Abstract
Prediction of protein functions is one of the major challenges of the post-genomic era. Comprehension of protein functions has an important role in understanding the complicated mechanisms in various organisms. Different projects have identified novel protein sequences through high-throughput experiments, and structural genomics approaches are being used to identify their 3D structures. The generation of information about protein function could increase biological understanding and drug development as many diseases change these protein functions. In-silico protein function prediction is a difficult task due to the diversity of protein functions and multi-functionality in proteins. There is a requirement for annotations in a standard and machine-readable form to integrate them into workflows. The quality of the predictions also needs to be checked. Machine learning methods can integrate different features in workflows, give accurate predictions, and also measure the quality of predictions. Machine learning methods are also more useful as compared to traditional methods due to their ability to understand input–output relation without using a fixed model and work efficiently using noisy and non-linear data. In this article, we review the application of different machine learning methods for protein function prediction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhao XM, Chen L, Aihara K (2008) Protein function prediction with high-throughput data. Amino Acids 35(3):517–530. https://doi.org/10.1007/s00726-008-0077-y
Chou K, Shen H (2007) Recent progress in protein subcellular location prediction. Anal Bio-chem 370(1):1–16. https://doi.org/10.1016/j.ab.2007.07.006
Ashburner M, Ball C, Blake J et al (2000) Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet 25(1):25–29. https://doi.org/10.1038/75556
Altschul SF, Madden TL, Schäffer AA et al (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25(17):3389–3402. https://doi.org/10.1093/nar/25.17.3389
Rost B (2002) Enzyme function less conserved than anticipated. J Mol Biol 318:595–608. https://doi.org/10.1016/S0022-2836(02)00016-5
Hulo N, Bairoch A, Bulliard V et al (2008) The 20 years of PROSITE. Nucleic Acids Res 36(1):D245–D249. https://doi.org/10.1093/nar/gkm977
Sigrist CJ, Cerutti L, Hulo N et al (2002) PROSITE: a documented database using patterns and profiles as motif descriptors. Brief Bioinform 3(3):265–274. https://doi.org/10.1093/bib/3.3.265
Enault F, Suhre K, Claverie JM (2005) Phydbac “Gene Function Predictor”: a gene annotation tool based on genomic context analysis. BMC Bioinform 6:247. https://doi.org/10.1186/1471-2105-6-247
Pavlidis P, Gillis J (2013) Progress and challenges in the computational prediction of gene function using networks: 2012–2013 update. F1000Res. 2:230. https://doi.org/10.12688/f1000research.2-230.v1
Ye Y, Godzik A (2004) FATCAT: a web server for Xexible structure comparison and structure similarity searching. Nucl Acids Res 32:W582–W585. https://doi.org/10.1093/nar/gkh430
Wallace AC, Laskowski RA, Thornton JM (1996) Derivation of 3D coordinate templates for searching structural databases: application to Ser-His-Asp catalytic triads in the serine proteinases and lipases. Protein Sci 5:1001–1013. https://doi.org/10.1002/pro.5560050603
Gherardini PF, Helmer-Citterich M (2008) Structure-based function prediction: approaches and applications. Brief Funct Genomics 7(4):291–330. https://doi.org/10.1093/bfgp/eln030
Chou K (1995) A novel approach to predicting protein structural classes in a (20–1)-D amino acid composition space. Proteins: Structure, Function, and Bioinformatics 21(4): 319–344. https://doi.org/10.1002/prot.340210406.
Chou K (2001) Prediction of protein cellular attributes using pseudoamino acid composition. Proteins: Struct Funct Bioinform 43(3):246–255. https://doi.org/10.1002/prot.1035
Scholkopf B, Smola AJ (2005) Learning with kernels: support vector machines, regularization, optimization, and beyond. IEEE Trans Neural Netw 16(3):781–781. https://doi.org/10.1109/TNN.2005.848998
Xie H, Wasserman A, Levine Z et al (2002) Large-scale protein annotation through gene ontology. Genome Res 12(5):785–794. https://doi.org/10.1101/gr.86902
Shah AR, Oehmen CS, Webb-Robertson B (2008) SVM-HUSTLE—an iterative semi-supervised machine learning approach for pairwise protein remote homology detection. Bioinformatics 24(6):783–790. https://doi.org/10.1093/bioinformatics/btn028
Enright AJ, Van Dongen S, Ouzounis CA (2002) An efficient algorithm for large-scale detection of protein families. Nucl Acids Res 30(7):1575–1584. https://doi.org/10.1093/nar/30.7.1575
Pasquier C, Promponas VJ, Hamodrakas SJ (2001) PRED-CLASS: cascading neural networks for generalized protein classification and genome-wide applications. Proteins: Struct Funct Bioinform 44(3):361–369. https://doi.org/10.1002/prot.1101
Nair R, Rost B (2003) LOC3D: annotate sub-cellular localization for protein structures. Nucl Acids Res 31(13):3337–3340. https://doi.org/10.1093/nar/gkg514
Jiang X, Wei R, Zhang T, Gu Q (2008) Using the concept of Chou’s pseudo amino acid composition to predict apoptosis proteins subcellular location: an approach by approximate entropy. Protein Pept Lett 15(4):392–396. https://doi.org/10.2174/092986608784246443
Sikder A, Zomaya A (2006) Improving the performance of Domain discovery of protein domain boundary assignment using inter-domain linker index. BMC Bioinform 7(5):S6. https://doi.org/10.1186/1471-2105-7-S5-S6
Thomson R, Hodgman TC, Yang ZR et al (2003) Characterizing proteolytic cleavage site activity using bio-basis function neural networks. Bioinformatics 19(14):1741–1747. https://doi.org/10.1093/bioinformatics/btg237
Niu B, Cai Y, Lu W (2006) Predicting protein structural class with AdaBoost learner. Protein Pept Lett 13(5):489–492. https://doi.org/10.2174/092986606776819619
Chen W, Liu X, Huang Y et al (2012) Improved method for predicting protein fold patterns with ensemble classifiers. Genet Mol Res 11(1):174–181. https://doi.org/10.4238/2012.January.27.4
Date SV, Marcotte EM (2003) Discovery of uncharacterized cellular systems by genome-wide analysis of functional linkages. Nat Biotechnol 21(9):1055–1062. https://doi.org/10.1038/nbt861
Dale JM, Popescu L, Karp PD (2010) Machine learning methods for metabolic pathway prediction. BMC Bioinform 11(15). https://doi.org/10.1186/1471-2105-11-15
Chen X, Han B, Fang J et al (2008) Large-scale protein-protein interaction prediction using novel kernel methods. Int J Data Min Bioinform 2(2):145–156. https://doi.org/10.1504/IJDMB.2008.019095
Lobley A, Swindells MB, Orengo CA et al (2007) Inferring function using patterns of native disorder in proteins. PLoS Comput Biol 3(8):e162. https://doi.org/10.1371/journal.pcbi.0030162
Conflict of Interest
The authors confirm there is no conflict of interest in this article.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Biswas, S., Hasija, Y. (2022). Machine Learning Methods for Protein Function Prediction. In: Bansal, R.C., Zemmari, A., Sharma, K.G., Gajrani, J. (eds) Proceedings of International Conference on Computational Intelligence and Emerging Power System. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-4103-9_8
Download citation
DOI: https://doi.org/10.1007/978-981-16-4103-9_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-4102-2
Online ISBN: 978-981-16-4103-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)