Biology subjects

Randolph, N.

Publications and source records attributed to Randolph, N..

3 recordsLinked to original sources

bioRxiv2023

Transfer learning to leverage larger datasets for improved prediction of protein stability changes

Amino acid mutations that lower a proteins 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, in silico 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 proteins 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.

bioinformatics↗

bioRxiv2023

In silico evolution of protein binders with deep learning models for structure prediction and sequence 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. Significance statementProtein-protein interactions are critical to most processes in biology, and improved methods for designing protein binders will enable the creation of new research reagents, diagnostics, and therapeutics. In this study, we show that a deep learning-based method for protein design can create high-affinity protein binders without the need for extensive screening or affinity maturation.

biophysics↗

bioRxiv2021

OMA1 mediates local and global stress responses against protein misfolding in CHCHD10 mitochondrial myopathy

Mitochondrial stress triggers a response in the cells 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 novel 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, inhibiting mitochondrial fusion, and signaled outside the mitochondria, activating the integrated stress response. We additionally identified an isoform switch in the terminal complex of the electron transport chain as a novel component of this response. Our results demonstrate that OMA1 is essential 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. Graphical Abtract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/473493v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@1ab5990org.highwire.dtl.DTLVardef@85080dorg.highwire.dtl.DTLVardef@1d3a036org.highwire.dtl.DTLVardef@bb4679_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗