Dataset Information

Elucidating the Structural Impacts of Protein InDels.

ABSTRACT: The effects of amino acid insertions and deletions (InDels) remain a rather under-explored area of structural biology. These variations oftentimes are the cause of numerous disease phenotypes. In spite of this, research to study InDels and their structural significance remains limited, primarily due to a lack of experimental information and computational methods. In this work, we fill this gap by modeling InDels computationally; we investigate the rigidity differences between the wildtype and a mutant variant with one or more InDels. Further, we compare how structural effects due to InDels differ from the effects of amino acid substitutions, which are another type of amino acid mutation. We finish by performing a correlation analysis between our rigidity-based metrics and wet lab data for their ability to infer the effects of InDels on protein fitness.

SUBMITTER: Jilani M

PROVIDER: S-EPMC9599607 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Elucidating the Structural Impacts of Protein InDels.

Jilani Muneeba M Turcan Alistair A Haspel Nurit N Jagodzinski Filip F

Biomolecules 20221007 10

The effects of amino acid insertions and deletions (InDels) remain a rather under-explored area of structural biology. These variations oftentimes are the cause of numerous disease phenotypes. In spite of this, research to study InDels and their structural significance remains limited, primarily due to a lack of experimental information and computational methods. In this work, we fill this gap by modeling InDels computationally; we investigate the rigidity differences between the wildtype and a ...[more]

PMID: 36291643

Similar Datasets

Project description:BackgroundQuantitative evaluation of protein structural evolution is important for our understanding of protein biological functions and their evolutionary adaptation, and is useful in guiding protein engineering. However, compared to the models for sequence evolution, the quantitative models for protein structural evolution received less attention. Ancient protein superfamilies are often considered versatile, allowing genetic and functional diversifications during long-term evolution. In this study, we investigated the quantitative impacts of sequence variations on the structural evolution of homologues in 68 ancient protein superfamilies that exist widely in sequenced eukaryotic, bacterial and archaeal genomes.ResultsWe found that the accumulated structural variations within ancient superfamilies could be explained largely by a bilinear model that simultaneously considers amino acid substitution and insertion/deletion (indel). Both substitutions and indels are essential for explaining the structural variations within ancient superfamilies. For those ancient superfamilies with high bilinear multiple correlation coefficients, the influence of each unit of substitution or indel on structural variations is almost constant within each superfamily, but varies greatly among different superfamilies. The influence of each unit indel on structural variations is always larger than that of each unit substitution within each superfamily, but the accumulated contributions of indels to structural variations are lower than those of substitutions in most superfamilies. The total contributions of sequence indels and substitutions (46% and 54%, respectively) to the structural variations that result from sequence variations are slightly different in ancient superfamilies.ConclusionsStructural variations within ancient protein superfamilies accumulated under the significantly bilinear influence of amino acid substitutions and indels in sequences. Both substitutions and indels are essential for explaining the structural variations within ancient superfamilies. For those structural variations resulting from sequence variations, the total contribution of indels is slightly lower than that of amino acid substitutions. The regular clock exists not only in protein sequences, but also probably in protein structures.

Project description:BackgroundRecent studies have demonstrated the genetic significance of insertions, deletions, and other more complex structural variants (SVs) in the human population. With the development of the next-generation sequencing technologies, high-throughput surveys of SVs on the whole-genome level have become possible. Here we present split-read identification, calibrated (SRiC), a sequence-based method for SV detection.ResultsWe start by mapping each read to the reference genome in standard fashion using gapped alignment. Then to identify SVs, we score each of the many initial mappings with an assessment strategy designed to take into account both sequencing and alignment errors (e.g. scoring more highly events gapped in the center of a read). All current SV calling methods have multilevel biases in their identifications due to both experimental and computational limitations (e.g. calling more deletions than insertions). A key aspect of our approach is that we calibrate all our calls against synthetic data sets generated from simulations of high-throughput sequencing (with realistic error models). This allows us to calculate sensitivity and the positive predictive value under different parameter-value scenarios and for different classes of events (e.g. long deletions vs. short insertions). We run our calculations on representative data from the 1000 Genomes Project. Coupling the observed numbers of events on chromosome 1 with the calibrations gleaned from the simulations (for different length events) allows us to construct a relatively unbiased estimate for the total number of SVs in the human genome across a wide range of length scales. We estimate in particular that an individual genome contains ~670,000 indels/SVs.ConclusionsCompared with the existing read-depth and read-pair approaches for SV identification, our method can pinpoint the exact breakpoints of SV events, reveal the actual sequence content of insertions, and cover the whole size spectrum for deletions. Moreover, with the advent of the third-generation sequencing technologies that produce longer reads, we expect our method to be even more useful.

Dataset Information

Elucidating the Structural Impacts of Protein InDels.

Publications

Elucidating the Structural Impacts of Protein InDels.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets