Structural Dynamics is a Determinant of the Functional Significance of Missense Variants

Ponzoni, Luca; Bahar, İvet

doi:10.1016/j.bpj.2017.11.1291

Cited by 12 publications

(22 citation statements)

References 31 publications

(42 reference statements)

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“…B‐factor values of atomic coordinates in crystal structures (Sun, Liu, Qu, Feng, & Reetz, ), NMR order parameters (Torchia, ) or fluctuation calculated from an alignment of multiple X‐ray structures, or NMR‐derived ensembles allows straightforward estimates on flexibility. Conformational flexibility estimated through MD has also been used as one of the many features in predicting for effects of a missense mutation (Ponzoni & Bahar, ), but not in a direct manner as us. We look at altered flexibility; therefore, if any protein segment, including nonflexible segment, if altered in flexibility, those effects are accounted for.…”

Section: Discussionmentioning

confidence: 99%

Exploring the use of molecular dynamics in assessing protein variants for phenotypic alterations

Garg¹,

Pal²

2019

Human Mutation

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With the advent of rapid sequencing technologies, making sense of all the genomic variations that we see among us has been a major challenge. A plethora of algorithms and methods exist that try to address genome interpretation through genotype–phenotype linkage analysis or evaluating the loss of function/stability mutations in protein. Critical Assessment of Genome Interpretation (CAGI) offers an exceptional platform to blind‐test all such algorithms and methods to assess their true ability. We take advantage of this opportunity to explore the use of molecular dynamics simulation as a tool to assess alteration of phenotype, loss of protein function, interaction, and stability. The results show that coarse‐grained dynamics based protein flexibility analysis on 34 CHEK2 and 1719 CALM1 single mutants perform reasonably well for class‐based predictions for phenotype alteration and two‐thirds of the predicted scores return a correlation coefficient of 0.6 or more. When all‐atom dynamics is used to predict altered stability due to mutations for Frataxin protein (8 cases), the predictions are comparable to the state‐of‐the‐art methods. The competitive performance of our straightforward approach to phenotype interpretation contrasts with heavily trained machine learning approaches, and open new avenues to rationally improve genome interpretation.

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Section: Discussionmentioning

confidence: 99%

Exploring the use of molecular dynamics in assessing protein variants for phenotypic alterations

Garg¹,

Pal²

2019

Human Mutation

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“…Finally, we integrated scores and generated topologic groups that we believe may indicate different molecular mechanisms and thereby probabilities of pathogenicity, but more quantitative groups generated by training against experimental functional assays is required for a more definitive assessment. Earlier studies have shown the importance of protein 3D structure [5,23,24] and dynamics [4] in the prediction functional impact of missense variants, in general. However, no study, to our knowledge, has combined these diverse scores together, or with DNA annotations, for the interpretation of genomic variants.…”

Section: Discussionmentioning

confidence: 99%

“…The fundamental concept underlying this idea is that the structure and dynamics of RNA and proteins ultimately determine whether a variation can be tolerated or becomes pathogenic. Further, current approaches aim to directly predict pathogenicity resulting in different levels of predictive performance [3][4][5]. We believe that this approach bypasses the necessary step of determining the molecular mechanism of dysfunction.…”

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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“…Another study tested the performance of 14 tools (SEQ+DYN, SEQ, DYN, MutationTaster2, PolyPhen2, MutationAssessor, CADD, SIFT, LRT, FATHMM-U, Gerp++, phyloP, Condel, Logit) in relation to structural dynamics, which was used as a proxy for functional significance of amino acid substitutions [46]. PON-P2 has the best sensitivity, specificity, NPV and MMC, it is the second best for accuracy but only 13 th for PPV.…”

Section: Benchmark Use Casementioning

confidence: 99%

Variation Benchmark Datasets: Update, Criteria, Quality and Applications

Sarkar

Yang

Vihinen

2019

Preprint

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Development of new computational methods and testing their performance has to be done on experimental data. Only in comparison to existing knowledge can method performance be assessed. For that purpose, benchmark datasets with known and verified outcome are needed.High-quality benchmark datasets are valuable and may be difficult, laborious and time consuming to generate. VariBench and VariSNP are the two existing databases for sharing

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Structural Dynamics is a Determinant of the Functional Significance of Missense Variants

Cited by 12 publications

References 31 publications

Exploring the use of molecular dynamics in assessing protein variants for phenotypic alterations

Exploring the use of molecular dynamics in assessing protein variants for phenotypic alterations

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

Variation Benchmark Datasets: Update, Criteria, Quality and Applications

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