Identifying the Molecular Drivers of Pathogenic Aldehyde Dehydrogenase Missense Mutations in Cancer and Non-Cancer Diseases

Abstract

Human aldehyde dehydrogenases (ALDHs) comprising 19 isoenzymes play a vital role on both endogenous and exogenous aldehyde metabolism. This NAD(P)-dependent catalytic process relies on the intact structural and functional activity of the cofactor binding, substrate interaction, and the oligomerization of ALDHs. Disruptions on the activity of ALDHs, however, could result in the accumulation of cytotoxic aldehydes, which have been linked with a wide range of diseases, including both cancers as well as neurological and developmental disorders. In our previous works, we have successfully characterised the structure-function relationships of the missense variants of other proteins. We, therefore, applied a similar analysis pipeline to identify potential molecular drivers of pathogenic ALDH missense mutations. Variants data were first carefully curated and labelled as cancer-risk, non-cancer diseases, and benign. We then leveraged various computational biophysical methods to describe the changes caused by missense mutations, informing a bias of detrimental mutations with destabilising effects. Cooperating with these insights, several machine learning approaches were further utilised to investigate the combination of features, revealing the necessity of the conservation of ALDHs. Our work aims to provide important biological perspectives on pathogenic consequences of missense mutations of ALDHs, which could be invaluable resources in the development of cancer treatment.

Keywords: aldehyde dehydrogenase; cancer; machine learning; missense mutations; pathogenic molecular driver.

Figure 1

Protein structures predicted by AlphaFold2 of human ALDHs. Each ALDH was assigned a colour, which was grouped into 11 different families, namely ALDH1 (A), ALDH2 (B), ALDH3 (C), ALDH4 (D), ALDH5 (E), ALDH6 (F), ALDH7 (G), ALDH8 (H), ALDH9 (I), ALDH16 (J), and ALDH18 (K). Human ALDHs share similar folding on cofactor binding and substrate binding regions, particularly found in the ALDH1 and ALDH3 family, while some ALDHs (ALDH1L1, ALDH1L2, ALDH16A1, and ALDH18A1) contain additional domains performing other cellular functions.

Figure 2

Mutation analysis pipeline to identify pathogenic molecular drivers of ALDHs. This workflow consists of three steps. Missense variants were first curated from multiple databases and labelled as cancer-risk, benign, and non-cancer diseases, respectively. ALDH protein bound with substrates was generated by AlphaFold2 and AutoDock Vina. After this, various computational biophysical measurements were used to annotate the missense mutations on different aspects such as protein interactions, conservation, and local residue environment. Lastly, we implemented both qualitative analysis and machine learning approaches to identify potential disease-causing risk factors.

Figure 3

Distributions of variants of three labels, namely cancer-risk (red), benign (blue), and non-cancer diseases (yellow) of the structures of human ALDHs. Each ALDH protein is coloured based on its different important regions, namely NAD(P)+ binding region (dark magenta), aldehyde binding region (dark green), protein–protein interaction region (dark yellow), and addition domains such as folate/glutamate binding region (dark cyan).

Figure 4

Visualisation of variants of three labels, namely cancer-risk (red), benign (blue), and non-cancer diseases (yellow) using dimensionality reduction methods, including Principal Component Analysis (PCA) (A), t-distributed stochastic neighbour embedding (t-SNE) (B) and Uniform Manifold Approximation and Projection (UMAP) (C), respectively. Data points were coloured based on their labels.

Figure 5

Potential molecular drivers leading to cancer of human ALDHs. Qualitative tests were performed using Wilcoxon signed-rank test on change of protein stability (A,B), Relative solvent accessibility (C), distance from mutation site to NAD+ (D), change of NAD+ binding affinity (E), and the Mutation Tolerance Ratio 2 (MTR2) (F) and between cancer-risk and benign mutations. Relative feature importance of the cancer-risk machine learning model was presented (G).

Figure 6

Potential molecular drivers leading to non-cancer diseases of human ALDHs. Qualitative tests were performed using Wilcoxon signed-rank test on change of protein stability (A,B), Relative solvent accessibility (C), distance from mutation site to NAD+ (D), change of NAD+ binding affinity (E), and the Mutation Tolerance Ratio 2 (MTR2) (F) and between non-cancer diseases and benign mutations. Relative feature importance of the non-cancer pathogenic machine learning model was presented (G).