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. 2023 Jul 18;24:287. doi: 10.1186/s12859-023-05407-9

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 3 — SNPeffect analysis of carcinoma cell line SHP-77. A Number of different mutation types in the small cell lung carcinoma cell line SHP-77. The impact of 90% of all missense variants was defined with SNPeffect 5.0. B Variant impact plot for the small cell lung carcinoma cell line SHP-77. The APR TANGO score and FoldX scores (maxed at ΔΔG = 5 in the plot) are plotted for all variants in a high-confidence domain. The two variants in known driver proteins, KRAS and p53, are highlighted in the plot. For variants that were matched to more than one PDB structure, only one structure (highest pLDDT) was used to avoid redundancy. C Condensed input and output files of cell line SHP-77 focusing on KRAS and p53. The FoldX report contains the calculated impact on structural stability (ΔΔG), information about the used structure, and the residue pLDDT score. The SEQANAL report presents the variant-containing domain, identification of APRs in that domain, and variant-impact prediction by established tools such as PROVEAN. Dotted columns represent extra data that can be found in the complete output files (Additional file 2)