Deconstruction of the Ras switching cycle through saturation mutagenesis

Bandaru, Pradeep; Shah, Neel H.; Bhattacharyya, Moitrayee; Barton, John P.; Kondo, Yasumasa; Cofsky, Joshua C; Gee, Christine L.; Chakraborty, Arup K.; Kortemme, Tanja; Ranganathan, Rama; Kuriyan, John

doi:10.7554/elife.27810

Cited by 107 publications

(150 citation statements)

References 90 publications

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“…3A, which show the residue-based pathogenicity profiles (red line) computed upon averaging the entries in the corresponding column of the maps. Analogous profiles obtained using PolyPhen-2 (blue), EVmutation (green) and experimental fitness scores for "regulated-Ras" (Bandaru et al, 2017) (-ΔE, gray) reveal an overall agreement between computational predictions and experimental data (see also Table S3).…”

Section: Contribution Of Selected Featuresmentioning

confidence: 56%

“…The assumption is that those substitutions seen in the 140,000 people tested (mostly normal population) are somewhat permissive. We therefore compared the distribution of predictions obtained by Rhapsody on this set of gnomAD SAVs with the corresponding fitness scores from the experimental study considered above (Bandaru et al, 2017).…”

Section: Analysis Of H-ras Variants In Gnomadmentioning

confidence: 99%

“…H-Ras SAVs (dark blue dots) collected from the gnomAD database found in healthy population are shown along with the results for all SAVS (light blue dots) on the scatter plot between Rhapsody pathogenicity probabilities and experimental fitness scores reported in (Bandaru et al, 2017). "High-count" SAVs (yellow stars) were seen in at least 10 individuals.…”

Section: Fig 6: Analysis Of H-ras Savs From Gnomad Databasementioning

confidence: 99%

“…The utility of Rhapsody is illustrated by way of application of human H-Ras, a highly conserved Gprotein belonging to Ras subfamily of small GTPases, and comparison with deep mutational scanning data for this protein reported by Kuriyan, Ranganathan and coworkers (Bandaru et al, 2017). The new tool provides not only an efficient independent assessment of the pathogenicity of mutations, but also mechanistic insights into the molecular basis of the observed and/or predicted effect.…”

Section: Introductionmentioning

confidence: 99%

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Rhapsody: Pathogenicity prediction of human missense variants based on protein sequence, structure and dynamics

Ponzoni

Oltvai

Bahar

2019

Preprint

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AbstractThe biological effects of human missense variants have been studied experimentally for decades but predicting their effects in clinical molecular diagnostics remains challenging. Available computational tools are usually based on the analysis of sequence conservation and structural properties of the mutant protein. We recently introduced a new machine learning method that demonstrated for the first time the significance of protein dynamics in determining the pathogenicity of missense variants. Here we present a significant extension that integrates coevolutionary data from Pfam database and we also introduce a new interface (Rhapsody) that enables fully automated assessment of pathogenicity. Benchmarked against a dataset of about 20,000 annotated variants, the methodology is shown to outperform well-established and/or advanced prediction tools. We illustrate the utility of our approach by in silico saturation mutagenesis study of human H-Ras. The tool is made available both as a webtool (rhapsody.csb.pitt.edu) and an open source Python package (pip install prody-rhapsody).

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Section: Contribution Of Selected Featuresmentioning

confidence: 56%

Section: Analysis Of H-ras Variants In Gnomadmentioning

confidence: 99%

Section: Fig 6: Analysis Of H-ras Savs From Gnomad Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rhapsody: Pathogenicity prediction of human missense variants based on protein sequence, structure and dynamics

Ponzoni

Oltvai

Bahar

2019

Preprint

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“…This genetic spectrum differs for each member of the RAS family. Experimental data indicates that each type of alteration at the hotspots can lead to different downstream effects [11][12][13]. Further, non-hotspot variants may not alter the protein in the same way as hotspot mutations and therefore may not have the same implications for clinical management.…”

Section: Introductionmentioning

confidence: 99%

Integration of Multi-level Molecular Scoring for the Interpretation of RAS-Family Genetic Variation

Tripathi

Dsouza

Urrutia

et al. 2019

Preprint

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Protein-coding genetic variants are the first considered in most studies and Precision Medicine workflows, but their interpretation is primarily driven by DNA sequence-based analytical tools and annotations. Thus, more specific and mechanistic interpretations should be attainable by integrating DNA-based scores with scores from the protein 3D structure. However, reliable and reproducible standardization of methods that use 3D structure for genomic variation is still lacking. Further, we believe that the current paradigm of aiming to directly predict the pathogenicity of variants skips the critical step of inferring, with precision, molecular mechanisms of dysfunction. Thus, we report herein the development and evaluation of single and composite 3D structurebased scores and their integration with protein and DNA sequence-based scores to better understand not only if a genomic variant alters a protein, but how. We believe this is a critical step for understanding mechanistic changes due to genomic variants, designing functional validation tests, and for improving disease classifications. We applied this approach to the RAS gene family encoding seven distinct proteins and their 935 unique missense variants present somatically in cancer, in rare diseases (termed RASopathies), and in the currently healthy adult population. This knowledge shows that protein structure-based scores are distinct from information available from genomic annotation, that they are useful for interpreting genomic variants, and they should be taken into consideration in future guidelines for genomic data interpretation. Significance StatementGenetic information from patients is a powerful data type for understanding individual differences in disease risk and treatment, but most of the genetic variation we observe has no mechanistic interpretation. This lack of interpretation limits the use of genomics data in clinical care. Standard methods for genomics data interpretation take advantage of annotations available for the human reference genome, but they do not consider the 3D protein molecule. We believe that changes to the 3D molecule must be considered, to augment current practice and lead to more precise interpretation. In this work, we present our initial process for systematic multi-level molecular scores, including 3D, to interrogate 935 RAS-family variants that are relevant in both cancer and rare diseases. \body

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Understanding oncogenicity of cancer driver genes and mutations in the cancer genomics era

2020

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One of the key challenges of cancer biology is to catalogue and understand the somatic genomic alterations leading to cancer. Although alternative definitions and search methods have been developed to identify cancer driver genes and mutations, analyses of thousands of cancer genomes return a remarkably similar catalogue of around 300 genes that are mutated in at least one cancer type. Yet, many features of these genes and their role in cancer remain unclear, first and foremost when a somatic mutation is truly oncogenic. In this review, we first summarize some of the recent efforts in completing the catalogue of cancer driver genes. Then, we give an overview of different aspects that influence the oncogenicity of somatic mutations in the core cancer driver genes, including their interactions with the germline genome, other cancer driver mutations, the immune system, or their potential role in healthy tissues. In the coming years, this research holds promise to illuminate how, when, and why cancer driver genes and mutations are really drivers, and thereby move personalized cancer medicine and targeted therapies forward.

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Deconstruction of the Ras switching cycle through saturation mutagenesis

Cited by 107 publications

References 90 publications

Rhapsody: Pathogenicity prediction of human missense variants based on protein sequence, structure and dynamics

Rhapsody: Pathogenicity prediction of human missense variants based on protein sequence, structure and dynamics

Integration of Multi-level Molecular Scoring for the Interpretation of RAS-Family Genetic Variation

Understanding oncogenicity of cancer driver genes and mutations in the cancer genomics era

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