Antibodies hold significant potential for inhibiting toxic protein aggregation associated with conformational disorders such as Alzheimer's and Huntington's diseases. However, near-stoichiometric antibody concentrations are typically required to completely inhibit protein aggregation. We posited that the molecular interactions mediating amyloid fibril formation could be harnessed to generate antibodies with potent antiaggregation. Here we report that grafting small amyloidogenic peptides (6-10 residues) into the complementarity-determining regions of a single-domain (V(H)) antibody yields potent domain antibody inhibitors of amyloid formation. Grafted AMyloid-Motif AntiBODIES (gammabodies) presenting hydrophobic peptides from Aβ (Alzheimer's disease), α-Synuclein (Parkinson's disease), and islet amyloid polypeptide (type 2 diabetes) inhibit fibril assembly of each corresponding polypeptide at low substoichiometric concentrations (1:10 gammabody:monomer molar ratio). In contrast, sequence- and conformation-specific antibodies that were obtained via immunization are unable to prevent fibrillization at the same substoichiometric concentrations. Gammabodies prevent amyloid formation by converting monomers and/or fibrillar intermediates into small complexes that are unstructured and benign. We expect that our antibody design approach--which eliminates the need for immunization or screening to identify sequence-specific domain antibody inhibitors--can be readily extended to generate potent aggregation inhibitors of other amyloidogenic polypeptides linked to human disease.

Background

Hypertrophic cardiomyopathy (HCM) patients have a high incidence of atrial fibrillation (AF) and increased stroke risk, even with low CHA₂DS₂-VASc (congestive heart failure, hypertension, age diabetes, previous stroke/transient ischemic attack) scores. Hence, there is a need to understand the pathophysiology of AF/stroke in HCM. In this retrospective study, we develop and apply a data-driven, machine learning-based method to identify AF cases, and clinical/imaging features associated with AF, using electronic health record data.

Methods

HCM patients with documented paroxysmal/persistent/permanent AF (n = 191) were considered AF cases, and the remaining patients in sinus rhythm (n = 640) were tagged as No-AF. We evaluated 93 clinical variables; the most informative variables useful for distinguishing AF from No-AF cases were selected based on the 2-sample t test and the information gain criterion.

Results

We identified 18 highly informative variables that are positively (n = 11) and negatively (n = 7) correlated with AF in HCM. Next, patient records were represented via these 18 variables. Data imbalance resulting from the relatively low number of AF cases was addressed via a combination of oversampling and undersampling strategies. We trained and tested multiple classifiers under this sampling approach, showing effective classification. Specifically, an ensemble of logistic regression and naïve Bayes classifiers, trained based on the 18 variables and corrected for data imbalance, proved most effective for separating AF from No-AF cases (sensitivity = 0.74, specificity = 0.70, C-index = 0.80).

Conclusions

Our model (HCM-AF-Risk Model) is the first machine learning-based method for identification of AF cases in HCM. This model demonstrates good performance, addresses data imbalance, and suggests that AF is associated with a more severe cardiac HCM phenotype.