Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine

Sanavia, Tiziana; Birolo, Giovanni; Montanucci, Ludovica; Turina, Paola; Capriotti, Emidio; Fariselli, Piero

doi:10.1016/j.csbj.2020.07.011

Cited by 110 publications

(120 citation statements)

References 88 publications

(114 reference statements)

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“…In the vast majority of cases, the folding-free energy of the mutants was found to be less favorable than of a wild type, and, thus, the dataset is biased toward de-stabilizing mutations. Series of works [ 26 , 43 , 44 , 45 ] were devoted on this topic and suggested that the training (and testing) dataset should have a similar number of cases of stabilizing and de-stabilizing mutations. While this is understandable from the point-of-view of statistics, we argue that this should not be necessarily applied in developing machine learning predictors of protein stability changes caused by mutations (especially for predictors that use only sequence information as SAAFEC-SEQ).…”

Section: Discussionmentioning

confidence: 99%

SAAFEC-SEQ: A Sequence-Based Method for Predicting the Effect of Single Point Mutations on Protein Thermodynamic Stability

Panday

Alexov

2021

IJMS

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Modeling the effect of mutations on protein thermodynamics stability is useful for protein engineering and understanding molecular mechanisms of disease-causing variants. Here, we report a new development of the SAAFEC method, the SAAFEC-SEQ, which is a gradient boosting decision tree machine learning method to predict the change of the folding free energy caused by amino acid substitutions. The method does not require the 3D structure of the corresponding protein, but only its sequence and, thus, can be applied on genome-scale investigations where structural information is very sparse. SAAFEC-SEQ uses physicochemical properties, sequence features, and evolutionary information features to make the predictions. It is shown to consistently outperform all existing state-of-the-art sequence-based methods in both the Pearson correlation coefficient and root-mean-squared-error parameters as benchmarked on several independent datasets. The SAAFEC-SEQ has been implemented into a web server and is available as stand-alone code that can be downloaded and embedded into other researchers’ code.

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Section: Discussionmentioning

confidence: 99%

SAAFEC-SEQ: A Sequence-Based Method for Predicting the Effect of Single Point Mutations on Protein Thermodynamic Stability

Panday

Alexov

2021

IJMS

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“…Computational tools accounting for anti-symmetric properties of variation i.e. ΔΔG (A->B) = - ΔΔG (B->A) [118] , [141] , [142] are able to achieve improved prediction performance complementing experimental studies [85] .…”

Section: Discussionmentioning

confidence: 99%

“…It is not meant to be an exhaustive list, with other tools available centred on important questions like assessing cancer variations and other human mutations. As such, these go beyond the scope of this review and have been extensively reviewed elsewhere [83] , [84] , [85] .…”

Section: Introductionmentioning

confidence: 99%

Combining structure and genomics to understand antimicrobial resistance

Tunstall

Portelli

Phelan

et al. 2020

Computational and Structural Biotechnology Journal

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Antimicrobials against bacterial, viral and parasitic pathogens have transformed human and animal health. Nevertheless, their widespread use (and misuse) has led to the emergence of antimicrobial resistance (AMR) which poses a potentially catastrophic threat to public health and animal husbandry. There are several routes, both intrinsic and acquired, by which AMR can develop. One major route is through non-synonymous single nucleotide polymorphisms (nsSNPs) in coding regions. Large scale genomic studies using high-throughput sequencing data have provided powerful new ways to rapidly detect and respond to such genetic mutations linked to AMR. However, these studies are limited in their mechanistic insight. Computational tools can rapidly and inexpensively evaluate the effect of mutations on protein function and evolution. Subsequent insights can then inform experimental studies, and direct existing or new computational methods. Here we review a range of sequence and structure-based computational tools, focussing on tools successfully used to investigate mutational effect on drug targets in clinically important pathogens, particularly Mycobacterium tuberculosis . Combining genomic results with the biophysical effects of mutations can help reveal the molecular basis and consequences of resistance development. Furthermore, we summarise how the application of such a mechanistic understanding of drug resistance can be applied to limit the impact of AMR.

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“…Recently, Gerasimavicius et al have highlighted an improvement in the identification of pathogenic variations using |ΔΔG| values ( Gerasimavicius et al, 2020 ). However, very little is known about thermodynamic changes in human protein variants so far ( Sanavia et al, 2020 ), and the processes establishing whether a variation perturbing the protein stability is or not disease-related are not clear yet. An extensive comparative analysis has proven that, on average, variations mostly involved in disease also associated with large effects on protein stability ( Casadio et al, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Protein Stability Perturbation Contributes to the Loss of Function in Haploinsufficient Genes

Birolo

Benevenuta

Fariselli

et al. 2021

Front. Mol. Biosci.

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Missense variants are among the most studied genome modifications as disease biomarkers. It has been shown that the “perturbation” of the protein stability upon a missense variant (in terms of absolute ΔΔG value, i.e., |ΔΔG|) has a significant, but not predictive, correlation with the pathogenicity of that variant. However, here we show that this correlation becomes significantly amplified in haploinsufficient genes. Moreover, the enrichment of pathogenic variants increases at the increasing protein stability perturbation value. These findings suggest that protein stability perturbation might be considered as a potential cofactor in diseases associated with haploinsufficient genes reporting missense variants.

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Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine

Abstract: Graphical abstract

Cited by 110 publications

References 88 publications

SAAFEC-SEQ: A Sequence-Based Method for Predicting the Effect of Single Point Mutations on Protein Thermodynamic Stability

SAAFEC-SEQ: A Sequence-Based Method for Predicting the Effect of Single Point Mutations on Protein Thermodynamic Stability

Combining structure and genomics to understand antimicrobial resistance

Protein Stability Perturbation Contributes to the Loss of Function in Haploinsufficient Genes

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