Enhanced interpretation of 935 hotspot and non-hotspot RAS variants using evidence-based structural bioinformatics
[Display omitted] •RAS mutations have been non-uniformly characterized biophysically and biochemically.•We assembled the broadest-to-date resource for KRAS mutation-specific activities.•Genomics tools do not correlate with KRAS experimental data, while 3D calculations do.•Calculations indicate local...
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| Vydané v: | Computational and structural biotechnology journal Ročník 20; s. 117 - 127 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Netherlands
Elsevier B.V
01.01.2022
Research Network of Computational and Structural Biotechnology Elsevier |
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| ISSN: | 2001-0370, 2001-0370 |
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| Abstract | [Display omitted]
•RAS mutations have been non-uniformly characterized biophysically and biochemically.•We assembled the broadest-to-date resource for KRAS mutation-specific activities.•Genomics tools do not correlate with KRAS experimental data, while 3D calculations do.•Calculations indicate local unfolding propensity correlates with enzymatic activity.
In the current study, we report computational scores for advancing genomic interpretation of disease-associated genomic variation in members of the RAS family of genes. For this purpose, we applied 31 sequence- and 3D structure-based computational scores, chosen by their breadth of biophysical properties. We parametrized our data by assembling a numerically homogenized experimentally-derived dataset, which when use in our calculations reveal that computational scores using 3D structure highly correlate with experimental measures (e.g., GAP-mediated hydrolysis RSpearman = 0.80 and RAF affinity Rspearman = 0.82), while sequence-based scores are discordant with this data. Performing all-against-all comparisons, we applied this parametrized modeling approach to the study of 935 RAS variants from 7 RAS genes, which led us to identify 4 groups of mutations according to distinct biochemical scores within each group. Each group was comprised of hotspot and non-hotspot KRAS variants, indicating that poorly characterized variants could functionally behave like pathogenic mutations. Combining computational scores using dimensionality reduction indicated that changes to local unfolding propensity associate with changes in enzyme activity by genomic variants. Hence, our systematic approach, combining methodologies from both clinical genomics and 3D structural bioinformatics, represents an expansion for interpreting genomic data, provides information of mechanistic value, and that is transferable to other proteins. |
|---|---|
| AbstractList | •
RAS mutations have been non-uniformly characterized biophysically and biochemically.
•
We assembled the broadest-to-date resource for KRAS mutation-specific activities.
•
Genomics tools do not correlate with KRAS experimental data, while 3D calculations do.
•
Calculations indicate local unfolding propensity correlates with enzymatic activity.
In the current study, we report computational scores for advancing genomic interpretation of disease-associated genomic variation in members of the RAS family of genes. For this purpose, we applied 31 sequence- and 3D structure-based computational scores, chosen by their breadth of biophysical properties. We parametrized our data by assembling a numerically homogenized experimentally-derived dataset, which when use in our calculations reveal that computational scores using 3D structure highly correlate with experimental measures (e.g., GAP-mediated hydrolysis RSpearman = 0.80 and RAF affinity Rspearman = 0.82), while sequence-based scores are discordant with this data. Performing all-against-all comparisons, we applied this parametrized modeling approach to the study of 935 RAS variants from 7 RAS genes, which led us to identify 4 groups of mutations according to distinct biochemical scores within each group. Each group was comprised of hotspot and non-hotspot KRAS variants, indicating that poorly characterized variants could functionally behave like pathogenic mutations. Combining computational scores using dimensionality reduction indicated that changes to local unfolding propensity associate with changes in enzyme activity by genomic variants. Hence, our systematic approach, combining methodologies from both clinical genomics and 3D structural bioinformatics, represents an expansion for interpreting genomic data, provides information of mechanistic value, and that is transferable to other proteins. In the current study, we report computational scores for advancing genomic interpretation of disease-associated genomic variation in members of the RAS family of genes. For this purpose, we applied 31 sequence- and 3D structure-based computational scores, chosen by their breadth of biophysical properties. We parametrized our data by assembling a numerically homogenized experimentally-derived dataset, which when use in our calculations reveal that computational scores using 3D structure highly correlate with experimental measures (e.g., GAP-mediated hydrolysis RSpearman = 0.80 and RAF affinity Rspearman = 0.82), while sequence-based scores are discordant with this data. Performing all-against-all comparisons, we applied this parametrized modeling approach to the study of 935 RAS variants from 7 RAS genes, which led us to identify 4 groups of mutations according to distinct biochemical scores within each group. Each group was comprised of hotspot and non-hotspot KRAS variants, indicating that poorly characterized variants could functionally behave like pathogenic mutations. Combining computational scores using dimensionality reduction indicated that changes to local unfolding propensity associate with changes in enzyme activity by genomic variants. Hence, our systematic approach, combining methodologies from both clinical genomics and 3D structural bioinformatics, represents an expansion for interpreting genomic data, provides information of mechanistic value, and that is transferable to other proteins. In the current study, we report computational scores for advancing genomic interpretation of disease-associated genomic variation in members of the RAS family of genes. For this purpose, we applied 31 sequence- and 3D structure-based computational scores, chosen by their breadth of biophysical properties. We parametrized our data by assembling a numerically homogenized experimentally-derived dataset, which when use in our calculations reveal that computational scores using 3D structure highly correlate with experimental measures (e.g., GAP-mediated hydrolysis RSₚₑₐᵣₘₐₙ = 0.80 and RAF affinity Rₛₚₑₐᵣₘₐₙ = 0.82), while sequence-based scores are discordant with this data. Performing all-against-all comparisons, we applied this parametrized modeling approach to the study of 935 RAS variants from 7 RAS genes, which led us to identify 4 groups of mutations according to distinct biochemical scores within each group. Each group was comprised of hotspot and non-hotspot KRAS variants, indicating that poorly characterized variants could functionally behave like pathogenic mutations. Combining computational scores using dimensionality reduction indicated that changes to local unfolding propensity associate with changes in enzyme activity by genomic variants. Hence, our systematic approach, combining methodologies from both clinical genomics and 3D structural bioinformatics, represents an expansion for interpreting genomic data, provides information of mechanistic value, and that is transferable to other proteins. In the current study, we report computational scores for advancing genomic interpretation of disease-associated genomic variation in members of the RAS family of genes. For this purpose, we applied 31 sequence- and 3D structure-based computational scores, chosen by their breadth of biophysical properties. We parametrized our data by assembling a numerically homogenized experimentally-derived dataset, which when use in our calculations reveal that computational scores using 3D structure highly correlate with experimental measures (e.g., GAP-mediated hydrolysis RSpearman = 0.80 and RAF affinity Rspearman = 0.82), while sequence-based scores are discordant with this data. Performing all-against-all comparisons, we applied this parametrized modeling approach to the study of 935 RAS variants from 7 RAS genes, which led us to identify 4 groups of mutations according to distinct biochemical scores within each group. Each group was comprised of hotspot and non-hotspot KRAS variants, indicating that poorly characterized variants could functionally behave like pathogenic mutations. Combining computational scores using dimensionality reduction indicated that changes to local unfolding propensity associate with changes in enzyme activity by genomic variants. Hence, our systematic approach, combining methodologies from both clinical genomics and 3D structural bioinformatics, represents an expansion for interpreting genomic data, provides information of mechanistic value, and that is transferable to other proteins.In the current study, we report computational scores for advancing genomic interpretation of disease-associated genomic variation in members of the RAS family of genes. For this purpose, we applied 31 sequence- and 3D structure-based computational scores, chosen by their breadth of biophysical properties. We parametrized our data by assembling a numerically homogenized experimentally-derived dataset, which when use in our calculations reveal that computational scores using 3D structure highly correlate with experimental measures (e.g., GAP-mediated hydrolysis RSpearman = 0.80 and RAF affinity Rspearman = 0.82), while sequence-based scores are discordant with this data. Performing all-against-all comparisons, we applied this parametrized modeling approach to the study of 935 RAS variants from 7 RAS genes, which led us to identify 4 groups of mutations according to distinct biochemical scores within each group. Each group was comprised of hotspot and non-hotspot KRAS variants, indicating that poorly characterized variants could functionally behave like pathogenic mutations. Combining computational scores using dimensionality reduction indicated that changes to local unfolding propensity associate with changes in enzyme activity by genomic variants. Hence, our systematic approach, combining methodologies from both clinical genomics and 3D structural bioinformatics, represents an expansion for interpreting genomic data, provides information of mechanistic value, and that is transferable to other proteins. In the current study, we report computational scores for advancing genomic interpretation of disease-associated genomic variation in members of the RAS family of genes. For this purpose, we applied 31 sequence- and 3D structure-based computational scores, chosen by their breadth of biophysical properties. We parametrized our data by assembling a numerically homogenized experimentally-derived dataset, which when use in our calculations reveal that computational scores using 3D structure highly correlate with experimental measures (e.g., GAP-mediated hydrolysis R = 0.80 and RAF affinity R = 0.82), while sequence-based scores are discordant with this data. Performing all-against-all comparisons, we applied this parametrized modeling approach to the study of 935 RAS variants from 7 RAS genes, which led us to identify 4 groups of mutations according to distinct biochemical scores within each group. Each group was comprised of hotspot and non-hotspot KRAS variants, indicating that poorly characterized variants could functionally behave like pathogenic mutations. Combining computational scores using dimensionality reduction indicated that changes to local unfolding propensity associate with changes in enzyme activity by genomic variants. Hence, our systematic approach, combining methodologies from both clinical genomics and 3D structural bioinformatics, represents an expansion for interpreting genomic data, provides information of mechanistic value, and that is transferable to other proteins. [Display omitted] •RAS mutations have been non-uniformly characterized biophysically and biochemically.•We assembled the broadest-to-date resource for KRAS mutation-specific activities.•Genomics tools do not correlate with KRAS experimental data, while 3D calculations do.•Calculations indicate local unfolding propensity correlates with enzymatic activity. In the current study, we report computational scores for advancing genomic interpretation of disease-associated genomic variation in members of the RAS family of genes. For this purpose, we applied 31 sequence- and 3D structure-based computational scores, chosen by their breadth of biophysical properties. We parametrized our data by assembling a numerically homogenized experimentally-derived dataset, which when use in our calculations reveal that computational scores using 3D structure highly correlate with experimental measures (e.g., GAP-mediated hydrolysis RSpearman = 0.80 and RAF affinity Rspearman = 0.82), while sequence-based scores are discordant with this data. Performing all-against-all comparisons, we applied this parametrized modeling approach to the study of 935 RAS variants from 7 RAS genes, which led us to identify 4 groups of mutations according to distinct biochemical scores within each group. Each group was comprised of hotspot and non-hotspot KRAS variants, indicating that poorly characterized variants could functionally behave like pathogenic mutations. Combining computational scores using dimensionality reduction indicated that changes to local unfolding propensity associate with changes in enzyme activity by genomic variants. Hence, our systematic approach, combining methodologies from both clinical genomics and 3D structural bioinformatics, represents an expansion for interpreting genomic data, provides information of mechanistic value, and that is transferable to other proteins. |
| Author | Urrutia, Raul Dsouza, Nikita R. Mathison, Angela J. Leverence, Elise Zimmermann, Michael T. Tripathi, Swarnendu |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34976316$$D View this record in MEDLINE/PubMed |
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| Keywords | Structural bioinformatics Protein science RAS mutation Genomics Functional genomics Data interpretation |
| Language | English |
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•RAS mutations have been non-uniformly characterized biophysically and biochemically.•We assembled the broadest-to-date resource for KRAS... In the current study, we report computational scores for advancing genomic interpretation of disease-associated genomic variation in members of the RAS family... • RAS mutations have been non-uniformly characterized biophysically and biochemically. • We assembled the broadest-to-date resource for KRAS mutation-specific... |
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| Title | Enhanced interpretation of 935 hotspot and non-hotspot RAS variants using evidence-based structural bioinformatics |
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