FoldX as Protein Engineering Tool: Better Than Random Based Approaches?

Protein rational design and modification of erythrose reductase for the improvement of erythritol production in Yarrowia lipolytica

Erythritol is a natural non-caloric sweetener, which is produced by fermentation and extensively applied in food, medicine and chemical industries. The final step of the erythritol synthesis pathway is involved in erythritol reductase, whose activity and NADPH-dependent become the limiting node of erythritol production efficiency. Herein, we implemented a strategy combining molecular docking and thermal stability screening to construct an ER mutant library. And we successfully obtained a double mutant ERK26N/V295M (ER*) whose catalytic activity was 1.48 times that of wild-type ER. Through structural analysis and MD analysis, we found that the catalytic pocket and the enzyme stability of ER* were both improved. We overexpressed ER* in the engineered strain ΔKU70 to obtain the strain YLE-1. YLE-1 can produce 39.47 g/L of erythritol within 144 h, representing a 35% increase compared to the unmodified strain, and a 10% increase compared to the strain overexpressing wild-type ER. Considering the essentiality of NADPH supply, we further co-expressed ER* with two genes from the oxidative phase of PPP, ZWF1 and GND1. This resulted in the construction of YLE-3, which exhibited a significant increase in production, producing 47.85 g/L of erythritol within 144 h, representing a 63.90% increase compared to the original chassis strain. The productivity and the yield of the engineered strain YLE-3 were 0.33 g/L/h and 0.48 g/g glycerol, respectively. This work provided an ER mutation with excellent performance, and also proved the importance of cofactors in the process of erythritol synthesis, which will promote the industrial production of erythritol by metabolic engineering of Y. lipolytica.

Mutations in LIFR rewire the JAK/STAT signaling pathway: A study unveiling mechanistic details of Stüve-Wiedemann syndrome

Stüve-Wiedemann syndrome (SWS), a rare autosomal recessive disorder, characterized by diminutive size, curvature of the elongated bones, bent fingers, episodes of heightened body temperature, respiratory distress or periods of breath-holding, and challenges with feeding, especially causes fatality in infants. SWS is an outcome of potential missense mutations in the leukemia inhibitory factor receptor gene reflected as numerous amino acid mutations at protein level. Employing in silico tools and techniques like mutational screening with Pred_MutHTP, I-Mutant2.0, PANTHER.db, PolyPhen, to classify mutations as deleterious/destabilizing, in conjunction with experimental data analysis, P136A and S279P emerged as 'effect'-causing mutations. Pre-existing knowledge suggests, SWS progression is effectuated conformationally altered and dysfunctional LIFR, unable to bind to LIF and further form the LIF/LIFR/gp130 signalling complex. To gain functional insights into the effect of the said mutations on the wild type protein, an all-atom, explicit, solvent molecular dynamics simulation was performed following docking approaches. Consequently, referring to the RMSD, RMSF, protein dynamic network analysis, energy landscape plots and domain motion analysis, it was revealed that unbound LIFR_WT was more prone to LIF binding as usual whereas the mutants exhibited considerable domain closure to inhibit LIF binding. We conducted binding affinity analysis via MM/GBSA and dissociation constant estimation after LIFR-LIF docking and found the WT_complex to be more stable and compact as a whole when compared to the flexible mutant complexes thus being associated with SWS. Our study offers a route for understanding molecular level implications upon LIFR mutations which opens an avenue for therapeutic interventions.

Evolving ω-amine transaminase AtATA guided by substrate-enzyme binding free energy for enhancing activity and stability against non-natural substrates

In the field of chiral amine synthesis, ω-amine transaminase (ω-ATA) is one of the most established enzymes capable of asymmetric amination under optimal conditions. However, the applicability of ω-ATA toward more non-natural complex molecules remains limited due to its low transamination activity, thermostability, and narrow substrate scope. Here, by employing a combined approach of computational virtual screening strategy and combinatorial active-site saturation test/iterative saturation mutagenesis strategy, we have constructed the best variant M14C3-V5 (M14C3-V62A-V116S-E117I-L118I-V147F) with improved ω-ATA from Aspergillus terreus (AtATA) activity and thermostability toward non-natural substrate 1-acetylnaphthalene, which is the ketone precursor for producing the intermediate (R)-(+)-1-(1-naphthyl)ethylamine [(R)-NEA] of cinacalcet hydrochloride, showing activity enhancement of up to 3.4-fold compared to parent enzyme M14C3 (AtATA-F115L-M150C-H210N-M280C-V149A-L182F-L187F). The computational tools YASARA, Discovery Studio, Amber, and FoldX were applied for predicting mutation hotspots based on substrate-enzyme binding free energies and to show the possible mechanism with features related to AtATA structure, catalytic activity, and stability in silico analyses. M14C3-V5 achieved 71.8% conversion toward 50 mM 1-acetylnaphthalene in a 50 mL preparative-scale reaction for preparing (R)-NEA. Moreover, M14C3-V5 expanded the substrate scope toward aromatic ketone compounds. The generated virtual screening strategy based on the changes in binding free energies has successfully predicted the AtATA activity toward 1-acetylnaphthalene and related substrates. Together with experimental data, these approaches can serve as a gateway to explore desirable performances, expand enzyme-substrate scope, and accelerate biocatalysis.IMPORTANCEChiral amine is a crucial compound with many valuable applications. Their asymmetric synthesis employing ω-amine transaminases (ω-ATAs) is considered an attractive method. However, most ω-ATAs exhibit low activity and stability toward various non-natural substrates, which limits their industrial application. In this work, protein engineering strategy and computer-aided design are performed to evolve the activity and stability of ω-ATA from Aspergillus terreus toward non-natural substrates. After five rounds of mutations, the best variant, M14C3-V5, is obtained, showing better catalytic efficiency toward 1-acetylnaphthalene and higher thermostability than the original enzyme, M14C3. The robust combinational variant acquired displayed significant application value for pushing the asymmetric synthesis of aromatic chiral amines to a higher level.

De novo structure prediction of meteorin and meteorin-like protein for identification of domains, functional receptor binding regions, and their high-risk missense variants

Meteorin (Metrn) and Meteorin-like (Metrnl) are homologous secreted proteins involved in neural development and metabolic regulation. In this study, we have performed de novo structure prediction and analysis of both Metrn and Metrnl using Alphafold2 (AF2) and RoseTTAfold (RF). Based on the domain and structural homology analysis of the predicted structures, we have identified that these proteins are composed of two functional domains, a CUB domain and an NTR domain, connected by a hinge/loop region. We have identified the receptor binding regions of Metrn and Metrnl using the machine-learning tools ScanNet and Masif. These were further validated by docking Metrnl with its reported KIT receptor, thus establishing the role of each domain in the receptor interaction. Also, we have studied the effect of non-synonymous SNPs on the structure and function of these proteins using an array of bioinformatics tools and selected 16 missense variants in Metrn and 10 in Metrnl that can affect the protein stability. This is the first study to comprehensively characterize the functional domains of Metrn and Metrnl at their structural level and identify the functional domains, and protein binding regions. This study also highlights the interaction mechanism of the KIT receptor and Metrnl. The predicted deleterious SNPs will allow further understanding of the role of these variants in modulating the plasma levels of these proteins in disease conditions such as diabetes.Communicated by Ramaswamy H. Sarma.

In silico energetic and molecular dynamic simulations studies demonstrate potential effect of the point mutations with implications for protein engineering in BDNF

Protein engineering by directed evolution is time-consuming. Hence, in silico techniques like FoldX-Yasara for ∆∆G calculation, and SNPeffect for predicting propensity for aggregation, amyloid formation, and chaperone binding are employed to design proteins. Here, we used in silico techniques to engineer BDNF-NTF3 interaction and validated it using mutations with known functional implications for NGF dimer. The structures of three mutants representing a positive, negative, or neutral ∆∆G involving two interface residues in BDNF and two mutations representing a neutral and positive ∆∆G in NGF, which is aligned with BDNF, were selected for molecular dynamics (MD) simulation. Our MD results conclude that the secondary structure of individual protomers of the positive and negative mutants displayed a similar or different conformation from the NTF3 monomer, respectively. The positive mutants showed fewer hydrophobic interactions and higher hydrogen bonds compared to the wild-type, negative, and neutral mutants with similar SASA, suggesting solvent-mediated disruption of hydrogen-bonded interactions. Similar results were obtained for mutations with known functional implications for NGF and BDNF. The results suggest that mutations with known effects in homologous proteins could help in validation, and in silico directed evolution experiments could be a viable alternative to the experimental technique used for protein engineering.

Application of an alchemical free energy method for the prediction of thermostable DuraPETase variants

Non-equilibrium (NEQ) alchemical free energy calculations are an emerging tool for accurately predicting changes in protein folding free energy resulting from amino acid mutations. In this study, this method in combination with the Rosetta ddg monomer tool was applied to predict more thermostable variants of the polyethylene terephthalate (PET) degrading enzyme DuraPETase. The Rosetta ddg monomer tool efficiently enriched promising mutations prior to more accurate prediction by NEQ alchemical free energy calculations. The relative change in folding free energy of 96 single amino acid mutations was calculated by NEQ alchemical free energy calculation. Experimental validation of ten of the highest scoring variants identified two mutations (DuraPETaseS61M and DuraPETaseS223Y) that increased the melting temperature (Tm) of the enzyme by up to 1 °C. The calculated relative change in folding free energy showed an excellent correlation with experimentally determined Tm resulting in a Pearson's correlation coefficient of r =  - 0.84. Limitations in the prediction of strongly stabilizing mutations were, however, encountered and are discussed. Despite these challenges, this study demonstrates the practical applicability of NEQ alchemical free energy calculations in prospective enzyme engineering projects. KEY POINTS: • Rosetta ddg monomer enriches stabilizing mutations in a library of DuraPETase variants • NEQ free energy calculations accurately predict changes in Tm of DuraPETase • The DuraPETase variants S223Y, S42M, and S61M have increased Tm.

Guidelines for releasing a variant effect predictor

Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released to date, and there is tremendous variability in their underlying algorithms and outputs, and in the ways in which the methodologies and predictions are shared. This leads to considerable challenges for end users in knowing which VEPs to use and how to use them. Here, to address these issues, we provide guidelines and recommendations for the release of novel VEPs. Emphasising open-source availability, transparent methodologies, clear variant effect score interpretations, standardised scales, accessible predictions, and rigorous training data disclosure, we aim to improve the usability and interpretability of VEPs, and promote their integration into analysis and evaluation pipelines. We also provide a large, categorised list of currently available VEPs, aiming to facilitate the discovery and encourage the usage of novel methods within the scientific community.

The polymorphism analysis for CD36 among platelet donors

CD36 may defect on platelets and/or monocytes in healthy individuals, which was defined as CD36 deficiency. However, we did not know the correlation between the molecular and protein levels completely. Here, we aim to determine the polymorphisms of the CD36 gene, RNA level, and CD36 on platelets and in plasma. The individuals were sequenced by Sanger sequencing. Bioinformational analysis was used by the HotMuSiC, CUPSAT, SAAFEC-SEQ, and FoldX. RNA analysis and CD36 protein detection were performed by qPCR, flow cytometry, and ELISA. In this study, we found c.1228_1239delATTGTGCCTATT (allele frequency = 0.0072) with the highest frequency among our cohort, and one mutation (c.1329_1354dupGATAGAAATGATCTTACTCAGTGTTG) was not present in the dbSNP database. 5 mutations located in the extracellular domain sequencing region with confirmation in deficient individuals, of which c.284T>C, c.512A>G, c.572C>T, and c.869T>C were found to have a deleterious impact on CD36 protein stability. Furthermore, the MFI of CD36 expression on platelets in the mutation-carry, deleterious-effect, and deficiency group was significantly lower than the no-mutation group (P < 0.0500). In addition, sCD36 levels in type II individuals were significantly lower compared with positive controls (P = 0.0060). Nevertheless, we found the presence of sCD36 in a type I individual. RNA analysis showed CD36 RNA levels in platelets of type II individuals were significantly lower than the positive individuals (P = 0.0065). However, no significant difference was observed in monocytes (P = 0.7500). We identified the most prevalent mutation (c.1228_1239delATTGTGCCTATT) among Kunming donors. Besides, our results suggested RNA level alterations could potentially underlie type II deficiency. Furthermore, sCD36 may hold promise for assessing immune reaction risk in CD36-deficient individuals, but more studies should be conducted to validate this hypothesis.

State-of-the-art in engineering small molecule biosensors and their applications in metabolic engineering

Genetically encoded biosensors are crucial for enhancing our understanding of how molecules regulate biological systems. Small molecule biosensors, in particular, help us understand the interaction between chemicals and biological processes. They also accelerate metabolic engineering by increasing screening throughput and eliminating the need for sample preparation through traditional chemical analysis. Additionally, they offer significantly higher spatial and temporal resolution in cellular analyte measurements. In this review, we discuss recent progress in in vivo biosensors and control systems-biosensor-based controllers-for metabolic engineering. We also specifically explore protein-based biosensors that utilize less commonly exploited signaling mechanisms, such as protein stability and induced degradation, compared to more prevalent transcription factor and allosteric regulation mechanism. We propose that these lesser-used mechanisms will be significant for engineering eukaryotic systems and slower-growing prokaryotic systems where protein turnover may facilitate more rapid and reliable measurement and regulation of the current cellular state. Lastly, we emphasize the utilization of cutting-edge and state-of-the-art techniques in the development of protein-based biosensors, achieved through rational design, directed evolution, and collaborative approaches.

Multi-strategy orthogonal enhancement and analysis of aldo-keto reductase thermal stability

Given their outstanding efficiency and selectivity, enzymes are integral in various domains such as drug synthesis, the food industry, and environmental management. However, the inherent instability of natural enzymes limits their widespread industrial application. In this study, we underscore the efficacy of enhancing protein thermal stability through comprehensive protein design strategies, encompassing elements such as the free energy of protein folding, internal forces within proteins, and the overall structural design. We also demonstrate the efficiency and precision of combinatorial screening in the thermal stability design of aldo-keto reductase (AKR7-2-1). In our research, three single-point mutations and five combinatorial mutations were strategically introduced into AKR7-2-1, using multiple computational techniques. Notably, the E12I/S235I mutant showed a significant increase of 25.4 °C in its melting temperature (Tm). Furthermore, the optimal mutant, E12V/S235I, maintained 80 % of its activity while realizing a 16.8 °C elevation in Tm. Remarkably, its half-life at 50 °C was increased to twenty times that of the wild type. Structural analysis indicates that this enhanced thermal stability primarily arises from reduced oscillation in the loop region and increased internal hydrogen bonding. The promising results achieved with AKR7-2-1 demonstrate that our strategy could serve as a valuable reference for enhancing the thermal stability of other industrial enzymes.

Integrating physics in deep learning algorithms: a force field as a PyTorch module

MOTIVATION

Deep learning algorithms applied to structural biology often struggle to converge to meaningful solutions when limited data is available, since they are required to learn complex physical rules from examples. State-of-the-art force-fields, however, cannot interface with deep learning algorithms due to their implementation.

RESULTS

We present MadraX, a forcefield implemented as a differentiable PyTorch module, able to interact with deep learning algorithms in an end-to-end fashion.

AVAILABILITY AND IMPLEMENTATION

MadraX documentation, together with tutorials and installation guide, is available at madrax.readthedocs.io.

Computational Tools to Assist in Analyzing Effects of the SERPINA1 Gene Variation on Alpha-1 Antitrypsin (AAT)

In the rapidly advancing field of bioinformatics, the development and application of computational tools to predict the effects of single nucleotide variants (SNVs) are shedding light on the molecular mechanisms underlying disorders. Also, they hold promise for guiding therapeutic interventions and personalized medicine strategies in the future. A comprehensive understanding of the impact of SNVs in the SERPINA1 gene on alpha-1 antitrypsin (AAT) protein structure and function requires integrating bioinformatic approaches. Here, we provide a guide for clinicians to navigate through the field of computational analyses which can be applied to describe a novel genetic variant. Predicting the clinical significance of SERPINA1 variation allows clinicians to tailor treatment options for individuals with alpha-1 antitrypsin deficiency (AATD) and related conditions, ultimately improving the patient's outcome and quality of life. This paper explores the various bioinformatic methodologies and cutting-edge approaches dedicated to the assessment of molecular variants of genes and their product proteins using SERPINA1 and AAT as an example.

Unveiling the affinity-stability relationship in anti-measles virus antibodies: a computational approach for hotspots prediction

Recent years have seen an uptick in the use of computational applications in antibody engineering. These tools have enhanced our ability to predict interactions with antigens and immunogenicity, facilitate humanization, and serve other critical functions. However, several studies highlight the concern of potential trade-offs between antibody affinity and stability in antibody engineering. In this study, we analyzed anti-measles virus antibodies as a case study, to examine the relationship between binding affinity and stability, upon identifying the binding hotspots. We leverage in silico tools like Rosetta and FoldX, along with molecular dynamics (MD) simulations, offering a cost-effective alternative to traditional in vitro mutagenesis. We introduced a pattern in identifying key residues in pairs, shedding light on hotspots identification. Experimental physicochemical analysis validated the predicted key residues by confirming significant decrease in binding affinity for the high-affinity antibodies to measles virus hemagglutinin. Through the nature of the identified pairs, which represented the relative hydropathy of amino acid side chain, a connection was proposed between affinity and stability. The findings of the study enhance our understanding of the interactions between antibody and measles virus hemagglutinin. Moreover, the implications of the observed correlation between binding affinity and stability extend beyond the field of anti-measles virus antibodies, thereby opening doors for advancements in antibody research.

Engineering the thermal stability of a polyphosphate kinase by ancestral sequence reconstruction to expand the temperature boundary for an industrially applicable ATP regeneration system

ATP-dependent energy-consuming enzymatic reactions are widely used in cell-free biocatalysis. However, the direct addition of large amounts of expensive ATP can greatly increase cost, and enzymatic production is often difficult to achieve as a result. Although a polyphosphate kinase (PPK)-polyphosphate-based ATP regeneration system has the potential to solve this challenge, the generally poor thermal stability of PPKs limits the widespread use of this method. In this paper, we evaluated the thermal stability of a PPK from Sulfurovum lithotrophicum (SlPPK2). After directed evolution and computation-supported design, we found that SlPPK2 is very recalcitrant and cannot acquire beneficial mutations. Inspired by the usually outstanding stability of ancestral enzymes, we reconstructed the ancestral sequence of the PPK family and used it as a guide to construct three heat-stable variants of SlPPK2, of which the L35F/T144S variant has a half-life of more than 14 h at 60°C. Molecular dynamics simulations were performed on all enzymes to analyze the reasons for the increased thermal stability. The results showed that mutations at these two positions act synergistically from the interior and surface of the protein, leading to a more compact structure. Finally, the robustness of the L35F/T144S variant was verified in the synthesis of nucleotides at high temperature. In practice, the use of this high-temperature ATP regeneration system can effectively avoid byproduct accumulation. Our work extends the temperature boundary of ATP regeneration and has great potential for industrial applications.IMPORTANCEATP regeneration is an important basic applied study in the field of cell-free biocatalysis. Polyphosphate kinase (PPK) is an enzyme tool widely used for energy regeneration during enzymatic reactions. However, the thermal stability of the PPKs reported to date that can efficiently regenerate ATP is usually poor, which greatly limits their application. In this study, the thermal stability of a difficult-to-engineer PPK from Sulfurovum lithotrophicum was improved, guided by an ancestral sequence reconstruction strategy. The optimal variant has a 4.5-fold longer half-life at 60°C than the wild-type enzyme, thus enabling the extension of the temperature boundary for ATP regeneration. The ability of this variant to regenerate ATP was well demonstrated during high-temperature enzymatic production of nucleotides.

Identification and mechanism determination of the efflux pump subunit amrB gene mutations linked to gentamicin susceptibility in clinical Burkholderia pseudomallei from Malaysian Borneo

The bacterium Burkholderia pseudomallei is typically resistant to gentamicin but rare susceptible strains have been isolated in certain regions, such as Thailand and Sarawak, Malaysia. Recently, several amino acid substitutions have been reported in the amrB gene (a subunit of the amrAB-oprA efflux pump gene) that confer gentamicin susceptibility. However, information regarding the mechanism of the substitutions conferring the susceptibility is lacking. To understand the mechanism of amino acid substitution that confers susceptibility, this study identifies the corresponding mutations in clinical gentamicin-susceptible B. pseudomallei isolates from the Malaysian Borneo (n = 46; Sarawak: 5; Sabah: 41). Three phenotypically confirmed gentamicin-susceptible (GENs) strains from Sarawak, Malaysia, were screened for mutations in the amrB gene using gene sequences of gentamicin-resistant (GENr) strains (QEH 56, QEH 57, QEH20, and QEH26) and publicly available sequences (AF072887.1 and BX571965.1) as the comparator. The effect of missense mutations on the stability of the AmrB protein was determined by calculating the average energy change value (ΔΔG). Mutagenesis analysis identified a polymorphism-associated mutation, g.1056 T > G, a possible susceptible-associated in-frame deletion, Delta V412, and a previously confirmed susceptible-associated amino acid substitution, T368R, in each of the three GENs isolates. The contribution of Delta V412 needs further confirmation by experimental mutagenesis analysis. The mechanism by which T368R confers susceptibility, as elucidated by in silico mutagenesis analysis using AmrB-modeled protein structures, is proposed to be due to the location of T368R in a highly conserved region, rather than destabilization of the AmrB protein structure.

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.

Improving the thermal stability and catalytic activity of ulvan lyase by the combination of FoldX and KnowVolution campaign

Thermal stability is one of the most important properties of ulvan lyases for their application in algae biomass degradation. The Knowledge gaining directed eVolution (KnowVolution) protein engineering strategy could be employed to improve thermostability of ulvan lyase with less screening effort. Herein, the unfolding free energies (ΔΔG) of the loop region were calculated using FoldX and four sites (D103, G104, T113, Q229) were selected for saturation mutagenesis, resulting in the identification of a favorable single-site mutant Q229M. Subsequently, iteration mutation was carried out with the mutant N57P (previously obtained by our group) to further enhance the performance of ulvan lyase. The results showed that the most beneficial variant N57P/Q229M exhibited a 1.67-fold and 2-fold increase in residual activity compared to the wild type after incubation at 40 °C and 50 °C for 1 h, respectively. In addition, the variant produced 1.06 mg/mL of reducing sugar in 2 h, which was almost four times as much as the wild type. Molecular dynamics simulations revealed that N57P/Q229M mutant enhanced the structural rigidity by augmenting intramolecular hydrogen bonds. Meanwhile, the shorter proton transmission distance between the general base of the enzyme and the substrate contributed to the glycosidic bond breakage. Our research showed that in silico saturation mutagenesis using position scan module in FoldX allowed for faster screening of mutants with improved thermal stability, and combining it with KnowVolution enabled a balanced effect of thermal stability and enzyme activity in protein engineering.

Improving the thermostability of Pseudoalteromonas Porphyrae κ-carrageenase by rational design and MD simulation

The industrial applications of the κ-carrageenases have been restricted by their poor thermostability. In this study, based on the folding free energy change (ΔΔG) and the flexibility analysis using molecular dynamics (MD) simulation for the alkaline κ-carrageenase KCgCD from Pseudoalteromonas porphyrae (WT), the mutant S190R was identified with improved thermostability. After incubation at 50 °C for 30 min, the residual activity of S190R was 63.7%, 25.7% higher than that of WT. The Tm values determined by differential scanning calorimetry were 66.2 °C and 64.4 °C for S190R and WT, respectively. The optimal temperature of S190R was 10 °C higher than that of WT. The κ-carrageenan hydrolysates produced by S190R showed higher xanthine oxidase inhibitory activity compared with the untreated κ-carrageenan. MD simulation analysis of S190R showed that the residues (V186-M194 and P196-G197) in F5 and the key residue R150 in F3 displayed the decreased flexibility, and residues of T169-N173 near the catalytic center displayed the increased flexibility. These changed flexibilities might be the reasons for the improved thermostability of mutant S190R. This study provides a useful rational design strategy of combination of ΔΔG calculation and MD simulation to improve the κ-carrageenase's thermostability for its better industrial applications.

A Computational Approach: The Functional Effects of Thyroid Peroxidase Variants in Thyroid Cancer and Genetic Disorders

PURPOSE

Thyroid peroxidase (TPO) is essential for the synthesis of thyroid hormones. However, specific mutations render TPO antigenic and prone to autoimmune attacks leading to thyroid cancer, TPO deficiency, and congenital hypothyroidism (CH). Despite technological advancement, most experimental procedures cannot quickly identify the genetic causes of CH nor detect thyroid cancer in the early stages.

METHODS

We performed saturated computational mutagenesis to calculate the folding energy changes (∆∆G) caused by missense mutations and analyzed the mutations involved in post-translational modifications (PTMs).

RESULTS

Our results showed that the functional important missense mutations occurred in the heme peroxidase domain. Through computational saturation mutagenesis, we identified the TPO mutations in G393 and G348 affecting protein stability and PTMs. Our folding energy calculations revealed that seven of nine somatic thyroid cancer mutations destabilized TPO.

CONCLUSION

These findings highlight the impact of these specific mutations on TPO stability, linking them to thyroid cancer and other genetic thyroid-related disorders. Our results show that computational mutagenesis of proteins provides a quick insight into rare mutations causing Mendelian disorders and cancers in humans.

Influence of Model Structures on Predictors of Protein Stability Changes from Single-Point Mutations

Missense variation in genomes can affect protein structure stability and, in turn, the cell physiology behavior. Predicting the impact of those variations is relevant, and the best-performing computational tools exploit the protein structure information. However, most of the current protein sequence variants are unresolved, and comparative or ab initio tools can provide a structure. Here, we evaluate the impact of model structures, compared to experimental structures, on the predictors of protein stability changes upon single-point mutations, where no significant changes are expected between the original and the mutated structures. We show that there are substantial differences among the computational tools. Methods that rely on coarse-grained representation are less sensitive to the underlying protein structures. In contrast, tools that exploit more detailed molecular representations are sensible to structures generated from comparative modeling, even on single-residue substitutions.

Prediction of Thermostability of Enzymes Based on the Amino Acid Index (AAindex) Database and Machine Learning

The combination of wet-lab experimental data on multi-site combinatorial mutations and machine learning is an innovative method in protein engineering. In this study, we used an innovative sequence-activity relationship (innov'SAR) methodology based on novel descriptors and digital signal processing (DSP) to construct a predictive model. In this paper, 21 experimental (R)-selective amine transaminases from Aspergillus terreus (AT-ATA) were used as an input to predict higher thermostability mutants than those predicted using the existing data. We successfully improved the coefficient of determination (R2) of the model from 0.66 to 0.92. In addition, root-mean-squared deviation (RMSD), root-mean-squared fluctuation (RMSF), solvent accessible surface area (SASA), hydrogen bonds, and the radius of gyration were estimated based on molecular dynamics simulations, and the differences between the predicted mutants and the wild-type (WT) were analyzed. The successful application of the innov'SAR algorithm in improving the thermostability of AT-ATA may help in directed evolutionary screening and open up new avenues for protein engineering.

Molecular Footprints on Osmoregulation-Related Genes Associated with Freshwater Colonization by Cetaceans and Sirenians

The genetic basis underlying adaptive physiological mechanisms has been extensively explored in mammals after colonizing the seas. However, independent lineages of aquatic mammals exhibit complex patterns of secondary colonization in freshwater environments. This change in habitat represents new osmotic challenges, and additional changes in key systems, such as the osmoregulatory system, are expected. Here, we studied the selective regime on coding and regulatory regions of 20 genes related to the osmoregulation system in strict aquatic mammals from independent evolutionary lineages, cetaceans, and sirenians, with representatives in marine and freshwater aquatic environments. We identified positive selection signals in genes encoding the protein vasopressin (AVP) in mammalian lineages with secondary colonization in the fluvial environment and in aquaporins for lineages inhabiting the marine and fluvial environments. A greater number of sites with positive selection signals were found for the dolphin species compared to the Amazonian manatee. Only the AQP5 and AVP genes showed selection signals in more than one independent lineage of these mammals. Furthermore, the vasopressin gene tree indicates greater similarity in river dolphin sequences despite the independence of their lineages based on the species tree. Patterns of distribution and enrichment of Transcription Factors in the promoter regions of target genes were analyzed and appear to be phylogenetically conserved among sister species. We found accelerated evolution signs in genes ACE, AQP1, AQP5, AQP7, AVP, NPP4, and NPR1 for the fluvial mammals. Together, these results allow a greater understanding of the molecular bases of the evolution of genes responsible for osmotic control in aquatic mammals.

Improvement of α-amino Ester Hydrolase Stability via Computational Protein Design

Amino ester hydrolases (AEHs) are capable of rapid synthesis of cephalexin but suffer from rapid deactivation even at low temperatures. Previous efforts to engineer AEH have generated several improved variants but have been limited in scope in part due to limitations in activity assay throughput for β-lactam synthesis reactions. Rational design of 'whole variants' was explored to rapidly improve AEH thermostability by mutating between 3-15% of residues. Most variants were found to be inactive due to a mutated calcium binding site, the function of which has not previously been described. Four active variants, all with improved melting temperatures, were characterized in terms of synthesis and hydrolysis activity, melting temperature, and deactivation at 25°C. Two variants were found to have improved total turnover numbers relative to the initial AEH variant; however, a clear tradeoff exists between improved stability and overall activity of each variant.

Simultaneous Improvement in the Thermostability and Catalytic Activity of Epoxidase Lsd18 for the Synthesis of Lasalocid A

Enzymes used in the synthesis of natural products are potent catalysts, capable of efficient and stereoselective chemical transformations. Lsd18 catalyzes two sequential epoxidations during the biosynthesis of lasalocid A, a polyether polyketide natural product. We performed protein engineering on Lsd18 to improve its thermostability and catalytic activity. Utilizing structure-guided methods of FoldX and Rosetta-ddG, we designed 15 mutants of Lsd18. Screening of these mutants using thermal shift assay identified stabilized variants Lsd18-T189M, Lsd18-S195M, and the double mutant Lsd18-T189M-S195M. Trypsin digestion, molecular dynamic simulation, circular dichroism (CD) spectroscopy, and X-ray crystallography provided insights into the molecular basis for the improved enzyme properties. Notably, enhanced hydrophobic interaction within the enzyme core and interaction of the protein with the FAD cofactor appear to be responsible for its better thermostability.

FireProt 2.0: web-based platform for the fully automated design of thermostable proteins

Thermostable proteins find their use in numerous biomedical and biotechnological applications. However, the computational design of stable proteins often results in single-point mutations with a limited effect on protein stability. However, the construction of stable multiple-point mutants can prove difficult due to the possibility of antagonistic effects between individual mutations. FireProt protocol enables the automated computational design of highly stable multiple-point mutants. FireProt 2.0 builds on top of the previously published FireProt web, retaining the original functionality and expanding it with several new stabilization strategies. FireProt 2.0 integrates the AlphaFold database and the homology modeling for structure prediction, enabling calculations starting from a sequence. Multiple-point designs are constructed using the Bron-Kerbosch algorithm minimizing the antagonistic effect between the individual mutations. Users can newly limit the FireProt calculation to a set of user-defined mutations, run a saturation mutagenesis of the whole protein or select rigidifying mutations based on B-factors. Evolution-based back-to-consensus strategy is complemented by ancestral sequence reconstruction. FireProt 2.0 is significantly faster and a reworked graphical user interface broadens the tool's availability even to users with older hardware. FireProt 2.0 is freely available at http://loschmidt.chemi.muni.cz/fireprotweb.

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.

Enhanced Activity and Stability of an Acetyl Xylan Esterase in Hydrophilic Alcohols through Site-Directed Mutagenesis

Current demands for the development of suitable biocatalysts showing high process performance is stimulated by the need to replace current chemical synthesis with cleaner alternatives. A drawback to the use of biocatalysts for unique applications is their low performance in industrial conditions. Hence, enzymes with improved performance are needed to achieve innovative and sustainable biocatalysis. In this study, we report the improved performance of an engineered acetyl xylan esterase (BaAXE) in a hydrophilic organic solvent. The structure of BaAXE was partitioned into a substrate-binding region and a solvent-affecting region. Using a rational design approach, charged residues were introduced at protein surfaces in the solvent-affecting region. Two sites present in the solvent-affecting region, A12D and Q143E, were selected for site-directed mutagenesis, which generated the mutants MUT12, MUT143 and MUT12-143. The mutants MUT12 and MUT143 reported lower Km (0.29 mM and 0.27 mM, respectively) compared to the wildtype (0.41 mM). The performance of the mutants in organic solvents was assessed after enzyme incubation in various strengths of alcohols. The mutants showed improved activity and stability compared to the wild type in low strengths of ethanol and methanol. However, the activity of MUT143 was lost in 40% methanol while MUT12 and MUT12-143 retained over 70% residual activity in this environment. Computational analysis links the improved performance of MUT12 and MUT12-143 to novel intermolecular interactions that are absent in MUT143. This work supports the rationale for protein engineering to augment the characteristics of wild-type proteins and provides more insight into the role of charged residues in conferring stability.

Molecular dynamics simulations identify the topological weak spots of a protease CN2S8A

Thermophilic enzymes are highly desired in industrial applications due to their efficient catalytic activity at high temperature. However, most enzymes exhibit inferior thermostability and it remains challenging to identify the optimal sites for designing mutations to improve protein stability. To tackle this issue, we integrated topological analysis and all-atom molecular dynamics simulations to efficiently pinpoint the thermally-unstable regions in protein structures. Using a protease CN2S8A as the model, we analyzed the intramolecular hydrogen bonding interactions between adjacent secondary structure elements, and then identified the topological weak spots of CN2S8A where weak hydrogen bonding interactions were formed. To examine the role of these sites in protein structural stability, we designed three virtual mutations at different weak spots and characterized the effects of these mutations on the structural properties of CN2S8A. The results showed that all three mutations increased the protein structural stability. In conclusion, these findings provide a novel method to identify the topological weak spots of proteins, with implications in the rational design of biocatalysts with superior thermostability.

Widespread impact of immunoglobulin V-gene allelic polymorphisms on antibody reactivity

The ability of the human immune system to generate antibodies to any given antigen can be strongly influenced by immunoglobulin V-gene allelic polymorphisms. However, previous studies have provided only limited examples. Therefore, the prevalence of this phenomenon has been unclear. By analyzing >1,000 publicly available antibody-antigen structures, we show that many V-gene allelic polymorphisms in antibody paratopes are determinants for antibody binding activity. Biolayer interferometry experiments further demonstrate that paratope allelic polymorphisms on both heavy and light chains often abolish antibody binding. We also illustrate the importance of minor V-gene allelic polymorphisms with low frequency in several broadly neutralizing antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza virus. Overall, this study not only highlights the pervasive impact of V-gene allelic polymorphisms on antibody binding but also provides mechanistic insights into the variability of antibody repertoires across individuals, which in turn have important implications for vaccine development and antibody discovery.

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.

Engineering and Expression Strategies for Optimization of L-Asparaginase Development and Production

Genetic engineering for heterologous expression has advanced in recent years. Model systems such as Escherichia coli, Bacillus subtilis and Pichia pastoris are often used as host microorganisms for the enzymatic production of L-asparaginase, an enzyme widely used in the clinic for the treatment of leukemia and in bakeries for the reduction of acrylamide. Newly developed recombinant L-asparaginase (L-ASNase) may have a low affinity for asparagine, reduced catalytic activity, low stability, and increased glutaminase activity or immunogenicity. Some successful commercial preparations of L-ASNase are now available. Therefore, obtaining novel L-ASNases with improved properties suitable for food or clinical applications remains a challenge. The combination of rational design and/or directed evolution and heterologous expression has been used to create enzymes with desired characteristics. Computer design, combined with other methods, could make it possible to generate mutant libraries of novel L-ASNases without costly and time-consuming efforts. In this review, we summarize the strategies and approaches for obtaining and developing L-ASNase with improved properties.

Interactions of Nucleosomes with Acidic Patch-Binding Peptides: A Combined Structural Bioinformatics, Molecular Modeling, Fluorescence Polarization, and Single-Molecule FRET Study

In eukaryotic organisms, genomic DNA associates with histone proteins to form nucleosomes. Nucleosomes provide a basis for genome compaction, epigenetic markup, and mediate interactions of nuclear proteins with their target DNA loci. A negatively charged (acidic) patch located on the H2A-H2B histone dimer is a characteristic feature of the nucleosomal surface. The acidic patch is a common site in the attachment of various chromatin proteins, including viral ones. Acidic patch-binding peptides present perspective compounds that can be used to modulate chromatin functioning by disrupting interactions of nucleosomes with natural proteins or alternatively targeting artificial moieties to the nucleosomes, which may be beneficial for the development of new therapeutics. In this work, we used several computational and experimental techniques to improve our understanding of how peptides may bind to the acidic patch and what are the consequences of their binding. Through extensive analysis of the PDB database, histone sequence analysis, and molecular dynamic simulations, we elucidated common binding patterns and key interactions that stabilize peptide-nucleosome complexes. Through MD simulations and FRET measurements, we characterized changes in nucleosome dynamics conferred by peptide binding. Using fluorescence polarization and gel electrophoresis, we evaluated the affinity and specificity of the LANA1-22 peptide to DNA and nucleosomes. Taken together, our study provides new insights into the different patterns of intermolecular interactions that can be employed by natural and designed peptides to bind to nucleosomes, and the effects of peptide binding on nucleosome dynamics and stability.

Surfaces: a software to quantify and visualize interactions within and between proteins and ligands

SUMMARY

Computational methods for the quantification and visualization of the relative contribution of molecular interactions to the stability of biomolecular structures and complexes are fundamental to understand, modulate and engineer biological processes. Here, we present Surfaces, an easy to use, fast and customizable software for quantification and visualization of molecular interactions based on the calculation of surface areas in contact. Surfaces calculations shows equivalent or better correlations with experimental data as computationally expensive methods based on molecular dynamics.

AVAILABILITY AND IMPLEMENTATION

All scripts are available at https://github.com/NRGLab/Surfaces. Surface's documentation is available at https://surfaces-tutorial.readthedocs.io/en/latest/index.html.

In Vitro Affinity Maturation of Nanobodies against Mpox Virus A29 Protein Based on Computer-Aided Design

Mpox virus (MPXV), the most pathogenic zoonotic orthopoxvirus, caused worldwide concern during the SARS-CoV-2 epidemic. Growing evidence suggests that the MPXV surface protein A29 could be a specific diagnostic marker for immunological detection. In this study, a fully synthetic phage display library was screened, revealing two nanobodies (A1 and H8) that specifically recognize A29. Subsequently, an in vitro affinity maturation strategy based on computer-aided design was proposed by building and docking the A29 and A1 three-dimensional structures. Ligand-receptor binding and molecular dynamics simulations were performed to predict binding modes and key residues. Three mutant antibodies were predicted using the platform, increasing the affinity by approximately 10-fold compared with the parental form. These results will facilitate the application of computers in antibody optimization and reduce the cost of antibody development; moreover, the predicted antibodies provide a reference for establishing an immunological response against MPXV.

Intra-FCY1: a novel system to identify mutations that cause protein misfolding

Protein misfolding is a common intracellular occurrence. Most mutations to coding sequences increase the propensity of the encoded protein to misfold. These misfolded molecules can have devastating effects on cells. Despite the importance of protein misfolding in human disease and protein evolution, there are fundamental questions that remain unanswered, such as, which mutations cause the most misfolding? These questions are difficult to answer partially because we lack high-throughput methods to compare the destabilizing effects of different mutations. Commonly used systems to assess the stability of mutant proteins in vivo often rely upon essential proteins as sensors, but misfolded proteins can disrupt the function of the essential protein enough to kill the cell. This makes it difficult to identify and compare mutations that cause protein misfolding using these systems. Here, we present a novel in vivo system named Intra-FCY1 that we use to identify mutations that cause misfolding of a model protein [yellow fluorescent protein (YFP)] in Saccharomyces cerevisiae. The Intra-FCY1 system utilizes two complementary fragments of the yeast cytosine deaminase Fcy1, a toxic protein, into which YFP is inserted. When YFP folds, the Fcy1 fragments associate together to reconstitute their function, conferring toxicity in media containing 5-fluorocytosine and hindering growth. But mutations that make YFP misfold abrogate Fcy1 toxicity, thus strains possessing misfolded YFP variants rise to high frequency in growth competition experiments. This makes such strains easier to study. The Intra-FCY1 system cancels localization of the protein of interest, thus can be applied to study the relative stability of mutant versions of diverse cellular proteins. Here, we confirm this method can identify novel mutations that cause misfolding, highlighting the potential for Intra-FCY1 to illuminate the relationship between protein sequence and stability.

EnCPdock: a web-interface for direct conjoint comparative analyses of complementarity and binding energetics in inter-protein associations

CONTEXT

Protein-protein interaction (PPI) is a key component linked to virtually all cellular processes. Be it an enzyme catalysis ('classic type functions' of proteins) or a signal transduction ('non-classic'), proteins generally function involving stable or quasi-stable multi-protein associations. The physical basis for such associations is inherent in the combined effect of shape and electrostatic complementarities (Sc, EC) of the interacting protein partners at their interface, which provides indirect probabilistic estimates of the stability and affinity of the interaction. While Sc is a necessary criterion for inter-protein associations, EC can be favorable as well as disfavored (e.g., in transient interactions). Estimating equilibrium thermodynamic parameters (∆G_binding, K_d) by experimental means is costly and time consuming, thereby opening windows for computational structural interventions. Attempts to empirically probe ∆G_binding from coarse-grain structural descriptors (primarily, surface area based terms) have lately been overtaken by physics-based, knowledge-based and their hybrid approaches (MM/PBSA, FoldX, etc.) that directly compute ∆G_binding without involving intermediate structural descriptors.

METHODS

Here, we present EnCPdock ( https://www.scinetmol.in/EnCPdock/ ), a user-friendly web-interface for the direct conjoint comparative analyses of complementarity and binding energetics in proteins. EnCPdock returns an AI-predicted ∆G_binding computed by combining complementarity (Sc, EC) and other high-level structural descriptors (input feature vectors), and renders a prediction accuracy comparable to the state-of-the-art. EnCPdock further locates a PPI complex in terms of its {Sc, EC} values (taken as an ordered pair) in the two-dimensional complementarity plot (CP). In addition, it also generates mobile molecular graphics of the interfacial atomic contact network for further analyses. EnCPdock also furnishes individual feature trends along with the relative probability estimates (Pr_fmax) of the obtained feature-scores with respect to the events of their highest observed frequencies. Together, these functionalities are of real practical use for structural tinkering and intervention as might be relevant in the design of targeted protein-interfaces. Combining all its features and applications, EnCPdock presents a unique online tool that should be beneficial to structural biologists and researchers across related fraternities.

Widespread impact of immunoglobulin V gene allelic polymorphisms on antibody reactivity

The ability of human immune system to generate antibodies to any given antigen can be strongly influenced by immunoglobulin V gene (IGV) allelic polymorphisms. However, previous studies have provided only a limited number of examples. Therefore, the prevalence of this phenomenon has been unclear. By analyzing >1,000 publicly available antibody-antigen structures, we show that many IGV allelic polymorphisms in antibody paratopes are determinants for antibody binding activity. Biolayer interferometry experiment further demonstrates that paratope allelic mutations on both heavy and light chain often abolish antibody binding. We also illustrate the importance of minor IGV allelic variants with low frequency in several broadly neutralizing antibodies to SARS-CoV-2 and influenza virus. Overall, this study not only highlights the pervasive impact of IGV allelic polymorphisms on antibody binding, but also provides mechanistic insights into the variability of antibody repertoires across individuals, which in turn have important implications for vaccine development and antibody discovery.

Molecular docking in organic, inorganic, and hybrid systems: a tutorial review

Molecular docking simulation is a very popular and well-established computational approach and has been extensively used to understand molecular interactions between a natural organic molecule (ideally taken as a receptor) such as an enzyme, protein, DNA, RNA and a natural or synthetic organic/inorganic molecule (considered as a ligand). But the implementation of docking ideas to synthetic organic, inorganic, or hybrid systems is very limited with respect to their use as a receptor despite their huge popularity in different experimental systems. In this context, molecular docking can be an efficient computational tool for understanding the role of intermolecular interactions in hybrid systems that can help in designing materials on mesoscale for different applications. The current review focuses on the implementation of the docking method in organic, inorganic, and hybrid systems along with examples from different case studies. We describe different resources, including databases and tools required in the docking study and applications. The concept of docking techniques, types of docking models, and the role of different intermolecular interactions involved in the docking process to understand the binding mechanisms are explained. Finally, the challenges and limitations of dockings are also discussed in this review.

Graphical abstract

Modulating Substrate Specificity of Rhizobium sp. Histamine Dehydrogenase through Protein Engineering for Food Quality Applications

Histamine is a biogenic amine found in fish-derived and fermented food products with physiological relevance since its concentration is proportional to food spoilage and health risk for sensitive consumers. There are various analytical methods for histamine quantification from food samples; however, a simple and quick enzymatic detection and quantification method is highly desirable. Histamine dehydrogenase (HDH) is a candidate for enzymatic histamine detection; however, other biogenic amines can change its activity or produce false positive results with an observed substrate inhibition at higher concentrations. In this work, we studied the effect of site saturation mutagenesis in Rhizobium sp. Histamine Dehydrogenase (Rsp HDH) in nine amino acid positions selected through structural alignment analysis, substrate docking, and proximity to the proposed histamine-binding site. The resulting libraries were screened for histamine and agmatine activity. Variants from two libraries (positions 72 and 110) showed improved histamine/agmatine activity ratio, decreased substrate inhibition, and maintained thermal resistance. In addition, activity characterization of the identified Phe72Thr and Asn110Val HDH variants showed a clear substrate inhibition curve for histamine and modified kinetic parameters. The observed maximum velocity (V_max) increased for variant Phe72Thr at the cost of an increased value for the Michaelis-Menten constant (K_m) for histamine. The increased K_m value, decreased substrate inhibition, and biogenic amine interference observed for variant Phe72Thr support a tradeoff between substrate affinity and substrate inhibition in the catalytic mechanism of HDHs. Considering this tradeoff for future enzyme engineering of HDH could lead to breakthroughs in performance increases and understanding of this enzyme class.

Enhanced Thermostability and Catalytic Activity of Streptomyces mobaraenesis Transglutaminase by Rationally Engineering Its Flexible Regions

Streptomyces mobaraenesis transglutaminase can catalyze the cross-linking of proteins, which has been widely used in food processing. In this study, we rationally modified flexible regions to further improve the thermostability of FRAPD-TGm2 (S2P-S23V-Y24N-E28T-S199A-A265P-A287P-K294L), a stable mutant of the transglutaminase constructed in our previous study. First, five flexible regions of FRAPD-TGm2 were identified by molecular dynamics simulations at 330 and 360 K. Second, a script based on Rosetta Cartesian_ddg was developed for virtual saturation mutagenesis within the flexible regions far from the substrate binding pocket, generating the top 18 mutants with remarkable decreases in folding free energy. Third, from the top 18 mutants, we identified two mutants (S116A and S179L) with increased thermostability and activity. Finally, the above favorable mutations were combined to obtain FRAPD-TGm2-S116A-S179L (FRAPD-TGm2A), exhibiting a half-life of 132.38 min at 60 °C (t_1/2(60 °C)) and a specific activity of 79.15 U/mg, 84 and 21% higher than those of FRAPD-TGm2, respectively. Therefore, the current result may benefit the application of S. mobaraenesis transglutaminase at high temperatures.

Simultaneous enhancement of thermostability and catalytic activity of κ-carrageenase from Pseudoalteromonas tetraodonis by rational design

κ-Carrageenase provides an attractive enzymatic approach to preparation of κ-carrageenan oligosaccharides. Pseudoalteromonas tetraodonis κ-carrageenase is active at the alkaline conditions but displays low thermostability. To further improve its enzymatic performance, two mutants of Q42V and I51H exhibiting both improved thermostability and enzyme activity were screened by the PoPMuSiC algorithm. Compared with the wild-type κ-carrageenase (WT), Q42V and I51H increased the enzyme activity by 20.9% and 25.4%, respectively. After treatment at 50 ℃ for 40 min, Q42V and I51H enhanced the residual activity by 31.1% and 25.9%, respectively. The T_m values of Q42V, I51H, and WT determined by differential scanning calorimetry were 58.2 ℃, 54.8 ℃, and 51.2 ℃, respectively. Compared with untreated and HCl-treated κ-carrageenans, Q42V-treated κ-carrageenan exhibited higher pancreatic lipase inhibitory activity. Molecular docking analysis indicated that the additional pi-sigma force and hydrophobic interaction in the enzyme-substrate complex could account for the increased catalytic activity of Q42V and I51H, respectively. Molecular dynamics analysis indicated that the improved thermostability of mutants Q42V and I51H could be attributed to the less structural deviation and the flexible changes of enzyme conformation at high temperature. This study provides new insight into κ-carrageenase performance improvement and identifies good candidates for their industrial applications.

Mutant p53 activates hnRNPA2B1-AGAP1-mediated exosome formation to promote esophageal squamous cell carcinoma progression

p53 mutations predispose cancer cell development, promote their survival and metastasis, and lead to ineffective therapeutic responses and unfavorable prognosis. No drug that abrogates the oncogenic functions of mutant p53 has been approved for cancer treatment. Here, we performed whole-genome sequencing of 663 esophageal squamous cell carcinoma (ESCC) tumor tissues and paired normal tissues. The results indicated that ESCC samples from our cohort had a more dispersed distribution of TP53 mutants and a higher proportion of nonsense mutants than European and American ESCC samples in the International Agency for Research on Cancer (IARC) database. The most frequent p53 mutations disrupt the inhibition of proliferation, migration, and invasion mediated by wild-type p53 in ESCC. Furthermore, p53 mutations alter its protein nucleoplasmic localization and protein stability. The p53 mutation G245S (p53-G245S) interacts with heterogeneous nuclear ribonucleoprotein A2B1 (hnRNPA2B1) to increase protein translation of phosphatidylinositol-dependent Arf GAP (AGAP1) by promoting AGAP1 mRNA stability. AGAP1 promotes cancer cell proliferation and metastasis by enhancing exosome formation. Furthermore, we explored the combination of the HSP90 inhibitor HSP90i and the AGAP1 inhibitor QS11 could inhibit ESCC cell proliferation and metastasis. Thus, the p53-G245S/hnRNPA2B1/AGAP1 axis promotes ESCC progression by enhancing exosome formation, and the combination of an HSP90 inhibitor and an AGAP1 inhibitor may serve as a potential therapeutic strategy.

Structural modeling and analyses of genetic variations in the human XPC nucleotide excision repair protein

Xeroderma pigmentosum C (XPC) is a key initiator in the global genome nucleotide excision repair pathway in mammalian cells. Inherited mutations in the XPC gene can cause xeroderma pigmentosum (XP) cancer predisposition syndrome that dramatically increases the susceptibility to sunlight-induced cancers. Various genetic variants and mutations of the protein have been reported in cancer databases and literature. The current lack of a high-resolution 3-D structure of human XPC makes it difficult to assess the structural impact of the mutations/genetic variations. Using the available high-resolution crystal structure of its yeast ortholog, Rad4, we built a homology model of human XPC protein and compared it with a model generated by AlphaFold. The two models are largely consistent with each other in the structured domains. We have also assessed the degree of conservation for each residue using 966 sequences of XPC orthologs. Our structure- and sequence conservation-based assessments largely agree with the variant's impact on the protein's structural stability, computed by FoldX and SDM. Known XP missense mutations such as Y585C, W690S, and C771Y are consistently predicted to destabilize the protein's structure. Our analyses also reveal several highly conserved hydrophobic regions that are surface-exposed, which may indicate novel intermolecular interfaces that are yet to be characterized.Communicated by Ramaswamy H. Sarma.

Bioinformatics Analysis of Mutations Sheds Light on the Evolution of Dengue NS1 Protein With Implications in the Identification of Potential Functional and Druggable Sites

Non-structural protein (NS1) is a 350 amino acid long conserved protein in the dengue virus. Conservation of NS1 is expected due to its importance in dengue pathogenesis. The protein is known to exist in dimeric and hexameric states. The dimeric state is involved in its interaction with host proteins and viral replication, and the hexameric state is involved in viral invasion. In this work, we performed extensive structure and sequence analysis of NS1 protein, and uncovered the role of NS1 quaternary states in its evolution. A three-dimensional modeling of unresolved loop regions in NS1 structure is performed. "Conserved" and "Variable" regions within NS1 protein were identified from sequences obtained from patient samples and the role of compensatory mutations in selecting destabilizing mutations were identified. Molecular dynamics (MD) simulations were performed to extensively study the effect of a few mutations on NS1 structure stability and compensatory mutations. Virtual saturation mutagenesis, predicting the effect of every individual amino acid substitution on NS1 stability sequentially, revealed virtual-conserved and variable sites. The increase in number of observed and virtual-conserved regions across NS1 quaternary states suggest the role of higher order structure formation in its evolutionary conservation. Our sequence and structure analysis could enable in identifying possible protein-protein interfaces and druggable sites. Virtual screening of nearly 10,000 small molecules, including FDA-approved drugs, permitted us to recognize six drug-like molecules targeting the dimeric sites. These molecules could be promising due to their stable interactions with NS1 throughout the simulation.

Targeting ‘immunogenic hotspots’ in Dengue and Zika virus: an in silico approach to a common vaccine candidate

Aim: Dengue and Zika viruses cause significant mortality globally. Considering high sequence similarity between the viral proteins, we designed common multi-epitope vaccine candidates against these pathogens. Methods: We identified multiple T and B cell epitope-rich conserved ‘immunogenic hotspots’ from highly antigenic and phylogenetically related viral proteins and used these to design the multi-epitope vaccine (MEV) candidates, ensuring high global population coverage. Results: Four MEV candidates containing conserved immunogenic hotspots from E and NS5 proteins with the highest structural integrity could favorably interact with TLR4-MD2 complex in molecular docking studies, indicating activation of TLR-mediated immune responses. MEVs also induced memory responses in silico, hallmarks of a good vaccine candidate. Conclusion: Conserved immunogenic hotspots can be utilized to design cross-protective MEV candidates.

Improved catalytic efficiency of chimeric xylanase 10B from Thermotoga petrophila RKU1 and its synergy with cellulases

TpXyl10B is a glycoside hydrolase family 10 xylanase of hyperthermophile Thermotoga petrophila RKU-1. This enzyme is of considerable importance due to its thermostability. However, in its native state, this enzyme does not possess any carbohydrate-binding module (CBM) for efficient binding to plant biomass. In this study CBM6 from Clostridium thermocellum was attached to the N- and C-termini of TpXyl10B, thereby producing the variants TpXyl10B-B6C and TpXyl10B-CB6, respectively. TpXyl10B-B6C showed 5-7 folds increased activity on Beechwood xylan and the different types of plant biomass as compared to that from the catalytic domain only. However, the activity of TpXyl10B-CB6 decreased 0.6-0.8 folds on Beechwood xylan and plant biomass compared to the catalytic domain. We explained these results through molecular modeling, which showed that binding residues of CBM6's cleft B, which were previously reported to show no contribution towards binding due to steric hindrance from a loop region, were exposed in a favorable position in TpXyl10B-B6C such that they efficiently bound the substrate. In contrast, these binding residues of CBM6 in TpXyl10B-CB6 were exposed opposite to the catalytic residues; thus, binding to the substrate resulted in decreased exposure of catalytic residues to the substrate. CD spectroscopy and thermostability assays showed that TpXyl10B-B6C was highly thermostable, having a melting point > 90 °C, which is relatively higher than that of the other variant, TpXyl10B-CB6. In addition, this xylanase variant showed synergism with cellulases for the hydrolysis of plant biomass. Therefore, TpXyl10B-B6C, an engineered xylanase in this study, can be a valuable candidate for industrial applications.

MHC2AffyPred: A machine-learning approach to estimate affinity of MHC class II peptides based on structural interaction fingerprints

Understanding how MHC class II (MHC-II) binding peptides with differing lengths exhibit specific interaction at the core and extended sites within the large MHC-II pocket is a very important aspect of immunological research for designing peptides. Certain efforts were made to generate peptide conformations amenable for MHC-II binding and calculate the binding energy of such complex formation but not directed toward developing a relationship between the peptide conformation in MHC-II structures and the binding affinity (BA) (IC₅₀ ). We present here a machine-learning approach to calculate the BA of the peptides within the MHC-II pocket for HLA-DRA1, HLA-DRB1, HLA-DP, and HLA-DQ allotypes. Instead of generating ensembles of peptide conformations conventionally, the biased mode of conformations was created by considering the peptides in the crystal structures of pMHC-II complexes as the templates, followed by site-directed peptide docking. The structural interaction fingerprints generated from such docked pMHC-II structures along with the Moran autocorrelation descriptors were trained using a random forest regressor specific to each MHC-II peptide lengths (9-19). The entire workflow is automated using Linux shell and Perl scripts to promote the utilization of MHC2AffyPred program to any characterized MHC-II allotypes and is made for free access at https://github.com/SiddhiJani/MHC2AffyPred. The MHC2AffyPred attained better performance (correlation coefficient [CC] of .612-.898) than MHCII3D (.03-.594) and NetMHCIIpan-3.2 (.289-.692) programs in the HLA-DRA1, HLA-DRB1 types. Similarly, the MHC2AffyPred program achieved CC between .91 and .98 for HLA-DP and HLA-DQ peptides (13-mer to 17-mer). Further, a case study on MHC-II binding 15-mer peptides of severe acute respiratory syndrome coronavirus-2 showed very close competency in computing the IC₅₀ values compared to the sequence-based NetMHCIIpan v3.2 and v4.0 programs with a correlation of .998 and .570, respectively.

The role of S477N mutation in the molecular behavior of SARS-CoV-2 spike protein: An in-silico perspective

The attachment of SARA-CoV-2 happens between ACE2 and the receptor binding domain (RBD) on the spike protein. Mutations in this domain can affect the binding affinity of the spike protein for ACE2. S477N, one of the most common mutations reported in the recent variants, is located in the RBD. Today's computational approaches in biology, especially during the SARS-CoV-2 pandemic, assist researchers in predicting a protein's behavior in contact with other proteins in more detail. In this study, we investigated the interactions of the S477N-hACE2 in silico to find the impact of this mutation on its binding affinity for ACE2 and immunity responses using dynamics simulation, protein-protein docking, and immunoinformatics methods. Our computational analysis revealed an increased binding affinity of N477 for ACE2. Four new hydrogen and hydrophobic bonds in the mutant RBD-ACE2 were formed (with S19 and Q24 of ACE2), which do not exist in the wild type. Also, the protein spike structure in this mutation was associated with an increase in stabilization and a decrease in its fluctuations at the atomic level. N477 mutation can be considered as the cause of increased escape from the immune system through MHC-II.

In silico engineering a CD80 variant with increased affinity to CTLA-4 and decreased affinity to CD28 for optimized cancer immunotherapy

CD80 or cluster of differentiation 80, also known as B7-1, is a member of the immunoglobulin super family, which binds to CTLA-4 and CD28 T cell receptors and induces inhibitory and inductive signals respectively. Although CTLA-4 and CD28 receptors belong to the same protein family, slight differences in their structures leads to CD80 having a higher binding affinity to CTLA-4 (-14.55 kcal/mol) compared with CD28(-12.51 kcal/mol). In this study, we constructed a variant of CD80 protein with increased binding affinity to CTLA-4 and decreased binding affinity to CD28. This variant has no signaling capability, and can act as a cap for these receptors to protect them from natural CD80 proteins existing in the body. The first step was the evolutionary and alanine scanning analysis of CD80 protein to determine conserved regions in this protein. Next, complex alanine scanning technique was employed to determine CD80 protein hotspots in CD80-CTLA-4 and CD80-CD28 protein complexes. This information was fed into a computational model developed in R for in silico mutagenesis and CD80 variant library construction. The 3D structures of variants were modeled using the Swiss model webserver. After modeling the 3D structures, HADDOCK server was employed to build all protein-protein complexes, which contain CTLA-4-CD80 variant complexes, Wild type CD80-CD28 complexes and CD28-CD80 variant complexes. Protein-protein binding free energy was determined using FoldX and the variant number 316 with mutations at 29, 31, 33 positions showed increased binding affinity to CTLA-4 (-21.43 kcal/mol) and decreased binding affinity to CD28 (- 9.54 kcal/mol). Finally, molecular dynamics (MD) simulations confirmed the stability of variant 316. In conclusion, we designed a new CD80 protein variant with potential immunotherapeutic applications.

Predicting protein stability changes upon mutation using a simple orientational potential

MOTIVATION

Structure-based stability prediction upon mutation is crucial for protein engineering and design, and for understanding genetic diseases or drug resistance events. For this task, we adopted a simple residue-based orientational potential that considers only three backbone atoms, previously applied in protein modeling. Its application to stability prediction only requires parametrizing 12 amino acid-dependent weights using cross-validation strategies on a curated dataset in which we tried to reduce the mutations that belong to protein-protein or protein-ligand interfaces, extreme conditions and the alanine over-representation.

RESULTS

Our method, called KORPM, accurately predicts mutational effects on an independent benchmark dataset, whether the wild-type or mutated structure is used as starting point. Compared with state-of-the-art methods on this balanced dataset, our approach obtained the lowest root mean square error (RMSE) and the highest correlation between predicted and experimental ΔΔG measures, as well as better receiver operating characteristics and precision-recall curves. Our method is almost anti-symmetric by construction, and it performs thus similarly for the direct and reverse mutations with the corresponding wild-type and mutated structures. Despite the strong limitations of the available experimental mutation data in terms of size, variability, and heterogeneity, we show competitive results with a simple sum of energy terms, which is more efficient and less prone to overfitting.

AVAILABILITY AND IMPLEMENTATION

https://github.com/chaconlab/korpm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.