Key Points
-
'Inheritance through homology' is the most common and generally more accessible approach to function prediction, but orthology should be established where possible to improve confidence in predictions.
-
The body of functional annotations of proteins is becoming increasingly computer-readable and is being organized in ways that can enhance the scope of in silico prediction methods.
-
Significant advances in complete genome sequencing have resulted in a new generation of methods that exploit sequence analysis on the genome level.
-
Curated protein family resources can often guide the assignment of protein functions and the detection of motifs or sequence patterns.
-
New approaches are being developed to identify functional residues in proteins; these can then be applied to divide larger protein families into more specific functional subfamilies.
-
There have been exciting new developments in databases of experimentally determined protein–protein interactions, as well as genomic inference methods for predicting these interactions.
-
Non-homology-based function prediction methods that exploit the properties of sequences and not their evolutionary history are also becoming more successful.
-
Recent Structural Genomics Initiatives (SGIs) are attempting to target functionally diverse relatives within protein families.
-
Function prediction from structure can be achieved by global comparison of protein structures to detect homology or through the use of structural templates derived from the active sites of enzymes. It is also possible to explore the protein surface for sequence-conserved patches, clefts and electrostatic potentials.
-
In general terms, it is best to seek and compare the results of several methods to predict the function of novel proteins. Meta-servers simplify this by providing easy access to a range of the best-performing methods.
-
Future developments will see more efficient integration of prediction methods and experimental data; for example, microarrays, yeast two-hybrid screens and tandem affinity purification. Better understanding of the diversification of function in protein families will permit more sophisticated means of predicting function and functional networks.
Abstract
While the number of sequenced genomes continues to grow, experimentally verified functional annotation of whole genomes remains patchy. Structural genomics projects are yielding many protein structures that have unknown function. Nevertheless, subsequent experimental investigation is costly and time-consuming, which makes computational methods for predicting protein function very attractive. There is an increasing number of noteworthy methods for predicting protein function from sequence and structural data alone, many of which are readily available to cell biologists who are aware of the strengths and pitfalls of each available technique.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$189.00 per year
only $15.75 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Liolios, K., Tavernarakis, N., Hugenholtz, P. & Kyrpides, N. C. The Genomes On Line Database (GOLD) v2: a monitor of genome projects worldwide. Nucleic Acids Res. 34, D332–D334 (2006).
Wu, C. H. et al. The Universal Protein Resource (UniProt): an expanding universe of protein information. Nucleic Acids Res. 34, D187–D191 (2006).
Benson, D. A., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J. & Wheeler, D. L. GenBank. Nucleic Acids Res. 34, D16–D20 (2006).
Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genet. 25, 25–29 (2000) www.nature.com/ng/journal/v25/n1/abs/ng0500_25.html. One of the best and most comprehensive attempts to standardize and organize the annotation of protein function.
Whisstock, J. C. & Lesk, A. M. Prediction of protein function from protein sequence and structure. Q. Rev. Biophys. 36, 307–340 (2003). A thorough and fairly recent review of the whole field of protein-function prediction from sequence and structure.
Bork, P. et al. Predicting function: from genes to genomes and back. J. Mol. Biol. 283, 707–725 (1998).
Watson, J. D., Laskowski, R. A. & Thornton, J. M. Predicting protein function from sequence and structural data. Curr. Opin. Struct. Biol. 15, 275–284 (2005).
Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).
Brenner, S. E. Errors in genome annotation. Trends Genet. 15, 132–133 (1999).
Devos, D. & Valencia, A. Intrinsic errors in genome annotation. Trends Genet. 17, 429–431 (2001).
Godzik, A., Jambon, M. & Friedberg, I. Computational protein function prediction: are we making progress? Cell Mol. Life Sci. 64, 2505–2511 (2007).
Fitch, W. M. Homology: a personal view on some of the problems. Trends Genet. 16, 227–231 (2000). An interesting discussion of some important concepts in the field of protein-function prediction.
Krallinger, M. & Valencia, A. Text-mining and information-retrieval services for molecular biology. Genome Biol. 6, 224 (2005).
Lord, P. W., Stevens, R. D., Brass, A. & Goble, C. A. Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation. Bioinformatics 19, 1275–1283 (2003).
Schlicker, A., Domingues, F. S., Rahnenfuhrer, J. & Lengauer, T. A new measure for functional similarity of gene products based on Gene Ontology. BMC Bioinformatics 7, 302 (2006).
Rison, S. C., Hodgman, T. C. & Thornton, J. M. Comparison of functional annotation schemes for genomes. Funct. Integr. Genomics 1, 56–69 (2000).
Mulder, N. J. et al. New developments in the InterPro database. Nucleic Acids Res. 35, D224–D228 (2007).
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).
Martin, D. M., Berriman, M. & Barton, G. J. GOtcha: a new method for prediction of protein function assessed by the annotation of seven genomes. BMC Bioinformatics 5, 178 (2004).
Hawkins, T., Luban, S. & Kihara, D. Enhanced automated function prediction using distantly related sequences and contextual association by PFP. Protein Sci. 15, 1550–1556 (2006). This method performed well in the CASP7 function-prediction category.
Blair, H. S. & Kumar, S. Genomic clocks and evolutionary timescales. Trends Genet. 19, 200–206 (2003).
Wall, D. P. et al. Functional genomic analysis of the rates of protein evolution. Proc. Natl. Acad. Sci. USA 102, 5483–5488 (2005).
Gattiker, A. et al. Automated annotation of microbial proteomes in SWISS-PROT. Comput. Biol. Chem. 27, 49–58 (2003).
Tatusov, R. L. et al. The COG database: an updated version includes eukaryotes. BMC Bioinformatics 4, 41 (2003).
O'Brien, K. P., Remm, M. & Sonnhammer, E. L. Inparanoid: a comprehensive database of eukaryotic orthologs. Nucleic Acids Res. 33, D476–D480 (2005).
Storm, C. E. & Sonnhammer, E. L. Automated ortholog inference from phylogenetic trees and calculation of orthology reliability. Bioinformatics 18, 92–99 (2002).
Mewes, H. W. et al. MIPS: analysis and annotation of proteins from whole genomes in 2005. Nucleic Acids Res. 34, D169–D172 (2006).
Bateman, A. et al. The Pfam protein families database. Nucleic Acids Res. 30, 276–280 (2002).
Apweiler, R. et al. The InterPro database, an integrated documentation resource for protein families, domains and functional sites. Nucleic Acids Res. 29, 37–40 (2001).
Pearl, F. et al. The CATH Domain Structure Database and related resources Gene3D and DHS provide comprehensive domain family information for genome analysis. Nucleic Acids Res. 33, D247–D251 (2005).
Todd, A. E., Orengo, C. A. & Thornton, J. M. Evolution of function in protein superfamilies, from a structural perspective. J. Mol. Biol. 307, 1113–1143 (2001). This paper examines the sequence–structure–function paradigm through an analysis of enzymes within superfamilies in the CATH database. It gives several examples of the different ways in which sequence and structure can change over evolution to produce new functions.
Tian, W. & Skolnick, J. How well is enzyme function conserved as a function of pairwise sequence identity? J. Mol. Biol. 333, 863–882 (2003).
Rost, B. Enzyme function less conserved than anticipated. J. Mol. Biol. 318, 595–608 (2002).
Marttinen, P., Corander, J., Toronen, P. & Holm, L. Bayesian search of functionally divergent protein subgroups and their function specific residues. Bioinformatics 22, 2466–2474 (2006).
Thomas, P. D. et al. PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 13, 2129–2141 (2003).
Krishnamurthy, N., Brown, D. P., Kirshner, D. & Sjolander, K. PhyloFacts: an online structural phylogenomic encyclopedia for protein functional and structural classification. Genome Biol. 7, R83 (2006).
del Sol, M. A., Pazos, F. & Valencia, A. Automatic methods for predicting functionally important residues. J. Mol. Biol. 326, 1289–1302 (2003).
Yao, H. et al. An accurate, sensitive, and scalable method to identify functional sites in protein structures. J. Mol. Biol. 326, 255–261 (2003).
Joachimiak, M. P. & Cohen, F. E. JEvTrace: refinement and variations of the evolutionary trace in JAVA. Genome Biol. 3, RESEARCH0077 (2002). genomebiology.com/2002/3/12/RESEARCH/0077
Morgan, D. H., Kristensen, D. M., Mittelman, D. & Lichtarge, O. ET viewer: an application for predicting and visualizing functional sites in protein structures. Bioinformatics 22, 2049–2050 (2006).
La, D. & Livesay, D. R. MINER: software for phylogenetic motif identification. Nucleic Acids Res. 33, W267–W270 (2005).
Chelliah, V., Chen, L., Blundell, T. L. & Lovell, S. C. Distinguishing structural and functional restraints in evolution in order to identify interaction sites. J. Mol. Biol. 342, 1487–1504 (2004).
Engelhardt, B. E., Jordan, M. I., Muratore, K. E. & Brenner, S. E. Protein molecular function prediction by Bayesian phylogenomics. PLoS Comput. Biol. 1, e45 (2005).
Yao, H., Mihalek, I. & Lichtarge, O. Rank information: a structure-independent measure of evolutionary trace quality that improves identification of protein functional sites. Proteins 65, 111–123 (2006).
Pazos, F., Rausell, A. & Valencia, A. Phylogeny-independent detection of functional residues. Bioinformatics 22, 1440–1448 (2006).
Ng, P. C. & Henikoff, S. Predicting the effects of amino acid substitutions on protein function. Annu. Rev. Genomics Hum. Genet. 7, 61–80 (2006).
Valdar, W. S. Scoring residue conservation. Proteins 48, 227–241 (2002).
Pirovano, W., Feenstra, K. A. & Heringa, J. Sequence comparison by sequence harmony identifies subtype-specific functional sites. Nucleic Acids Res. 34, 6540–6548 (2006).
Abhiman, S. & Sonnhammer, E. L. FunShift: a database of function shift analysis on protein subfamilies. Nucleic Acids Res. 33, D197–D200 (2005).
Tian, W., Arakaki, A. K. & Skolnick, J. EFICAz: a comprehensive approach for accurate genome-scale enzyme function inference. Nucleic Acids Res. 32, 6226–6239 (2004).
Thompson, J. D., Higgins, D. G. & Gibson, T. J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680 (1994).
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).
Katoh, K., Kuma, K., Toh, H. & Miyata, T. MAFFT version 5: improvement in accuracy of multiple sequence alignment. Nucleic Acids Res. 33, 511–518 (2005).
Notredame, C., Higgins, D. G. & Heringa, J. T-Coffee: a novel method for fast and accurate multiple sequence alignment. J. Mol. Biol. 302, 205–217 (2000).
Porter, C. T., Bartlett, G. J. & Thornton, J. M. The Catalytic Site Atlas: a resource of catalytic sites and residues identified in enzymes using structural data. Nucleic Acids Res. 32, D129–D133 (2004).
George, R. A. et al. Effective function annotation through catalytic residue conservation. Proc. Natl. Acad. Sci. USA 102, 12299–12304 (2005).
Shoemaker, B. A. & Panchenko, A. R. Deciphering protein–protein interactions. Part II. Computational methods to predict protein and domain interaction partners. PLoS Comput Biol. 3, e43 (2007). An accessible introduction to computational methods for predicting protein-interaction partners.
Aloy, P. & Russell, R. B. Structural systems biology: modelling protein interactions. Nature Rev. Mol. Cell Biol. 7, 188–197 (2006).
Guldener, U. et al. MPact: the MIPS protein interaction resource on yeast. Nucleic Acids Res. 34, D436–D441 (2006).
von Mering, C. et al. STRING 7 — recent developments in the integration and prediction of protein interactions. Nucleic Acids Res. 35, D358–D362 (2007). A good example of a state-of-the-art protein-interaction database.
Krull, M. et al. TRANSPATH: an information resource for storing and visualizing signaling pathways and their pathological aberrations. Nucleic Acids Res. 34, D546–D551 (2006).
Vastrik, I. et al. Reactome: a knowledge base of biologic pathways and processes. Genome Biol. 8, R39 (2007).
Mishra, G. R. et al. Human protein reference database — 2006 update. Nucleic Acids Res. 34, D411–D414 (2006).
Dandekar, T., Snel, B., Huynen, M. & Bork, P. Conservation of gene order: a fingerprint of proteins that physically interact. Trends Biochem. Sci. 23, 324–328 (1998).
Overbeek, R., Fonstein, M., D'Souza, M., Pusch, G. D. & Maltsev, N. The use of gene clusters to infer functional coupling. Proc. Natl. Acad. Sci. USA 96, 2896–2901 (1999).
Teichmann, S. A. & Babu, M. M. Conservation of gene co-regulation in prokaryotes and eukaryotes. Trends Biotechnol. 20, 407–410 (2002).
Korbel, J. O., Jensen, L. J., von Mering, C. & Bork, P. Analysis of genomic context: prediction of functional associations from conserved bidirectionally transcribed gene pairs. Nature Biotechnol. 22, 911–917 (2004).
Marcotte, E. M. et al. Detecting protein function and protein–protein interactions from genome sequences. Science 285, 751–753 (1999).
Burns, D. M., Horn, V., Paluh, J. & Yanofsky, C. Evolution of the tryptophan synthetase of fungi. Analysis of experimentally fused Escherichia coli tryptophan synthetase α and β chains. J. Biol. Chem. 265, 2060–2069 (1990).
Marcotte, C. J. & Marcotte, E. M. Predicting functional linkages from gene fusions with confidence. Appl. Bioinformatics. 1, 93–100 (2002).
Pellegrini, M., Marcotte, E. M., Thompson, M. J., Eisenberg, D. & Yeates, T. O. Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc. Natl. Acad. Sci. USA 96, 4285–4288 (1999).
Pagel, P., Wong, P. & Frishman, D. A domain interaction map based on phylogenetic profiling. J. Mol. Biol. 344, 1331–1346 (2004).
Ranea, J. A. G., Yeats, C., Grant, A. & Orengo, C. A. Predicting protein function with hierarchical phylogenetic profiles: the Gene3D “Phylo-Tuner” method applied to eukaryotic genomes. PLoS Comput. Biol. (in the press).
Pazos, F. & Valencia, A. Similarity of phylogenetic trees as indicator of protein–protein interaction. Protein Eng. 14, 609–614 (2001).
Pazos, F., Ranea, J. A., Juan, D. & Sternberg, M. J. Assessing protein co-evolution in the context of the tree of life assists in the prediction of the interactome. J. Mol. Biol. 352, 1002–1015 (2005).
Qi, Y., Bar-Joseph, Z. & Klein-Seetharaman, J. Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Proteins 63, 490–500 (2006).
Lee, D., Grant, A., Marsden, R. L. & Orengo, C. Identification and distribution of protein families in 120 completed genomes using Gene3D. Proteins 59, 603–615 (2005).
Gardy, J. L. & Brinkman, F. S. Methods for predicting bacterial protein subcellular localization. Nature Rev. Microbiol. 4, 741–751 (2006).
Donnes, P. & Hoglund, A. Predicting protein subcellular localization: past, present, and future. Genomics Proteomics Bioinformatics 2, 209–215 (2004).
Jensen, L. J. et al. Prediction of human protein function from post-translational modifications and localization features. J. Mol. Biol. 319, 1257–1265 (2002).
de Lichtenberg, U., Jensen, T. S., Jensen, L. J. & Brunak, S. Protein feature based identification of cell cycle regulated proteins in yeast. J. Mol. Biol. 329, 663–674 (2003).
Lobley, A., Swindells, M. B., Orengo, C. A. & Jones, D. T. Inferring function using patterns of native disorder in proteins. PLoS Comput. Biol. 3, e162 (2007).
Chothia, C. & Lesk, A. M. The relation between the divergence of sequence and structure in proteins. EMBO J. 5, 823–826 (1986).
Greene, L. H. et al. The CATH domain structure database: new protocols and classification levels give a more comprehensive resource for exploring evolution. Nucleic Acids Res. 35, D291–D297 (2007).
Holm, L. & Sander, C. Protein structure comparison by alignment of distance matrices. J. Mol. Biol. 233, 123–138 (1993).
Shindyalov, I. N. & Bourne, P. E. Protein structure alignment by incremental combinatorial extension (CE) of the optimal path. Protein Eng. 11, 739–747 (1998).
Taylor, W. R. & Orengo, C. A. Protein structure alignment. J. Mol. Biol. 208, 1–22 (1989).
Kolodny, R., Koehl, P. & Levitt, M. Comprehensive evaluation of protein structure alignment methods: scoring by geometric measures. J. Mol. Biol. 346, 1173–1188 (2005).
Reeves, G. A., Dallman, T. J., Redfern, O. C., Akpor, A. & Orengo, C. A. Structural diversity of domain superfamilies in the CATH database. J. Mol. Biol. 360, 725–741 (2006).
Orengo, C. A., Sillitoe, I., Reeves, G. & Pearl, F. M. Review: what can structural classifications reveal about protein evolution? J. Struct. Biol. 134, 145–165 (2001).
Lisewski, A. M. & Lichtarge, O. Rapid detection of similarity in protein structure and function through contact metric distances. Nucleic Acids Res. 34, e152 (2006).
Barker, J. A. & Thornton, J. M. An algorithm for constraint-based structural template matching: application to 3D templates with statistical analysis. Bioinformatics 19, 1644–1649 (2003).
Laskowski, R. A., Watson, J. D. & Thornton, J. M. Protein function prediction using local 3D templates. J. Mol. Biol. 351, 614–626 (2005).
Ivanisenko, V. A. et al. PDBSiteScan: a tool for search for the best-matching superposition in the database PDBSite. Third International Conference on Bioinformatics of Genome Regulation and Structure 3, 149–152 (2002). Description of the PDBSiteScan server, which allows the user to compare a query protein structure against known functional sites in solved structures in the PDB.
Golovin, A., Dimitropoulos, D., Oldfield, T., Rachedi, A. & Henrick, K. MSDsite: a database search and retrieval system for the analysis and viewing of bound ligands and active sites. Proteins 58, 190–199 (2005).
Stark, A. & Russell, R. B. Annotation in three dimensions. PINTS: Patterns In Non-homologous Tertiary Structures. Nucleic Acids Res. 31, 3341–3344 (2003).
Wangikar, P. P., Tendulkar, A. V., Ramya, S., Mali, D. N. & Sarawagi, S. Functional sites in protein families uncovered via an objective and automated graph theoretic approach. J. Mol. Biol. 326, 955–978 (2003).
Polacco, B. J. & Babbitt, P. C. Automated discovery of 3D motifs for protein function annotation. Bioinformatics 22, 723–730 (2006).
Laskowski, R. A., Luscombe, N. M., Swindells, M. B. & Thornton, J. M. Protein clefts in molecular recognition and function. Protein Sci. 5, 2438–2452 (1996).
Binkowski, T. A., Joachimiak, A. & Liang, J. Protein surface analysis for function annotation in high-throughput structural genomics pipeline. Protein Sci. 14, 2972–2981 (2005).
Shulman-Peleg, A., Nussinov, R. & Wolfson, H. J. SiteEngines: recognition and comparison of binding sites and protein–protein interfaces. Nucleic Acids Res. 33, W337–W341 (2005).
Kinoshita, K. & Nakamura, H. eF-site and PDBjViewer: database and viewer for protein functional sites. Bioinformatics 20, 1329–1330 (2004).
Pawlowski, K. & Godzik, A. Surface map comparison: studying function diversity of homologous proteins. J. Mol. Biol. 309, 793–806 (2001).
Ko, J., Murga, L. F., Wei, Y. & Ondrechen, M. J. Prediction of active sites for protein structures from computed chemical properties. Bioinformatics 21 (Suppl. 1), i258–i265 (2005).
Laskowski, R. A., Watson, J. D. & Thornton, J. M. ProFunc: a server for predicting protein function from 3D structure. Nucleic Acids Res. 33, W89–W93 (2005). Description of the ProFunc server, which combines sequence and structure comparison methods to predict protein function from a given structure.
Pal, D. & Eisenberg, D. Inference of protein function from protein structure. Structure 13, 121–130 (2005). Description of the ProKnow server, which, like ProFunc, aims to combine a range of homology-detection methods for a given structure to predict function. Gene Ontology terms from matched proteins are combined using a statistical framework to provide the user with a combined significance score for each predicted function.
Parkinson, H. et al. ArrayExpress — a public database of microarray experiments and gene expression profiles. Nucleic Acids Res. 35, D747–D750 (2007).
Kahlem, P. & Birney, E. Dry work in a wet world: computation in systems biology. Mol. Syst. Biol. 2, 40 (2006).
Breitling, R., Amtmann, A. & Herzyk, P. Iterative Group Analysis (iGA): a simple tool to enhance sensitivity and facilitate interpretation of microarray experiments. BMC Bioinformatics 5, 34 (2004).
Breslin, T., Eden, P. & Krogh, M. Comparing functional annotation analyses with Catmap. BMC Bioinformatics 5, 193 (2004).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).
Hu, P., Bader, G., Wigle, D. A. & Emili, A. Computational prediction of cancer-gene function. Nature Rev. Cancer 7, 23–34 (2007).
Editorial. A decade of genome-wide biology. Nature Genetics 37, S3 (2005).
Hinsby, A. M. et al. A wiring of the human nucleolus. Mol. Cell 22, 285–295 (2006).
Shulman-Peleg, A., Nussinov, R. & Wolfson, H. J. Recognition of functional sites in protein structures. J. Mol. Biol. 339, 607–633 (2004).
Acknowledgements
We would particularly like to acknowledge E. Sideris for help with the figures in this manuscript.
Author information
Authors and Affiliations
Supplementary information
Supplementary information S1 (table): Online resources
This site is not intended to list all available online resources but rather those that are widely used, of high quality, and publicly available as of June 2007. (PDF 382 kb)
Related links
Glossary
- Orthologue
-
A homologue that is found in separate species and has been separated by speciation rather than by a gene duplication event.
- Homologue
-
Protein sequences are homologous if they have descended, usually with divergence, from a common ancestral sequence.
- Paralogue
-
A homologue that is the product of a gene duplication event within a species.
- Phylogenetic tree
-
Shows the evolutionary inter-relationships among various species or other entities that are believed to have a common ancestor. Each node that has descendants represents the most recent common ancestor of those descendants, with edge lengths sometimes corresponding to time estimates.
- TIM barrel
-
Consists of eight α-helices and eight parallel β-strands that alternate along the peptide backbone. The structure is named after triose phosphate isomerase, a conserved glycolytic enzyme.
- Superposition
-
After equivalent residues in two protein structures have been determined, the coordinates of one protein can be transformed onto the other.
- Rossmann fold
-
Composed of three or more parallel β-strands linked by two α-helices and is found in proteins that bind nucleotides, such as the NAD and FMN co-factors.
- Superfamily
-
A group of evolutionarily related proteins that often have the same overall domain structure, but may have diverged beyond recognition at the sequence level.
- Structural template
-
Many methods of predicting function from structure involve listing specific residues and expected inter-atom distances in a template file, which can then be compared against other structures.
- SITE record
-
Part of a Protein Data Bank file containing details of which residues are relevant to the protein function (for example, those involved in substrate binding).
- De novo sequence method
-
A method that does not rely upon homology between sequences for transferring functional annotations but rather on the recognition of features such as residue composition and subcellular localization signals.
- Meta-server
-
In the context of this review, a meta-server is a gateway to a well-benchmarked set of prediction methods.
Rights and permissions
About this article
Cite this article
Lee, D., Redfern, O. & Orengo, C. Predicting protein function from sequence and structure. Nat Rev Mol Cell Biol 8, 995–1005 (2007). https://doi.org/10.1038/nrm2281
Issue Date:
DOI: https://doi.org/10.1038/nrm2281
This article is cited by
-
One substrate many enzymes virtual screening uncovers missing genes of carnitine biosynthesis in human and mouse
Nature Communications (2024)
-
Uncovering supramolecular chirality codes for the design of tunable biomaterials
Nature Communications (2024)
-
Discovering functionally important sites in proteins
Nature Communications (2023)
-
Favourable Interfacial Characteristics of A2 Milk Protein Monolayer
The Journal of Membrane Biology (2023)
-
The CmMYB3 transcription factors isolated from the Chrysanthemum morifolium regulate flavonol biosynthesis in Arabidopsis thaliana
Plant Cell Reports (2023)
