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Predict impact of single amino acid change upon protein structure.

ABSTRACT: BACKGROUND: Amino acid point mutations (nsSNPs) may change protein structure and function. However, no method directly predicts the impact of mutations on structure. Here, we compare pairs of pentamers (five consecutive residues) that locally change protein three-dimensional structure (3D, RMSD>0.4Å) to those that do not alter structure (RMSD<0.2Å). Mutations that alter structure locally can be distinguished from those that do not through a machine-learning (logistic regression) method. RESULTS: The method achieved a rather high overall performance (AUC>0.79, two-state accuracy >72%). This discriminative power was particularly unexpected given the enormous structural variability of pentamers. Mutants for which our method predicted a change of structure were also enriched in terms of disrupting stability and function. Although distinguishing change and no change in structure, the new method overall failed to distinguish between mutants with and without effect on stability or function. CONCLUSIONS: Local structural change can be predicted. Future work will have to establish how useful this new perspective on predicting the effect of nsSNPs will be in combination with other methods.

SUBMITTER: Schaefer C

PROVIDER: S-EPMC3395892 | biostudies-other | 2012

REPOSITORIES: biostudies-other

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Predict impact of single amino acid change upon protein structure.

Schaefer Christian C Rost Burkhard B

BMC genomics 20120618

<h4>Background</h4>Amino acid point mutations (nsSNPs) may change protein structure and function. However, no method directly predicts the impact of mutations on structure. Here, we compare pairs of pentamers (five consecutive residues) that locally change protein three-dimensional structure (3D, RMSD>0.4Å) to those that do not alter structure (RMSD<0.2Å). Mutations that alter structure locally can be distinguished from those that do not through a machine-learning (logistic regression) method.<h ...[more]

PMID: 22759652

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Project description:BackgroundGenomic variations may cause deleterious effects on protein functionality and perturb biological processes. Elucidating the effects of variations is critical for developing novel treatment strategies for diseases of genetic origin. Computational approaches have been aiding the work in this field by modeling and analyzing the mutational landscape. However, new approaches are required, especially for accurate representation and data-centric analysis of sequence variations.MethodIn this study, we propose ASCARIS (Annotation and StruCture-bAsed RepresentatIon of Single amino acid variations), a method for the featurization (i.e., quantitative representation) of single amino acid variations (SAVs), which could be used for a variety of purposes, such as predicting their functional effects or building multi-omics-based integrative models. ASCARIS utilizes the direct and spatial correspondence between the location of the SAV on the sequence/structure and 30 different types of positional feature annotations (e.g., active/lipidation/glycosylation sites; calcium/metal/DNA binding, inter/transmembrane regions, etc.), along with structural features and physicochemical properties. The main novelty of this method lies in constructing reusable numerical representations of SAVs via functional annotations.ResultsWe statistically analyzed the relationship between these features and the consequences of variations and found that each carries information in this regard. To investigate potential applications of ASCARIS, we trained variant effect prediction models that utilize our SAV representations as input. We carried out an ablation study and a comparison against the state-of-the-art methods and observed that ASCARIS has a competing and complementary performance against widely-used predictors. ASCARIS can be used alone or in combination with other approaches to represent SAVs from a functional perspective. ASCARIS is available as a programmatic tool at https://github.com/HUBioDataLab/ASCARIS and as a web-service at https://huggingface.co/spaces/HUBioDataLab/ASCARIS.

Project description:Prediction of protein secondary structure from FTIR spectra usually relies on the absorbance in the amide I-amide II region of the spectrum. It assumes that the absorbance in this spectral region, i.e., roughly 1700-1500 cm-1 is solely arising from amide contributions. Yet, it is accepted that, on the average, about 20% of the absorbance is due to amino acid side chains. The present paper evaluates the contribution of amino acid side chains in this spectral region and the potential to improve secondary structure prediction after correcting for their contribution. We show that the β-sheet content prediction is improved upon subtraction of amino acid side chain contributions in the amide I-amide II spectral range. Improvement is relatively important, for instance, the error of prediction of β-sheet content decreases from 5.42 to 4.97% when evaluated by ascending stepwise regression. Other methods tested such as partial least square regression and support vector machine have also improved accuracy for β-sheet content evaluation. The other structures such as α-helix do not significantly benefit from side chain contribution subtraction, in some cases prediction is even degraded. We show that co-linearity between secondary structure content and amino acid composition is not a main limitation for improving secondary structure prediction. We also show that, even though based on different criteria, secondary structures defined by DSSP and XTLSSTR both arrive at the same conclusion: only the β-sheet structure clearly benefits from side chain subtraction. It must be concluded that side chain contribution subtraction benefit for the evaluation of other secondary structure contents is limited by the very rough description of side chain absorbance which does not take into account the variations related to their environment. The study was performed on a large protein set. To deal with the large number of proteins present, we worked on protein microarrays deposited on BaF2 slides and FTIR spectra were acquired with an imaging system.

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Predict impact of single amino acid change upon protein structure.

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Predict impact of single amino acid change upon protein structure.

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