Stabilizing proteins, simplified: A Rosetta-based webtool for predicting favorable mutations

Enhanced l-serine synthesis in Corynebacterium glutamicum by exporter engineering and Bayesian optimization of the medium composition

l-serine is a versatile, high value-added amino acid, widely used in food, medicine and cosmetics. However, the low titer of l-serine has limited its industrial production. In this study, a cell factory without plasmid for efficient production of l-serine was constructed based on transport engineering. Firstly, the effects of l-serine exporter SerE overexpression and deletion on the cell growth and l-serine titer were investigated in Corynebacterium glutamicum (C. glutamicum) A36, overexpression of s erE using a plasmid led to a 15.1% increase in l-serine titer but also caused a 15.1% decrease in cell growth. Subsequently, to increase the export capacity of SerE, we conducted semi-rational design and bioinformatics analysis, combined with alanine mutation and site-specific saturation mutation. The mutant E277K was obtained and exhibited a 53.2% higher export capacity compared to wild-type SerE, resulting in l-serine titer increased by 39.6%. Structural analysis and molecular dynamics simulations were performed to elucidate the mechanism. The results showed that the mutation shortened the hydrogen bond distance between the exporter and l-serine, enhanced complex stability, and reduced the binding energy. Finally, Bayesian optimization was employed to further improve l-serine titer of the mutant strain C-E277K. Under the optimized conditions, 47.77 g/L l-serine was achieved in a 5-L bioreactor, representing the highest reported titer for C. glutamicum to date. This study provides a basis for the transformation of l-serine export pathway and offers a new strategy for increasing l-serine titer.

Stabilizing Mutations Enhance Evolvability of BlaC β-lactamase by Widening the Mutational Landscape

Antimicrobial resistance is fueled by the rapid evolution of β-lactamases. However, a gain of new enzyme activity often comes at the expense of reduced protein stability. This evolutionary constraint is often overcome by the acquisition of stabilizing mutations that compensate for the loss of stability invoked by new function mutations. Here, we report three stabilizing mutations (I105F, H184R, and V263I) in BlaC, a serine β-lactamase from Mycobacterium tuberculosis. Using a severely destabilized variant as a template for random mutagenesis and selection, these three mutations emerged together and were able to fully restore resistance toward the antibiotic carbenicillin. In vitro characterization shows that all three mutations increase chemical and thermal stability, which leads to elevated protein levels in the periplasm of Escherichia coli. We demonstrate that the introduction of stabilizing mutations substantially enhances the evolvability of the enzyme. These findings illustrate the important role of stabilizing mutations in enzyme evolution by alleviating function-stability trade-offs and broadening the accessible evolutionary landscape.

Molecular and functional profiling of Gαi as an intracellular pH sensor

Heterotrimeric G proteins (Gα, Gβ and Gγ) act downstream of G-protein-coupled receptors (GPCRs) to mediate signaling pathways that regulate various physiological processes and human disease conditions. While human Gαi and its yeast homolog Gpa1 were previously postulated to function as intracellular pH sensors, the pH-sensing capabilities of Gαi and the underlying mechanism remain to be established. Our research shows that variations in pH significantly affect the structure and stability of Gαi-GDP. Specifically, at the lower end of the physiological pH range, the protein undergoes an order-to-disorder transition due to the loss of electrostatic interactions within the Gαi Switch regions, resulting in a reduction in agonist-mediated Gαi-Gβγ release. Further, we identified key residues within the Gαi Switch regions that form the pH-sensing network. Mutation of these residues in Gαi gives rise to 'low pH mimetics' that abolish pH-dependent thermostability changes and reduce Gαi-Gβγ release. Overall, our findings suggest that pH-sensitive structural changes in Gαi impact the agonist-mediated dissociation of Gβγ, which is essential for proper signaling.

PSSKB: A Web Application to Study Protein Structures

The proteins expressed during the cell cycle determine cell function and ensure signaling pathway activation in response to environmental influences. Developments in structural biology, biophysics, and bioinformatics provide information on the structure and function of particular proteins including that on the structural changes in proteins due to post-translational modification (PTM) and amino acid substitutions (AAS), which is essential for understanding protein function and life cycle. These are PTMs and AASs that often modulate the function and alter the stability and localization of a protein in a cell. PSSKB is a platform that integrates all necessary tools for modeling the five common natural modifications and all canonical AASs in proteins. The available tools are not limited to the local database, so the user can select a protein from Uniprot ID or PDB ID. The result will be a three-dimensional (3D) representation of the modified structure, as well as an analysis of the changes in the performance of the intact and modified structures after energy minimization compared with the original structure, which not only makes it possible to evaluate AAS/PTM influence of on a protein's characteristics but also to use the 3D model for further studies. Additionally, PSSKB enables the user to search, align, overlay, and determine the exact coordinates of protein structure fragments. The search results are a set of structural motifs similar to the query and ranked by statistical significance. The platform is fully functional and publicly available at https://psskb.org/. No registration is required to access the platform. A tutorial video can be found at https://psskb.org/page/about. Services provided on the platform are based on previously developed and published software. SCPacker applied for PTM Modeling and AAS services available at GitHub (https://github.com/protdb/SCPacker). SaFoldNet applied for a Similar Search service is also available at GitHub (https://github.com/protdb/ABBNet).

Protein stability models fail to capture epistatic interactions of double point mutations

There is strong interest in accurate methods for predicting changes in protein stability resulting from amino acid mutations to the protein sequence. Recombinant proteins must often be stabilized to be used as therapeutics or reagents, and destabilizing mutations are implicated in a variety of diseases. Due to increased data availability and improved modeling techniques, recent studies have shown advancements in predicting changes in protein stability when a single-point mutation is made. Less focus has been directed toward predicting changes in protein stability when there are two or more mutations. Here, we analyze the largest available dataset of double point mutation stability and benchmark several widely used protein stability models on this and other datasets. We find that additive models of protein stability perform surprisingly well on this task, achieving similar performance to comparable non-additive predictors according to most metrics. Accordingly, we find that neither artificial intelligence-based nor physics-based protein stability models consistently capture epistatic interactions between single mutations. We observe one notable deviation from this trend, which is that epistasis-aware models provide marginally better predictions than additive models on stabilizing double point mutations. We develop an extension of the ThermoMPNN framework for double mutant modeling, as well as a novel data augmentation scheme, which mitigates some of the limitations in currently available datasets. Collectively, our findings indicate that current protein stability models fail to capture the nuanced epistatic interactions between concurrent mutations due to several factors, including training dataset limitations and insufficient model sensitivity.

Quantifying Protein-Nucleic Acid Interactions for Engineering Useful CRISPR-Cas9 Genome-Editing Variants

Numerous high-specificity Cas9 variants have been engineered for precision genome editing. These variants typically harbor multiple mutations designed to alter the Cas9-single guide RNA (sgRNA)-DNA complex interactions for reduced off-target cleavage. By dissecting the contributions of individual mutations, we attempt to derive principles for designing high-specificity Cas9 variants. Here, we computationally modeled the specificity harnessing mutations of the widely used Cas9 isolated from Streptococcus pyogenes (SpCas9) and investigated their individual mutational effects. We quantified the mutational effects in terms of energy and contact changes by comparing the wild-type and mutant structures. We found that these mutations disrupt the protein-protein or protein-DNA contacts within the Cas9-sgRNA-DNA complex. We also identified additional impacted amino acid sites via energy changes that constitute the structural microenvironment encompassing the focal mutation, giving insights into how the mutations contribute to the high-specificity phenotype of SpCas9. Our method outlines a strategy to evaluate mutational effects that can facilitate rational design for Cas9 optimization.

Protein stability models fail to capture epistatic interactions of double point mutations

There is strong interest in accurate methods for predicting changes in protein stability resulting from amino acid mutations to the protein sequence. Recombinant proteins must often be stabilized to be used as therapeutics or reagents, and destabilizing mutations are implicated in a variety of diseases. Due to increased data availability and improved modeling techniques, recent studies have shown advancements in predicting changes in protein stability when a single point mutation is made. Less focus has been directed toward predicting changes in protein stability when there are two or more mutations, despite the significance of mutation clusters for disease pathways and protein design studies. Here, we analyze the largest available dataset of double point mutation stability and benchmark several widely used protein stability models on this and other datasets. We identify a blind spot in how predictors are typically evaluated on multiple mutations, finding that, contrary to assumptions in the field, current stability models are unable to consistently capture epistatic interactions between double mutations. We observe one notable deviation from this trend, which is that epistasis-aware models provide marginally better predictions on stabilizing double point mutations. We develop an extension of the ThermoMPNN framework for double mutant modeling as well as a novel data augmentation scheme which mitigates some of the limitations in available datasets. Collectively, our findings indicate that current protein stability models fail to capture the nuanced epistatic interactions between concurrent mutations due to several factors, including training dataset limitations and insufficient model sensitivity.

Increasing the Soluble Expression and Whole-Cell Activity of the Plastic-Degrading Enzyme MHETase through Consensus Design

The mono(2-hydroxyethyl) terephthalate hydrolase (MHETase) from Ideonella sakaiensis carries out the second step in the enzymatic depolymerization of poly(ethylene terephthalate) (PET) plastic into the monomers terephthalic acid (TPA) and ethylene glycol (EG). Despite its potential industrial and environmental applications, poor recombinant expression of MHETase has been an obstacle to its industrial application. To overcome this barrier, we developed an assay allowing for the medium-throughput quantification of MHETase activity in cell lysates and whole-cell suspensions, which allowed us to screen a library of engineered variants. Using consensus design, we generated several improved variants that exhibit over 10-fold greater whole-cell activity than wild-type (WT) MHETase. This is revealed to be largely due to increased soluble expression, which biochemical and structural analysis indicates is due to improved protein folding.

An integrative approach to protein sequence design through multiobjective optimization

With recent methodological advances in the field of computational protein design, in particular those based on deep learning, there is an increasing need for frameworks that allow for coherent, direct integration of different models and objective functions into the generative design process. Here we demonstrate how evolutionary multiobjective optimization techniques can be adapted to provide such an approach. With the established Non-dominated Sorting Genetic Algorithm II (NSGA-II) as the optimization framework, we use AlphaFold2 and ProteinMPNN confidence metrics to define the objective space, and a mutation operator composed of ESM-1v and ProteinMPNN to rank and then redesign the least favorable positions. Using the two-state design problem of the foldswitching protein RfaH as an in-depth case study, and PapD and calmodulin as examples of higher-dimensional design problems, we show that the evolutionary multiobjective optimization approach leads to significant reduction in the bias and variance in RfaH native sequence recovery, compared to a direct application of ProteinMPNN. We suggest that this improvement is due to three factors: (i) the use of an informative mutation operator that accelerates the sequence space exploration, (ii) the parallel, iterative design process inherent to the genetic algorithm that improves upon the ProteinMPNN autoregressive sequence decoding scheme, and (iii) the explicit approximation of the Pareto front that leads to optimal design candidates representing diverse tradeoff conditions. We anticipate this approach to be readily adaptable to different models and broadly relevant for protein design tasks with complex specifications.

High-throughput system for the thermostability analysis of proteins

Thermal stability of proteins is a primary metric for evaluating their physical properties. Although researchers attempted to predict it using machine learning frameworks, their performance has been dependent on the quality and quantity of published data. This is due to the technical limitation that thermodynamic characterization of protein denaturation by fluorescence or calorimetry in a high-throughput manner has been challenging. Obtaining a melting curve that derives solely from the target protein requires laborious purification, making it far from practical to prepare a hundred or more samples in a single workflow. Here, we aimed to overcome this throughput limitation by leveraging the high protein secretion efficacy of Brevibacillus and consecutive treatment with plate-scale purification methodologies. By handling the entire process of expression, purification, and analysis on a per-plate basis, we enabled the direct observation of protein denaturation in 384 samples within 4 days. To demonstrate a practical application of the system, we conducted a comprehensive analysis of 186 single mutants of a single-chain variable fragment of nivolumab, harvesting the melting temperature (Tm) ranging from -9.3 up to +10.8°C compared to the wild-type sequence. Our findings will allow for data-driven stabilization in protein design and streamlining the rational approaches.

Molecular and Functional Profiling of Gαi as an Intracellular pH Sensor

Heterotrimeric G proteins (Gα, Gβ and Gγ) act downstream of G-protein-coupled receptors (GPCRs) to mediate signaling pathways that regulate various physiological processes and human disease conditions. Previously, human Gαi and its yeast homolog Gpa1 have been reported to function as intracellular pH sensors, yet the pH sensing capabilities of Gαi and the underlying mechanism remain to be established. Herein, we identify a pH sensing network within Gαi, and evaluate the consequences of pH modulation on the structure and stability of the G-protein. We find that changes over the physiological pH range significantly alter the structure and stability of Gαi-GDP, with the protein undergoing a disorder-to-order transition as the pH is raised from 6.8 to 7.5. Further, we find that modulation of intracellular pH in HEK293 cells regulates Gαi-Gβγ release. Identification of key residues in the pH-sensing network allowed the generation of low pH mimetics that attenuate Gαi-Gβγ release. Our findings, taken together, indicate that pH-dependent structural changes in Gαi alter the agonist-mediated Gβγ dissociation necessary for proper signaling.

Transfer learning to leverage larger datasets for improved prediction of protein stability changes

Amino acid mutations that lower a protein's thermodynamic stability are implicated in numerous diseases, and engineered proteins with enhanced stability can be important in research and medicine. Computational methods for predicting how mutations perturb protein stability are, therefore, of great interest. Despite recent advancements in protein design using deep learning, in silico prediction of stability changes has remained challenging, in part due to a lack of large, high-quality training datasets for model development. Here, we describe ThermoMPNN, a deep neural network trained to predict stability changes for protein point mutations given an initial structure. In doing so, we demonstrate the utility of a recently released megascale stability dataset for training a robust stability model. We also employ transfer learning to leverage a second, larger dataset by using learned features extracted from ProteinMPNN, a deep neural network trained to predict a protein's amino acid sequence given its three-dimensional structure. We show that our method achieves state-of-the-art performance on established benchmark datasets using a lightweight model architecture that allows for rapid, scalable predictions. Finally, we make ThermoMPNN readily available as a tool for stability prediction and design.

PTRH2 Gene Variants: Recent Review of the Phenotypic Features and Their Bioinformatics Analysis

Peptidyl-tRNA hydrolase 2 (PTRH2) is an evolutionarily highly conserved mitochondrial protein. The biallelic mutations in the PTRH2 gene have been suggested to cause a rare autosomal recessive disorder characterized by an infantile-onset multisystem neurologic endocrine and pancreatic disease (IMNEPD). Patients with IMNEPD present varying clinical manifestations, including global developmental delay associated with microcephaly, growth retardation, progressive ataxia, distal muscle weakness with ankle contractures, demyelinating sensorimotor neuropathy, sensorineural hearing loss, and abnormalities of thyroid, pancreas, and liver. In the current study, we conducted an extensive literature review with an emphasis on the variable clinical spectrum and genotypes in patients. Additionally, we reported on a new case with a previously documented mutation. A bioinformatics analysis of the various PTRH2 gene variants was also carried out from a structural perspective. It appears that the most common clinical characteristics among all patients include motor delay (92%), neuropathy (90%), distal weakness (86.4%), intellectual disability (84%), hearing impairment (80%), ataxia (79%), and deformity of head and face (~70%). The less common characteristics include hand deformity (64%), cerebellar atrophy/hypoplasia (47%), and pancreatic abnormality (35%), while the least common appear to be diabetes mellitus (~30%), liver abnormality (~22%), and hypothyroidism (16%). Three missense mutations were revealed in the PTRH2 gene, the most common one being Q85P, which was shared by four different Arab communities and was presented in our new case. Moreover, four different nonsense mutations in the PTRH2 gene were detected. It may be concluded that disease severity depends on the PTRH2 gene variant, as most of the clinical features are manifested by nonsense mutations, while only the common features are presented by missense mutations. A bioinformatics analysis of the various PTRH2 gene variants also suggested the mutations to be deleterious, as they seem to disrupt the structural confirmation of the enzyme, leading to loss of stability and functionality.

GABRG2 Variants Associated with Febrile Seizures

Febrile seizures (FS) are the most common form of epilepsy in children between six months and five years of age. FS is a self-limited type of fever-related seizure. However, complicated prolonged FS can lead to complex partial epilepsy. We found that among the GABAA receptor subunit (GABR) genes, most variants associated with FS are harbored in the γ2 subunit (GABRG2). Here, we characterized the effects of eight variants in the GABAA receptor γ2 subunit on receptor biogenesis and channel function. Two-thirds of the GABRG2 variants followed the expected autosomal dominant inheritance in FS and occurred as missense and nonsense variants. The remaining one-third appeared as de novo in the affected probands and occurred only as missense variants. The loss of GABAA receptor function and dominant negative effect on GABAA receptor biogenesis likely caused the FS phenotype. In general, variants in the GABRG2 result in a broad spectrum of phenotypic severity, ranging from asymptomatic, FS, genetic epilepsy with febrile seizures plus (GEFS+), and Dravet syndrome individuals. The data presented here support the link between FS, epilepsy, and GABRG2 variants, shedding light on the relationship between the variant topological occurrence and disease severity.