CISD3/MiNT is required for complex I function, mitochondrial integrity, and skeletal muscle maintenance.

Mitochondria play a central role in muscle metabolism and function. A unique family of iron-sulfur proteins, termed CDGSH Iron Sulfur Domain-containing (CISD/NEET) proteins, support mitochondrial function in skeletal muscles. The abundance of these proteins declines during aging leading to muscle degeneration. Although the function of the outer mitochondrial CISD/NEET proteins, CISD1/mitoNEET and CISD2/NAF-1, has been defined in skeletal muscle cells, the role of the inner mitochondrial CISD protein, CISD3/MiNT, is currently unknown. Here, we show that CISD3 deficiency in mice results in muscle atrophy that shares proteomic features with Duchenne muscular dystrophy. We further reveal that CISD3 deficiency impairs the function and structure of skeletal muscles, as well as their mitochondria, and that CISD3 interacts with, and donates its [2Fe-2S] clusters to, complex I respiratory chain subunit NADH Ubiquinone Oxidoreductase Core Subunit V2 (NDUFV2). Using coevolutionary and structural computational tools, we model a CISD3-NDUFV2 complex with proximal coevolving residue interactions conducive of [2Fe-2S] cluster transfer reactions, placing the clusters of the two proteins 10 to 16 Å apart. Taken together, our findings reveal that CISD3/MiNT is important for supporting the biogenesis and function of complex I, essential for muscle maintenance and function. Interventions that target CISD3 could therefore impact different muscle degeneration syndromes, aging, and related conditions.

The physical and evolutionary energy landscapes of devolved protein sequences corresponding to pseudogenes.

Protein evolution is guided by structural, functional, and dynamical constraints ensuring organismal viability. Pseudogenes are genomic sequences identified in many eukaryotes that lack translational activity due to sequence degradation and thus over time have undergone "devolution." Previously pseudogenized genes sometimes regain their protein-coding function, suggesting they may still encode robust folding energy landscapes despite multiple mutations. We study both the physical folding landscapes of protein sequences corresponding to human pseudogenes using the Associative Memory, Water Mediated, Structure and Energy Model, and the evolutionary energy landscapes obtained using direct coupling analysis (DCA) on their parent protein families. We found that generally mutations that have occurred in pseudogene sequences have disrupted their native global network of stabilizing residue interactions, making it harder for them to fold if they were translated. In some cases, however, energetic frustration has apparently decreased when the functional constraints were removed. We analyzed this unexpected situation for Cyclophilin A, Profilin-1, and Small Ubiquitin-like Modifier 2 Protein. Our analysis reveals that when such mutations in the pseudogene ultimately stabilize folding, at the same time, they likely alter the pseudogenes' former biological activity, as estimated by DCA. We localize most of these stabilizing mutations generally to normally frustrated regions required for binding to other partners.

Coevolutionary Information Captures Catalytic Functions and Reveals Divergent Roles of Terpene Synthase Interdomain Connections.

Inferring the historical and biophysical causes of diversity within protein families is a complex puzzle. A key to unraveling this problem is characterizing the rugged topography of sequence-function adaptive landscapes. Using biochemical data from a 29 = 512 combinatorial library of tobacco 5-epi-aristolochene synthase (TEAS) mutants engineered to make the native major product of Egyptian henbane premnaspirodiene synthase (HPS) and a complementary 512 mutant HPS library, we address the question of how product specificity is controlled. These data sets reveal that HPS is far more robust and resistant to mutations than TEAS, where most mutants are promiscuous. We also combine experimental data with a sequence Potts Hamiltonian model and direct coupling analysis to quantify mutant fitness. Our results demonstrate that the Hamiltonian captures variation in product outputs across both libraries, clusters native family members based on their substrate specificities, and exposes the divergent catalytic roles of couplings between the catalytic and noncatalytic domains of TEAS versus HPS. Specifically, we found that the role of the interdomain connectivities in specifying product output is more important in TEAS than connectivities within the catalytic domain. Despite being 75% identical, this property is not shared by HPS, where connectivities within the catalytic domain are more important for specificity. By solving the X-ray crystal structure of HPS, we assessed structural bases for their interdomain network differences. Last, we calculate the product profile Shannon entropies of the two libraries, which showcases that site-site connectivities also play divergent roles in catalytic accuracy.

In vivo functional phenotypes from a computational epistatic model of evolution.

Computational models of evolution are valuable for understanding the dynamics of sequence variation, to infer phylogenetic relationships or potential evolutionary pathways and for biomedical and industrial applications. Despite these benefits, few have validated their propensities to generate outputs with in vivo functionality, which would enhance their value as accurate and interpretable evolutionary algorithms. We demonstrate the power of epistasis inferred from natural protein families to evolve sequence variants in an algorithm we developed called sequence evolution with epistatic contributions (SEEC). Utilizing the Hamiltonian of the joint probability of sequences in the family as fitness metric, we sampled and experimentally tested for in vivo [Formula: see text]-lactamase activity in Escherichia coli TEM-1 variants. These evolved proteins can have dozens of mutations dispersed across the structure while preserving sites essential for both catalysis and interactions. Remarkably, these variants retain family-like functionality while being more active than their wild-type predecessor. We found that depending on the inference method used to generate the epistatic constraints, different parameters simulate diverse selection strengths. Under weaker selection, local Hamiltonian fluctuations reliably predict relative changes to variant fitness, recapitulating neutral evolution. SEEC has the potential to explore the dynamics of neofunctionalization, characterize viral fitness landscapes, and facilitate vaccine development.

Lipid discovery enabled by sequence statistics and machine learning.

Bacterial membranes are complex and dynamic, arising from an array of evolutionary pressures. One enzyme that alters membrane compositions through covalent lipid modification is MprF. We recently identified that Streptococcus agalactiae MprF synthesizes lysyl-phosphatidylglycerol (Lys-PG) from anionic PG, and a novel cationic lipid, lysyl-glucosyl-diacylglycerol (Lys-Glc-DAG), from neutral glycolipid Glc-DAG. This unexpected result prompted us to investigate whether Lys-Glc-DAG occurs in other MprF-containing bacteria, and whether other novel MprF products exist. Here, we studied protein sequence features determining MprF substrate specificity. First, pairwise analyses identified several streptococcal MprFs synthesizing Lys-Glc-DAG. Second, a restricted Boltzmann machine-guided approach led us to discover an entirely new substrate for MprF in Enterococcus , diglucosyl-diacylglycerol (Glc2-DAG), and an expanded set of organisms that modify glycolipid substrates using MprF. Overall, we combined the wealth of available sequence data with machine learning to model evolutionary constraints on MprF sequences across the bacterial domain, thereby identifying a novel cationic lipid.

In vivo functional phenotypes from a computational epistatic model of evolution.

Computational models of evolution are valuable for understanding the dynamics of sequence variation, to infer phylogenetic relationships or potential evolutionary pathways and for biomedical and industrial applications. Despite these benefits, few have validated their propensities to generate outputs with in vivo functionality, which would enhance their value as accurate and interpretable evolutionary algorithms. We demonstrate the power of epistasis inferred from natural protein families to evolve sequence variants in an algorithm we developed called Sequence Evolution with Epistatic Contributions. Utilizing the Hamiltonian of the joint probability of sequences in the family as fitness metric, we sampled and experimentally tested for in vivo ß-lactamase activity in E. coli TEM-1 variants. These evolved proteins can have dozens of mutations dispersed across the structure while preserving sites essential for both catalysis and interactions. Remarkably, these variants retain family-like functionality while being more active than their WT predecessor. We found that depending on the inference method used to generate the epistatic constraints, different parameters simulate diverse selection strengths. Under weaker selection, local Hamiltonian fluctuations reliably predict relative changes to variant fitness, recapitulating neutral evolution. SEEC has the potential to explore the dynamics of neofunctionalization, characterize viral fitness landscapes and facilitate vaccine development.

Latent generative landscapes as maps of functional diversity in protein sequence space.

Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, ß-lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design.

Coevolution and smFRET Enhances Conformation Sampling and FRET Experimental Design in Tandem PDZ1-2 Proteins.

The structural flexibility of proteins is crucial for their functions. Many experimental and computational approaches can probe protein dynamics across a range of time and length-scales. Integrative approaches synthesize the complementary outputs of these techniques and provide a comprehensive view of the dynamic conformational space of proteins, including the functionally relevant limiting conformational states and transition pathways between them. Here, we introduce an integrative paradigm to model the conformational states of multidomain proteins. As a model system, we use the first two tandem PDZ domains of postsynaptic density protein 95. First, we utilize available sequence information collected from genomic databases to identify potential amino acid interactions in the PDZ1-2 tandem that underlie modeling of the functionally relevant conformations maintained through evolution. This was accomplished through combination of coarse-grained structural modeling with outputs from direct coupling analysis measuring amino acid coevolution, a hybrid approach called SBM+DCA. We recapitulated five distinct, experimentally derived PDZ1-2 tandem conformations. In addition, SBM+DCA unveiled an unidentified, twisted conformation of the PDZ1-2 tandem. Finally, we implemented an integrative framework for the design of single-molecule Förster resonance energy transfer (smFRET) experiments incorporating the outputs of SBM+DCA with simulated FRET observables. This resulting FRET network is designed to mutually resolve the predicted limiting state conformations through global analysis. Using simulated FRET observables, we demonstrate that structural modeling with the newly designed FRET network is expected to outperform a previously used empirical FRET network at resolving all states simultaneously. Integrative approaches to experimental design have the potential to provide a new level of detail in characterizing the evolutionarily conserved conformational landscapes of proteins, and thus new insights into functional relevance of protein dynamics in biological function.

Computational compensatory mutation discovery approach: Predicting a PARP1 variant rescue mutation.

The prediction of protein mutations that affect function may be exploited for multiple uses. In the context of disease variants, the prediction of compensatory mutations that reestablish functional phenotypes could aid in the development of genetic therapies. In this work, we present an integrated approach that combines coevolutionary analysis and molecular dynamics (MD) simulations to discover functional compensatory mutations. This approach is employed to investigate possible rescue mutations of a poly(ADP-ribose) polymerase 1 (PARP1) variant, PARP1 V762A, associated with lung cancer and follicular lymphoma. MD simulations show PARP1 V762A exhibits noticeable changes in structural and dynamical behavior compared with wild-type (WT) PARP1. Our integrated approach predicts A755E as a possible compensatory mutation based on coevolutionary information, and molecular simulations indicate that the PARP1 A755E/V762A double mutant exhibits similar structural and dynamical behavior to WT PARP1. Our methodology can be broadly applied to a large number of systems where single-nucleotide polymorphisms have been identified as connected to disease and can shed light on the biophysical effects of such changes as well as provide a way to discover potential mutants that could restore WT-like functionality. This can, in turn, be further utilized in the design of molecular therapeutics that aim to mimic such compensatory effect.

Coupling a Live Cell Directed Evolution Assay with Coevolutionary Landscapes to Engineer an Improved Fluorescent Rhodopsin Chloride Sensor.

Our understanding of chloride in biology has been accelerated through the application of fluorescent protein-based sensors in living cells. These sensors can be generated and diversified to have a range of properties using laboratory-guided evolution. Recently, we established that the fluorescent proton-pumping rhodopsin wtGR from Gloeobacter violaceus can be converted into a fluorescent sensor for chloride. To unlock this non-natural function, a single point mutation at the Schiff counterion position (D121V) was introduced into wtGR fused to cyan fluorescent protein (CFP) resulting in GR1-CFP. Here, we have integrated coevolutionary analysis with directed evolution to understand how the rhodopsin sequence space can be explored and engineered to improve this starting point. We first show how evolutionary couplings are predictive of functional sites in the rhodopsin family and how a fitness metric based on a sequence can be used to quantify the known proton-pumping activities of GR-CFP variants. Then, we couple this ability to predict potential functional outcomes with a screening and selection assay in live Escherichia coli to reduce the mutational search space of five residues along the proton-pumping pathway in GR1-CFP. This iterative selection process results in GR2-CFP with four additional mutations: E132K, A84K, T125C, and V245I. Finally, bulk and single fluorescence measurements in live E. coli reveal that GR2-CFP is a reversible, ratiometric fluorescent sensor for extracellular chloride with an improved dynamic range. We anticipate that our framework will be applicable to other systems, providing a more efficient methodology to engineer fluorescent protein-based sensors with desired properties.

A VDAC1-mediated NEET protein chain transfers [2Fe-2S] clusters between the mitochondria and the cytosol and impacts mitochondrial dynamics.

Mitochondrial inner NEET (MiNT) and the outer mitochondrial membrane (OMM) mitoNEET (mNT) proteins belong to the NEET protein family. This family plays a key role in mitochondrial labile iron and reactive oxygen species (ROS) homeostasis. NEET proteins contain labile [2Fe-2S] clusters which can be transferred to apo-acceptor proteins. In eukaryotes, the biogenesis of [2Fe-2S] clusters occurs within the mitochondria by the iron-sulfur cluster (ISC) system; the clusters are then transferred to [2Fe-2S] proteins within the mitochondria or exported to cytosolic proteins and the cytosolic iron-sulfur cluster assembly (CIA) system. The last step of export of the [2Fe-2S] is not yet fully characterized. Here we show that MiNT interacts with voltage-dependent anion channel 1 (VDAC1), a major OMM protein that connects the intermembrane space with the cytosol and participates in regulating the levels of different ions including mitochondrial labile iron (mLI). We further show that VDAC1 is mediating the interaction between MiNT and mNT, in which MiNT transfers its [2Fe-2S] clusters from inside the mitochondria to mNT that is facing the cytosol. This MiNT-VDAC1-mNT interaction is shown both experimentally and by computational calculations. Additionally, we show that modifying MiNT expression in breast cancer cells affects the dynamics of mitochondrial structure and morphology, mitochondrial function, and breast cancer tumor growth. Our findings reveal a pathway for the transfer of [2Fe-2S] clusters, which are assembled inside the mitochondria, to the cytosol.

Divergence in Dimerization and Activity of Primate APOBEC3C.

The APOBEC3 (A3) family of single-stranded DNA cytidine deaminases are host restriction factors that inhibit lentiviruses, such as HIV-1, in the absence of the Vif protein that causes their degradation. Deamination of cytidine in HIV-1 (-)DNA forms uracil that causes inactivating mutations when uracil is used as a template for (+)DNA synthesis. For APOBEC3C (A3C), the chimpanzee and gorilla orthologues are more active than human A3C, and we determined that Old World Monkey A3C from rhesus macaque (rh) is not active against HIV-1. Biochemical, virological, and coevolutionary analyses combined with molecular dynamics simulations showed that the key amino acids needed to promote rhA3C antiviral activity, 44, 45, and 144, also promoted dimerization and changes to the dynamics of loop 1, near the enzyme active site. Although forced evolution of rhA3C resulted in a similar dimer interface with hominid A3C, the key amino acid contacts were different. Overall, our results determine the basis for why rhA3C is less active than human A3C and establish the amino acid network for dimerization and increased activity. Based on identification of the key amino acids determining Old World Monkey antiviral activity we predict that other Old World Monkey A3Cs did not impart anti-lentiviral activity, despite fixation of a key residue needed for hominid A3C activity. Overall, the coevolutionary analysis of the A3C dimerization interface presented also provides a basis from which to analyze dimerization interfaces of other A3 family members.

Coevolutionary methods enable robust design of modular repressors by reestablishing intra-protein interactions.

Genetic sensors with unique combinations of DNA recognition and allosteric response can be created by hybridizing DNA-binding modules (DBMs) and ligand-binding modules (LBMs) from distinct transcriptional repressors. This module swapping approach is limited by incompatibility between DBMs and LBMs from different proteins, due to the loss of critical module-module interactions after hybridization. We determine a design strategy for restoring key interactions between DBMs and LBMs by using a computational model informed by coevolutionary traits in the LacI family. This model predicts the influence of proposed mutations on protein structure and function, quantifying the feasibility of each mutation for rescuing hybrid repressors. We accurately predict which hybrid repressors can be rescued by mutating residues to reinstall relevant module-module interactions. Experimental results confirm that dynamic ranges of gene expression induction were improved significantly in these mutants. This approach enhances the molecular and mechanistic understanding of LacI family proteins, and advances the ability to design modular genetic parts.

A single point mutation converts a proton-pumping rhodopsin into a red-shifted, turn-on fluorescent sensor for chloride.

The visualization of chloride in living cells with fluorescent sensors is linked to our ability to design hosts that can overcome the energetic penalty of desolvation to bind chloride in water. Fluorescent proteins can be used as biological supramolecular hosts to address this fundamental challenge. Here, we showcase the power of protein engineering to convert the fluorescent proton-pumping rhodopsin GR from Gloeobacter violaceus into GR1, a red-shifted, turn-on fluorescent sensor for chloride in detergent micelles and in live Escherichia coli. This non-natural function was unlocked by mutating D121, which serves as the counterion to the protonated retinylidene Schiff base chromophore. Substitution from aspartate to valine at this position (D121V) creates a binding site for chloride. The binding of chloride tunes the pK a of the chromophore towards the protonated, fluorescent state to generate a pH-dependent response. Moreover, ion pumping assays combined with bulk fluorescence and single-cell fluorescence microscopy experiments with E. coli, expressing a GR1 fusion with a cyan fluorescent protein, show that GR1 does not pump ions nor sense membrane potential but instead provides a reversible, ratiometric readout of changes in extracellular chloride at the membrane. This discovery sets the stage to use natural and laboratory-guided evolution to build a family of rhodopsin-based fluorescent chloride sensors with improved properties for cellular applications and learn how proteins can evolve and adapt to bind anions in water.

Information Theory in Molecular Evolution: From Models to Structures and Dynamics.

Historically, information theory has been closely interconnected with evolutionary theory [...].