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Mitochondrial stress triggers a response in the cell's mitochondria and nucleus, but how these stress responses are coordinated in vivo is poorly understood. Here, we characterize a family with myopathy caused by a dominant p.G58R mutation in the mitochondrial protein CHCHD10. To understand the disease etiology, we developed a knock-in mouse model and found that mutant CHCHD10 aggregates in affected tissues, applying a toxic protein stress to the inner mitochondrial membrane. Unexpectedly, survival of CHCHD10 knock-in mice depended on a protective stress response mediated by OMA1. The OMA1 stress response acted both locally within mitochondria, causing mitochondrial fragmentation, and signaled outside the mitochondria, activating the integrated stress response through cleavage of DELE1. We additionally identified an isoform switch in the terminal complex of the electron transport chain as a component of this response. Our results demonstrate that OMA1 is critical for neonatal survival conditionally in the setting of inner mitochondrial membrane stress, coordinating local and global stress responses to reshape the mitochondrial network and proteome.

Protein design plays a key role in our efforts to work out how genetic coding began. That effort entails urzymes. Urzymes are small, conserved excerpts from full-length aminoacyl-tRNA synthetases that remain active. Urzymes require design to connect disjoint pieces and repair naked nonpolar patches created by removing large domains. Rosetta allowed us to create the first urzymes, but those urzymes were only sparingly soluble. We could measure activity, but it was hard to concentrate those samples to levels required for structural biology. Here, we used the deep learning algorithms ProteinMPNN and AlphaFold2 to redesign a set of optimized LeuAC urzymes derived from leucyl-tRNA synthetase. We select a balanced, representative subset of eight variants for testing using principal component analysis. Most tested variants are much more soluble than the original LeuAC. They also span a range of catalytic proficiency and amino acid specificity. The data enable detailed statistical analyses of the sources of both solubility and specificity. In that way, we show how to begin to unwrap the elements of protein chemistry that were hidden within the neural networks. Deep learning networks have thus helped us surmount several vexing obstacles to further investigations into the nature of ancestral proteins. Finally, we discuss how the eight variants might resemble a sample drawn from a population similar to one subject to natural selection.

Protein side chain packing (PSCP) is a fundamental problem in the field of protein engineering, as high-confidence and low-energy conformations of amino acid side chains are crucial for understanding (and designing) protein folding, protein-protein interactions, and protein-ligand interactions. Traditional PSCP methods (such as the Rosetta Packer) often rely on a library of discrete side chain conformations, or rotamers, and a forcefield to guide the structure to low-energy conformations. Recently, deep learning (DL) based methods (such as DLPacker, AttnPacker, and DiffPack) have demonstrated state-of-the-art predictions and speed in the PSCP task. Building off the success of geometric graph neural networks for protein modeling, we present the Protein Invariant Point Packer (PIPPack) which effectively processes local structural and sequence information to produce realistic, idealized side chain coordinates using -angle distribution predictions and geometry-aware invariant point message passing (IPMP). On a test set of ~1,400 high-quality protein chains, PIPPack is highly competitive with other state-of-the-art PSCP methods in rotamer recovery and per-residue RMSD but is significantly faster.

De novo protein design has gone from science fiction to reality, thanks to the tight coupling of theory, computation, and experiments. Recently, deep learning (DL) approaches have taken advantage of expanding databases and growing compute infrastructure to achieve remarkable results for many design tasks. In this dissertation, we develop DL-based methods to facilitate efficient protein learning and enhance current design capabilities. In the second chapter, we present a novel architectural component for graph neural networks (GNNs) that directly injects geometric information into network updates, called invariant point message passing (IPMP). We demonstrate IPMP’s effectiveness by integrating it into a new protein side chain packing method called Protein Invariant Point Packer (PIPPack). With IPMP, PIPPack rapidly and accurately generates dihedral angle distributions for each amino acid in a protein.In the third chapter, we propose a novel DL-based framework for scaffolding atomic constraints by matching. Representing the constraints as geometric interaction graphs (GIGs), we train two GNNs to sequentially design matches into protein scaffolds and then place their side chains to closely mirror the desired constraint geometry. The resulting method, GIGMatcher, proposes matches for a given scaffold-GIG pair and ranks them based on geometric agreement, producing similar distributions to enumerative methods.

Mitochondrial dysfunction causes devastating disorders, including mitochondrial myopathy. Here, we identified that diverse mitochondrial myopathy models elicit a protective mitochondrial integrated stress response (mt-ISR), mediated by OMA1-DELE1 signaling. The response was similar following disruptions in mtDNA maintenance, from knockout of , and mitochondrial protein unfolding, from disease-causing mutations in CHCHD10 (G58R and S59L). The preponderance of the response was directed at upregulating pathways for aminoacyl-tRNA biosynthesis, the intermediates for protein synthesis, and was similar in heart and skeletal muscle but more limited in brown adipose challenged with cold stress. Strikingly, models with early DELE1 mt-ISR activation failed to grow and survive to adulthood in the absence of , accounting for some but not all of OMA1's protection. Notably, the DELE1 mt-ISR did not slow net protein synthesis in stressed striated muscle, but instead prevented loss of translation-associated proteostasis in muscle fibers. Together our findings identify that the DELE1 mt-ISR mediates a stereotyped response to diverse forms of mitochondrial stress and is particularly critical for maintaining growth and survival in early-onset mitochondrial myopathy.

Amino acid mutations that lower a protein's thermodynamic stability are implicated in numerous diseases, and engineered proteins with enhanced stability are important in research and medicine. Computational methods for predicting how mutations perturb protein stability are therefore of great interest. Despite recent advancements in protein design using deep learning, prediction of stability changes has remained challenging, in part due to a lack of large, high-quality training datasets for model development. Here we introduce ThermoMPNN, a deep neural network trained to predict stability changes for protein point mutations given an initial structure. In doing so, we demonstrate the utility of a newly released mega-scale stability dataset for training a robust stability model. We also employ transfer learning to leverage a second, larger dataset by using learned features extracted from a deep neural network trained to predict a protein's amino acid sequence given its three-dimensional structure. We show that our method achieves competitive performance on established benchmark datasets using a lightweight model architecture that allows for rapid, scalable predictions. Finally, we make ThermoMPNN readily available as a tool for stability prediction and design.

There has been considerable progress in the development of computational methods for designing protein-protein interactions, but engineering high-affinity binders without extensive screening and maturation remains challenging. Here, we test a protein design pipeline that uses iterative rounds of deep learning (DL)-based structure prediction (AlphaFold2) and sequence optimization (ProteinMPNN) to design autoinhibitory domains (AiDs) for a PD-L1 antagonist. Inspired by recent advances in therapeutic design, we sought to create autoinhibited (or masked) forms of the antagonist that can be conditionally activated by proteases. Twenty-three de novo designed AiDs, varying in length and topology, were fused to the antagonist with a protease sensitive linker, and binding to PD-L1 was tested with and without protease treatment. Nine of the fusion proteins demonstrated conditional binding to PD-L1 and the top performing AiDs were selected for further characterization as single domain proteins. Without any experimental affinity maturation, four of the AiDs bind to the PD-L1 antagonist with equilibrium dissociation constants (KDs) below 150 nM, with the lowest KD equal to 0.9 nM. Our study demonstrates that DL-based protein modeling can be used to rapidly generate high affinity protein binders.There has been considerable progress in the development of computational methods for designing protein-protein interactions, but engineering high-affinity binders without extensive screening and maturation remains challenging. Here, we test a protein design pipeline that uses iterative rounds of deep learning (DL)-based structure prediction (AlphaFold2) and sequence optimization (ProteinMPNN) to design autoinhibitory domains (AiDs) for a PD-L1 antagonist. Inspired by recent advances in therapeutic design, we sought to create autoinhibited (or masked) forms of the antagonist that can be conditionally activated by proteases. Twenty-three de novo designed AiDs, varying in length and topology, were fused to the antagonist with a protease sensitive linker, and binding to PD-L1 was tested with and without protease treatment. Nine of the fusion proteins demonstrated conditional binding to PD-L1 and the top performing AiDs were selected for further characterization as single domain proteins. Without any experimental affinity maturation, four of the AiDs bind to the PD-L1 antagonist with equilibrium dissociation constants (KDs) below 150 nM, with the lowest KD equal to 0.9 nM. Our study demonstrates that DL-based protein modeling can be used to rapidly generate high affinity protein binders.

In this study, we develop a methodology for model reduction and selection informed by global sensitivity analysis (GSA) methods. We apply these techniques to a control model that takes systolic blood pressure and thoracic tissue pressure data as inputs and predicts heart rate in response to the Valsalva maneuver (VM). The study compares four GSA methods based on Sobol' indices (SIs) quantifying the parameter influence on the difference between the model output and the heart rate data. The GSA methods include standard scalar SIs determining the average parameter influence over the time interval studied and three time-varying methods analyzing how parameter influence changes over time. The time-varying methods include a new technique, termed limited-memory SIs, predicting parameter influence using a moving window approach. Using the limited-memory SIs, we perform model reduction and selection to analyze the necessity of modeling both the aortic and carotid baroreceptor regions in response to the VM. We compare the original model to three systematically reduced models including (i) the aortic and carotid regions, (ii) the aortic region only, and (iii) the carotid region only. Model selection is done quantitatively using the Akaike and Bayesian Information Criteria and qualitatively by comparing the neurological predictions. Results show that it is necessary to incorporate both the aortic and carotid regions to model the VM.

Delay differential equations (DDEs) are widely used in mathematical modeling to describe physical and biological systems. Delays can impact model dynamics, resulting in oscillatory behavior. In physiological systems, this instability may signify (i) an attempt to return to homeostasis or (ii) system dysfunction. In this study, we analyze a nonlinear, nonautonomous, nonhomogeneous open-loop neurological control model describing the autonomic nervous system response to the Valsalva maneuver. Unstable modes have been identified as a result of parameter interactions between the sympathetic delay and time-scale. In a two-parameter bifurcation analysis, we examine both the homogeneous and nonhomogeneous systems. Discrepancies between solutions result from the presence of the forcing functions which stabilize the system. We use analytical methods to determine stability regions for the homogeneous system, identifying transcendental relationships between the parameters. We also use computational methods to determine stability regions for the nonhomogeneous system. The presence of a Hopf bifurcation within the system is discussed and solution types from the sink and stable focus regions are compared to two control patients and a patient with postural orthostatic tachycardia syndrome (POTS). The model and its analysis support the current clinical hypotheses that patients suffering from POTS experience altered nervous system activity.

The vision of achieving a completely in-vacuum process for fabricating HgCdTe Infrared detector arrays is contingent on the availability of a vacuum-compatible lithography technology. One such technology for vacuum-lithography involves the use of amorphous hydrogenated Si (a-Si:H) as a dry photoresist. The basic concept has recently been demonstrated whereby a-Si:H resists were deposited via plasma enhanced chemical vapor deposition (PECVD), and then patterned using an excimer laser. The patterns were then hydrogen plasma developed to remove unirradiated areas. Finally, an Ar/H2 electron cyclotron resonance (ECR) plasma was used to transfer patterns to underlying Hg 1−xCdxTe film layers. This thesis presents a continued investigation of a-Si:H as a resist material wherein the resists are deposited using an Ar-diluted silane precursor. To determine the best conditions for the technique, the effects of different laser fluences, and exposure environments were studied. Analysis via transmission electron microscopy (TEM) reveals that the excimer-exposed surfaces are polycrystalline in nature, indicating that the mechanism for pattern generation in this study is based on melting and crystallization of the exposed areas. To reduce undesirable surface roughness induced by laser irradiation, a step-wise crystallization/dehydrogenation technique is demonstrated. Fundamental aspects of pattern transfer (via ECR plasma etching) to CdTe and HgCdTe films are also demonstrated, where etch selectivities of 8:1 and 16:1 (respectively) are observed. These values represent a significant improvement to etch selectivities obtained using commercially available organic resists. To address concerns regarding possible damage to HgCdTe caused by the a-Si:H dry lithography process, preliminary studies were carried out using double-crystal rocking curve X-Ray diffraction and high-resolution TEM. The results indicate no evidence of microstructural damage to the HgCdTe film. Other characterization techniques used throughout this thesis include Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and stylus profilometry. The implementation of the a-Si:H dry lithography process represents a crucial step toward achieving totally integrated fabrication of HgCdTe IR detector arrays. In addition this lithography technique is both low temperature and contamination-free, so that other semiconductor microfabrication processes could potentially benefit from its use.