Thermostability of in vitro evolved Bacillus subtilis lipase A: a network and dynamics perspective

Computer-aided mining of a psychrophilic cellobiose 2-epimerase from the Qinghai-Tibet Plateau gene catalogue

Cellobiose 2-epimerase (CE) catalyzes the conversion of the lactose into its high-value derivatives, epilactose and lactulose, which has great prospects in food applications. In this study, CE sequences from the Qinghai-Tibet Plateau gene catalogue, we screened these for structural flexibility through molecular dynamics simulation to identify potential psychrophilic CE candidates. One such psychrophilic CE we termed psyCE demonstrated exceptional epimerization activity, achieving an optimum activity of 122.2 ± 1.6 U/mg. Its kinetic parameters (Kcat and Km) for epimerization activity were 219.9 ± 5.6 s-1 and 261.9 ± 18.1 mM, respectively, representing the highest Kcat recorded among known cold-active CEs. Notably, this is the first report of a psychrophilic CE. The psyCE can effectively produce epilactose at 8 °C, converting 20.3 % of 200 mM lactose into epilactose within four hours. These findings suggest that psyCE is highly suitable for cryogenic food processing, and glaciers may serve as a valuable repository of psychrophilic enzymes.

Probing structural basis for enhanced binding of SARS-CoV-2 P.1 variant spike protein with the human ACE2 receptor

The initial step of infection by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) involves the binding of receptor binding domain (RBD) of the spike protein to the angiotensin converting enzyme 2 (ACE2) receptor. Each successive wave of SARS‐CoV‐2 reports emergence of many new variants, which is associated with mutations in the RBD as well as other parts of the spike protein. These mutations are reported to have enhanced affinity towards the ACE2 receptor as well as are also crucial for the virus transmission. Many computational and experimental studies have demonstrated the effect of individual mutation on the RBD‐ACE2 binding. However, the cumulative effect of mutations on the RBD and away from the RBD was not investigated in detail. We report here a comparative analysis on the structural communication and dynamics of the RBD and truncated S1 domain of spike protein in complex with the ACE2 receptor from SARS‐CoV‐2 wild type and its P.1 variant. Our integrative network and dynamics approaches highlighted a subtle conformational changes in the RBD as well as truncated S1 domain of spike protein at the protein contact level, responsible for the increased affinity with the ACE2 receptor. Moreover, our study also identified the commonalities and differences in the dynamics of the interactions between spike protein of SARS‐CoV‐2 wild type and its P.1 variant with the ACE2 receptor. Further, our investigation yielded an understanding towards identification of the unique RBD residues crucial for the interaction with the ACE2 host receptor. Overall, the study provides an insight for designing better therapeutics against the circulating P.1 variants as well as other future variants.

Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects

Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug–disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.

Thermostable lipases and their dynamics of improved enzymatic properties

Thermal stability is one of the most desirable characteristics in the search for novel lipases. The search for thermophilic microorganisms for synthesising functional enzyme biocatalysts with the ability to withstand high temperature, and capacity to maintain their native state in extreme conditions opens up new opportunities for their biotechnological applications. Thermophilic organisms are one of the most favoured organisms, whose distinctive characteristics are extremely related to their cellular constituent particularly biologically active proteins. Modifications on the enzyme structure are critical in optimizing the stability of enzyme to thermophilic conditions. Thermostable lipases are one of the most favourable enzymes used in food industries, pharmaceutical field, and actively been studied as potential biocatalyst in biodiesel production and other biotechnology application. Particularly, there is a trade-off between the use of enzymes in high concentration of organic solvents and product generation. Enhancement of the enzyme stability needs to be achieved for them to maintain their enzymatic activity regardless the environment. Various approaches on protein modification applied since decades ago conveyed a better understanding on how to improve the enzymatic properties in thermophilic bacteria. In fact, preliminary approach using advanced computational analysis is practically conducted before any modification is being performed experimentally. Apart from that, isolation of novel extremozymes from various microorganisms are offering great frontier in explaining the crucial native interaction within the molecules which could help in protein engineering. In this review, the thermostability prospect of lipases and the utility of protein engineering insights into achieving functional industrial usefulness at their high temperature habitat are highlighted. Similarly, the underlying thermodynamic and structural basis that defines the forces that stabilize these thermostable lipase is discussed. KEY POINTS: • The dynamics of lipases contributes to their non-covalent interactions and structural stability. • Thermostability can be enhanced by well-established genetic tools for improved kinetic efficiency. • Molecular dynamics greatly provides structure-function insights on thermodynamics of lipase.

Comparative protein structure network analysis on 3CLpro from SARS-CoV-1 and SARS-CoV-2

The main protease M^pro, 3CL^pro is an important target from coronaviruses. In spite of having 96% sequence identity among M^pros from SARS‐CoV‐1 and SARS‐CoV‐2; the inhibitors used to block the activity of SARS‐CoV‐1 M^pro so far, were found to have differential inhibitory effect on M^pro of SARS‐CoV‐2. The possible reason could be due to the difference of few amino acids among the peptidases. Since, overall 3‐D crystallographic structure of M^pro from SARS‐CoV‐1 and SARS‐CoV‐2 is quite similar and mapping a subtle structural variation is seemingly impossible. Hence, we have attempted to study a structural comparison of SARS‐CoV‐1 and SARS‐CoV‐2 M^pro in apo and inhibitor bound states using protein structure network (PSN) based approach at contacts level. The comparative PSNs analysis of apo M^pros from SARS‐CoV‐1 and SARS‐CoV‐2 uncovers small but significant local changes occurring near the active site region and distributed throughout the structure. Additionally, we have shown how inhibitor binding perturbs the PSG and the communication pathways in M^pros. Moreover, we have also investigated the network connectivity on the quaternary structure of M^pro and identified critical residue pairs for complex formation using three centrality measurement parameters along with the modularity analysis. Taken together, these results on the comparative PSN provide an insight into conformational changes that may be used as an additional guidance towards specific drug development.

Understanding the Effect of Multiple Domain Deletion in DNA Polymerase I from Geobacillus Sp. Strain SK72

The molecular structure of DNA polymerase I or family A polymerases is made up of three major domains that consist of a single polymerase domain with two extra exonuclease domains. When the N-terminal was deleted, the enzyme was still able to perform basic polymerase activity with additional traits that used isothermal amplification. However, the 3′-5′ exonuclease domain that carries a proofreading activity was disabled. Yet, the structure remained attached to the 5′-3′ polymerization domain without affecting its ability. The purpose of this non-functional domain still remains scarce. It either gives negative effects or provides structural support to the DNA polymerase. Here, we compared the effect of deleting each domain against the polymerase activity. The recombinant wild type and its variants were successfully purified and characterized. Interestingly, SK72-Exo (a large fragment excluding the 5′-3′ exonuclease domain) exhibited better catalytic activity than the native SK72 (with all three domains) at similar optimum temperature and pH profile, and it showed longer stability at 70 °C. Meanwhile, SK72-Exo2 (polymerization domain without both the 5′-3′ and 3′-5′ exonuclease domain) displayed the lowest activity with an optimum at 40 °C and favored a more neutral environment. It was also the least stable among the variants, with almost no activity at 50 °C for the first 10 min. In conclusion, cutting both exonuclease domains in DNA polymerase I has a detrimental effect on the polymerization activity and structural stability.

Computer-aided search for a cold-active cellobiose 2-epimerase

Cellobiose 2-epimerase (CE) is a promising industrial enzyme that can catalyze bioconversion of lactose to its high-value derivatives, namely epilactose and lactulose. A need exists in the dairy industry to catalyze lactose bioconversions at low temperatures to avoid microbial growth. We focused on the discovery of cold-active CE in this study. A genome mining method based on computational prediction was used to screen the potential genes encoding cold-active enzymes. The CE-encoding gene from Roseburia intestinalis, with a predicted high structural flexibility, was expressed heterologously in Escherichia coli. The catalytic property of the recombinant enzyme was extensively studied. The optimum temperature and pH of the enzyme were 45°C and 7.0, respectively. The specific activity of this enzyme to catalyze conversion of lactose to epilactose was measured to be 77.3 ± 1.6 U/mg. The kinetic parameters, including turnover number (k_cat), Michaelis constant (K_m), and catalytic efficiency (k_cat/K_m) using lactose as a substrate were 117.0 ± 7.7 s^-1, 429.9 ± 57.3 mM, and 0.27 mM^-1s^-1, respectively. In situ production of epilactose was carried out at 8°C: 20.9% of 68.4 g/L lactose was converted into epilactose in 4 h using 0.02 mg/mL (1.5 U/mL, measured at 45°C) of recombinant enzyme. The enzyme discovered by this in silico method is suitable for low-temperature applications.

Network analysis of hydroxymethylbilane synthase dynamics

Hydroxymethylbilane synthase (HMBS) is one of the key enzymes of the heme biosynthetic pathway that catalyzes porphobilinogen to form the linear tetrapyrrole 1-hydroxymethylbilane through four intermediate steps. Mutations in the human HMBS (hHMBS) can lead to acute intermittent porphyria (AIP), a lethal metabolic disorder. The molecular basis of importance of the amino acid residues at the catalytic site of hHMBS has been well studied. However, the role of non-active site residues toward the activity of the enzyme and hence the association of their mutations with AIP is not known. Network-based analyses of protein structures provide a systems approach to understand the correlations of the residues through a series of inter-residue interactions. We analyzed the dynamic network representation of HMBS protein derived from five molecular dynamics trajectories corresponding to the five steps of pyrrole polymerization. We analyzed the network clusters for each stage and identified the amino acid residues and interactions responsible for the structural stability and catalytic function of the protein. The analysis of high betweenness nodes and interaction paths from the active site help in understanding the molecular basis of the effect of non-active site AIP-causing mutations on the catalytic activity.

Small Conformational Changes Underlie Evolution of Resistance to NNRTI in HIV Reverse Transcriptase

Despite achieving considerable success in reducing the number of fatalities due to acquired immunodeficiency syndrome, emergence of resistance against the reverse transcriptase (RT) inhibitor drugs remains one of the biggest challenges of the human immunodeficiency virus antiretroviral therapy (ART). Non-nucleoside reverse transcriptase inhibitors (NNRTIs) form a large class of drugs and a crucial component of ART. In NNRTIs, even a single resistance mutation is known to make the drugs completely ineffective. Additionally, several inhibitor-bound RTs with single resistance mutations do not exhibit any significant variations in their three-dimensional structures compared with the inhibitor-bound RT but completely nullify their inhibitory functions. This makes understanding the structural mechanism of these resistance mutations crucial for drug development. Here, we study several single resistance mutations in the allosteric inhibitor (nevirapine)-bound RT to analyze the mechanism of small structural changes leading to these large functional effects. In this study, we have shown that in absence of significant conformational variations in the inhibitor-bound wild-type RT and RT with single resistance mutations, the protein contact network analysis of their static structures, along with molecular dynamics simulations, can be a useful approach to understand the functional effect of small local conformational variations. The simple network analysis exposes the localized contact changes that lead to global rearrangement in the communication pattern within RT. Furthermore, these conformational changes have implications on the overall dynamics of RT. Using various measures, we show that a single resistance mutation can change the network structure and dynamics of RT to behave more like unbound RT, even in the presence of the inhibitor. This combined coarse-grained contact network and molecular dynamics approach promises to be a useful tool to analyze structure-function studies of proteins that show large functional changes with negligible variations in their overall conformation.

Thermostability of Lipase A and Dynamic Communication Based on Residue Interaction Network

OBJECTIVE

Dynamic communication caused by mutation affects protein stability. The main objective of this study is to explore how mutations affect communication and to provide further insight into the relationship between heat resistance and signal propagation of Bacillus subtilis lipase (Lip A).

METHODS

The relationship between dynamic communication and Lip A thermostability is studied by long-time MD simulation and residue interaction network. The Dijkstra algorithm is used to get the shortest path of each residue pair. Subsequently, time-series frequent paths and spatio-temporal frequent paths are mined through an Apriori-like algorithm.

RESULTS

Time-series frequent paths show that the communication between residue pairs, both in wild-type lipase (WTL) and mutant 6B, becomes chaotic with an increase in temperature; however, more residues in 6B can maintain stable communication at high temperature, which may be associated with the structural rigidity. Furthermore, spatio-temporal frequent paths reflect the interactions among secondary structures. For WTL at 300K, β7, αC, αB, the longest loop, αA and αF contact frequently. The 310-helix between β3 and αA is penetrated by spatio-temporal frequent paths. At 400K, only αC can be frequently transmitted. For 6B, when at 300K, αA and αF are in more tight contact by spatio-temporal frequent paths though I157M and N166Y. Moreover, the rigidity of the active site His156 and the C-terminal of Lip A are increased, as reflected by the spatio-temporal frequent paths. At 400K, αA and αF, 310-helix between β3 and αA, the longest loop, and the loop where the active site Asp133 is located can still maintain stable communication.

CONCLUSION

From the perspective of residue dynamic communication, it is obviously found that mutations cause changes in interactions between secondary structures and enhance the rigidity of the structure, contributing to the thermal stability and functional activity of 6B.

RankProt: A multi criteria-ranking platform to attain protein thermostabilizing mutations and its in vitro applications - Attribute based prediction method on the principles of Analytical Hierarchical Process

Attaining recombinant thermostable proteins is still a challenge for protein engineering. The complexity is the length of time and enormous efforts required to achieve the desired results. Present work proposes a novel and economic strategy of attaining protein thermostability by predicting site-specific mutations at the shortest possible time. The success of the approach can be attributed to Analytical Hierarchical Process and the outcome was a rationalized thermostable mutation(s) prediction tool- RankProt. Briefly the method involved ranking of 17 biophysical protein features as class predictors, derived from 127 pairs of thermostable and mesostable proteins. Among the 17 predictors, ionic interactions and main-chain to main-chain hydrogen bonds were the highest ranked features with eigen value of 0.091. The success of the tool was judged by multi-fold in silico validation tests and it achieved the prediction accuracy of 91% with AUC 0.927. Further, in vitro validation was carried out by predicting thermostabilizing mutations for mesostable Bacillus subtilis lipase and performing the predicted mutations by multi-site directed mutagenesis. The rationalized method was successful to render the lipase thermostable with optimum temperature stability and T_m increase by 20°C and 7°C respectively. Conclusively it can be said that it was the minimum number of mutations in comparison to the number of mutations incorporated to render Bacillus subtilis lipase thermostable, by directed evolution techniques. The present work shows that protein stabilizing mutations can be rationally designed by balancing the biophysical pleiotropy of proteins, in accordance to the selection pressure.

Complex network analysis of thermostable mutants of Bacillus subtilis Lipase A

Three-dimensional structures of proteins that regulate their functions can be modelled using complex network based approaches for understanding the structure-function relationship. The six mutants of the protein Lipase A from Bacillus subtilis, harbouring 2 to 12 mutations, retain their function at higher temperatures with negligible variation in their overall three-dimensional crystallographic structures. This enhanced thermostability of the mutants questions the structure-function paradigm. In this paper, a coarse-grained complex network approach is used to elucidate the structural basis of enhanced thermostability in the mutant proteins, by uncovering small but significant local changes distributed throughout the structure, rendering stability to the mutants at higher temperatures. Community structure analysis of the six mutant protein networks uncovers the specific reorganisations among the nodes/residues that occur, in absence of overall structural variations, which induce enhanced rigidity underlying the increased thermostability. This study offers a novel and significant application of complex network analysis that proposes to be useful in the understanding and designing of thermostable proteins.

Multifactorial level of extremostability of proteins: can they be exploited for protein engineering?

Research on extremostable proteins has seen immense growth in the past decade owing to their industrial importance. Basic research of attributes related to extreme-stability requires further exploration. Modern mechanistic approaches to engineer such proteins in vitro will have more impact in industrial biotechnology economy. Developing a priori knowledge about the mechanism behind extreme-stability will nurture better understanding of pathways leading to protein molecular evolution and folding. This review is a vivid compilation about all classes of extremostable proteins and the attributes that lead to myriad of adaptations divulged after an extensive study of 6495 articles belonging to extremostable proteins. Along with detailing on the rationale behind extreme-stability of proteins, emphasis has been put on modern approaches that have been utilized to render proteins extremostable by protein engineering. It was understood that each protein shows different approaches to extreme-stability governed by minute differences in their biophysical properties and the milieu in which they exist. Any general rule has not yet been drawn regarding adaptive mechanisms in extreme environments. This review was further instrumental to understand the drawback of the available 14 stabilizing mutation prediction algorithms. Thus, this review lays the foundation to further explore the biophysical pleiotropy of extreme-stable proteins to deduce a global prediction model for predicting the effect of mutations on protein stability.

Uncoupling of an ammonia channel as a mechanism of allosteric inhibition in anthranilate synthase of Serratia marcescens: dynamic and graph theoretical analysis

Network modeling and molecular dynamic studies reveal the perturbation in communication pathways as a mechanism of allosteric inhibition in anthranilate synthase.

Understanding the thermostability and activity of Bacillus subtilis lipase mutants: insights from molecular dynamics simulations

Improving the thermostability of industrial enzymes is an important protein engineering challenge. Point mutations, induced to increase thermostability, affect the structure and dynamics of the target protein in several ways and thus can also affect its activity. There appears to be no general rules for improving the thermostabilty of enzymes without adversely affecting their enzymatic activity. We report MD simulations, of wild type Bacillus subtilis lipase (WT) and its six progressively thermostable mutants (2M, 3M, 4M, 6M, 9M, and 12M), performed at different temperatures, to address this issue. Less thermostable mutants (LTMs), 2M to 6M, show WT-like dynamics at all simulation temperatures. However, the two more thermostable mutants (MTMs) show the required flexibility at appropriate temperature ranges and maintain conformational stability at high temperature. They show a deep and rugged free-energy landscape, confining them within a near-native conformational space by conserving noncovalent interactions, and thus protecting them from possible aggregation. In contrast, the LTMs having marginally higher thermostabilities than WT show greater probabilities of accessing non-native conformations, which, due to aggregation, have reduced possibilities of reverting to their respective native states under refolding conditions. Our analysis indicates the possibility of nonadditive effects of point mutations on the conformational stability of LTMs.