Dataset Information

ISEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations.

ABSTRACT: Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning-based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using a small number of sensitive predictive features is vital to the generalization and robustness of such machine learning methods. Here we introduce a fast and reliable predictor of binding affinity changes upon single point mutation, based on a random forest approach. Our method, iSEE, uses a limited number of interface Structure, Evolution, and Energy-based features for the prediction. iSEE achieves, using only 31 features, a high prediction performance with a Pearson correlation coefficient (PCC) of 0.80 and a root mean square error of 1.41?kcal/mol on a diverse training dataset consisting of 1102 mutations in 57 protein-protein complexes. It competes with existing state-of-the-art methods on two blind test datasets. Predictions for a new dataset of 487 mutations in 56 protein complexes from the recently published SKEMPI 2.0 database reveals that none of the current methods perform well (PCC?

SUBMITTER: Geng C

PROVIDER: S-EPMC6587874 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Publications

iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations.

Geng Cunliang C Vangone Anna A Folkers Gert E GE Xue Li C LC Bonvin Alexandre M J J AMJJ

Proteins 20181203 2

Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning-based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using a small number of sensitive predictive features is vital to the generalization and robustness of such machine learning methods. Here we introduce a fast and reliable predictor of binding affinity changes upon single point mutation, based on a random ...[more]

PMID: 30417935

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Project description:Predicting the effects of mutations on the kinetic rate constants of protein-protein interactions is central to both the modeling of complex diseases and the design of effective peptide drug inhibitors. However, while most studies have concentrated on the determination of association rate constants, dissociation rates have received less attention. In this work we take a novel approach by relating the changes in dissociation rates upon mutation to the energetics and architecture of hotspots and hotregions, by performing alanine scans pre- and post-mutation. From these scans, we design a set of descriptors that capture the change in hotspot energy and distribution. The method is benchmarked on 713 kinetically characterized mutations from the SKEMPI database. Our investigations show that, with the use of hotspot descriptors, energies from single-point alanine mutations may be used for the estimation of off-rate mutations to any residue type and also multi-point mutations. A number of machine learning models are built from a combination of molecular and hotspot descriptors, with the best models achieving a Pearson's Correlation Coefficient of 0.79 with experimental off-rates and a Matthew's Correlation Coefficient of 0.6 in the detection of rare stabilizing mutations. Using specialized feature selection models we identify descriptors that are highly specific and, conversely, broadly important to predicting the effects of different classes of mutations, interface regions and complexes. Our results also indicate that the distribution of the critical stability regions across protein-protein interfaces is a function of complex size more strongly than interface area. In addition, mutations at the rim are critical for the stability of small complexes, but consistently harder to characterize. The relationship between hotregion size and the dissociation rate is also investigated and, using hotspot descriptors which model cooperative effects within hotregions, we show how the contribution of hotregions of different sizes, changes under different cooperative effects.

Project description:Adenoviruses (AdVs) constitute a diverse family with many pathogenic types that infect a broad range of hosts. Understanding the pathogenesis of adenoviral infections is not only clinically relevant but also important to elucidate the potential use of AdVs as vectors in therapeutic applications. For an adenoviral infection to occur, attachment of the viral ligand to a cellular receptor on the host organism is a prerequisite and, in this sense, it is a criterion to decide whether an adenoviral infection can potentially happen. The interaction between any virus and its corresponding host organism is a specific kind of protein-protein interaction (PPI) and several experimental techniques, including high-throughput methods are being used in exploring such interactions. As a result, there has been accumulating data on virus-host interactions including a significant portion reported at publicly available bioinformatics resources. There is not, however, a computational model to integrate and interpret the existing data to draw out concise decisions, such as whether an infection happens or not. In this study, accepting the cellular entry of AdV as a decisive parameter for infectivity, we have developed a machine learning, more precisely support vector machine (SVM), based methodology to predict whether adenoviral infection can take place in a given host. For this purpose, we used the sequence data of the known receptors of AdVs, we identified sets of adenoviral ligands and their respective host species, and eventually, we have constructed a comprehensive adenovirus-host interaction dataset. Then, we committed interaction predictions through publicly available virus-host PPI tools and constructed an AdV infection predictor model using SVM with RBF kernel, with the overall sensitivity, specificity, and AUC of 0.88 ± 0.011, 0.83 ± 0.064, and 0.86 ± 0.030, respectively. ML-AdVInfect is the first of its kind as an effective predictor to screen the infection capacity along with anticipating any cross-species shifts. We anticipate our approach led to ML-AdVInfect can be adapted in making predictions for other viral infections.

Dataset Information

ISEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations.

Publications

iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations.

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