebrate inversion rates, although this is likely due to underestimation of these rates in previous studies. Most microinversions were lineage-specific or united well-accepted groups. However, some homoplastic microinversions were evident among the informative characters. Hemiplasy, which reflects differences between gene trees and the species tree, did not explain the observed homoplasy. Two specific loci were microinversion hotspots, with high numbers of inversions that included both the homoplastic as well as some overlapping microinversions. Neither stem-loop structures nor detectable sequence motifs were associated with microinversions in the hotspots. CONCLUSIONS Microinversions can provide valuable phylogenetic information, although power analysis indicates that large amounts of sequence data will be necessary to identify enough inversions (and similar RGCs) to resolve short branches in the tree of life. Moreover, microinversions are not perfect characters and should be interpreted with caution, just as with any other character type. Independent of their use for phylogenetic analyses, microinversions are important because they have the potential to complicate alignment of non-coding sequences. Despite their low rate of accumulation, they have clearly contributed to genome evolution, suggesting that active identification of microinversions will prove useful in future phylogenomic studies.
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Affiliation(s)
- Edward L Braun
- Department of Biology, University of Florida, Gainesville, FL 32611, USA
| | - Rebecca T Kimball
- Department of Biology, University of Florida, Gainesville, FL 32611, USA
| | - Kin-Lan Han
- Department of Biology, University of Florida, Gainesville, FL 32611, USA
| | | | - Amber J Bonilla
- Department of Biology, University of Florida, Gainesville, FL 32611, USA
| | - Jena L Chojnowski
- Department of Biology, University of Florida, Gainesville, FL 32611, USA
| | - Jordan V Smith
- Department of Biology, University of Florida, Gainesville, FL 32611, USA
| | - Rauri CK Bowie
- Zoology Department, Field Museum of Natural History, 1400 S. Lakeshore Drive, Chicago, IL 60605, USA
- Museum of Vertebrate Zoology and Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Michael J Braun
- Department of Vertebrate Zoology, Smithsonian Institution, 4210 Silver Hill Road, Suitland, MD 20746, USA
- Behavior, Ecology, Evolution, and Systematics Program, University of Maryland, College Park, MD 20742, USA
| | - Shannon J Hackett
- Zoology Department, Field Museum of Natural History, 1400 S. Lakeshore Drive, Chicago, IL 60605, USA
| | - John Harshman
- Zoology Department, Field Museum of Natural History, 1400 S. Lakeshore Drive, Chicago, IL 60605, USA
- 4869 Pepperwood Way, San Jose, CA 95124, USA
| | - Christopher J Huddleston
- Department of Vertebrate Zoology, Smithsonian Institution, 4210 Silver Hill Road, Suitland, MD 20746, USA
| | - Ben D Marks
- Museum of Natural Science and Department of Biological Sciences, 119 Foster Hall, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Kathleen J Miglia
- Department of Biological Sciences, Wayne State University, 5047 Gullen Mall, Detroit, MI 48202, USA
| | - William S Moore
- Department of Biological Sciences, Wayne State University, 5047 Gullen Mall, Detroit, MI 48202, USA
| | - Sushma Reddy
- Zoology Department, Field Museum of Natural History, 1400 S. Lakeshore Drive, Chicago, IL 60605, USA
- Biology Department, Loyola University Chicago, Chicago, IL 60626, USA
| | - Frederick H Sheldon
- Museum of Natural Science and Department of Biological Sciences, 119 Foster Hall, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Christopher C Witt
- Museum of Natural Science and Department of Biological Sciences, 119 Foster Hall, Louisiana State University, Baton Rouge, LA 70803, USA
- Department of Biology and Museum of Southwestern Biology, University of New Mexico, Albuquerque, NM 87131, USA
| | - Tamaki Yuri
- Department of Biology, University of Florida, Gainesville, FL 32611, USA
- Department of Vertebrate Zoology, Smithsonian Institution, 4210 Silver Hill Road, Suitland, MD 20746, USA
- Sam Noble Oklahoma Museum of Natural History, University of Oklahoma, Norman, OK 73072, USA
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Thompson JD, Linard B, Lecompte O, Poch O. A comprehensive benchmark study of multiple sequence alignment methods: current challenges and future perspectives. PLoS One 2011; 6:e18093. [PMID: 21483869 PMCID: PMC3069049 DOI: 10.1371/journal.pone.0018093] [Citation(s) in RCA: 129] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2010] [Accepted: 02/21/2011] [Indexed: 12/18/2022] Open
Abstract
Multiple comparison or alignmentof protein sequences has become a fundamental tool in many different domains in modern molecular biology, from evolutionary studies to prediction of 2D/3D structure, molecular function and inter-molecular interactions etc. By placing the sequence in the framework of the overall family, multiple alignments can be used to identify conserved features and to highlight differences or specificities. In this paper, we describe a comprehensive evaluation of many of the most popular methods for multiple sequence alignment (MSA), based on a new benchmark test set. The benchmark is designed to represent typical problems encountered when aligning the large protein sequence sets that result from today's high throughput biotechnologies. We show that alignmentmethods have significantly progressed and can now identify most of the shared sequence features that determine the broad molecular function(s) of a protein family, even for divergent sequences. However,we have identified a number of important challenges. First, the locally conserved regions, that reflect functional specificities or that modulate a protein's function in a given cellular context,are less well aligned. Second, motifs in natively disordered regions are often misaligned. Third, the badly predicted or fragmentary protein sequences, which make up a large proportion of today's databases, lead to a significant number of alignment errors. Based on this study, we demonstrate that the existing MSA methods can be exploited in combination to improve alignment accuracy, although novel approaches will still be needed to fully explore the most difficult regions. We then propose knowledge-enabled, dynamic solutions that will hopefully pave the way to enhanced alignment construction and exploitation in future evolutionary systems biology studies.
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Affiliation(s)
- Julie D Thompson
- Département de Biologie Structurale et Génomique, IGBMC (Institut de Génétique et de Biologie Moléculaire et Cellulaire), CNRS/INSERM/Université de Strasbourg, Illkirch, France.
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Exploring laccase-like multicopper oxidase genes from the ascomycete Trichoderma reesei: a functional, phylogenetic and evolutionary study. BMC BIOCHEMISTRY 2010; 11:32. [PMID: 20735824 PMCID: PMC2939539 DOI: 10.1186/1471-2091-11-32] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2010] [Accepted: 08/24/2010] [Indexed: 11/10/2022]
Abstract
BACKGROUND The diversity and function of ligninolytic genes in soil-inhabiting ascomycetes has not yet been elucidated, despite their possible role in plant litter decay processes. Among ascomycetes, Trichoderma reesei is a model organism of cellulose and hemicellulose degradation, used for its unique secretion ability especially for cellulase production. T. reesei has only been reported as a cellulolytic and hemicellulolytic organism although genome annotation revealed 6 laccase-like multicopper oxidase (LMCO) genes. The purpose of this work was i) to validate the function of a candidate LMCO gene from T. reesei, and ii) to reconstruct LMCO phylogeny and perform evolutionary analysis testing for positive selection. RESULTS After homologous overproduction of a candidate LMCO gene, extracellular laccase activity was detected when ABTS or SRG were used as substrates, and the recombinant protein was purified to homogeneity followed by biochemical characterization. The recombinant protein, called TrLAC1, has a molecular mass of 104 kDa. Optimal temperature and pH were respectively 40-45°C and 4, by using ABTS as substrate. TrLAC1 showed broad pH stability range of 3 to 7. Temperature stability revealed that TrLAC1 is not a thermostable enzyme, which was also confirmed by unfolding studies monitored by circular dichroism. Evolutionary studies were performed to shed light on the LMCO family, and the phylogenetic tree was reconstructed using maximum-likelihood method. LMCO and classical laccases were clearly divided into two distinct groups. Finally, Darwinian selection was tested, and the results showed that positive selection drove the evolution of sequences leading to well-known laccases involved in ligninolysis. Positively-selected sites were observed that could be used as targets for mutagenesis and functional studies between classical laccases and LMCO from T. reesei. CONCLUSIONS Homologous production and evolutionary studies of the first LMCO from the biomass-degrading fungus T. reesei gives new insights into the physicochemical parameters and biodiversity in this family.
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Vandenkoornhuyse P, Dufresne A, Quaiser A, Gouesbet G, Binet F, Francez AJ, Mahé S, Bormans M, Lagadeuc Y, Couée I. Integration of molecular functions at the ecosystemic level: breakthroughs and future goals of environmental genomics and post-genomics. Ecol Lett 2010; 13:776-91. [PMID: 20426792 PMCID: PMC2901524 DOI: 10.1111/j.1461-0248.2010.01464.x] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2009] [Revised: 11/16/2009] [Accepted: 02/24/2010] [Indexed: 11/30/2022]
Abstract
Environmental genomics and genome-wide expression approaches deal with large-scale sequence-based information obtained from environmental samples, at organismal, population or community levels. To date, environmental genomics, transcriptomics and proteomics are arguably the most powerful approaches to discover completely novel ecological functions and to link organismal capabilities, organism-environment interactions, functional diversity, ecosystem processes, evolution and Earth history. Thus, environmental genomics is not merely a toolbox of new technologies but also a source of novel ecological concepts and hypotheses. By removing previous dichotomies between ecophysiology, population ecology, community ecology and ecosystem functioning, environmental genomics enables the integration of sequence-based information into higher ecological and evolutionary levels. However, environmental genomics, along with transcriptomics and proteomics, must involve pluridisciplinary research, such as new developments in bioinformatics, in order to integrate high-throughput molecular biology techniques into ecology. In this review, the validity of environmental genomics and post-genomics for studying ecosystem functioning is discussed in terms of major advances and expectations, as well as in terms of potential hurdles and limitations. Novel avenues for improving the use of these approaches to test theory-driven ecological hypotheses are also explored.
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Abstract
Our understanding of how evolution acts on biological networks remains patchy, as is our knowledge of how that action is best identified, modelled and understood. Starting with network structure and the evolution of protein-protein interaction networks, we briefly survey the ways in which network evolution is being addressed in the fields of systems biology, development and ecology. The approaches highlighted demonstrate a movement away from a focus on network topology towards a more integrated view, placing biological properties centre-stage. We argue that there remains great potential in a closer synergy between evolutionary biology and biological network analysis, although that may require the development of novel approaches and even different analogies for biological networks themselves.
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Affiliation(s)
- Christopher G Knight
- Faculty of Life Sciences, The University of Manchester, Michael Smith Building, Manchester, UK.
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