Value of predictive bioinformatics in inherited metabolic diseases

MutateX: an automated pipeline for in silico saturation mutagenesis of protein structures and structural ensembles

Mutations, which result in amino acid substitutions, influence the stability of proteins and their binding to biomolecules. A molecular understanding of the effects of protein mutations is both of biotechnological and medical relevance. Empirical free energy functions that quickly estimate the free energy change upon mutation (ΔΔG) can be exploited for systematic screenings of proteins and protein complexes. In silico saturation mutagenesis can guide the design of new experiments or rationalize the consequences of known mutations. Often software such as FoldX, while fast and reliable, lack the necessary automation features to apply them in a high-throughput manner. We introduce MutateX, a software to automate the prediction of ΔΔGs associated with the systematic mutation of each residue within a protein, or protein complex to all other possible residue types, using the FoldX energy function. MutateX also supports ΔΔG calculations over protein ensembles, upon post-translational modifications and in multimeric assemblies. At the heart of MutateX lies an automated pipeline engine that handles input preparation, parallelization and outputs publication-ready figures. We illustrate the MutateX protocol applied to different case studies. The results of the high-throughput scan provided by our tools can help in different applications, such as the analysis of disease-associated mutations, to complement experimental deep mutational scans, or assist the design of variants for industrial applications. MutateX is a collection of Python tools that relies on open-source libraries. It is available free of charge under the GNU General Public License from https://github.com/ELELAB/mutatex.

In Silico Analysis of the Effects of Point Mutations on α-Globin: Implications for α-Thalassemia

Hemoglobinopathies are inherited diseases that impair the structure and function of the oxygen-carrying pigment hemoglobin (Hb). Adult Hb consists of two α and two β subunits. α-Thalassemia (α-thal) affects the genes that code for the α-globin chains, HBA1 and HBA2. Mutations can result in asymptomatic, mild or severe outcomes depending on several factors, such as mutation type, number of mutations and the location at which they occur. PredictSNP was used to estimate whether every possible single nucleotide polymorphism (SNP) would have a neutral or deleterious effect on the protein. These results were then used to create a plot of predicted tolerance to change for each residue in the protein. Tolerance to change was negatively correlated with the residue's sequence conservation score. The PredictSNP data were compared to clinical reports of 110 selected variants in the literature. There were 29 disagreements between the two data types. Some of these could be resolved by considering the role of the affected residue in binding other molecules. The three-dimensional structures of some of these variant proteins were modeled. These models helped explain variants which affect heme binding. We predict that where a point mutation alters a residue that is intolerant to change, is well conserved and or involved in interactions, it is likely to be associated with disease. Overall, the data from this study could be used alongside biochemical and clinical data to assess novel α-globin variants.

In silico prediction of the effects of mutations in the human triose phosphate isomerase gene: Towards a predictive framework for TPI deficiency

Triose phosphate isomerase (TPI) deficiency is a rare, but highly debilitating, inherited metabolic disease. Almost all patients suffer severe neurological effects and the most severely affected are unlikely to live beyond early childhood. Here, we describe an in silico study into well-characterised variants which are associated with the disease alongside an investigation into 79 currently uncharacterised TPI variants which are known to occur in the human population. The majority of the disease-associated mutations affected amino acid residues close to the dimer interface or the active site. However, the location of the altered amino acid residue did not predict the severity of the resulting disease. Prediction of the effect on protein stability using a range of different programs suggested a relationship between the degree of instability caused by the sequence variation and the severity of the resulting disease. Disease-associated variations tended to affect well-conserved residues in the protein's sequence. However, the degree of conservation of the residue was not predictive of disease severity. The majority of the 79 uncharacterised variants are potentially associated with disease since they were predicted to destabilise the protein and often occur in well-conserved residues. We predict that individuals homozygous for the corresponding mutations would be likely to suffer from TPI deficiency.