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Structural dynamics is a determinant of the functional significance of missense variants

Significance Discrimination of clinically relevant mutations from neutral mutations is of paramount importance in precision medicine and pharmacogenomics. Our study shows that current computational predictions of pathogenicity, mostly based on analysis of sequence conservation, may be improved by considering the changes in the structural dynamics of the protein due to point mutations. We introduce and demonstrate the utility of a classifier that takes advantage of efficient evaluation of structural dynamics by elastic network models.

• Solvent-accessible Surface Area (SASA): it is a common observation that mutations at less exposed, buried sites are usually associated with a higher probability of being pathogenic. For this reason, we included the solvent accessibility, computed with the DSSP program (6,7), among the structure-derived features. Such feature has been used in other prediction tools as well (PolyPhen-2, for instance, uses a version of the solvent accessibility normalized with respect to the maximum value observed for that specific residue). The conformational mobility of residues is significantly affected by their SASA. We have shown earlier that GNM-predicted mobilities provide an accurate description of H/D exchange ability of residues, in support of the interrelation between SASA and equilibrium dynamics (8).
• Sequence-based (SEQ) features (WT PSIC, ΔPSIC): These are based on the analysis of MSA built for the examined protein.
SEQ properties are extracted directly from the output of PolyPhen-2. Specifically, we adopted the Position-Specific Independent Counts (PSIC) score (9), which computes the likelihood of observing a given amino acid at a specific position, and compares it with the background probability of observing it at any position. The first feature is the PSIC score, or conservation, of the wt amino acid that underwent a mutation. As a second SEQ feature, we used the difference between the PSIC scores of the mutated and wt amino acid at the investigated position. Such conservation properties are extremely informative, as shown in Fig. 1F, 2B and 3 in the main text, but their applicability is limited by the necessity of having a large set of homologous proteins aligned in the MSA. It is important to note that all dynamic/structural features have been computed for the single chain/subunit of the protein containing the given variant. In some cases, we observed that the final prediction could benefit from the inclusion of all other chains of the biological assembly, whose presence might affect the dynamics, for example by constraining particularly flexible regions (see Fig. S8). Such calculations can be performed with the so-called environment ENM (10), implemented in ProDy (1).

Random Forest (RF) classifier
The supervised binary classification of variants into deleterious and neutral, based on the features described in the previous section, has been accomplished through a Random Forest (RF) classifier. This algorithm builds an ensemble of decision trees, fitted to the training data, and assigns a label based on the consensus from all trees. In this work, we used the implementation of RF algorithm included in the open-source machine learning Python library Scikit-learn (11). The main parameters of the algorithm, namely the number of trees and the maximum number of features used for fitting them, have been optimized through cross-validation (see Fig. S1). All cross-validation tests presented here are based on a 10-fold stratified partitioning of the given dataset, which therefore preserves the proportion between classes when splitting into folds. Since most datasets are strongly imbalanced, with generally a much larger number of deleterious variants than neutral (see Table S1), we used the option "balanced" provided with the implementation of RF in Scikit-learn, which automatically assigns weights to classes that are inversely proportional to their frequency in the training set.
Particular attention was paid to eliminating duplicates in the datasets, which is essential to avoid encountering identical samples in the training and in the test sets. This situation can arise when combining samples from different datasets (e.g. HumVar and ExoVar, which have hundreds of SNPs in common), or when considering only a subset of features. By ignoring some features, in fact, some previously distinct training examples may become degenerate, as in the case of same-site variants, which are described by a distinct set of features only when sequence-based properties are included.
The accuracy of the classification was evaluated by means of the area under curve (AUC) computed over the ROC plot. When a binary classification is required, like in the case of the examples illustrated in Fig. 5 in the main text and in Fig. S9, a threshold for the prediction score is set as the value that maximizes the Youden's index (12), which corresponds to the point of the ROC plot that is the farthest away from the chance line. Fig. S1: Parameter optimization for Random Forest classifiers. The two main parameters to be set in a RF algorithm are the maximum number of features used for the training of each tree in the forest, and the total number of trees. As shown in the three panels, the latter can be safely set to 1000, when the performance measure (AUC) reaches a plateau. Similarly, slightly better results are obtained when setting the maximum number of features to 2. The AUC plots refer to classifications performed on the Integrated Dataset.     Fig. 2C). The best fitting curve (orange curve) has been adopted as the probability of being deleterious for a given variant, released as output in the RAPSODY webserver.    Fig. 4, the DYN classification of SAVs for human complement component C3 (PDB ID: 2a73) chain B is even poorer than a random assignment of variant labels. We computed the overall collectivity of the 10 GNM slowest modes of motions for each of the 20 cases analyzed in Fig. 4. Collectivity measures the degree at which the principal modes capture the global motions of the protein (14), rather than the local movements at flexible regions (e.g. protruding loops). Results in panel (A) show that the proteins whose dynamics is less collective are more likely to yield less accurate predictions. This issue can be partially addressed by including the effect of the environment in the GNM mode calculation (15). For example, in the case of 2a73:B, inclusion of chain A (in white in panel (B)) in the model construction leads to a significant increase in the collectivity of the slowest modes (black arrow and blue square in panel (A)) due to restrictions imposed on the mobility of a flexible loop belonging to chain B. Another example is the human sterol transporter (PDB ID: 5do7, chain B) which is noted in Fig. 4 to yield a lower AUC for SEQ+DYN evaluation than that for SEQ only, i.e. the inclusion of DYN properties lowers the AUC. The green arrow in panel (A) and the corresponding blue square show the increase in DYN AUC (from 0.61 to 0.67) upon consideration of the other chains in the same structure. Although the final revised SEQ+DYN accuracy (AUC = 0.82, as opposed to the original AUC of 0.76) is still smaller than the one from SEQ features only (AUC = 0.87), the inclusion of environmental effects (due to the presence of other three chains in the complex) improves the accuracy of both DYN and SEQ+DYN classifiers.

Fig. S9: Illustration of the confusion plots and color-coded diagrams for variants classification Band 3 anion transport protein, chain A (PDB ID: 4yzf).
Three different mutants are reported for site 589, which have been assigned to different pathogenicity classes by the classifiers involving SEQ features. For this reason, residue R589 is labeled as both True Positive (red space-fill) and False Negative (orange wireframe) on the 3D representation. Same property holds for residue T837.      (b) "true" labels found in the Integrated Dataset (ID). In a few cases, we found conflicting labels ("?"). Two mutations, marked "new", were not present in the ID. (c) predicted labels, scores and associated PDB structures computed by PolyPhen-2. We note that in most cases the server failed to report a valid protein structure.