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Related Concept Videos

Mutations01:39

Mutations

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Overview
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Mutations01:35

Mutations

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Mutations are changes in the sequence of DNA. These changes can occur spontaneously or they can be induced by exposure to environmental factors. Mutations can be characterized in a number of different ways: whether and how they alter the amino acid sequence of the protein, whether they occur over a small or large area of DNA, and whether they occur in somatic cells or germline cells.
Chromosomal Alterations Are Large-Scale Mutations
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Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...
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Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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RNA Stability01:53

RNA Stability

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Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
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Related Experiment Video

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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STRUM: structure-based prediction of protein stability changes upon single-point mutation.

Lijun Quan1, Qiang Lv2, Yang Zhang3

  • 1School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China Department of Computational Medicine and Bioinformatics, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA.

Bioinformatics (Oxford, England)
|June 19, 2016
PubMed
Summary

We developed STRUM, a new method for predicting protein stability changes from mutations using low-resolution models. STRUM achieves high accuracy, outperforming existing methods by leveraging physics-based energy terms and conservation scores.

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Area of Science:

  • Computational Biology
  • Protein Engineering
  • Bioinformatics

Background:

  • Single nucleotide polymorphisms (SNPs) in the human genome can alter protein stability and function, leading to diseases.
  • Existing methods for predicting mutation-induced protein stability changes often require experimental structures, limiting their applicability.
  • Advancements in protein structure prediction offer new opportunities for developing accurate computational tools.

Purpose of the Study:

  • To develop a novel method for predicting the stability changes caused by single-point mutations using low-resolution protein structure models.
  • To assess the accuracy and performance of the new method against state-of-the-art approaches.

Main Methods:

  • Developed STRUM, a method that utilizes 3D protein models generated by iterative threading assembly refinement (I-TASSER) simulations.
  • Trained STRUM models using gradient boosting regression, incorporating physics- and knowledge-based energy functions derived from I-TASSER models.
  • Validated STRUM using 5-fold cross-validation on 3421 experimentally determined mutations across 150 proteins.

Main Results:

  • STRUM achieved a Pearson correlation coefficient (PCC) of 0.79 and a root-mean-square error of 1.2 kcal/mol for predicting changes in Gibbs free-energy gap (ΔΔG).
  • The method significantly outperformed existing state-of-the-art prediction tools, including those based on experimental structures.
  • Prediction accuracy showed minimal dependence on the precise accuracy of structure models, provided the global fold was correct.

Conclusions:

  • Low-resolution protein structure modeling is feasible for high-accuracy prediction of stability changes upon point mutations.
  • STRUM offers a powerful computational tool for understanding mutation effects on protein stability.
  • The findings highlight the potential of integrating structure prediction with stability analysis for disease-related mutation studies.