Vibrational entropy differences between mesophile and thermophile proteins and their use in protein engineering

Improving the Thermostability of Serine Protease PB92 from Bacillus alcalophilus via Site-Directed Mutagenesis Based on Semi-Rational Design

Proteases have been widely employed in many industrial processes. In this work, we aimed to improve the thermostability of the serine protease PB92 from Bacillus alcalophilus to meet the high-temperature requirements of biotechnological treatments. Eight mutation sites (N18, S97-S101, E110, and R143) were identified, and 21 mutants were constructed from B-factor comparison and multiple sequence alignment and expressed via Bacillus subtilis. Among them, fifteen mutants exhibited increased half-life (t1/2) values at 65 °C (1.13-31.61 times greater than that of the wild type). Based on the composite score of enzyme activity and thermostability, six complex mutants were implemented. The t1/2 values of these six complex mutants were 2.12-10.05 times greater than that of the wild type at 65 °C. In addition, structural analysis revealed that the increased thermal stability of complex mutants may be related to the formation of additional hydrophobic interactions due to increased hydrophobicity and the decreased flexibility of the structure. In brief, the thermal stability of the complex mutants N18L/R143L/S97A, N18L/R143L/S99L, and N18L/R143L/G100A was increased 4-fold, which reveals application potential in industry.

Enhanced Extracellular Expression of a Ca2+- and Mg2+-Dependent Hyperthermostable Protease EA1 in Bacillus subtilis via Systematic Screening of Optimal Signal Peptides

Proteases have been widely applied in various industries, including tanning, silk, feed, medicine, food, and environmental protection. Herein, the protease EA1 (GenBank accession no. U25630.1) was successfully expressed in Bacillus subtilis and demonstrated to function as a Ca²⁺- and Mg²⁺-dependent hyperthermostable neutral protease. At 80 °C, its half-life (t_1/2) in the presence of 10 mM Mg²⁺ and Ca²⁺ was 50.4-fold longer than that in their absence (7.4 min), which can be explained by structural analysis. Compared with the currently available commercial proteases, protease EA1 has obvious advantages in heat resistance. The largest peptide library was used to enhance the extracellular expression of protease EA1 via constructing and screening 244 signal peptides (SPs). Eleven SPs with high yields of protease EA1 were identified from 5000 clones using a high-throughput assay. Specifically, the enzyme activity of protease produced by the strain (217.6 U/mL) containing the SP XynD was 5.2-fold higher than that of the strain with the initial SP. In brief, the protease is a potential candidate for future use in the high-temperature industry.

Sequence-sensitive elastic network captures dynamical features necessary for miR-125a maturation

The Elastic Network Contact Model (ENCoM) is a coarse-grained normal mode analysis (NMA) model unique in its all-atom sensitivity to the sequence of the studied macromolecule and thus to the effect of mutations. We adapted ENCoM to simulate the dynamics of ribonucleic acid (RNA) molecules, benchmarked its performance against other popular NMA models and used it to study the 3D structural dynamics of human microRNA miR-125a, leveraging high-throughput experimental maturation efficiency data of over 26 000 sequence variants. We also introduce a novel way of using dynamical information from NMA to train multivariate linear regression models, with the purpose of highlighting the most salient contributions of dynamics to function. ENCoM has a similar performance profile on RNA than on proteins when compared to the Anisotropic Network Model (ANM), the most widely used coarse-grained NMA model; it has the advantage on predicting large-scale motions while ANM performs better on B-factors prediction. A stringent benchmark from the miR-125a maturation dataset, in which the training set contains no sequence information in common with the testing set, reveals that ENCoM is the only tested model able to capture signal beyond the sequence. This ability translates to better predictive power on a second benchmark in which sequence features are shared between the train and test sets. When training the linear regression model using all available data, the dynamical features identified as necessary for miR-125a maturation point to known patterns but also offer new insights into the biogenesis of microRNAs. Our novel approach combining NMA with multivariate linear regression is generalizable to any macromolecule for which relatively high-throughput mutational data is available.

Prediction of infectivity of SARS-CoV-2 virus based on Spike-hACE-2 interaction

The COVID-19 pandemic caused by SARS-CoV-2 results almost 3 M death worldwide and till continuing in spite of having several vaccine against the virus. One of the main reasons is the mutations occur in the virus to cope with the environment. Detail study of genomics and proteomics level of each components may help to combat the situation. Spike (S) protein that covers the surface of the virus helps in entry by encountering the host receptor Human Angiotensin-Converting Enzyme-2 (hACE-2) with other different roles. In this study, we accomplish our work with the mutations in receptor binding domain (RBD) of Spike (S) protein considering different aspects like the hACE-2 variants in human populations to get an idea about the varying infectivity of different strains for different population. Several other parameters affecting the viral infectivity and in different diseased condition were also studied which may guide to a better insight in developing future therapeutics.

Response of Terahertz Protein Vibrations to Ligand Binding: Calmodulin-Peptide Complexes as a Case Study

Terahertz vibrations are sensitive reporters of the structure and interactions of proteins. Ligand binding alters the nature and distribution of these collective vibrations. The ligand-induced changes in the terahertz protein vibrations contribute to the binding entropy and to the overall thermodynamic stability of the resultant protein-ligand complexes. Here, we have examined the response of the low-frequency (below 6 terahertz) collective vibrations of the calcium-loaded calmodulin (CaM) to binding to five different ligands, both in the presence and absence of water, using normal-mode analysis and molecular dynamics simulations. A comparison of the vibrational spectra of hydrated and dry systems reveals that protein-solvent interactions stiffen the terahertz protein vibrations and that these solvent-coupled collective vibrations contribute significantly to the hydration-sensitive variation in the vibrational entropy of CaM. In the absence of water, the low-frequency vibrations of CaM are stiffened by ligand binding. On the contrary, the number and the cumulative vibrational entropy of low-frequency vibrational modes (ω < 200 cm^-1) of the hydrated CaM are increased noticeably after binding to the peptides, indicating binding-induced softening of collective vibrations of the protein. Although the calculated and experimental binding affinities of the chosen complexes correlated reasonably well, no systematic correlation was observed between the protein vibrational entropy and the binding affinity. The results underscored the importance of the interplay of protein-ligand and solvent interactions in modulating the low-frequency vibrations of proteins.

Modelling conformational state dynamics and its role on infection for SARS-CoV-2 Spike protein variants

The SARS-CoV-2 Spike protein needs to be in an open-state conformation to interact with ACE2 to initiate viral entry. We utilise coarse-grained normal mode analysis to model the dynamics of Spike and calculate transition probabilities between states for 17081 variants including experimentally observed variants. Our results correctly model an increase in open-state occupancy for the more infectious D614G via an increase in flexibility of the closed-state and decrease of flexibility of the open-state. We predict the same effect for several mutations on glycine residues (404, 416, 504, 252) as well as residues K417, D467 and N501, including the N501Y mutation recently observed within the B.1.1.7, 501.V2 and P1 strains. This is, to our knowledge, the first use of normal mode analysis to model conformational state transitions and the effect of mutations on such transitions. The specific mutations of Spike identified here may guide future studies to increase our understanding of SARS-CoV-2 infection mechanisms and guide public health in their surveillance efforts.

The present work explores the molecular mechanisms underlying and potentially helping new strains of SARS-CoV-2 to gain an evolutionary advantage during the ongoing COVID-19 pandemics. We show how a computational method called normal mode analysis that treats protein dynamics in a simplified manner is capable to predict the higher propensity of the Spike protein to be in the open state in which it is capable to interact with the human ACE2 receptor and thus facilitate cell entry. Because the simulation of the simplified computational model is relatively less demanding on resources than alternative methods, we were able to simulate over 17000 mutations in the SARS-CoV-2 Spike protein to identify multiple mutations that if they were to appear as the virus continues to evolve, could confer an evolutionary advantage. As a matter of fact, our predictions foresaw the emergence of particular mutations such as N501Y that appeared in several variants of concern. Our results can inform public health regarding new variants and serves as a proof of concept for the application of normal mode analysis to study the effect of mutations on both, protein dynamics and conformational transitions in a high-throughput manner.

The NRGTEN Python package: an extensible toolkit for coarse-grained normal mode analysis of proteins, nucleic acids, small molecules and their complexes

SUMMARY

The Najmanovich Research Group Toolkit for Elastic Networks (NRGTEN) is a Python toolkit that implements four different NMA models in addition to popular and novel metrics to benchmark and measure properties from these models. Furthermore, the toolkit is available as a public Python package and is easily extensible for the development or implementation of additional NMA models. The inclusion of the ENCoM model (Elastic Network Contact Model) developed in our group within NRGTEN is noteworthy, owing to its account for the specific chemical nature of atomic interactions.

AVAILABILITY AND IMPLEMENTATION

https://github.com/gregorpatof/nrgten_package/.

Expression and characterization of a thermotolerant and pH-stable hyaluronate lyase from Thermasporomyces composti DSM22891

Hyaluronate lyases have received extensive attention due to their applications in medical science, drug and biochemical engineering. However, few thermotolerant and pH-stable hyaluronate lyases have been found. In this study, hyaluronate lyase TcHly8B from Thermasporomyces composti DSM22891 was expressed in Escherichia coli BL21(DE3), purified, and characterized. Phylogenetic analysis revealed that TcHly8B belonged to a new subfamily in PL8. The molecular mass of recombinant TcHly8B determined by SDS-PAGE was approximately 86 kDa. The optimal temperature of TcHly8B was 70 °C, which was higher than that of previously reported hyaluronate lyases. TcHly8B was very stable at temperatures from 0 to 60 °C. The optimal pH of TcHly8B was 6.6. It could retain more than 80% of its original enzyme activity after incubation for 12 h in the pH range of 3.0-10.6. TcHly8B degraded hyaluronic acid into unsaturated disaccharides as the end products. The amino acid sequence and structure analysis of TcHly8B demonstrated that the amino acid composition and salt bridges might contribute to the thermostability of TcHly8B. Overall, this study provides an excellent example for the discovery of thermotolerant hyaluronate lyases and can be applied to the industrialized production and basic research of hyaluronate oligosaccharides.

Biochemical characterization of a thermophilic hyaluronate lyase TcHly8C from Thermasporomyces composti DSM22891

Hyaluronic acid (HA) is an anionic linear polysaccharide abundantly distributed in the extracellular matrix of mammalian connective, growing, and tumor tissues. Hyaluronidase is used as an important drug diffusion promoter and a tool enzyme to produce HA oligosaccharides. However, there is no thermostable hyaluronidase suitable for application to date. In this study, a thermophilic hyaluronate lyase, TcHly8C, from Thermasporomyces composti DSM22891 was expressed in Escherichia coli. The recombinant TcHly8C was most active at 70 °C, and it retained about 30% of initial activity after incubation at 60 °C for 28 days. The half-lives of TcHly8C at 60 °C and 70 °C were 16.1 d and 2.3 h, respectively. The optimum pH of TcHly8C is 5.93, and it was stable at pH 6.15-10.90. The presence of Mg²⁺ could enhance its enzymatic activity significantly. K_m, k_cat, and k_cat/K_m of TcHly8C towards HA were 3.69 mg∙ml^-1, 17.82 s^-1, and 4.82 ml∙mg^-1∙s^-1, respectively. TcHly8C degraded HA in an exolytic mode, and the end product was unsaturated HA disaccharide (ΔUA-GlcNAc). Overall, our results show that TcHly8C is the first reported PL8 exo-type hyaluronate lyase with high thermostability, which provides a potential enzyme used in medicine and production of HA oligosaccharides.

Mutations that Allow SIR2 Orthologs to Function in a NAD+-Depleted Environment

Sirtuin enzymes depend on NAD⁺ to catalyze protein deacetylation. Therefore, the lowering of NAD⁺ during aging leads to decreased sirtuin activity and may speed up aging processes in laboratory animals and humans. In this study, we used a genetic screen to identify two mutations in the catalytic domain of yeast Sir2 that allow the enzyme to function in an NAD⁺-depleted environment. These mutant enzymes give rise to a significant increase of yeast replicative lifespan and increase deacetylation by the Sir2 ortholog, SIRT1, in mammalian cells. Our data suggest that these mutations increase the stability of the conserved catalytic sirtuin domain, thereby increasing the catalytic efficiency of the mutant enzymes. Our approach to identifying sirtuin mutants that permit function in NAD⁺-limited environments may inform the design of small molecules that can maintain sirtuin activity in aging organisms.

Applications of Normal Mode Analysis Methods in Computational Protein Design

Recent advances in coarse-grained normal mode analysis methods make possible the large-scale prediction of the effect of mutations on protein stability and dynamics as well as the generation of biologically relevant conformational ensembles. Given the interplay between flexibility and enzymatic activity, the combined analysis of stability and dynamics using the Elastic Network Contact Model (ENCoM) method has ample applications in protein engineering in industrial and medical applications such as in computational antibody design. Here, we present a detailed tutorial on how to perform such calculations using ENCoM.

Evolutionary studies of ligand binding sites in proteins

Biological processes at their most fundamental molecular aspects are defined by molecular interactions with ligand-protein interactions in particular at the core of cellular functions such as metabolism and signalling. Divergent and convergent processes shape the evolution of ligand binding sites. The competition between similar ligands and binding sites across protein families create evolutionary pressures that affect the specificity and selectivity of interactions. This short review showcases recent studies of the evolution of ligand binding-sites and methods used to detect binding-site similarities.

Delineating the relationship between amyotrophic lateral sclerosis and frontotemporal dementia: Sequence and structure-based predictions

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are related neurodegenerative disorders which are characterized by a rapid decline in cognitive and motor functions, and short survival. Both syndromes may be present within the same family or even in the same person. The genetic findings for both diseases also support the existence of a continuum, with mutations in the same genes being found in patients with ALS, FTD or FTD/ALS. Little is known about the molecular mechanisms underlying the differences in mutations of the same protein causing either ALS or FTD. Here, we shed light on 348 ALS and FTD missense mutations in 14 genes focusing on genic intolerance and protein stability based on available 3D structures. Using EvoTol, we prioritized the disease-causing genes and their domain. The most intolerant genes predicted by EvoTol are SQSTM1 and OPTN which are involved in protein homeostasis. Further, using ENCoM (Elastic Network Contact Model) that predicts stability based on vibrational entropy, we predicted that most of the missense mutations with destabilizing energies are in the structural regions that control the protein-protein interaction, and only a few mutations affect protein folding. We found a trend that energy changes are higher for ALS compared to FTD mutations. The stability of the ALS mutants correlated well with the duration of disease progression as compared to FTD-ALS mutants. This study provides a comprehensive understanding of the mechanism of ALS and illustrates the significance of structure-energy based studies in differentiating ALS and FTD mutations.

DIRECT-ID: An automated method to identify and quantify conformational variations--application to β2 -adrenergic GPCR

The conformational dynamics of a macromolecule can be modulated by a number of factors, including changes in environment, ligand binding, and interactions with other macromolecules, among others. We present a method that quantifies the differences in macromolecular conformational dynamics and automatically extracts the structural features responsible for these changes. Given a set of molecular dynamics (MD) simulations of a macromolecule, the norms of the differences in covariance matrices are calculated for each pair of trajectories. A matrix of these norms thus quantifies the differences in conformational dynamics across the set of simulations. For each pair of trajectories, covariance difference matrices are parsed to extract structural elements that undergo changes in conformational properties. As a demonstration of its applicability to biomacromolecular systems, the method, referred to as DIRECT-ID, was used to identify relevant ligand-modulated structural variations in the β2 -adrenergic (β2 AR) G-protein coupled receptor. Micro-second MD simulations of the β2 AR in an explicit lipid bilayer were run in the apo state and complexed with the ligands: BI-167107 (agonist), epinephrine (agonist), salbutamol (long-acting partial agonist), or carazolol (inverse agonist). Each ligand modulated the conformational dynamics of β2 AR differently and DIRECT-ID analysis of the inverse-agonist vs. agonist-modulated β2 AR identified residues known through previous studies to selectively propagate deactivation/activation information, along with some previously unidentified ligand-specific microswitches across the GPCR. This study demonstrates the utility of DIRECT-ID to rapidly extract functionally relevant conformational dynamics information from extended MD simulations of large and complex macromolecular systems.

Detection of Binding Site Molecular Interaction Field Similarities

Protein binding-site similarity detection methods can be used to predict protein function and understand molecular recognition, as a tool in drug design for drug repurposing and polypharmacology, and for the prediction of the molecular determinants of drug toxicity. Here, we present IsoMIF, a method able to identify binding site molecular interaction field similarities across protein families. IsoMIF utilizes six chemical probes and the detection of subgraph isomorphisms to identify geometrically and chemically equivalent sections of protein cavity pairs. The method is validated using six distinct data sets, four of those previously used in the validation of other methods. The mean area under the receiver operator curve (AUC) obtained across data sets for IsoMIF is higher than those of other methods. Furthermore, while IsoMIF obtains consistently high AUC values across data sets, other methods perform more erratically across data sets. IsoMIF can be used to predict function from structure, to detect potential cross-reactivity or polypharmacology targets, and to help suggest bioisosteric replacements to known binding molecules. Given that IsoMIF detects spatial patterns of molecular interaction field similarities, its predictions are directly related to pharmacophores and may be readily translated into modeling decisions in structure-based drug design. IsoMIF may in principle detect similar binding sites with distinct amino acid arrangements that lead to equivalent interactions within the cavity. The source code to calculate and visualize MIFs and MIF similarities are freely available.

ENCoM server: exploring protein conformational space and the effect of mutations on protein function and stability

ENCoM is a coarse-grained normal mode analysis method recently introduced that unlike previous such methods is unique in that it accounts for the nature of amino acids. The inclusion of this layer of information was shown to improve conformational space sampling and apply for the first time a coarse-grained normal mode analysis method to predict the effect of single point mutations on protein dynamics and thermostability resulting from vibrational entropy changes. Here we present a web server that allows non-technical users to have access to ENCoM calculations to predict the effect of mutations on thermostability and dynamics as well as to generate geometrically realistic conformational ensembles. The server is accessible at: http://bcb.med.usherbrooke.ca/encom.