CUPSAT: prediction of protein stability upon point mutations

By Vijaya Parthiban, M. Michael Gromiha, Dietmar Schomburg. Nucleic Acids Res., 2006

665 citations recorded by the source corpus.

CUPSAT (Cologne University Protein Stability Analysis Tool) is a web tool to analyse and predict protein stability changes upon point mutations (single amino acid mutations). This program uses structural environment specific atom potentials and torsion angle potentials to predict ΔΔG, the difference in free energy of unfolding between wild-type and mutant proteins. It requires the protein structure in Protein Data Bank format and the location of the residue to be mutated. The output consists information about mutation site, its structural features (solvent accessibility, secondary structure and torsion angles), and comprehensive information about changes in protein stability for 19 possible substitutions of a specific amino acid mutation. Additionally, it also analyses the ability of the mutated amino acids to adapt the observed torsion angles. Results were tested on 1538 mutations from thermal denaturation and 1603 mutations from chemical denaturation experiments. Several validation tests (split-sample, jack-knife and k-fold) were carried out to ensure the reliability, accuracy and transferability of the prediction method that gives >80% prediction accuracy for most of these validation tests. Thus, the program serves as a valuable tool for the analysis of protein design and stability. The tool is accessible from the link .

Subfields: Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5bde26603e2bbb2004984fc487901f4d17698338

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

In Molecular Biology

Citations in the corpus

Stability effects of mutations and protein evolvability. (2009, methodology): “Some prediction methods are based on sequence [18–20], others on 3D structures [21–24].”
Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0 (2009, background): “Availability: http://babylone.ulb.ac.be/popmusic Contact: ydehouck@ulb.ac.be Supplementary information: Supplementary data are available at Bioinformatics online.”
Inferring Stabilizing Mutations from Protein Phylogenies: Application to Influenza Hemagglutinin (2009, methodology): “The ‘‘informative priors’’ are peaked at the DDG values predicted by the state-of-the-art physicochemically based program CUPSAT [8], and so are designed to leverage extensive pre-existing knowledge about DDG values.”
PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality (2011, methodology): “We may distinguish the approaches based on statistical potentials extracted from datasets of protein structures [15-19], from those based on empirical potentials built from optimised combinations of various physical energy terms [20-23].”
Variation Interpretation Predictors: Principles, Types, Performance, and Choice (2016, methodology): “The energy function-based methods can be grouped into physical potential function, statistical potential function, and empirical potential function methods [Guerois et al., 2002; Capriotti et al., 2004; Parthiban et al., 2006].”
Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks (2020, methodology): “These 85 methods either rely on explicit biophysical modeling of amino acid interactions coupled with 86 conformational sampling of protein structures (7-11) or apply machine/statistical learning to 87 extract patterns from various types of amino acid sequence, evolutionary, and/or protein 88 struct”
Improving the prediction of disease-related variants using protein three-dimensional structure (2011, methodology): “These algorithms are able to predict the effect of the mutation on protein stability [13,14,19,23-26], protein functional activity [9,21], and insurgence of human pathologies [7,8,10-12,1518,20-22].”
WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation (2013, background): “Thus, during the last few years, several methods have been developed to predict the impact of nsSNPs on protein stability [10-16], protein function [17,18] and to detect their pathologic effect [19-29].”
SPROUTS: a database for the evaluation of protein stability upon point mutation (2008, methodology): “Other methods exist but have been rejected due to some restrictions: CUPSAT (18) was not available in a stand-alone version and the current version of FoldX (19) only computes mutations to Alanine.”