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Point and Frameshift Mutations

Point and Frameshift Mutations

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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Mutations

Conserved Binding Sites

Conserved Binding Sites

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.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

Mismatch Repair

Mismatch Repair

Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...

Spontaneous and Induced Mutations

Spontaneous and Induced Mutations

Spontaneous mutations arise infrequently during DNA replication due to errors in the process. A key factor behind these errors is tautomeric shifts in nitrogenous bases, where bases transition from keto to enol forms or amino to imino forms. This shift can alter base-pairing rules, leading to mutations. Additionally, reactive oxygen species (ROS) arising from aerobic metabolism can damage DNA, resulting in depurination (loss of a purine base) or depyrimidination (loss of a pyrimidine base).

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Related Experiment Video

Updated: Oct 21, 2025

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Identification and Classification of Position-specific GABA_A Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

Packpred: Predicting the Functional Effect of Missense Mutations.

Kuan Pern Tan^1,2, Tejashree Rajaram Kanitkar³, Chee Keong Kwoh²

¹Bioinformatics Institute, Singapore, Singapore.

Frontiers in Molecular Biosciences

|September 7, 2021

Summary

Packpred accurately predicts the functional impact of protein mutations using a novel statistical potential. This tool aids in protein function annotation and clinical diagnosis by identifying disease-causing variants.

Keywords:

amino acid depth local environment/clique meta predictor missense mutation effect prediction statistical potential

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Area of Science:

Computational Biology
Bioinformatics
Protein Science

Background:

Predicting the functional consequences of single point mutations is crucial for protein function annotation and clinical diagnosis.
Existing methods for mutation effect prediction require improvement in accuracy and scope.

Purpose of the Study:

To develop and evaluate Packpred, a novel computational method for predicting the functional consequences of protein point mutations.
To compare Packpred's performance against existing state-of-the-art prediction tools.

Main Methods:

Packpred integrates a multi-body clique statistical potential, a depth-dependent amino acid substitution matrix (FADHM), and positional Shannon entropy.
Model parameters were trained using a saturation mutagenesis dataset of T4-lysozyme.
Performance was validated on independent saturation mutagenesis (CcdB) and Missense3D datasets.

Main Results:

Packpred achieved Matthew's Correlation Coefficient (MCC) values of 0.42, 0.47, and 0.36 on training and testing datasets, respectively.
Packpred demonstrated superior performance compared to six other contemporary methods across all tested datasets.
A meta-server analysis combining multiple predictors further enhanced prediction accuracy, achieving MCC values of 0.40 and 0.51.

Conclusions:

Packpred is a highly effective tool for predicting the functional impact of protein point mutations.
The findings suggest that meta-predictors can significantly improve prediction accuracy.
Further development of meta-predictors holds promise for even greater accuracy in mutation effect prediction.