Visualizing the Complexity of Proteins in Living Cells With Genetic Code Expansion

Genetic code expansion has emerged as an enabling tool to provide insight into functions of understudied proteinogenic species such as small proteins and peptides, and to probe protein biophysics in the cellular context. Here we discuss recent technical advances and applications of genetic code expansion in cellular imaging of complex mammalian protein species, along with considerations and challenges upon using the method.

Reviewing Challenges of Predicting Protein Melting Temperature Change Upon Mutation Through the Full Analysis of a Highly Detailed Dataset with High-Resolution Structures

Predicting the effects of mutations on protein stability is a key problem in fundamental and applied biology, still unsolved even for the relatively simple case of small, soluble, globular, monomeric, two-state-folder proteins. Many articles discuss the limitations of prediction methods and of the datasets used to train them, which result in low reliability for actual applications despite globally capturing trends. Here, we review these and other issues by analyzing one of the most detailed, carefully curated datasets of melting temperature change (ΔTm) upon mutation for proteins with high-resolution structures. After examining the composition of this dataset to discuss imbalances and biases, we inspect several of its entries assisted by an online app for data navigation and structure display and aided by a neural network that predicts ΔTm with accuracy close to that of programs available to this end. We pose that the ΔTm predictions of our network, and also likely those of other programs, account only for a baseline-like general effect of each type of amino acid substitution which then requires substantial corrections to reproduce the actual stability changes. The corrections are very different for each specific case and arise from fine structural details which are not well represented in the dataset and which, despite appearing reasonable upon visual inspection of the structures, are hard to encode and parametrize. Based on these observations, additional analyses, and a review of recent literature, we propose recommendations for developers of stability prediction methods and for efforts aimed at improving the datasets used for training. We leave our interactive interface for analysis available online at http://lucianoabriata.altervista.org/papersdata/proteinstability2021/s1626navigation.html so that users can further explore the dataset and baseline predictions, possibly serving as a tool useful in the context of structural biology and protein biotechnology research and as material for education in protein biophysics.

One-pot Synthesis of Enzyme@Metal-Organic Materials (MOM) Biocomposites for Enzyme Biocatalysis

Metal-Organic Frameworks/Materials (MOFs/MOMs) are advanced enzyme immobilization platforms that improve biocatalysis, materials science, and protein biophysics. A unique way to immobilize enzymes is co-crystallization/co-precipitation, which removes the limitation on enzyme/substrate...

Fake It ‘Till You Make It—The Pursuit of Suitable Membrane Mimetics for Membrane Protein Biophysics

Membrane proteins evolved to reside in the hydrophobic lipid bilayers of cellular membranes. Therefore, membrane proteins bridge the different aqueous compartments separated by the membrane, and furthermore, dynamically interact with their surrounding lipid environment. The latter not only stabilizes membrane proteins, but directly impacts their folding, structure and function. In order to be characterized with biophysical and structural biological methods, membrane proteins are typically extracted and subsequently purified from their native lipid environment. This approach requires that lipid membranes are replaced by suitable surrogates, which ideally closely mimic the native bilayer, in order to maintain the membrane proteins structural and functional integrity. In this review, we survey the currently available membrane mimetic environments ranging from detergent micelles to bicelles, nanodiscs, lipidic-cubic phase (LCP), liposomes, and polymersomes. We discuss their respective advantages and disadvantages as well as their suitability for downstream biophysical and structural characterization. Finally, we take a look at ongoing methodological developments, which aim for direct in-situ characterization of membrane proteins within native membranes instead of relying on membrane mimetics.

Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks

Predicting mutation-induced changes in protein thermodynamic stability (ΔΔG) is of great interest in protein engineering, variant interpretation, and protein biophysics. We introduce ThermoNet, a deep, 3D-convolutional neural network (3D-CNN) designed for structure-based prediction of ΔΔGs upon point mutation. To leverage the image-processing power inherent in CNNs, we treat protein structures as if they were multi-channel 3D images. In particular, the inputs to ThermoNet are uniformly constructed as multi-channel voxel grids based on biophysical properties derived from raw atom coordinates. We train and evaluate ThermoNet with a curated data set that accounts for protein homology and is balanced with direct and reverse mutations; this provides a framework for addressing biases that have likely influenced many previous ΔΔG prediction methods. ThermoNet demonstrates performance comparable to the best available methods on the widely used Ssym test set. In addition, ThermoNet accurately predicts the effects of both stabilizing and destabilizing mutations, while most other methods exhibit a strong bias towards predicting destabilization. We further show that homology between Ssym and widely used training sets like S2648 and VariBench has likely led to overestimated performance in previous studies. Finally, we demonstrate the practical utility of ThermoNet in predicting the ΔΔGs for two clinically relevant proteins, p53 and myoglobin, and for pathogenic and benign missense variants from ClinVar. Overall, our results suggest that 3D-CNNs can model the complex, non-linear interactions perturbed by mutations, directly from biophysical properties of atoms.

Beyond History: The List of The Most Well Studied Human Protein Structures

Of 20,000 or so canonical human protein sequences, as of July 2020, 6,747 proteins have had their full or partial medium to high-resolution structures determined by x-ray crystallography or other methods. Which of these proteins dominate the protein database (the PDB) and why? In this paper, we list the 272 top protein structures based on the number of their PDB depositions. This set of proteins accounts for more than 40% of all available human PDB entries and represent past trend and current status for protein science. We briefly discuss the relationship which some of the prominent protein structures have with protein biophysics research and mention their relevance to human diseases. The information may inspire researchers who are new to protein science, but it also provides a year 2020 snap-shot for the state of protein science.

Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks

Predicting mutation-induced changes in protein thermodynamic stability (∆∆G) is of great interest in protein engineering, variant interpretation, and understanding protein biophysics. We introduce ThermoNet, a deep, 3D-convolutional neural network designed for structure-based prediction of ∆∆Gs upon point mutation. To leverage the image-processing power inherent in convolutional neural networks, we treat protein structures as if they were multi-channel 3D images. In particular, the inputs to ThermoNet are uniformly constructed as multi-channel voxel grids based on biophysical properties derived from raw atom coordinates. We train and evaluate ThermoNet with a curated data set that accounts for protein homology and is balanced with direct and reverse mutations; this provides a framework for addressing biases that have likely influenced many previous ∆∆G prediction methods. ThermoNet demonstrates performance comparable to the best available methods on the widely used Ssym test set. However, ThermoNet accurately predicts the effects of both stabilizing and destabilizing mutations, while most other methods exhibit a strong bias towards predicting destabilization. We further show that homology between Ssym and widely used training sets like S2648 and VariBench has likely led to overestimated performance in previous studies. Finally, we demonstrate the practical utility of ThermoNet in predicting the ∆∆Gs for two clinically relevant proteins, p53 and myoglobin, and for pathogenic and benign missense variants from ClinVar. Overall, our results suggest that 3D convolutional neural networks can model the complex, non-linear interactions perturbed by mutations, directly from biophysical properties of atoms.Author SummaryThe thermodynamic stability of a protein, usually represented as the Gibbs free energy for the biophysical process of protein folding (∆G), is a fundamental thermodynamic quantity. Predicting mutation-induced changes in protein thermodynamic stability (∆∆G) is of great interest in protein engineering, variant interpretation, and understanding protein biophysics. However, predicting ∆∆Gs in an accurate and unbiased manner has been a long-standing challenge in the field of computational biology. In this work, we introduce ThermoNet, a deep, 3D-convolutional neural network designed for structure-based ∆∆G prediction. To leverage the image-processing power inherent in convolutional neural networks, we treat protein structures as if they were multi-channel 3D images. ThermoNet demonstrates performance comparable to the best available methods. However, ThermoNet accurately predicts the effects of both stabilizing and destabilizing mutations, while most other methods exhibit a strong bias towards predicting destabilization. We also demonstrate that the presence of homologous proteins in commonly used training and testing sets for ∆∆G prediction methods has likely influenced previous performance estimates. Finally, we highlight the practical utility of ThermoNet by applying it to predicting the ∆∆Gs for two clinically relevant proteins, p53 and myoglobin, and for pathogenic and benign missense variants from ClinVar.

How A Novel Scientific Concept Was Coined the “Molten Globule State”

As a tribute to Professor Oleg B. Ptitsyn, we organized an interview with Professor Akiyoshi Wada held in Tokyo in the middle of September 2019. Both Professor A. Wada and the late Professor O. B. Ptitsyn greatly contributed to the field of protein biophysics, and they played leading roles in establishing the concept of the “Molten Globule state” 35–40 years ago. This editorial is intended to recount, as accurately as possible, some episodes during the early days of protein research that led to the discovery of this state, and how this concept was coined the “Molten Globule state” and came to be widely accepted by biophysicists, biochemists, and molecular biologists.

Computation-guided optimization of split protein systems

ABSTRACTSplitting bioactive proteins, such as enzymes or fluorescent reporters, into conditionally reconstituting fragments is a powerful strategy for building tools to study and control biochemical systems. However, split proteins often exhibit a high propensity to reconstitute even in the absence of the conditional trigger, which limits their utility. Current approaches for tuning reconstitution propensity are laborious, context-specific, or often ineffective. Here, we report a computational design-driven strategy that is grounded in fundamental protein biophysics and which guides the experimental evaluation of a focused, sparse set of mutants—which vary in the degree of interfacial destabilization while preserving features such as stability and catalytic activity—to identify an optimal functional window. We validate our method by solving two distinct split protein design challenges, generating both broad insights and new technology platforms. This method will streamline the generation and use of split protein systems for diverse applications.