Stability change detection software tools | Protein structure data analysis
Mutation of a single amino acid residue can cause changes in a protein, which could then lead to a loss of protein function. Predicting the protein stability changes can provide several possible candidates for the novel protein designing.
A support vector machine (SVM)-based tool for the automatic prediction of protein stability changes upon single point mutations. I-Mutant predictions are performed starting either from the protein structure or, more importantly, from the protein sequence.
Investigates thermodynamic stability changes resulting from single site mutations in globular proteins. PoPMuSiC intends to highlight stability changes subsequent to all possible mutations in a medium size protein. Additionnally, the application can also be used to assess the optimality of each residue in a protein’s sequence while considering the stability of its structure. This program can be run including for large-scale analysis.
Prediction of protein stability changes for single site mutations from sequences. Because MUpro can accurately predict protein stability changes using primary sequence information only, it is applicable to many situations where the tertiary structure is unknown, overcoming a major limitation of previous methods which require tertiary information.
Identifies kinase activating mutations from a combination of sequence and structural information. Kinact is a machine learning-based predictive model implemented as a web server. It provides a set of analyses to help users investigate in detail the impact of the mutation. It uses graph-based signatures, sequence and structural data. This method also displays information on the group of homologue protein kinases according to the Standard Kinase Classification Scheme.
A tool to predict changes in protein stability upon point mutations. The prediction model uses amino acid-atom potentials and torsion angle distribution to assess the amino acid environment of the mutation site. Additionally, the prediction model can distinguish the amino acid environment using its solvent accessibility and secondary structure specificity.
Predicts the impact of single-point mutations on protein stability and protein–protein and protein–nucleic acid affinity. mCSM is an approach, which relies on graph-based signatures, for studying the impact of missense mutations in proteins. The software perceives residue environment density and depth implicitly, without relying on direct calculations or thresholds. It was applied to predict stability changes of mutations occurring in p53, demonstrating its applicability in a challenging disease scenario.