Evolutionary Coupling Research Articles

Inhibitory Potential of the Truncated Isoforms on Glutamate Transporter Oligomerization Identified by Computational Analysis of Gene-Centric Isoform Maps.

Glutamate transporters play a key role in central nervous system physiology by maintaining excitatory neurotransmitter homeostasis. Biological assemblies of the transporters, consisting of cyclic homotrimers, emerge as a crucial aspect of glutamate transporter modulation. Hence targeting heteromerization promises an effective approach for modulator design. On the other hand, the dynamic nature of transcription allows for the generation of transporter isoforms in structurally distinct manners. The potential isoforms were identified through the analysis of computationally generated gene-centric isoform maps. The conserved features of isoform sequences were revealed by computational chemistry methods and subsequent structural analysis of AlphaFold2 predictions. Truncated isoforms were further subjected to a wide range of docking analyses, 50ns molecular dynamics simulations, and evolutionary coupling analyses. Energetic landscapes of isoform-canonical transporter complexes suggested an inhibitory potential of truncated isoforms on glutamate transporter bio-assembly. Moreover, isoforms that mimic the trimerization domain (in particular, TM2 helices) exhibited stronger interactions with canonical transporters, underscoring the role of transmembrane helices in isoform interactions. Additionally, self-assembly dynamics observed in truncated isoforms mimicking canonical TM5 helices indicate a potential protective role against unwanted interactions with canonical transporters. Our computational studies on glutamate transporters offer insights into the roles of alternative splicing on protein interactions and identifies potential drug targets for physiological or pathological processes.

Functional contacts for activation of urease from Helicobacter pylori: an integrated approach using evolutionary couplings, in-cell enzymatic assays, and computational docking

Introduction: Urease is an enzyme exploited by many virulent bacteria and fungi to infect the host and exert their virulence. The Gram-negative bacterium Helicobacter pylori relies on the activity of urease to infect the highly acidic human stomach. The activity of urease depends on the presence of a catalytic site containing two Ni(II) ions. In vivo, urease is initially synthesized as an inactive apo-enzyme and requires a post-translational activation process that involves the incorporation of the metal ions into its buried active site. In H. pylori, as well as in other bacteria, this activation process is mediated by four accessory proteins, named UreD, UreF, UreG, and UreE. Targeting the interactions between urease chaperones could potentially inhibit the activation of urease through blocking the Ni(II) ions incorporation, providing a route for the development of antimicrobial strategies against ureolytic pathogens.Methods: In this paper, an evolutionary couplings (EC) approach was adopted to determine the interaction surface between urease and UreD, the first protein that binds the enzyme, preparing it for the subsequent activation steps. Site-directed mutagenesis and an in-cell assay were used to detect urease activity in recombinant bacteria expressing the mutated operon. The obtained data were used to drive a protein-protein docking computational approach.Results and Discussion: The EC prediction retrieved ten pairs of residues lying at the interface between UreD and the urease subunit UreB, likely involved in contacts essential to build the protein complex. These contacts were largely confirmed experimentally, leading to the obtainment of a model for the urease-UreD complex that agrees well with the recently reported experimental cryo-EM structure. This work represents a proof of concept for the calculation of reliable models of protein interaction surfaces in the absence of experimental structures of critical assemblies.

Evolutionary Airfoil Shape Optimization Coupling Parameterization Methods with Lagrangian Vortex Method

In the present paper, the evolutionary algorithm for single-objective optimization is developed using a genetic algorithm and employing polynomial-based (PARSEC) and radial basis function (RBF) functions for NACA 2412 airfoil parameterization. The determination of the objective functions, being aerodynamic coefficients, are performed using the Lagrangian vortex particle method. The results of the lift coefficient at the wide range of angle of attack using the vortex particle method shows a good agreement with experimental data listed in the literature. For the optimization results, the lift coefficient obtained from the PARSEC method is optimized to be larger for the whole range of angle of attack; while it still keeps the stall region at the upper surface of the airfoil to be the same as that of the original airfoil. In addition, the RBF method illustrates the lift coefficients larger at the range of angle of attack from -50 to 140 but stall occurs earlier than the original airfoil.

CovET: A covariation-evolutionary trace method that identifies protein structure–function modules

Measuring the relative effect that any two sequence positions have on each other may improve protein design or help better interpret coding variants. Current approaches use statistics and machine learning but rarely consider phylogenetic divergences which, as shown by Evolutionary Trace studies, provide insight into the functional impact of sequence perturbations. Here, we reframe covariation analyses in the Evolutionary Trace framework to measure the relative tolerance to perturbation of each residue pair during evolution. This approach (CovET) systematically accounts for phylogenetic divergences: at each divergence event, we penalize covariation patterns that belie evolutionary coupling. We find that while CovET approximates the performance of existing methods to predict individual structural contacts, it performs significantly better at finding structural clusters of coupled residues and ligand binding sites. For example, CovET found more functionally critical residues when we examined the RNA recognition motif and WW domains. It correlates better with large-scale epistasis screen data. In the dopamine D2 receptor, top CovET residue pairs recovered accurately the allosteric activation pathway characterized for Class A G protein-coupled receptors. These data suggest that CovET ranks highest the sequence position pairs that play critical functional roles through epistatic and allosteric interactions in evolutionarily relevant structure-function motifs. CovET complements current methods and may shed light on fundamental molecular mechanisms of protein structure and function.

The relaxin receptor RXFP1 signals through a mechanism of autoinhibition.

The relaxin family peptide receptor 1 (RXFP1) is the receptor for relaxin-2, an important regulator of reproductive and cardiovascular physiology. RXFP1 is a multi-domain G protein-coupled receptor (GPCR) with an ectodomain consisting of a low-density lipoprotein receptor class A (LDLa) module and leucine-rich repeats. The mechanism of RXFP1 signal transduction is clearly distinct from that of other GPCRs, but remains very poorly understood. In the present study, we determine the cryo-electron microscopy structure of active-state human RXFP1, bound to a single-chain version of the endogenous agonist relaxin-2 and the heterotrimeric Gs protein. Evolutionary coupling analysis and structure-guided functional experiments reveal that RXFP1 signals through a mechanism of autoinhibition. Our results explain how an unusual GPCR family functions, providing a path to rational drug development targeting the relaxin receptors.

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BiopolymersVolume 114, Issue 3 e23498 COVER IMAGEFree Access Cover Image First published: 24 March 2023 https://doi.org/10.1002/bip.23498AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Graphical Abstract Correlated mutations in a protein sequence are a hallmark of coevolution and are normally interpreted as contacts in the threedimensional structure of a protein or a protein complex. However, other factors such as function, allostery or regulatory mechanisms influence the evolutionary record of protein sequences. A new method is presented that is tuned to specifically identify such long-range evolutionary couplings by taking advantage of the recent developments in machinelearning guided protein structure prediction. DOI: 10.1002/bip.23530 Volume114, Issue3March 2023e23498 RelatedInformation

Evolutionary gate recurrent unit coupling convolutional neural network and improved manta ray foraging optimization algorithm for performance degradation prediction of PEMFC

Performance degradation prediction is an effective method to improve the durability of proton exchange membrane fuel cell (PEMFC). In this study, a hybrid deep learning model based on two-dimensional convolutional neural network (CNN2D), gate recurrent unit (GRU), and improved manta ray foraging optimization (IMRFO) algorithm is proposed for performance degradation prediction of PEMFC. Firstly, the mutual information (MI) and the locally weighted scatterplot smoothing (LOESS) are used to preprocess the data in order to boost the sample quality and reduce the influence of insignificant and noisy data on the model prediction. Secondly, CNN2D is used to deeply explore the nonlinear degradation characteristics in the data. Thirdly, three strategies including half uniform initialization, exponential weight coefficient and fitness-distance balance (FDB) are added to the algorithm to improve the defect that the optimization algorithm is easy to fall into local optimum. Finally, the GRU model optimized by the improved MRFO algorithm is used to predict the degradation data and obtain the final prediction results. The experimental results show that the prediction accuracy of the proposed prediction model in this study is 99.79%, and the RMSE and MAE are 0.0072 and 0.0042, respectively. Therefore, the method can effectively explore the deep features in the data and improve the accuracy, reliability, and robustness of PEMFC performance degradation prediction.

Coevolution-based prediction of key allosteric residues for protein function regulation.

Allostery is fundamental to many biological processes. Due to the distant regulation nature, how allosteric mutations, modifications, and effector binding impact protein function is difficult to forecast. In protein engineering, remote mutations cannot be rationally designed without large-scale experimental screening. Allosteric drugs have raised much attention due to their high specificity and possibility of overcoming existing drug-resistant mutations. However, optimization of allosteric compounds remains challenging. Here, we developed a novel computational method KeyAlloSite to predict allosteric site and to identify key allosteric residues (allo-residues) based on the evolutionary coupling model. We found that protein allosteric sites are strongly coupled to orthosteric site compared to non-functional sites. We further inferred key allo-residues by pairwise comparing the difference of evolutionary coupling scores of each residue in the allosteric pocket with the functional site. Our predicted key allo-residues are in accordance with previous experimental studies for typical allosteric proteins like BCR-ABL1, Tar, and PDZ3, as well as key cancer mutations. We also showed that KeyAlloSite can be used to predict key allosteric residues distant from the catalytic site that are important for enzyme catalysis. Our study demonstrates that weak coevolutionary couplings contain important information of protein allosteric regulation function. KeyAlloSite can be applied in studying the evolution of protein allosteric regulation, designing and optimizing allosteric drugs, and performing functional protein design and enzyme engineering.

Evolutionary Couplings and Molecular Dynamic Simulations Highlight Details of GPCRs Heterodimers' Interfaces.

A growing body of evidence suggests that only a few amino acids ("hot-spots") at the interface contribute most of the binding energy in transient protein-protein interactions. However, experimental protocols to identify these hot-spots are highly labor-intensive and expensive. Computational methods, including evolutionary couplings, have been proposed to predict the hot-spots, but they generally fail to provide details of the interacting amino acids. Here we showed that unbiased evolutionary methods followed by biased molecular dynamic simulations could achieve this goal and reveal critical elements of protein complexes. We applied the methodology to selected G-protein coupled receptors (GPCRs), known for their therapeutic properties. We used the structure-prior-assisted direct coupling analysis (SP-DCA) to predict the binding interfaces of A2aR/D2R, CB1R/D2R, A2aR/CB1R, 5HT2AR/D2R, and 5-HT2AR/mGluR2 receptor heterodimers, which all agreed with published data. In order to highlight details of the interactions, we performed molecular dynamic (MD) simulations using the newly developed AWSEM energy model. We found that these receptors interact primarily through critical residues at the C and N terminal domains and the third intracellular loop (ICL3). The MD simulations showed that these residues are energetically necessary for dimerization and revealed their native conformational state. We subsequently applied the methodology to the 5-HT2AR/5-HTR4R heterodimer, given its implication in drug addiction and neurodegenerative pathologies such as Alzheimer's disease (AD). Further, the SP-DCA analysis showed that 5-HT2AR and 5-HTR4R heterodimerize through the C-terminal domain of 5-HT2AR and ICL3 of 5-HT4R. However, elucidating the details of GPCR interactions would accelerate the discovery of druggable sites and improve our knowledge of the etiology of common diseases, including AD.

An Ancient Transcriptional Hub Couples Developmentally Regulated Gene Expression with Metabolism during Erythropoiesis

The α-globin genes and their regulation by the α-globin superenhancer have been extensively characterised. An adjacent gene, Nprl3, contains 4 of the 5 α-globin enhancers in mice, and 3 of 4 in humans. The neighbouring genomic positions of Nprl3 and α-globin, which share a topologically associated domain, have been maintained for >500 million years. However, the potential functional requirement of this genomic linkage has not been investigated until now. Nprl3 is conserved across Animalia and Fungi, and has been characterised to provide negative regulation of mTORC1, a critical controller of cellular metabolism. Here we report phenotypes of Nprl3-deficient erythropoiesis in mouse fetal liver and bone marrow, and in human peripheral blood. We provide molecular mechanistic insight that Nprl3 is essential for optimal erythropoiesis and for responding to fluctuating nutrient and growth factor concentrations. Finally, we show that erythroid Nprl3 function depends on transcriptional support arising from the α-globin enhancers. Analysis of Nprl3-/- fetal livers revealed severe impairment of erythroid development. On day E13.5, erythroid cells fail to develop beyond the proerythroblast stage. Loss of Nprl3 is accompanied by elevated mTORC1 signalling, confirming the canonical signalling role of Nprl3 in erythroblasts. Active mTORC1 typically inhibits autophagy; in consistence with this, we observed suppressed autophagic activity in Nprl3-/- erythroblasts. We next investigated whether the erythroid defect results from haematopoietic-intrinsic Nprl3 requirements. This was tested using competitive bone marrow - fetal liver chimera experiments, in which Nprl3-/- fetal liver cells contributed poorly to erythroid lineage reconstitution of irradiated adult bone marrow. Thus, Nprl3 regulates baseline erythropoiesis in the mouse. To discover whether NPRL3 is also required for human erythropoiesis, NPRL3-knockout was induced in primary human CD34+ progenitors using a CRISPR-Cas9 RNP-editing system. These progenitors had reduced ability to produce enucleated erythroid cells compared to their negative control counterparts. NPRL3-knockout erythroblasts also demonstrated defective mTORC1 signalling responses to iron deficiency, amino acid withdrawal and erythropoietin stimulation. These results indicate that NPRL3 has a critical role in interpreting fluctuating nutritional environments, and in tuning the metabolic response of developing erythroid cells to the extracellular milieu. Importantly, Nprl3 expression is known to be elevated in erythroid cells, increasing ~30-fold during erythroid lineage commitment due to α-globin enhancer activity. We sought to discover whether this erythroid-specific increase in Nprl3 expression is required for the functionality outlined above. Using genetic engineering to delete the Nprl3 promoter on one allele, and to delete all α-globin enhancers on the other, we eliminated all interactions between Nprl3 and α-globin regulatory elements. Embryos heterozygous for both alleles (Nprl3+/-α-globin-enhancers+/-,'Nalph') retain non-erythroid 'un-enhanced' Nprl3 expression levels, and enhancer-regulated α-globin expression. Remarkably, at E13.5, the Nalph genotype presented impaired erythropoiesis reminiscent of that observed in Nprl3-/- embryos, with development inhibited at the same stage of differentiation. Thus, we conclude that the genomic contact hub Nprl3 shares with α-globin and its enhancers bestows the erythroid-specific transcriptional upregulation required for Nprl3 to perform its important erythropoietic role. This finding suggests that the deep evolutionary coupling of these two genes has enabled the α-globin enhancers to control metabolism, as well as α-globin production, in developing erythroid cells.

Evolutionary coupling analysis guides identification of mistrafficking-sensitive variants in cardiac K+ channels: Validation with hERG.

Loss of function (LOF) mutations of voltage sensitive K+ channel proteins hERG (Kv11.1) and KCNQ1 (Kv7.1) account for the majority of instances of congenital Long QT Syndrome (cLQTS) with the dominant molecular phenotype being a mistrafficking one resulting from protein misfolding. We explored the use of Evolutionary Coupling (EC) analysis, which identifies evolutionarily conserved pairwise amino acid interactions that may contribute to protein structural stability, to identify regions of the channels susceptible to misfolding mutations. Comparison with published experimental trafficking data for hERG and KCNQ1 showed that the method strongly predicts "scaffolding" regions of the channel membrane domains and has useful predictive power for trafficking phenotypes of individual variants. We identified a region in and around the cytoplasmic S2-S3 loop of the hERG Voltage Sensor Domain (VSD) as susceptible to destabilising mutation, and this was confirmed using a quantitative LI-COR ® based trafficking assay that showed severely attenuated trafficking in eight out of 10 natural hERG VSD variants selected using EC analysis. Our analysis highlights an equivalence in the scaffolding structures of the hERG and KCNQ1 membrane domains. Pathogenic variants of ion channels with an underlying mistrafficking phenotype are likely to be located within similar scaffolding structures that are identifiable by EC analysis.

The SEIR Dynamic Evolutionary Model with Markov Chains in Hyper Networks

In real life, individuals play an important role in the social networking system. When an epidemic breaks out the individual’s recovery rate depends heavily on the social network in which he or she lives. For this reason, in this paper a nonlinear coupling dynamic model on the hyper network was built. The upper layer is the dynamic social network under the hypernetwork vision, and the lower layer is the physical contact layer. Thus, the dynamic evolutionary coupling mechanism between the social network and epidemic transmission was established. At the same time, this paper deduced the evolution process of the dynamic system according to the Markov chain method. The probability equation of the dynamic evolution process was determined, and the threshold of epidemic spread on the non-uniform network was obtained. In addition, numerical simulations verified the correctness of the theory and the validity of the model. The results show that an individual’s recovery state will be affected by the individual’s social ability and the degree of information forgetting. Finally, suitable countermeasures are suggested to suppress the pandemic from spreading in response to the coupling model’s affecting factors.

Protein language-model embeddings for fast, accurate, and alignment-free protein structure prediction

Advanced protein structure prediction requires evolutionary information from multiple sequence alignments (MSAs) from evolutionary couplings that are not always available. Artificial intelligence (AI)-based predictions inputting only single sequences are faster but so inaccurate as to render speed irrelevant. Here, we described a competitive prediction of inter-residue distances (2D structure) exclusively inputting embeddings from pre-trained protein language models (pLMs), namely ProtT5, from single sequences into a convolutional neural network (CNN) with relatively few layers. The major advance used the ProtT5 attention heads. Our new method, EMBER2, which never requires any MSAs, performed similarly to other methods that fully rely on co-evolution. Although clearly not reaching AlphaFold2, our leaner solution came somehow close at substantially lower costs. By generating protein-specific rather than family-averaged predictions, EMBER2 might better capture some features of particular protein structures. Results from using protein engineering and deep mutational scanning (DMS) experiments provided at least a proof of principle for such a speculation.

Identification of Eral1 Recognition Sequences for Unfolding and Degradation by Mitochondrial ClpXP

Eral1 is a GTPase and ribosomal assembly factor of the mitochondrial small subunit (SSU). Eral1 binds 12SrRNA at a critical juncture, and its association and dissociation are necessary for mitoribosome maturation. Previous work in the literature indicated the proteolytic complex mitochondrial ClpXP (mtClpXP) as a regulator of dissociation: mtClpXP physically associates with Eral1 in vivo and knockout of ClpP results in Eral1 stabilization, accumulation of Eral1‐bound small subunit precursors, and reduced levels of mature mitoribosomes. In order for mtClpXP to both permit Eral1 association and enact its dissociation from the assembling small subunit, it must either recognize a specific Eral1 conformation that indicates completion of its assembly factor function or precisely tune Eral1 levels to achieve a metastable state. We aim to describe the recognition strategy that mtClpXP utilizes to unfold and degrade Eral1 and enable ribosome assembly. We have demonstrated that mtClpXP directly unfolds and degrades Eral1 in vitro. Evolutionary coupling analysis between human Eral1 and mtClpX has pointed to select residues that may mediate the interaction between these proteins. We have shown that mutation of at least one such Eral1 residue blocks its unfolding and degradation. We are utilizing a peptide SPOT array to characterize additional Eral1 motifs that assist in recognition and unfolding by mtClpX and hypothesize that the exposure of these sequences aligns with the Eral1 state that is recognized by mtClpX.

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.

An Efficient Hybrid Evolutionary Optimization Method Coupling Cultural Algorithm with Genetic Algorithms and Its Application to Aerodynamic Shape Design

Evolutionary algorithms have been widely used to solve complex engineering optimization problems with large search spaces and nonlinearity. Both cultural algorithm (CA) and genetic algorithms (GAs) have a broad prospect in the optimization field. The traditional CA has poor precision in solving complex engineering optimization problems and easily falls into local optima. An efficient hybrid evolutionary optimization method coupling CA with GAs (HCGA) is proposed in this paper. HCGA reconstructs the cultural framework, which uses three kinds of knowledge to build the belief space, and the GAs are used as an evolutionary model for the population space. In addition, a knowledge-guided t-mutation operator is developed to dynamically adjust the mutation step and introduced into the influence function. HCGA achieves a balance between exploitation and exploration through the above strategies, and thus effectively avoids falling into local optima and improves the optimization efficiency. Numerical experiments and comparisons with several benchmark functions show that the proposed HCGA significantly outperforms the other compared algorithms in terms of comprehensive performance, especially for high-dimensional problems. HCGA is further applied to aerodynamic optimization design, with the wing cruise factor being improved by 23.21%, demonstrating that HCGA is an efficient optimization algorithm with potential applications in aerodynamic optimization design.

Evolutionary genomic relationships and coupling in MK-STYX and STYX pseudophosphatases

The dual specificity phosphatase (DUSP) family has catalytically inactive members, called pseudophosphatases. They have mutations in their catalytic motifs that render them enzymatically inactive. This study analyzes the significance of two pseudophosphatases, MK-STYX [MAPK (mitogen-activated protein kinase phosphoserine/threonine/tyrosine-binding protein]) and STYX (serine/threonine/tyrosine-interacting protein), throughout their evolution and provides measurements and comparison of their evolutionary conservation. Phylogenetic trees were constructed to show any deviation from various species evolutionary paths. Data was collected on a large set of proteins that have either one of the two domains of MK-STYX, the DUSP domain or the cdc-25 homology (CH2) /rhodanese-like domain. The distance between species pairs for MK-STYX or STYX and Ka/Ks ratio were calculated. In addition, both pseudophosphatases were ranked among a large set of related proteins, including the active homologs of MK-STYX, MKP (MAPK phosphatase)-1 and MKP-3. MK-STYX had one of the highest species-species protein distances and was under weaker purifying selection pressure than most proteins with its domains. In contrast, the protein distances of STYX were lower than 82% of the DUSP-containing proteins and was under one of the strongest purifying selection pressures. However, there was similar selection pressure on the N-terminal sequences of MK-STYX, STYX, MKP-1, and MKP-3. We next perform statistical coupling analysis, a process that reveals interconnected regions within the proteins. We find that while MKP-1,-3, and STYX all have 2 functional units (sectors), MK-STYX only has one, and that MK-STYX is similar to MKP-3 in the evolutionary coupling of the active site and KIM domain. Within those two domains, the mean coupling is also most similar for MK-STYX and MKP-3. This study reveals striking distinctions between the evolutionary patterns of MK-STYX and STYX, suggesting a very specific role for each pseudophosphatase, further highlighting the relevance of these atypical members of DUSP as signaling regulators. Therefore, our study provides computational evidence and evolutionary reasons to further explore the properties of pseudophosphatases, in particular MK-STYX and STYX.

Structural evolution of the ancient enzyme, dissimilatory sulfite reductase.

Dissimilatory sulfite reductase is an ancient enzyme that has linked the global sulfur and carbon biogeochemical cycles since at least 3.47 Gya. While much has been learned about the phylogenetic distribution and diversity of DsrAB across environmental gradients, far less is known about the structural changes that occurred to maintain DsrAB function as the enzyme accompanied diversification of sulfate/sulfite reducing organisms (SRO) into new environments. Analyses of available crystal structures of DsrAB from Archaeoglobus fulgidus and Desulfovibrio vulgaris, representing early and late evolving lineages, respectively, show that certain features of DsrAB are structurally conserved, including active siro-heme binding motifs. Whether such structural features are conserved among DsrAB recovered from varied environments, including hot spring environments that host representatives of the earliest evolving SRO lineage (e.g., MV2-Eury), is not known. To begin to overcome these gaps in our understanding of the evolution of DsrAB, structural models from MV2.Eury were generated and evolutionary sequence co-variance analyses were conducted on a curated DsrAB database. Phylogenetically diverse DsrAB harbor many conserved functional residues including those that ligate active siro-heme(s). However, evolutionary co-variance analysis of monomeric DsrAB subunits revealed several False Positive Evolutionary Couplings (FPEC) that correspond to residues that have co-evolved despite being too spatially distant in the monomeric structure to allow for direct contact. One set of FPECs corresponds to residues that form a structural path between the two active siro-heme moieties across the interface between heterodimers, suggesting the potential for allostery or electron transfer within the enzyme complex. Other FPECs correspond to structural loops and gaps that may have been selected to stabilize enzyme function in different environments. These structural bioinformatics results suggest that DsrAB has maintained allosteric communication pathways between subunits as SRO diversified into new environments. The observations outlined here provide a framework for future biochemical and structural analyses of DsrAB to examine potential allosteric control of this enzyme.

Systematic discovery of non-native protein conformations and their molecular and cellular phenotypes

Native structures of many proteins have been solved experimentally or predicted with increasing accuracy using evolutionary couplings and machine learning. However, no current approach is well-suited for elucidating misfolded, partially disordered, or transient conformational states. Such altered conformations are of high interest because they drive protein misfolding diseases, determine success in protein design efforts, and provide evolutionary routes toward conformational switching. To discover non-native structures a protein may adopt (upon random fluctuation, environmental perturbation, or mutation) and to map them to their molecular, cellular, or organismal phenotypic consequences, we have developed a new experimental structural technique: high-throughput disulfide scanning (HTDS).

A reproducibility analysis-based statistical framework for residue-residue evolutionary coupling detection.

Direct coupling analysis (DCA) has been widely used to infer evolutionary coupled residue pairs from the multiple sequence alignment (MSA) of homologous sequences. However, effectively selecting residue pairs with significant evolutionary couplings according to the result of DCA is a non-trivial task. In this study, we developed a general statistical framework for significant evolutionary coupling detection, referred to as irreproducible discovery rate (IDR)-DCA, which is based on reproducibility analysis of the coupling scores obtained from DCA on manually created MSA replicates. IDR-DCA was applied to select residue pairs for contact prediction for monomeric proteins, protein-protein interactions and monomeric RNAs, in which three different versions of DCA were applied. We demonstrated that with the application of IDR-DCA, the residue pairs selected using a universal threshold always yielded stable performance for contact prediction. Comparing with the application of carefully tuned coupling score cutoffs, IDR-DCA always showed better performance. The robustness of IDR-DCA was also supported through the MSA downsampling analysis. We further demonstrated the effectiveness of applying constraints obtained from residue pairs selected by IDR-DCA to assist RNA secondary structure prediction.