#Automute protein free
In order to help understand the impact of amino acid changes, the ProTherm database collects the free Gibbs energy for thousands wild type and mutant proteins. Interestingly, the assessment of stability changes has been shown to be critical for the interpretation of variants in key proteins like TP53, which is known to have a strong connection with cancer development. The most relevant factors affecting protein folding and stability are hydrogen bonds, van der Waals, electrostatic and hydrophobic interactions, backbone angle preferences and protein chain entropy. Polypeptide chains are held together by non-covalent interactions between the residues forming them. The difference between wild type and mutant polypeptide energy (ΔΔG = ΔGwt - ΔGmut) is a measure of how the amino acid change affects protein stability. The Gibbs free energy (ΔG) defines the thermodynamic energy of folding compared to the denatured state. Finally, designing mutants for protein design and to evaluate their effects on function requires a deeper understanding of the mechanisms by which single variants affect stability. This is not only important for healthcare, but also for biotechnology, where alanine-scanning mutagenesis is commonly used to study the effect of amino acid variants on protein function and interactions. This gap represents a problem for understanding disease development, as the proper characterization of variant effects may require expensive experiments. In humans, dbSNP reports more than one million such variants, while only 1% of them have functional annotation or are referenced in the literature.
The development of Next Generation Sequencing technologies has a tremendous impact on the discovery of missense variants. NeEMO may suggest innovative strategies for bioinformatics tools beyond protein stability prediction. Interestingly, the approach is very general, and can motivate the development of a new family of RIN-based protein structure analyzers. A key contribution are RINs, which can be used for modeling proteins and their interactions effectively. NeEMO offers an innovative and reliable tool for the annotation of amino acid changes. The NeEMO web server can be freely accessed from URL. Validation on a previously published independent dataset shows that NeEMO has a Pearson correlation coefficient of 0.77 and a standard error of 1 Kcal/mol, outperforming nine recent methods. Benchmarking shows NeEMO to be very effective, allowing reliable predictions in different parts of the protein such as β-strands and buried residues. RINs are used to extract useful features describing interactions of the mutant amino acid with its structural environment. Here we propose NeEMO, a tool for the evaluation of stability changes using an effective representation of proteins based on residue interaction networks (RINs). This is of great relevance for the study of diseases and protein design, justifying the development of prediction methods for variant-induced stability changes. From the thermodynamic point of view, amino acid changes can lead to a change in the internal energy of a protein and induce structural rearrangements. The rapid growth of un-annotated missense variants poses challenges requiring novel strategies for their interpretation.
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