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

Enhancing the Stability of Sweet Protein Neoculin for Food Applications Using Computational Strategies

Sweet proteins, known for their high sweetness and low caloric content, offer great potential as safe, low-calorie sweeteners without the negative health effects associated with sugar or artificial alternatives. Neoculin, unique for its dual properties of sweetness and taste-modifying, holds significant promise for food industry applications. However, its low thermal stability limits its broader use in food processing, highlighting the need to improve its stability. In this study, we applied disulfide bonds design and saturation mutagenesis based on the PyRosetta algorithm to improve neoculin's thermal stability. Experimental screening revealed that introducing a pair of disulfide bonds increased the melting temperature (Tm) by 4.7 °C, and subsequent saturation mutagenesis led to a mutant with a significant Tm increase of 14.5 °C. This combined approach resulted in a final mutant (FM) with a Tm of 84.7, 20.3 °C higher than the wild-type (WT), while maintaining sweetness. Furthermore, Gaussian network model (GNM) analysis indicated that the cross-correlation of specific residues in FM was enhanced compared to WT, which may explain the improved thermal stability. This study presents a neoculin variant with substantial industrial application potential in the food and beverage sectors and offers valuable strategies for future protein engineering of low-calorie sweeteners.

Computational design towards a boiling-resistant single-chain sweet protein monellin

Sweet proteins offer a promising solution as sugar substitutes by providing a sugar-like sweetness without the negative health impacts linked to sugar or artificial sweeteners. However, the low thermal stability of sweet proteins has hindered their applications. In this study, we took a computational approach utilizing ΔΔG calculations in PyRosetta to enhance the thermostability of single-chain monellin (MNEI). By generating and characterizing 21 variants with single mutation, we identified 11 variants with higher melting temperature (Tm) than that of MNEI. To further enhance the thermal stability, we conducted structural analysis and designed an additional set of 14 variants with multiple mutations. Among these variants, four exhibited a significant improvement in thermal stability, with an increase of at least 20 °C (Tm > 96 °C) compared to MNEI, while maintaining their sweetness. Remarkably, these variants remained soluble even after being heated in boiling water for one hour. Moreover, they displayed exceptional stability across alkaline, acidic and neutral environments. These findings highlight the tremendous potential of these variants for applications in the food and beverage industry. Additionally, this study provides valuable strategies for protein engineering to enhance the thermal stability of sweet proteins.

Revisiting the hydrolysis of ampicillin catalyzed by Temoneira-1 β-lactamase, and the effect of Ni(II), Cd(II) and Hg(II)

β-Lactamases grant resistance to bacteria against β-lactam antibiotics. The active center of TEM-1 β-lactamase accommodates a Ser-Xaa-Xaa-Lys motif. TEM-1 β-lactamase is not a metalloenzyme but it possesses several putative metal ion binding sites. The sites composed of His residue pairs chelate borderline transition metal ions such as Ni(II). In addition, there are many sulfur-containing donor groups that can coordinate soft metal ions such as Hg(II). Cd(II) may bind to both types of the above listed donor groups. No significant change was observed in the circular dichroism spectra of TEM-1 β-lactamase on increasing the metal ion content of the samples, with the exception of Hg(II) inducing a small change in the secondary structure of the protein. A weak nonspecific binding of Hg(II) was proven by mass spectrometry and 119m Hg perturbed angular correlation spectroscopy. The hydrolytic process of ampicillin catalyzed by TEM-1 β-lactamase was described by the kinetic analysis of the set of full catalytic progress curves, where the slow, yet observable conversion of the primary reaction product into a second one, identified as ampilloic acid by mass spectrometry, needed also to be considered in the applied model. Ni(II) and Cd(II) slightly promoted the catalytic activity of the enzyme while Hg(II) exerted a noticeable inhibitory effect. Hg(II) and Ni(II), applied at 10 μM concentration, inhibited the growth of E. coli BL21(DE3) in M9 minimal medium in the absence of ampicillin, but addition of the antibiotic could neutralize this toxic effect by complexing the metal ions.

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.

High-Accuracy Prediction of Stabilizing Surface Mutations to the Three-Helix Bundle, UBA(1), with EmCAST

The accurate modeling of energetic contributions to protein structure is a fundamental challenge in computational approaches to protein analysis and design. We describe a general computational method, EmCAST (empirical Cα stabilization), to score and optimize the sequence to the structure in proteins. The method relies on an empirical potential derived from the database of the Cα dihedral angle preferences for all possible four-residue sequences, using the data available in the Protein Data Bank. Our method produces stability predictions that naturally correlate one-to-one with the experimental results for solvent-exposed mutation sites. EmCAST predicted four mutations that increased the stability of a three-helix bundle, UBA(1), from 2.4 to 4.8 kcal/mol by optimizing residues in both helices and turns. For a set of eight variants, the predicted and experimental stabilizations correlate very well (R2 = 0.97) with a slope near 1 and with a 0.16 kcal/mol standard error for EmCAST predictions. Tests against literature data for the stability effects of surface-exposed mutations show that EmCAST outperforms the existing stability prediction methods. UBA(1) variants were crystallized to verify and analyze their structures at an atomic resolution. Thermodynamic and kinetic folding experiments were performed to determine the magnitude and mechanism of stabilization. Our method has the potential to enable the rapid, rational optimization of natural proteins, expand the analysis of the sequence/structure relationship, and supplement the existing protein design strategies.

Predicting binding affinity changes from long-distance mutations using molecular dynamics simulations and Rosetta

Computationally modeling how mutations affect protein-protein binding not only helps uncover the biophysics of protein interfaces, but also enables the redesign and optimization of protein interactions. Traditional high-throughput methods for estimating binding free energy changes are currently limited to mutations directly at the interface due to difficulties in accurately modeling how long-distance mutations propagate their effects through the protein structure. However, the modeling and design of such mutations is of substantial interest as it allows for greater control and flexibility in protein design applications. We have developed a method that combines high-throughput Rosetta-based side-chain optimization with conformational sampling using classical molecular dynamics simulations, finding significant improvements in our ability to accurately predict long-distance mutational perturbations to protein binding. Our approach uses an analytical framework grounded in alchemical free energy calculations while enabling exploration of a vastly larger sequence space. When comparing to experimental data, we find that our method can predict internal long-distance mutational perturbations with a level of accuracy similar to that of traditional methods in predicting the effects of mutations at the protein-protein interface. This work represents a new and generalizable approach to optimize protein free energy landscapes for desired biological functions.

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.

LYRUS: a machine learning model for predicting the pathogenicity of missense variants

SUMMARY

Single amino acid variations (SAVs) are a primary contributor to variations in the human genome. Identifying pathogenic SAVs can provide insights to the genetic architecture of complex diseases. Most approaches for predicting the functional effects or pathogenicity of SAVs rely on either sequence or structural information. This study presents 〈Lai Yang Rubenstein Uzun Sarkar〉 (LYRUS), a machine learning method that uses an XGBoost classifier to predict the pathogenicity of SAVs. LYRUS incorporates five sequence-based, six structure-based and four dynamics-based features. Uniquely, LYRUS includes a newly proposed sequence co-evolution feature called the variation number. LYRUS was trained using a dataset that contains 4363 protein structures corresponding to 22 639 SAVs from the ClinVar database, and tested using the VariBench testing dataset. Performance analysis showed that LYRUS achieved comparable performance to current variant effect predictors. LYRUS's performance was also benchmarked against six Deep Mutational Scanning datasets for PTEN and TP53.

AVAILABILITY AND IMPLEMENTATION

LYRUS is freely available and the source code can be found at https://github.com/jiaying2508/LYRUS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics Advances online.

The Interplay of Electrostatics and Chemical Positioning in the Evolution of Antibiotic Resistance in TEM β-Lactamases

The interplay of enzyme active site electrostatics and chemical positioning is important for understanding the origin(s) of enzyme catalysis and the design of novel catalysts. We reconstruct the evolutionary trajectory of TEM-1 β-lactamase to TEM-52 toward extended-spectrum activity to better understand the emergence of antibiotic resistance and to provide insights into the structure-function paradigm and noncovalent interactions involved in catalysis. Utilizing a detailed kinetic analysis and the vibrational Stark effect, we quantify the changes in rates and electric fields in the Michaelis and acyl-enzyme complexes for penicillin G and cefotaxime to ascertain the evolutionary role of electric fields to modulate function. These data are combined with MD simulations to interpret and quantify the substrate-dependent structural changes during evolution. We observe that this evolutionary trajectory utilizes a large preorganized electric field and substrate-dependent chemical positioning to facilitate catalysis. This governs the evolvability, substrate promiscuity, and protein fitness landscape in TEM β-lactamase antibiotic resistance.

Switching an active site helix in dihydrofolate reductase reveals limits to subdomain modularity

To what degree are individual structural elements within proteins modular such that similar structures from unrelated proteins can be interchanged? We study subdomain modularity by creating 20 chimeras of an enzyme, Escherichia coli dihydrofolate reductase (DHFR), in which a catalytically important, 10-residue α-helical sequence is replaced by α-helical sequences from a diverse set of proteins. The chimeras stably fold but have a range of diminished thermal stabilities and catalytic activities. Evolutionary coupling analysis indicates that the residues of this α-helix are under selection pressure to maintain catalytic activity in DHFR. Reversion to phenylalanine at key position 31 was found to partially restore catalytic activity, which could be explained by evolutionary coupling values. We performed molecular dynamics simulations using replica exchange with solute tempering. Chimeras with low catalytic activity exhibit nonhelical conformations that block the binding site and disrupt the positioning of the catalytically essential residue D27. Simulation observables and in vitro measurements of thermal stability and substrate-binding affinity are strongly correlated. Several E. coli strains with chromosomally integrated chimeric DHFRs can grow, with growth rates that follow predictions from a kinetic flux model that depends on the intracellular abundance and catalytic activity of DHFR. Our findings show that although α-helices are not universally substitutable, the molecular and fitness effects of modular segments can be predicted by the biophysical compatibility of the replacement segment.

Evolution, folding, and design of TIM barrels and related proteins

Proteins are chief actors in life that perform a myriad of exquisite functions. This diversity has been enabled through the evolution and diversification of protein folds. Analysis of sequences and structures strongly suggest that numerous protein pieces have been reused as building blocks and propagated to many modern folds. This information can be traced to understand how the protein world has diversified. In this review, we discuss the latest advances in the analysis of protein evolutionary units, and we use as a model system one of the most abundant and versatile topologies, the TIM-barrel fold, to highlight the existing common principles that interconnect protein evolution, structure, folding, function, and design.

Computational Studies of the Structural Basis of Human RPS19 Mutations Associated With Diamond-Blackfan Anemia

Diamond-Blackfan Anemia (DBA) is an inherited rare disease characterized with severe pure red cell aplasia, and it is caused by the defective ribosome biogenesis stemming from the impairment of ribosomal proteins. Among all DBA-associated ribosomal proteins, RPS19 affects most patients and carries most DBA mutations. Revealing how these mutations lead to the impairment of RPS19 is highly demanded for understanding the pathogenesis of DBA, but a systematic study is currently lacking. In this work, based on the complex structure of human ribosome, we comprehensively studied the structural basis of DBA mutations of RPS19 by using computational methods. Main structure elements and five conserved surface patches involved in RPS19-18S rRNA interaction were identified. We further revealed that DBA mutations would destabilize RPS19 through disrupting the hydrophobic core or breaking the helix, or perturb the RPS19-18S rRNA interaction through destroying hydrogen bonds, introducing steric hindrance effect, or altering surface electrostatic property at the interface. Moreover, we trained a machine-learning model to predict the pathogenicity of all possible RPS19 mutations. Our work has laid a foundation for revealing the pathogenesis of DBA from the structural perspective.

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.

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.

Selection enhances protein evolvability by increasing mutational robustness and foldability

Natural selection can promote or hinder a population’s evolvability—the ability to evolve new and adaptive phenotypes—but the underlying mechanisms are poorly understood. To examine how the strength of selection affects evolvability, we subjected populations of yellow fluorescent protein to directed evolution under different selection regimes and then evolved them toward the new phenotype of green fluorescence. Populations under strong selection for the yellow phenotype evolved the green phenotype most rapidly. They did so by accumulating mutations that increase both robustness to mutations and foldability. Under weak selection, neofunctionalizing mutations rose to higher frequency at first, but more frequent deleterious mutations undermined their eventual success. Our experiments show how selection can enhance evolvability by enhancing robustness and create the conditions necessary for evolutionary success.