LIST | Gsponer Lab (original) (raw)

LIST predicts the deleteriousness of variants in protein sequences based on Local Identity and Shared Taxa.

Background

The contribution of a protein to the observed phenotype is a complex function that depends on proper folding and activity as well as cellular localization and interactions with partners. Differences in the environment of homologous proteins are likely to increase the further apart species are. Thus, when assessing the deleteriousness of a given mutation, both sequence and genome similarities are important.

Output

LIST scores are in the range [0..1] such that variants with higher scores are more deleterious than those with lower scores. If a cut-off value is needed, we suggest 0.85, so that variants with scores equal to or greater than 0.85 are considered to be deleterious and those with lower scores benign.

Availability

LIST-S2 (Species Specific):

• Online HTML Jobs Interface here.
• LIST-S2 precomputed deleteriousness for multiple species including human here.
• Download precomputed LIST-S2 human data here.
• RESTful web server: LIST-S2 RESTful web server can be accessed online through programming languages here.
• To download LIST-S2 source code in C++ here.

LIST (Human only):

• LIST pre-computed deleteriousness of all possible mutations in individual Swiss-Prot human protein sequences please click here, and to download all these predictions here.
• To download LIST source code in C++ (academic use only), please click here. And to download LIST-2 (academic and commercial use) please click here.

The set of proteins used in the optimization of LIST (and LIST-S2) here. The set of proteins used in the benchmarking of LIST (and LIST-S2) here.

Citation

Malhis N, Jacobson M, Jones SJM, and Gsponer J. LIST-S2: Taxonomy based sorting of deleterious missense mutations across species. Nucleic Acids Research (2020) Apr 30. doi: 10.1093/nar/gkaa288. pmid: 32352516.

Malhis N, Jones SJM, Gsponer J. Improved measures for evolutionary conservation that exploit taxonomy distances. Nature Communications (2019) Apr 5;10(1):1556. doi: 10.1038/s41467-019-09583-2. pmid: 30952844.

Contacts: nmalhis@msl.ubc.ca and gsponer@msl.ubc.ca.