Human Milk Protein-Derived Bioactive Peptides from In Vitro-Digested Colostrum Exert Antimicrobial Activities against Common Neonatal Pathogens.

Human milk reduces risk for necrotizing enterocolitis in preterm infants. Necrotizing enterocolitis occurs in the ileocecal region where thousands of milk protein-derived peptides have been released from digestion. Digestion-released peptides may exert bioactivity, such as antimicrobial and immunomodulatory activities, in the gut. In this study, we applied mass spectrometry-based peptidomics to characterize peptides present in colostrum before and after in vitro digestion. Sequence-based computational modeling was applied to predict peptides with antimicrobial activity. We identified more peptides in undigested samples, yet the abundances were much higher in the digested samples. Heatmapping demonstrated highly different peptide profiles between undigested and digested samples. Four peptides (αS1-casein [157-163], αS1-casein [157-165], ß-casein [153-159] and plasminogen [591-597]) were selected, synthesized and tested against common pathogenic bacteria associated with necrotizing enterocolitis. All four exhibited bacteriostatic, though not bactericidal, activities against Klebsiella aerogenes, Citrobacter freundii and Serratia marcescens, but not Escherichia coli.

Enhanced catabolism of glycine betaine and derivatives provides improved osmotic stress protection in Methylorubrum extorquens PA1.

Integration of metabolites into the overall metabolic network of a cell requires careful coordination dependent upon the ultimate usage of the metabolite. Different stoichiometric needs, and thus pathway fluxes, must exist for compounds destined for diverse uses, such as carbon sources, nitrogen sources, or stress-protective agents. Herein, we expand upon our previous work that highlighted the nature of glycine betaine (GB) metabolism in Methylobacteria to examine the utilization of GB-derivative compounds dimethylglycine (DMG) and sarcosine into Methylorubrum extorquens in different metabolic capacities, including as sole nitrogen and/or carbon sources. We isolated gain-of-function mutations that allowed M. extorquens PA1 to utilize dimethylglycine as a carbon source and dimethylglycine and sarcosine as nitrogen source. Characterization of mutants demonstrated selection for variants of the AraC-like regulator Mext_3735 that confer constitutive expression of the GB metabolic gene cluster, allowing direct utilization of the downstream GB derivatives. Finally, among the distinct isolates examined, we found that catabolism of the osmoprotectant used for selection (GB or dimethylglycine) enhanced osmotic stress resistance provided in the presence of that particular osmolyte. Thus, access to the carbon and nitrogen and osmoprotective effects of GB and DMG are made readily accessible through adaptive mutations. In M. extorquens PA1, the limitations to exploiting this group of compounds appear to exist predominantly at the levels of gene regulation and functional activity, rather than being constrained by transport or toxicity.IMPORTANCEOsmotic stress is a common challenge for bacteria colonizing the phyllosphere, where glycine betaine (GB) can be found as a prevalent osmoprotectant. Though Methylorubrum extorquens PA1 cannot use GB or its demethylation products, dimethylglycine (DMG) and sarcosine, as a sole carbon source, utilization is highly selectable via single nucleotide changes for both GB and DMG growth. The innate inability to use these compounds is due to limited flux through steps in the pathway and regulatory constraints. Herein, the characterization of the transcriptional regulator, Mext_3735 (GbdR), expands our understanding of the various roles in which GB derivatives can be used in M. extorquens PA1. Interestingly, increased catabolism of GB and derivatives does not interfere with, but rather improves, the ability of cells to thrive under increased salt stress conditions, suggesting that metabolic flux improves stress tolerance rather than providing a distinct tension between uses.

Statistical modeling to quantify the uncertainty of FoldX-predicted protein folding and binding stability.

BACKGROUND: Computational methods of predicting protein stability changes upon missense mutations are invaluable tools in high-throughput studies involving a large number of protein variants. However, they are limited by a wide variation in accuracy and difficulty of assessing prediction uncertainty. Using a popular computational tool, FoldX, we develop a statistical framework that quantifies the uncertainty of predicted changes in protein stability. RESULTS: We show that multiple linear regression models can be used to quantify the uncertainty associated with FoldX prediction for individual mutations. Comparing the performance among models with varying degrees of complexity, we find that the model precision improves significantly when we utilize molecular dynamics simulation as part of the FoldX workflow. Based on the model that incorporates information from molecular dynamics, biochemical properties, as well as FoldX energy terms, we can generally expect upper bounds on the uncertainty of folding stability predictions of ± 2.9 kcal/mol and ± 3.5 kcal/mol for binding stability predictions. The uncertainty for individual mutations varies; our model estimates it using FoldX energy terms, biochemical properties of the mutated residue, as well as the variability among snapshots from molecular dynamics simulation. CONCLUSIONS: Using a linear regression framework, we construct models to predict the uncertainty associated with FoldX prediction of stability changes upon mutation. This technique is straightforward and can be extended to other computational methods as well.

The effect of mutations on binding interactions between the SARS-CoV-2 receptor binding domain and neutralizing antibodies B38 and CB6.

SARS-CoV-2 is the pathogen responsible for COVID-19 that has claimed over six million lives as of July 2022. The severity of COVID-19 motivates a need to understand how it could evolve to escape potential treatments and to find ways to strengthen existing treatments. Here, we used the molecular modeling methods MD + FoldX and PyRosetta to study the SARS-CoV-2 spike receptor binding domain (S-RBD) bound to two neutralizing antibodies, B38 and CB6 and generated lists of antibody escape and antibody strengthening mutations. Our resulting watchlist contains potential antibody escape mutations against B38/CB6 and consists of 211/186 mutations across 35/22 S-RBD sites. Some of these mutations have been identified in previous studies as being significant in human populations (e.g., N501Y). The list of potential antibody strengthening mutations that are predicted to improve binding of B38/CB6 to S-RBD consists of 116/45 mutations across 29/13 sites. These mutations could be used to improve the therapeutic value of these antibodies.

Defining the HIV Capsid Binding Site of Nucleoporin 153.

The interaction between the HIV-1 capsid and human nucleoporin 153 (NUP153) is vital for delivering the HIV-1 preintegration complex into the nucleus via the nuclear pore complex. The interaction with the capsid requires a phenylalanine/glycine-containing motif in the C-terminus of NUP153 (NUP153C). This study used molecular modeling and biochemical assays to comprehensively determine the amino acids in NUP153 that are important for capsid interaction. Molecular dynamics, FoldX, and PyRosetta simulations delineated the minimal capsid binding motif of NUP153 based on the known structure of NUP153 bound to the HIV-1 capsid hexamer. Computational predictions were experimentally validated by testing the interaction of NUP153 with capsid using an in vitro binding assay and a cell-based TRIM-NUP153C restriction assay. This work identified eight amino acids from P1411 to G1418 that stably engage with capsid, with significant correlations between the interactions predicted by molecular models and empirical experiments. This validated the usefulness of this multidisciplinary approach to rapidly characterize the interaction between human proteins and the HIV-1 capsid. IMPORTANCE The human immunodeficiency virus (HIV) can infect nondividing cells by interacting with the host nuclear pore complex. The host nuclear pore protein NUP153 directly interacts with the HIV capsid to promote viral nuclear entry. This study used a multidisciplinary approach combining computational and experimental techniques to comprehensively map the effect of mutating the amino acids of NUP153 on HIV capsid interaction. This work showed a significant correlation between computational and empirical data sets, revealing that the HIV capsid interacted specifically with only six amino acids of NUP153. The simplicity of the interaction motif suggested other FG-containing motifs could also interact with the HIV-1 capsid. Furthermore, it was predicted that naturally occurring polymorphisms in human and nonhuman primates would disrupt NUP153 interaction with capsid, potentially protecting certain populations from HIV-1 infection.

An EvoDevo Study of Salmonid Visual Opsin Dynamics and Photopigment Spectral Sensitivity.

Salmonids are ideal models as many species follow a distinct developmental program from demersal eggs and a large yolk sac to hatching at an advanced developmental stage. Further, these economically important teleosts inhabit both marine- and freshwaters and experience diverse light environments during their life histories. At a genome level, salmonids have undergone a salmonid-specific fourth whole genome duplication event (Ss4R) compared to other teleosts that are already more genetically diverse compared to many non-teleost vertebrates. Thus, salmonids display phenotypically plastic visual systems that appear to be closely related to their anadromous migration patterns. This is most likely due to a complex interplay between their larger, more gene-rich genomes and broad spectrally enriched habitats; however, the molecular basis and functional consequences for such diversity is not fully understood. This study used advances in genome sequencing to identify the repertoire and genome organization of visual opsin genes (those primarily expressed in retinal photoreceptors) from six different salmonids [Atlantic salmon (Salmo salar), brown trout (Salmo trutta), Chinook salmon (Oncorhynchus tshawytcha), coho salmon (Oncorhynchus kisutch), rainbow trout (Oncorhynchus mykiss), and sockeye salmon (Oncorhynchus nerka)] compared to the northern pike (Esox lucius), a closely related non-salmonid species. Results identified multiple orthologues for all five visual opsin classes, except for presence of a single short-wavelength-sensitive-2 opsin gene. Several visual opsin genes were not retained after the Ss4R duplication event, which is consistent with the concept of salmonid rediploidization. Developmentally, transcriptomic analyzes of Atlantic salmon revealed differential expression within each opsin class, with two of the long-wavelength-sensitive opsins not being expressed before first feeding. Also, early opsin expression in the retina was located centrally, expanding dorsally and ventrally as eye development progressed, with rod opsin being the dominant visual opsin post-hatching. Modeling by spectral tuning analysis and atomistic molecular simulation, predicted the greatest variation in the spectral peak of absorbance to be within the Rh2 class, with a ∼40 nm difference in λ max values between the four medium-wavelength-sensitive photopigments. Overall, it appears that opsin duplication and expression, and their respective spectral tuning profiles, evolved to maximize specialist color vision throughout an anadromous lifecycle, with some visual opsin genes being lost to tailor marine-based vision.

Molecular Modeling Predicts Novel Antibody Escape Mutations in the Respiratory Syncytial Virus Fusion Glycoprotein.

Monoclonal antibodies are increasingly used for the prevention and/or treatment of viral infections. One caveat of their use is the ability of viruses to evolve resistance to antibody binding and neutralization. Computational strategies to identify viral mutations that may disrupt antibody binding would leverage the wealth of viral genomic sequence data to monitor for potential antibody-resistant mutations. The respiratory syncytial virus is an important pathogen for which monoclonal antibodies against the fusion (F) protein are used to prevent severe disease in high-risk infants. In this study, we used an approach that combines molecular dynamics simulations with FoldX to estimate changes in free energy in F protein folding and binding to the motavizumab antibody upon each possible amino acid change. We systematically selected 8 predicted escape mutations and tested them in an infectious clone. Consistent with our F protein stability predictions, replication-effective viruses were observed for each selected mutation. Six of the eight variants showed increased resistance to neutralization by motavizumab. Flow cytometry was used to validate the estimated (model-predicted) effects on antibody binding to F. Using surface plasmon resonance, we determined that changes in the on-rate of motavizumab binding were associated with the reduced affinity for two novel escape mutations. Our study empirically validated the accuracy of our molecular modeling approach and emphasized the role of biophysical protein modeling in predicting viral resistance to antibody-based therapeutics that can be used to monitor the emergence of resistant viruses and to design improved therapeutic antibodies. IMPORTANCE Respiratory syncytial virus (RSV) causes severe disease in young infants, particularly those with heart or lung diseases or born prematurely. Because no vaccine is currently available, monoclonal antibodies are used to prevent severe RSV disease in high-risk infants. While it is known that RSV evolves to avoid recognition by antibodies, screening tools that can predict which changes to the virus may lead to antibody resistance are greatly needed.

The Characterization of a Novel Virus Discovered in the Yeast Pichia membranifaciens.

Mycoviruses are widely distributed across fungi, including the yeasts of the Saccharomycotina subphylum. This manuscript reports the first double-stranded RNA (dsRNA) virus isolated from Pichia membranifaciens. This novel virus has been named Pichia membranifaciens virus L-A (PmV-L-A) and is a member of the Totiviridae. PmV-L-A is 4579 bp in length, with RNA secondary structures similar to the packaging, replication, and frameshift signals of totiviruses that infect Saccharomycotina yeasts. PmV-L-A was found to be part of a monophyletic group within the I-A totiviruses, implying a shared ancestry between mycoviruses isolated from the Pichiaceae and Saccharomycetaceae yeasts. Energy-minimized AlphaFold2 molecular models of the PmV-L-A Gag protein revealed structural conservation with the Gag protein of Saccharomyces cerevisiae virus L-A (ScV-L-A). The predicted tertiary structure of the PmV-L-A Pol and other homologs provided a possible mechanism for totivirus RNA replication due to structural similarities with the RNA-dependent RNA polymerases of mammalian dsRNA viruses. Insights into the structure, function, and evolution of totiviruses gained from yeasts are essential because of their emerging role in animal disease and their parallels with mammalian viruses.

Paired Simulations and Experimental Investigations into the Calcium-Dependent Conformation of Albumin.

Serum albumin is the most abundant protein in blood plasma, and it is involved in multiple biological processes. Serum albumin has recently been adapted for improving biomaterial integration with bone tissue, and studies have shown the importance of this protein in bone repair and regeneration. However, the mechanism of action is not yet clear. In stark contrast, other studies have demonstrated that albumin blocks cell adhesion to surfaces, which is seen as a limitation to its bone healing role. These apparent contradictions suggest that the conformation of albumin facilitates its bioactivity, leading to enhanced bone repair. Serum albumin is known to play a major role in maintaining the calcium ion concentration in blood plasma. Due to the prevalence of calcium at bone repair and regeneration sites, it has been hypothesized that calcium binding to serum albumin triggers a conformational change, leading to bioactivity. In the current study, molecular modeling approaches including molecular docking, atomic molecular dynamics (MD) simulation, and coarse-grained MD simulation were used to test this hypothesis by investigating the conformational changes induced in bovine serum albumin by interaction with calcium ions. The computational results were qualitatively validated with experimental Fourier-transform infrared spectroscopy analysis. We find that free calcium ions in solution transiently bind with the three major loops in albumin, triggering a conformational change where N-terminal and C-terminal domains separate from each other in a partial unfolding process. The separation distance between these domains was found to correlate with the calcium ion concentration. The experimental data support the simulation results showing that albumin has enhanced conformational heterogeneity upon exposure to intermediate levels of calcium, without any significant secondary structure changes.

Nuku, a family of primate retrocopies derived from KU70.

The gene encoding the ubiquitous DNA repair protein, Ku70p, has undergone extensive copy number expansion during primate evolution. Gene duplications of KU70 have the hallmark of long interspersed element-1 mediated retrotransposition with evidence of target-site duplications, the poly-A tails, and the absence of introns. Evolutionary analysis of this expanded family of KU70-derived "NUKU" retrocopies reveals that these genes are both ancient and also actively being created in extant primate species. NUKU retrocopies show evidence of functional divergence away from KU70, as evinced by their altered pattern of tissue expression and possible tissue-specific translation. Molecular modeling predicted that amino acid changes in Nuku2p at the interaction interface with Ku80p would prevent the assembly of the Ku heterodimer. The lack of Nuku2p-Ku80p interaction was confirmed by yeast two-hybrid assay, which contrasts the robust interaction of Ku70p-Ku80p. While several NUKU retrocopies appear to have been degraded by mutation, NUKU2 shows evidence of positive natural selection, suggesting that this retrocopy is undergoing neofunctionalization. Although Nuku proteins do not appear to antagonize retrovirus transduction in cell culture, the observed expansion and rapid evolution of NUKUs could be being driven by alternative selective pressures related to infectious disease or an undefined role in primate physiology.

The pesticide chlorpyrifos promotes obesity by inhibiting diet-induced thermogenesis in brown adipose tissue.

Obesity results from a caloric imbalance between energy intake, absorption and expenditure. In both rodents and humans, diet-induced thermogenesis contributes to energy expenditure and involves the activation of brown adipose tissue (BAT). We hypothesize that environmental toxicants commonly used as food additives or pesticides might reduce BAT thermogenesis through suppression of uncoupling protein 1 (UCP1) and this may contribute to the development of obesity. Using a step-wise screening approach, we discover that the organophosphate insecticide chlorpyrifos suppresses UCP1 and mitochondrial respiration in BAT at concentrations as low as 1 pM. In mice housed at thermoneutrality and fed a high-fat diet, chlorpyrifos impairs BAT mitochondrial function and diet-induced thermogenesis, promoting greater obesity, non-alcoholic fatty liver disease (NAFLD) and insulin resistance. This is associated with reductions in cAMP; activation of p38MAPK and AMPK; protein kinases critical for maintaining UCP1 and mitophagy, respectively in BAT. These data indicate that the commonly used pesticide chlorpyrifos, suppresses diet-induced thermogenesis and the activation of BAT, suggesting its use may contribute to the obesity epidemic.

EfgA is a conserved formaldehyde sensor that leads to bacterial growth arrest in response to elevated formaldehyde.

Normal cellular processes give rise to toxic metabolites that cells must mitigate. Formaldehyde is a universal stressor and potent metabolic toxin that is generated in organisms from bacteria to humans. Methylotrophic bacteria such as Methylorubrum extorquens face an acute challenge due to their production of formaldehyde as an obligate central intermediate of single-carbon metabolism. Mechanisms to sense and respond to formaldehyde were speculated to exist in methylotrophs for decades but had never been discovered. Here, we identify a member of the DUF336 domain family, named efgA for enhanced formaldehyde growth, that plays an important role in endogenous formaldehyde stress response in M. extorquens PA1 and is found almost exclusively in methylotrophic taxa. Our experimental analyses reveal that EfgA is a formaldehyde sensor that rapidly arrests growth in response to elevated levels of formaldehyde. Heterologous expression of EfgA in Escherichia coli increases formaldehyde resistance, indicating that its interaction partners are widespread and conserved. EfgA represents the first example of a formaldehyde stress response system that does not involve enzymatic detoxification. Thus, EfgA comprises a unique stress response mechanism in bacteria, whereby a single protein directly senses elevated levels of a toxic intracellular metabolite and safeguards cells from potential damage.

Targeting SUMOylation dependency in human cancer stem cells through a unique SAE2 motif revealed by chemical genomics.

Natural products (NPs) encompass a rich source of bioactive chemical entities. Here, we used human cancer stem cells (CSCs) in a chemical genomics campaign with NP chemical space to interrogate extracts from diverse strains of actinomycete for anti-cancer properties. We identified a compound (McM25044) capable of selectively inhibiting human CSC function versus normal stem cell counterparts. Biochemical and molecular studies revealed that McM025044 exerts inhibition on human CSCs through the small ubiquitin-like modifier (SUMO) cascade, found to be hyperactive in a variety of human cancers. McM025044 impedes the SUMOylation pathway via direct targeting of the SAE1/2 complex. Treatment of patient-derived CSCs resulted in reduced levels of SUMOylated proteins and suppression of progenitor and stem cell capacity measured in vitro and in vivo. Our study overcomes a barrier in chemically inhibiting oncogenic SUMOylation activity and uncovers a unique role for SAE2 in the biology of human cancers.

Implementing and Assessing an Alchemical Method for Calculating Protein-Protein Binding Free Energy.

Protein-protein binding is fundamental to most biological processes. It is important to be able to use computation to accurately estimate the change in protein-protein binding free energy due to mutations in order to answer biological questions that would be experimentally challenging, laborious, or time-consuming. Although nonrigorous free-energy methods are faster, rigorous alchemical molecular dynamics-based methods are considerably more accurate and are becoming more feasible with the advancement of computer hardware and molecular simulation software. Even with sufficient computational resources, there are still major challenges to using alchemical free-energy methods for protein-protein complexes, such as generating hybrid structures and topologies, maintaining a neutral net charge of the system when there is a charge-changing mutation, and setting up the simulation. In the current study, we have used the pmx package to generate hybrid structures and topologies, and a double-system/single-box approach to maintain the net charge of the system. To test the approach, we predicted relative binding affinities for two protein-protein complexes using a nonequilibrium alchemical method based on the Crooks fluctuation theorem and compared the results with experimental values. The method correctly identified stabilizing from destabilizing mutations for a small protein-protein complex, and a larger, more challenging antibody complex. Strong correlations were obtained between predicted and experimental relative binding affinities for both protein-protein systems.

Assessment of software methods for estimating protein-protein relative binding affinities.

A growing number of computational tools have been developed to accurately and rapidly predict the impact of amino acid mutations on protein-protein relative binding affinities. Such tools have many applications, for example, designing new drugs and studying evolutionary mechanisms. In the search for accuracy, many of these methods employ expensive yet rigorous molecular dynamics simulations. By contrast, non-rigorous methods use less exhaustive statistical mechanics, allowing for more efficient calculations. However, it is unclear if such methods retain enough accuracy to replace rigorous methods in binding affinity calculations. This trade-off between accuracy and computational expense makes it difficult to determine the best method for a particular system or study. Here, eight non-rigorous computational methods were assessed using eight antibody-antigen and eight non-antibody-antigen complexes for their ability to accurately predict relative binding affinities (ΔΔG) for 654 single mutations. In addition to assessing accuracy, we analyzed the CPU cost and performance for each method using a variety of physico-chemical structural features. This allowed us to posit scenarios in which each method may be best utilized. Most methods performed worse when applied to antibody-antigen complexes compared to non-antibody-antigen complexes. Rosetta-based JayZ and EasyE methods classified mutations as destabilizing (ΔΔG < -0.5 kcal/mol) with high (83-98%) accuracy and a relatively low computational cost for non-antibody-antigen complexes. Some of the most accurate results for antibody-antigen systems came from combining molecular dynamics with FoldX with a correlation coefficient (r) of 0.46, but this was also the most computationally expensive method. Overall, our results suggest these methods can be used to quickly and accurately predict stabilizing versus destabilizing mutations but are less accurate at predicting actual binding affinities. This study highlights the need for continued development of reliable, accessible, and reproducible methods for predicting binding affinities in antibody-antigen proteins and provides a recipe for using current methods.

Short-wavelength-sensitive 2 (Sws2) visual photopigment models combined with atomistic molecular simulations to predict spectral peaks of absorbance.

For many species, vision is one of the most important sensory modalities for mediating essential tasks that include navigation, predation and foraging, predator avoidance, and numerous social behaviors. The vertebrate visual process begins when photons of the light interact with rod and cone photoreceptors that are present in the neural retina. Vertebrate visual photopigments are housed within these photoreceptor cells and are sensitive to a wide range of wavelengths that peak within the light spectrum, the latter of which is a function of the type of chromophore used and how it interacts with specific amino acid residues found within the opsin protein sequence. Minor differences in the amino acid sequences of the opsins are known to lead to large differences in the spectral peak of absorbance (i.e. the λmax value). In our prior studies, we developed a new approach that combined homology modeling and molecular dynamics simulations to gather structural information associated with chromophore conformation, then used it to generate statistical models for the accurate prediction of λmax values for photopigments derived from Rh1 and Rh2 amino acid sequences. In the present study, we test our novel approach to predict the λmax of phylogenetically distant Sws2 cone opsins. To build a model that can predict the λmax using our approach presented in our prior studies, we selected a spectrally-diverse set of 11 teleost Sws2 photopigments for which both amino acid sequence information and experimentally measured λmax values are known. The final first-order regression model, consisting of three terms associated with chromophore conformation, was sufficient to predict the λmax of Sws2 photopigments with high accuracy. This study further highlights the breadth of our approach in reliably predicting λmax values of Sws2 cone photopigments, evolutionary-more distant from template bovine RH1, and provided mechanistic insights into the role of known spectral tuning sites.

Predicting the viability of beta-lactamase: How folding and binding free energies correlate with beta-lactamase fitness.

One of the long-standing holy grails of molecular evolution has been the ability to predict an organism's fitness directly from its genotype. With such predictive abilities in hand, researchers would be able to more accurately forecast how organisms will evolve and how proteins with novel functions could be engineered, leading to revolutionary advances in medicine and biotechnology. In this work, we assemble the largest reported set of experimental TEM-1 ß-lactamase folding free energies and use this data in conjunction with previously acquired fitness data and computational free energy predictions to determine how much of the fitness of ß-lactamase can be directly predicted by thermodynamic folding and binding free energies. We focus upon ß-lactamase because of its long history as a model enzyme and its central role in antibiotic resistance. Based upon a set of 21 ß-lactamase single and double mutants expressly designed to influence protein folding, we first demonstrate that modeling software designed to compute folding free energies such as FoldX and PyRosetta can meaningfully, although not perfectly, predict the experimental folding free energies of single mutants. Interestingly, while these techniques also yield sensible double mutant free energies, we show that they do so for the wrong physical reasons. We then go on to assess how well both experimental and computational folding free energies explain single mutant fitness. We find that folding free energies account for, at most, 24% of the variance in ß-lactamase fitness values according to linear models and, somewhat surprisingly, complementing folding free energies with computationally-predicted binding free energies of residues near the active site only increases the folding-only figure by a few percent. This strongly suggests that the majority of ß-lactamase's fitness is controlled by factors other than free energies. Overall, our results shed a bright light on to what extent the community is justified in using thermodynamic measures to infer protein fitness as well as how applicable modern computational techniques for predicting free energies will be to the large data sets of multiply-mutated proteins forthcoming.

Electrostatic Switching Controls Channel Dynamics of the Sensor Protein VirB10 in A. tumefaciens Type IV Secretion System.

Type IV secretion systems are large nanomachines assembled across the bacterial cell envelope for effector translocation and conjugation. VirB10 traverses the inner and outer membranes, sensing cellular signals for coordinating the conformational switch for pilus biogenesis and/or secretion. Mutations uncoupling secretion from pilus biogenesis were identified in Agrobacterium tumefaciens VirB10 including a gating defect mutation G272R that made VirB10 unresponsive to intracellular ATP, causing unregulated secretion of VirE2 in a contact-independent manner. Comparative long-timescale molecular dynamics of the wild type and G272R mutant of the A. tumefaciens VirB10CTD tetradecamer reveals how the G272R mutation locks the oligomer in a rigid conformation by swapping the ionic interactions between the loops from the ß-barrel close to the inner leaflet of the outer membrane. This electrostatic switching changes the allosteric communication pathway from the extracellular loop to the base of the barrel, suggesting that the local conformational dynamics in the loops can gate information across VirB10.

Vision using multiple distinct rod opsins in deep-sea fishes.

Vertebrate vision is accomplished through light-sensitive photopigments consisting of an opsin protein bound to a chromophore. In dim light, vertebrates generally rely on a single rod opsin [rhodopsin 1 (RH1)] for obtaining visual information. By inspecting 101 fish genomes, we found that three deep-sea teleost lineages have independently expanded their RH1 gene repertoires. Among these, the silver spinyfin (Diretmus argenteus) stands out as having the highest number of visual opsins in vertebrates (two cone opsins and 38 rod opsins). Spinyfins express up to 14 RH1s (including the most blueshifted rod photopigments known), which cover the range of the residual daylight as well as the bioluminescence spectrum present in the deep sea. Our findings present molecular and functional evidence for the recurrent evolution of multiple rod opsin-based vision in vertebrates.