Patient contrastive learning: A performant, expressive, and practical approach to electrocardiogram modeling.

Supervised machine learning applications in health care are often limited due to a scarcity of labeled training data. To mitigate the effect of small sample size, we introduce a pre-training approach, Patient Contrastive Learning of Representations (PCLR), which creates latent representations of electrocardiograms (ECGs) from a large number of unlabeled examples using contrastive learning. The resulting representations are expressive, performant, and practical across a wide spectrum of clinical tasks. We develop PCLR using a large health care system with over 3.2 million 12-lead ECGs and demonstrate that training linear models on PCLR representations achieves a 51% performance increase, on average, over six training set sizes and four tasks (sex classification, age regression, and the detection of left ventricular hypertrophy and atrial fibrillation), relative to training neural network models from scratch. We also compared PCLR to three other ECG pre-training approaches (supervised pre-training, unsupervised pre-training with an autoencoder, and pre-training using a contrastive multi ECG-segment approach), and show significant performance benefits in three out of four tasks. We found an average performance benefit of 47% over the other models and an average of a 9% performance benefit compared to best model for each task. We release PCLR to enable others to extract ECG representations at https://github.com/broadinstitute/ml4h/tree/master/model_zoo/PCLR.

Molecular basis for specific regulation of neuronal kinesin-3 motors by doublecortin family proteins.

Doublecortin (Dcx) defines a growing family of microtubule (MT)-associated proteins (MAPs) involved in neuronal migration and process outgrowth. We show that Dcx is essential for the function of Kif1a, a kinesin-3 motor protein that traffics synaptic vesicles. Neurons lacking Dcx and/or its structurally conserved paralogue, doublecortin-like kinase 1 (Dclk1), show impaired Kif1a-mediated transport of Vamp2, a cargo of Kif1a, with decreased run length. Human disease-associated mutations in Dcx's linker sequence (e.g., W146C, K174E) alter Kif1a/Vamp2 transport by disrupting Dcx/Kif1a interactions without affecting Dcx MT binding. Dcx specifically enhances binding of the ADP-bound Kif1a motor domain to MTs. Cryo-electron microscopy and subnanometer-resolution image reconstruction reveal the kinesin-dependent conformational variability of MT-bound Dcx and suggest a model for MAP-motor crosstalk on MTs. Alteration of kinesin run length by MAPs represents a previously undiscovered mode of control of kinesin transport and provides a mechanism for regulation of MT-based transport by local signals.

Solution structure and biochemical characterization of a spare part protein that restores activity to an oxygen-damaged glycyl radical enzyme.

Glycyl radical enzymes (GREs) utilize a glycyl radical cofactor to carry out a diverse array of chemically challenging enzymatic reactions in anaerobic bacteria. Although the glycyl radical is a powerful catalyst, it is also oxygen sensitive such that oxygen exposure causes cleavage of the GRE at the site of the radical. This oxygen sensitivity presents a challenge to facultative anaerobes dwelling in areas prone to oxygen exposure. Once GREs are irreversibly oxygen damaged, cells either need to make new GREs or somehow repair the damaged one. One particular GRE, pyruvate formate lyase (PFL), can be repaired through the binding of a 14.3 kDa protein, termed YfiD, which is constitutively expressed in E. coli. Herein, we have solved a solution structure of this 'spare part' protein using nuclear magnetic resonance spectroscopy. These data, coupled with data from circular dichroism, indicate that YfiD has an inherently flexible N-terminal region (residues 1-60) that is followed by a C-terminal region (residues 72-127) that has high similarity to the glycyl radical domain of PFL. Reconstitution of PFL activity requires that YfiD binds within the core of the PFL barrel fold; however, modeling suggests that oxygen-damaged, i.e. cleaved, PFL cannot fully accommodate YfiD. We further report that a PFL variant that mimics the oxygen-damaged enzyme is highly susceptible to proteolysis, yielding additionally truncated forms of PFL. One such PFL variant of ~ 77 kDa makes an ideal scaffold for the accommodation of YfiD. A molecular model for the rescue of PFL activity by YfiD is presented.

Expanding the Range of Protein Function at the Far End of the Order-Structure Continuum.

The traditional view of the structure-function paradigm is that a protein's function is inextricably linked to a well defined, three-dimensional structure, which is determined by the protein's primary amino acid sequence. However, it is now accepted that a number of proteins do not adopt a unique tertiary structure in solution and that some degree of disorder is required for many proteins to perform their prescribed functions. In this review, we highlight how a number of protein functions are facilitated by intrinsic disorder and introduce a new protein structure taxonomy that is based on quantifiable metrics of a protein's disorder.

Hidden States within Disordered Regions of the CcdA Antitoxin Protein.

The bacterial toxin-antitoxin system CcdB-CcdA provides a mechanism for the control of cell death and quiescence. The antitoxin protein CcdA is a homodimer composed of two monomers that each contain a folded N-terminal region and an intrinsically disordered C-terminal arm. Binding of the intrinsically disordered C-terminal arm of CcdA to the toxin CcdB prevents CcdB from inhibiting DNA gyrase and thereby averts cell death. Accurate models of the unfolded state of the partially disordered CcdA antitoxin can therefore provide insight into general mechanisms whereby protein disorder regulates events that are crucial to cell survival. Previous structural studies were able to model only two of three distinct structural states, a closed state and an open state, that are adopted by the C-terminal arm of CcdA. Using a combination of free energy simulations, single-pair Förster resonance energy transfer experiments, and existing NMR data, we developed structural models for all three states of the protein. Contrary to prior studies, we find that CcdA samples a previously unknown state where only one of the disordered C-terminal arms makes extensive contacts with the folded N-terminal domain. Moreover, our data suggest that previously unobserved conformational states play a role in regulating antitoxin concentrations and the activity of CcdA's cognate toxin. These data demonstrate that intrinsic disorder in CcdA provides a mechanism for regulating cell fate.

Mollack: a web server for the automated creation of conformational ensembles for intrinsically disordered proteins.

UNLABELLED: Intrinsically disordered proteins (IDPs) play central roles in many biological processes. Consequently, an accurate description of the disordered state is an important step towards a comprehensive understanding of a number of important biological functions. In this work we describe a new web server, Mollack, for the automated construction of unfolded ensembles that uses both experimental and molecular simulation data to construct models for the unfolded state. An important aspect of the method is that it calculates a quantitative estimate of the uncertainty in the constructed ensemble, thereby providing an objective measure of the quality of the final model. Overall, Mollack facilitates structure-function studies of disordered proteins. AVAILABILITY AND IMPLEMENTATION: http://cmstultz-mollack.mit.edu CONTACT: cmstultz@mit.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mechanism of amyloid-ß fibril elongation.

Amyloid-ß is an intrinsically disordered protein that forms fibrils in the brains of patients with Alzheimer's disease. To explore factors that affect the process of fibril growth, we computed the free energy associated with disordered amyloid-ß monomers being added to growing amyloid fibrils using extensive molecular dynamics simulations coupled with umbrella sampling. We find that the mechanisms of Aß40 and Aß42 fibril elongation have many features in common, including the formation of an obligate on-pathway ß-hairpin intermediate that hydrogen bonds to the fibril core. In addition, our data lead to new hypotheses for how fibrils may serve as secondary nucleation sites that can catalyze the formation of soluble oligomers, a finding in agreement with recent experimental observations. These data provide a detailed mechanistic description of amyloid-ß fibril elongation and a structural link between the disordered free monomer and the growth of amyloid fibrils and soluble oligomers.

SLLISWD sequence in the 10FNIII domain initiates fibronectin fibrillogenesis.

Fibronectin (FN) assembly into extracellular matrix is tightly regulated and essential to embryogenesis and wound healing. FN fibrillogenesis is initiated by cytoskeleton-derived tensional forces transmitted across transmembrane integrins onto RGD binding sequences within the tenth FN type III (10FNIII) domains. These forces unfold 10FNIII to expose cryptic FN assembly sites; however, a specific sequence has not been identified in 10FNIII. Our past steered molecular dynamics simulations modeling 10FNIII unfolding by force at its RGD loop predicted a mechanical intermediate with a solvent-exposed N terminus spanning the A and B ß-strands. Here, we experimentally confirm that the predicted 23-residue cryptic peptide 1 (CP1) initiates FN multimerization, which is mediated by interactions with 10FNIII that expose hydrophobic surfaces that support 8-anilino-1-napthalenesulfonic acid binding. Localization of multimerization activity to the C terminus led to the discovery of a minimal 7-amino acid "multimerization sequence" (SLLISWD), which induces polymerization of FN and the clotting protein fibrinogen in addition to enhancing FN fibrillogenesis in fibroblasts. A point mutation at Trp-6 that reduces exposure of hydrophobic sites for 8-anilino-1-napthalenesulfonic acid binding and ß-structure formation inhibits FN multimerization and prevents physiological cell-based FN assembly in culture. We propose a model for cell-mediated fibrillogenesis whereby cell traction force initiates a cascade of intermolecular exchange starting with the unfolding of 10FNIII to expose the multimerization sequence, which interacts with strand B of another 10FNIII domain via a Trp-mediated ß-strand exchange to stabilize a partially unfolded intermediate that propagates FN self-assembly.

Detecting QT prolongation from a single-lead ECG with deep learning.

For a number of antiarrhythmics, drug loading requires a 3-day hospitalization with continuous monitoring for QT-prolongation. Automated QT monitoring with wearable ECG monitors would enable out-of-hospital care. We therefore develop a deep learning model that infers QT intervals from ECG Lead-I-the lead that is often available in ambulatory ECG monitors-and use this model to detect clinically meaningful QT-prolongation episodes during Dofetilide drug loading. QTNet-a deep neural network that infers QT intervals from Lead-I ECG-was trained using over 3 million ECGs from 653 thousand patients at the Massachusetts General Hospital and tested on an internal-test set consisting of 633 thousand ECGs from 135 thousand patients. QTNet is further evaluated on an external-validation set containing 3.1 million ECGs from 667 thousand patients at another healthcare institution. On both evaluations, the model achieves mean absolute errors of 12.63ms (internal-test) and 12.30ms (external-validation) for estimating absolute QT intervals. The associated Pearson correlation coefficients are 0.91 (internal-test) and 0.92 (external-validation). Finally, QTNet was used to detect Dofetilide-induced QT prolongation in a publicly available database (ECGRDVQ-dataset) containing ECGs from subjects enrolled in a clinical trial evaluating the effects of antiarrhythmic drugs. QTNet detects Dofetilide-induced QTc prolongation with 87% sensitivity and 77% specificity. The negative predictive value of the model is greater than 95% when the pre-test probability of drug-induced QTc prolongation is below 25%. These results show that drug-induced QT prolongation risk can be tracked from ECG Lead-I using deep learning.

Comparative studies of disordered proteins with similar sequences: application to Aß40 and Aß42.

Quantitative comparisons of intrinsically disordered proteins (IDPs) with similar sequences, such as mutant forms of the same protein, may provide insights into IDP aggregation-a process that plays a role in several neurodegenerative disorders. Here we describe an approach for modeling IDPs with similar sequences that simplifies the comparison of the ensembles by utilizing a single library of structures. The relative population weights of the structures are estimated using a Bayesian formalism, which provides measures of uncertainty in the resulting ensembles. We applied this approach to the comparison of ensembles for Aß40 and Aß42. Bayesian hypothesis testing finds that although both Aß species sample ß-rich conformations in solution that may represent prefibrillar intermediates, the probability that Aß42 samples these prefibrillar states is roughly an order of magnitude larger than the frequency in which Aß40 samples such structures. Moreover, the structure of the soluble prefibrillar state in our ensembles is similar to the experimentally determined structure of Aß that has been implicated as an intermediate in the aggregation pathway. Overall, our approach for comparative studies of IDPs with similar sequences provides a platform for future studies on the effect of mutations on the structure and function of disordered proteins.

Using intramolecular disulfide bonds in tau protein to deduce structural features of aggregation-resistant conformations.

Because tau aggregation likely plays a role in a number of neurodegenerative diseases, understanding the processes that affect tau aggregation is of considerable importance. One factor that has been shown to influence the aggregation propensity is the oxidation state of the protein itself. Tau protein, which contains two naturally occurring cysteine residues, can form both intermolecular disulfide bonds and intramolecular disulfide bonds. Several studies suggest that intermolecular disulfide bonds can promote tau aggregation in vitro. By contrast, although there are data to suggest that intramolecular disulfide bond formation retards tau aggregation in vitro, the precise mechanism underlying this observation remains unclear. While it has been hypothesized that a single intramolecular disulfide bond in tau leads to compact conformations that cannot form extended structure consistent with tau fibrils, there are few data to support this conjecture. In the present study we generate oxidized forms of the truncation mutant, K18, which contains all four microtubule binding repeats, and isolate the monomeric fraction, which corresponds to K18 monomers that have a single intramolecular disulfide bond. We study the aggregation propensity of the oxidized monomeric fraction and relate these data to an atomistic model of the K18 unfolded ensemble. Our results argue that the main effect of intramolecular disulfide bond formation is to preferentially stabilize conformers within the unfolded ensemble that place the aggregation-prone tau subsequences, PHF6* and PHF6, in conformations that are inconsistent with the formation of cross-ß-structure. These data further our understanding of the precise structural features that retard tau aggregation.

The dynamic structure of α-synuclein multimers.

α-Synuclein, a protein that forms ordered aggregates in the brains of patients with Parkinson's disease, is intrinsically disordered in the monomeric state. Several studies, however, suggest that it can form soluble multimers in vivo that have significant secondary structure content. A number of studies demonstrate that α-synuclein can form ß-strand-rich oligomers that are neurotoxic, and recent observations argue for the existence of soluble helical tetrameric species in cellulo that do not form toxic aggregates. To gain further insight into the different types of multimeric states that this protein can adopt, we generated an ensemble for an α-synuclein construct that contains a 10-residue N-terminal extension, which forms multimers when isolated from Escherichia coli. Data from NMR chemical shifts and residual dipolar couplings were used to guide the construction of the ensemble. Our data suggest that the dominant state of this ensemble is a disordered monomer, complemented by a small fraction of helical trimers and tetramers. Interestingly, the ensemble also contains trimeric and tetrameric oligomers that are rich in ß-strand content. These data help to reconcile seemingly contradictory observations that indicate the presence of a helical tetramer in cellulo on the one hand, and a disordered monomer on the other. Furthermore, our findings are consistent with the notion that the helical tetrameric state provides a mechanism for storing α-synuclein when the protein concentration is high, thereby preventing non-membrane-bound monomers from aggregating.

Perturbing the folding energy landscape of the bacterial immunity protein Im7 by site-specific N-linked glycosylation.

N-linked glycosylation modulates protein folding and stability through a variety of mechanisms. As such there is considerable interest in the development of general rules to predict the structural consequences of site-specific glycosylation and to understand how these effects can be exploited in the design and development of modified proteins with advantageous properties. In this study, expressed protein ligation is used to create site-specifically glycosylated variants of the bacterial immunity protein Im7 modified with the chitobiose disaccharide (GlcNAc-GlcNAc). Glycans were introduced at seven solvent exposed sites within the Im7 sequence and the kinetic and thermodynamic consequences of N-linked glycosylation analyzed. The ΔΔG° values for glycan incorporation were found to range from +5.2 to -3.8 kJ·mol(-1). In several cases, glycosylation influences folding by modulating the local conformational preferences of the glycosylated sequence. These locally mediated effects are most prominent in the center of α-helices where glycosylation negatively effects folding and in compact turn motifs between segments of ordered secondary structure where glycosylation promotes folding and enhances the overall stability of the native protein. The studies also provide insight into why glycosylation is commonly identified at the transition between different types of secondary structure and when glycosylation may be used to elaborate protein structure to protect disordered sequences from proteolysis or immune system recognition.

QTNet: Deep Learning for Estimating QT Intervals Using a Single Lead ECG.

QT prolongation often leads to fatal arrhythmia and sudden cardiac death. Antiarrhythmic drugs can increase the risk of QT prolongation and therefore require strict post-administration monitoring and dosage control. Measurement of the QT interval from the 12-lead electrocardiogram (ECG) by a trained expert, in a clinical setting, is the accepted method for tracking QT prolongation. Recent advances in wearable ECG technology, however, raise the possibility of automated out-of-hospital QT tracking. Applications of Deep Learning (DL) - a subfield within Machine Learning - in ECG analysis holds the promise of automation for a variety of classification and regression tasks. In this work, we propose a residual neural network, QTNet, for the regression of QT intervals from a single lead (Lead-I) ECG. QTNet is trained in a supervised manner on a large ECG dataset from a U.S. hospital. We demonstrate the robustness and generalizability of QTNet on four test-sets; one from the same hospital, one from another U.S. hospital, and two public datasets. Over all four datasets, the mean absolute error (MAE) in the estimated QT interval ranges between 9ms and 15.8ms. Pearson correlation coefficients vary between 0.899 and 0.914. By contrast, QT interval estimation on these datasets with a standard method for automated ECG analysis (NeuroKit2) yields MAEs between 22.29ms and 90.79ms, and Pearson correlation coefficients 0.345 and 0.620. These results demonstrate the utility of QTNet across distinct datasets and patient populations, thereby highlighting the potential utility of DL models for ubiquitous QT tracking.Clinical Relevance- QTNet can be applied to inpatient or ambulatory Lead-I ECG signals to track QT intervals. The method facilitates ambulatory monitoring of patients at risk of QT prolongation.

ECG-guided non-invasive estimation of pulmonary congestion in patients with heart failure.

Quantifying hemodynamic severity in patients with heart failure (HF) is an integral part of clinical care. A key indicator of hemodynamic severity is the mean Pulmonary Capillary Wedge Pressure (mPCWP), which is ideally measured invasively. Accurate non-invasive estimates of the mPCWP in patients with heart failure would help identify individuals at the greatest risk of a HF exacerbation. We developed a deep learning model, HFNet, that uses the 12-lead electrocardiogram (ECG) together with age and sex to identify when the mPCWP > 18 mmHg in patients who have a prior diagnosis of HF. The model was developed using retrospective data from the Massachusetts General Hospital and evaluated on both an internal test set and an independent external validation set, from another institution. We developed an uncertainty score that identifies when model performance is likely to be poor, thereby helping clinicians gauge when to trust a given model prediction. HFNet AUROC for the task of estimating mPCWP > 18 mmHg was 0.8 [Formula: see text] 0.01 and 0.[Formula: see text] 0.01 on the internal and external datasets, respectively. The AUROC on predictions with the highest uncertainty are 0.50 [Formula: see text] 0.02 (internal) and 0.[Formula: see text] 0.04 (external), while the AUROC on predictions with the lowest uncertainty were 0.86 ± 0.01 (internal) and 0.82 ± 0.01 (external). Using estimates of the prevalence of mPCWP > 18 mmHg in patients with reduced ventricular function, and a decision threshold corresponding to an 80% sensitivity, the calculated positive predictive value (PPV) is 0.[Formula: see text] 0.01when the corresponding chest x-ray (CXR) is consistent with interstitial edema HF. When the CXR is not consistent with interstitial edema, the estimated PPV is 0.[Formula: see text] 0.02, again at an 80% sensitivity threshold. HFNet can accurately predict elevated mPCWP in patients with HF using the 12-lead ECG and age/sex. The method also identifies cohorts in which the model is more/less likely to produce accurate outputs.

Predicting outcomes in patients with aortic stenosis using machine learning: the Aortic Stenosis Risk (ASteRisk) score.

OBJECTIVE: To use echocardiographic and clinical features to develop an explainable clinical risk prediction model in patients with aortic stenosis (AS), including those with low-gradient AS (LGAS), using machine learning (ML). METHODS: In 1130 patients with moderate or severe AS, we used bootstrap lasso regression (BLR), an ML method, to identify echocardiographic and clinical features important for predicting the combined outcome of all-cause mortality or aortic valve replacement (AVR) within 5 years after the initial echocardiogram. A separate hold out set, from a different centre (n=540), was used to test the generality of the model. We also evaluated model performance with respect to each outcome separately and in different subgroups, including patients with LGAS. RESULTS: Out of 69 available variables, 26 features were identified as predictive by BLR and expert knowledge was used to further reduce this set to 9 easily available and input features without loss of efficacy. A ridge logistic regression model constructed using these features had an area under the receiver operating characteristic curve (AUC) of 0.74 for the combined outcome of mortality/AVR. The model reliably identified patients at high risk of death in years 2-5 (HRs ≥2.0, upper vs other quartiles, for years 2-5, p<0.05, p=not significant in year 1) and was also predictive in the cohort with LGAS (n=383, HRs≥3.3, p<0.05). The model performed similarly well in the independent hold out set (AUC 0.78, HR ≥2.5 in years 1-5, p<0.05). CONCLUSION: In two separate longitudinal databases, ML identified prognostic features and produced an algorithm that predicts outcome for up to 5 years of follow-up in patients with AS, including patients with LGAS. Our algorithm, the Aortic Stenosis Risk (ASteRisk) score, is available online for public use.

A Deep Learning Model for Inferring Elevated Pulmonary Capillary Wedge Pressures From the 12-Lead Electrocardiogram.

Background: Central hemodynamic parameters are typically measured via pulmonary artery catherization-an invasive procedure that involves some risk to the patient and is not routinely available in all settings. Objectives: This study sought to develop a noninvasive method to identify elevated mean pulmonary capillary wedge pressure (mPCWP). Methods: We leveraged data from 248,955 clinical records at the Massachusetts General Hospital to develop a deep learning model that can infer when the mPCWP >15 mmHg using the 12-lead electrocardiogram (ECG). Of these data, 242,216 records were used to pre-train a model that generates useful ECG representations. The remaining 6,739 records contain encounters with direct measurements of the mPCWP. Eighty percent of these data were used for model development and testing (5,390), and the remaining records comprise a holdout set (1,349) that was used to evaluate the model. We developed an associated unreliability score that identifies when model predictions are likely to be untrustworthy. Results: The model achieves an area under the receiver operating characteristic curve (AUC) of 0.80 ± 0.02 (test set) and 0.79 ± 0.01 (holdout set). Model performance varies as a function of the unreliability, where patients with high unreliability scores correspond to a subgroup where model performance is poor: for example, patients in the holdout set with unreliability scores in the highest decile have a reduced AUC of 0.70 ± 0.06. Conclusions: The mPCWP can be inferred from the ECG, and the reliability of this inference can be measured. When invasive monitoring cannot be expeditiously performed, deep learning models may provide information that can inform clinical care.

Protein structure along the order-disorder continuum.

Thermal fluctuations cause proteins to adopt an ensemble of conformations wherein the relative stability of the different ensemble members is determined by the topography of the underlying energy landscape. "Folded" proteins have relatively homogeneous ensembles, while "unfolded" proteins have heterogeneous ensembles. Hence, the labels "folded" and "unfolded" represent attempts to provide a qualitative characterization of the extent of structural heterogeneity within the underlying ensemble. In this work, we introduce an information-theoretic order parameter to quantify this conformational heterogeneity. We demonstrate that this order parameter can be estimated in a straightforward manner from an ensemble and is applicable to both unfolded and folded proteins. In addition, a simple formula for approximating the order parameter directly from crystallographic B factors is presented. By applying these metrics to a large sample of proteins, we show that proteins span the full range of the order-disorder axis.

Explaining the structural plasticity of α-synuclein.

Given that α-synuclein has been implicated in the pathogenesis of several neurodegenerative disorders, deciphering the structure of this protein is of particular importance. While monomeric α-synuclein is disordered in solution, it can form aggregates rich in cross-ß structure, relatively long helical segments when bound to micelles or lipid vesicles, and a relatively ordered helical tetramer within the native cell environment. To understand the physical basis underlying this structural plasticity, we generated an ensemble for monomeric α-synuclein using a Bayesian formalism that combines data from NMR chemical shifts, RDCs, and SAXS with molecular simulations. An analysis of the resulting ensemble suggests that a non-negligible fraction of the ensemble (0.08, 95% confidence interval 0.03-0.12) places the minimal toxic aggregation-prone segment in α-synuclein, NAC(8-18), in a solvent exposed and extended conformation that can form cross-ß structure. Our data also suggest that a sizable fraction of structures in the ensemble (0.14, 95% confidence interval 0.04-0.23) contains long-range contacts between the N- and C-termini. Moreover, a significant fraction of structures that contain these long-range contacts also place the NAC(8-18) segment in a solvent exposed orientation, a finding in contrast to the theory that such long-range contacts help to prevent aggregation. Lastly, our data suggest that α-synuclein samples structures with amphipathic helices that can self-associate via hydrophobic contacts to form tetrameric structures. Overall, these observations represent a comprehensive view of the unfolded ensemble of monomeric α-synuclein and explain how different conformations can arise from the monomeric protein.

DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics.

Myocardial strain analysis from cinematic magnetic resonance imaging (cine-MRI) data provides a more thorough characterization of cardiac mechanics than volumetric parameters such as left-ventricular ejection fraction, but sources of variation including segmentation and motion estimation have limited its wider clinical use. We designed and validated a fast, fully-automatic deep learning (DL) workflow to generate both volumetric parameters and strain measures from cine-MRI data consisting of segmentation and motion estimation convolutional neural networks. The final motion network design, loss function, and associated hyperparameters are the result of a thorough ad hoc implementation that we carefully planned specific for strain quantification, tested, and compared to other potential alternatives. The optimal configuration was trained using healthy and cardiovascular disease (CVD) subjects (n = 150). DL-based volumetric parameters were correlated (>0.98) and without significant bias relative to parameters derived from manual segmentations in 50 healthy and CVD test subjects. Compared to landmarks manually-tracked on tagging-MRI images from 15 healthy subjects, landmark deformation using DL-based motion estimates from paired cine-MRI data resulted in an end-point-error of 2.9 ± 1.5 mm. Measures of end-systolic global strain from these cine-MRI data showed no significant biases relative to a tagging-MRI reference method. On 10 healthy subjects, intraclass correlation coefficient for intra-scanner repeatability was good to excellent (>0.75) for all global measures and most polar map segments. In conclusion, we developed and evaluated the first end-to-end learning-based workflow for automated strain analysis from cine-MRI data to quantitatively characterize cardiac mechanics of healthy and CVD subjects.