Limitations and Challenges in Protein Stability Prediction Upon Genome Variations: Towards Future Applications in Precision Medicine

Overview

Journal Comput Struct Biotechnol J

Specialty Biotechnology

Date 2020 Aug 11

PMID 32774791

Citations 45

Authors

Tiziana Sanavia

Giovanni Birolo

Ludovica Montanucci

Paola Turina

Emidio Capriotti

Piero Fariselli

Affiliations

Soon will be listed here.

Abstract

Protein stability predictions are becoming essential in medicine to develop novel immunotherapeutic agents and for drug discovery. Despite the large number of computational approaches for predicting the protein stability upon mutation, there are still critical unsolved problems: 1) the limited number of thermodynamic measurements for proteins provided by current databases; 2) the large intrinsic variability of ΔΔG values due to different experimental conditions; 3) biases in the development of predictive methods caused by ignoring the anti-symmetry of ΔΔG values between mutant and native protein forms; 4) over-optimistic prediction performance, due to sequence similarity between proteins used in training and test datasets. Here, we review these issues, highlighting new challenges required to improve current tools and to achieve more reliable predictions. In addition, we provide a perspective of how these methods will be beneficial for designing novel precision medicine approaches for several genetic disorders caused by mutations, such as cancer and neurodegenerative diseases.

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References

Maryam A, Vedithi S, Khalid R, Alsulami A, Monteiro Torres P, Siddiqi A . The Molecular Organization of Human cGMP Specific Phosphodiesterase 6 (PDE6): Structural Implications of Somatic Mutations in Cancer and Retinitis Pigmentosa. Comput Struct Biotechnol J. 2019; 17:378-389. PMC: 6434069. DOI: 10.1016/j.csbj.2019.03.004. View

Savojardo C, Petrosino M, Babbi G, Bovo S, Corbi-Verge C, Casadio R . Evaluating the predictions of the protein stability change upon single amino acid substitutions for the FXN CAGI5 challenge. Hum Mutat. 2019; 40(9):1392-1399. PMC: 6744327. DOI: 10.1002/humu.23843. View

Yang Y, Ding X, Zhu G, Niroula A, Lv Q, Vihinen M . ProTstab - predictor for cellular protein stability. BMC Genomics. 2019; 20(1):804. PMC: 6830000. DOI: 10.1186/s12864-019-6138-7. View

Kumar M, Bava K, Gromiha M, Prabakaran P, Kitajima K, Uedaira H . ProTherm and ProNIT: thermodynamic databases for proteins and protein-nucleic acid interactions. Nucleic Acids Res. 2005; 34(Database issue):D204-6. PMC: 1347465. DOI: 10.1093/nar/gkj103. View

Scheiblhofer S, Laimer J, Machado Y, Weiss R, Thalhamer J . Influence of protein fold stability on immunogenicity and its implications for vaccine design. Expert Rev Vaccines. 2017; 16(5):479-489. PMC: 5490637. DOI: 10.1080/14760584.2017.1306441. View

Singh V, Pacitto A, Donini S, Ferraris D, Boros S, Illyes E . Synthesis and Structure-Activity relationship of 1-(5-isoquinolinesulfonyl)piperazine analogues as inhibitors of Mycobacterium tuberculosis IMPDH. Eur J Med Chem. 2019; 174:309-329. PMC: 6990405. DOI: 10.1016/j.ejmech.2019.04.027. View

Usmanova D, Bogatyreva N, Arino Bernad J, Eremina A, Gorshkova A, Kanevskiy G . Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation. Bioinformatics. 2018; 34(21):3653-3658. PMC: 6198859. DOI: 10.1093/bioinformatics/bty340. View

Quan L, Lv Q, Zhang Y . STRUM: structure-based prediction of protein stability changes upon single-point mutation. Bioinformatics. 2016; 32(19):2936-46. PMC: 5039926. DOI: 10.1093/bioinformatics/btw361. View

Hassan M, Shaalan A, Dessouky M, Abdelnaiem A, ElHefnawi M . A review study: Computational techniques for expecting the impact of non-synonymous single nucleotide variants in human diseases. Gene. 2018; 680:20-33. DOI: 10.1016/j.gene.2018.09.028. View

10.

Rodrigues C, Pires D, Ascher D . DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability. Nucleic Acids Res. 2018; 46(W1):W350-W355. PMC: 6031064. DOI: 10.1093/nar/gky300. View

11.

Tate J, Bamford S, Jubb H, Sondka Z, Beare D, Bindal N . COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Res. 2018; 47(D1):D941-D947. PMC: 6323903. DOI: 10.1093/nar/gky1015. View

12.

Fariselli P, Martelli P, Savojardo C, Casadio R . INPS: predicting the impact of non-synonymous variations on protein stability from sequence. Bioinformatics. 2015; 31(17):2816-21. DOI: 10.1093/bioinformatics/btv291. View

13.

Hunt J, Ingram V . Allelomorphism and the chemical differences of the human haemoglobins A, S and C. Nature. 1958; 181(4615):1062-3. DOI: 10.1038/1811062a0. View

14.

Chen C, Lin J, Chu Y . iStable: off-the-shelf predictor integration for predicting protein stability changes. BMC Bioinformatics. 2013; 14 Suppl 2:S5. PMC: 3549852. DOI: 10.1186/1471-2105-14-S2-S5. View

15.

Nair P, Vihinen M . VariBench: a benchmark database for variations. Hum Mutat. 2012; 34(1):42-9. DOI: 10.1002/humu.22204. View

16.

Montanucci L, Capriotti E, Frank Y, Ben-Tal N, Fariselli P . DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations. BMC Bioinformatics. 2019; 20(Suppl 14):335. PMC: 6606456. DOI: 10.1186/s12859-019-2923-1. View

17.

Nussinov R, Jang H, Tsai C, Cheng F . Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers. PLoS Comput Biol. 2019; 15(3):e1006658. PMC: 6438456. DOI: 10.1371/journal.pcbi.1006658. View

18.

Pucci F, Bernaerts K, Kwasigroch J, Rooman M . Quantification of biases in predictions of protein stability changes upon mutations. Bioinformatics. 2018; 34(21):3659-3665. DOI: 10.1093/bioinformatics/bty348. View

19.

Pandurangan A, Ochoa-Montano B, Ascher D, Blundell T . SDM: a server for predicting effects of mutations on protein stability. Nucleic Acids Res. 2017; 45(W1):W229-W235. PMC: 5793720. DOI: 10.1093/nar/gkx439. View

20.

Amberger J, Bocchini C, Schiettecatte F, Scott A, Hamosh A . OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 2014; 43(Database issue):D789-98. PMC: 4383985. DOI: 10.1093/nar/gku1205. View