Mutatomics analysis of the systematic thermostability profile of Bacillus subtilis lipase A

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

With the development of artificial intelligence (AI), tailoring methods for enzyme engineering have been widely expanded. Additional protocols based on optimized network models have been used to predict and optimize lipase production as well as properties, namely, catalytic activity, stability, and substrate specificity. Here, different network models and algorithms for the prediction and reforming of lipase, focusing on its modification methods and cases based on AI, are reviewed in terms of both their advantages and disadvantages. Different neural networks coupled with various algorithms are usually applied to predict the maximum yield of lipase by optimizing the external cultivations for lipase production, while one part is used to predict the molecule variations affecting the properties of lipase. However, few studies have directly utilized AI to engineer lipase by affecting the structure of the enzyme, and a set of research gaps needs to be explored. Additionally, future perspectives of AI application in enzymes, including lipase engineering, are deduced to help the redesign of enzymes and the reform of new functional biocatalysts. This review provides a new horizon for developing effective and innovative AI tools for lipase production and engineering and facilitating lipase applications in the food industry and biomass conversion.

Computer-Aided Lipase Engineering for Improving Their Stability and Activity in the Food Industry: State of the Art

As some of the most widely used biocatalysts, lipases have exhibited extreme advantages in many processes, such as esterification, amidation, and transesterification reactions, which causes them to be widely used in food industrial production. However, natural lipases have drawbacks in terms of organic solvent resistance, thermostability, selectivity, etc., which limits some of their applications in the field of foods. In this systematic review, the application of lipases in various food processes was summarized. Moreover, the general structure of lipases is discussed in-depth, and the engineering strategies that can be used in lipase engineering are also summarized. The protocols of some classical methods are compared and discussed, which can provide some information about how to choose methods of lipase engineering. Thermostability engineering and solvent tolerance engineering are highlighted in this review, and the basic principles for improving thermostability and solvent tolerance are summarized. In the future, comput er-aided technology should be more emphasized in the investigation of the mechanisms of reactions catalyzed by lipases and guide the engineering of lipases. The engineering of lipase tunnels to improve the diffusion of substrates is also a promising prospect for further enhanced lipase activity and selectivity.

Critical assessment of structure-based approaches to improve protein resistance in aqueous ionic liquids by enzyme-wide saturation mutagenesis

Ionic liquids (IL) and aqueous ionic liquids (aIL) are attractive (co-)solvents for green industrial processes involving biocatalysts, but often reduce enzyme activity. Experimental and computational methods are applied to predict favorable substitution sites and, most often, subsequent site-directed surface charge modifications are introduced to enhance enzyme resistance towards aIL. However, almost no studies evaluate the prediction precision with random mutagenesis or the application of simple data-driven filtering processes. Here, we systematically and rigorously evaluated the performance of 22 previously described structure-based approaches to increase enzyme resistance to aIL based on an experimental complete site-saturation mutagenesis library of Bacillus subtilis Lipase A (BsLipA) screened against four aIL. We show that, surprisingly, most of the approaches yield low gain-in-precision (GiP) values, particularly for predicting relevant positions: 14 approaches perform worse than random mutagenesis. Encouragingly, exploiting experimental information on the thermostability of BsLipA or structural weak spots of BsLipA predicted by rigidity theory yields GiP = 3.03 and 2.39 for relevant variants and GiP = 1.61 and 1.41 for relevant positions. Combining five simple-to-compute physicochemical and evolutionary properties substantially increases the precision of predicting relevant variants and positions, yielding GiP = 3.35 and 1.29. Finally, combining these properties with predictions of structural weak spots identified by rigidity theory additionally improves GiP for relevant variants up to 4-fold to ∼10 and sustains or increases GiP for relevant positions, resulting in a prediction precision of ∼90% compared to ∼9% in random mutagenesis. This combination should be applicable to other enzyme systems for guiding protein engineering approaches towards improved aIL resistance.

Enhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning

We developed a method to improve protein thermostability, “loop-walking method”. Three consecutive positions in 12 loops of Burkholderia cepacia lipase were subjected to random mutagenesis to make 12 libraries. Screening allowed us to identify L7 as a hot-spot loop having an impact on thermostability, and the P233G/L234E/V235M mutant was found from 214 variants in the L7 library. Although a more excellent mutant might be discovered by screening all the 8000 P233X/L234X/V235X mutants, it was difficult to assay all of them. We therefore employed machine learning. Using thermostability data of the 214 mutants, a computational discrimination model was constructed to predict thermostability potentials. Among 7786 combinations ranked in silico, 20 promising candidates were selected and assayed. The P233D/L234P/V235S mutant retained 66% activity after heat treatment at 60 °C for 30 min, which was higher than those of the wild-type enzyme (5%) and the P233G/L234E/V235M mutant (35%).

Identification, characterization, and comparison of n-alkanols and anesthetics binding to the C1b subdomain of protein kinase cα: similar function with different binding sites

Protein kinase C (PKC) is a family of lipid-activated enzymes involved in anesthetic preconditioning signaling pathways. Previously, n-alkanols and general anesthetics have been found to activate PKC by binding to the kinase C1B subdomain. In the present study, we attempt to ascertain the molecular mechanism and interaction mode of human PKCα C1B subdomain with a variety of exogenous n-alkanols and volatile general anesthetics as well as endogenous activator phorbol ester (PE) and co-activator diacylglycerol (DG). Systematic bioinformatics analysis identifies three spatially vicinal sites on the subdomain surface to potentially accommodate small-molecule ligands, where the site 1 is a narrow, amphipathic pocket, the site 2 is a wide, flat and hydrophobic pocket, and the site 3 is a rugged, polar pocket. Further interaction modeling reveals that site 1 is the cognate binding region of natural PE activator, which can moderately simulate the kinase activity in an independent manner. The short-chain n-alkanols are speculated to also bind at the site to competitively inhibit PE-induced kinase activation. The long-chain n-alkanols and co-activator DG are found to target site 2 in a nonspecific manner, while the volatile anesthetics prefer to interact with site 3 in a specific manner. Since the site 1 is composed of two protein loops that are also shared by sites 2 and 3, binding of n-alkanols, DG and anesthetics to sites 2 and 3 can trigger a conformational displacement on the two loops, which enlarges the pocket size and changes the pocket configuration of site 1 through an allosteric mechanism, consequently enhancing kinase activation by improving PE affinity to the site.

Systematic profiling of staralog response to acquired drug resistant kinase gatekeeper mutations in targeted cancer therapy

Kinase-targeted therapy has been widely used as a lifesaving strategy for cancer patients. However, many patients treated with targeted cancer drugs are clinically observed to rapidly develop acquired resistance. Kinase gatekeeper mutation is one of the most chief factors contributing to the resistance, which modulates the accessibility of kinase's ATP-binding pocket. Previously, the pan-kinase inhibitor Staurosporine and its analogs (termed as Staralogs) have been reported to exhibit wild-type sparing selectivity for some kinase gatekeeper mutants, such as EGFR T790M, Her2 T798M and cSrc T338M. Here, we describe an integrative approach to systematically profile the molecular response of 15 representative Staralogs to 17 kinase gatekeeper mutations in targeted cancer therapy. With the profile we are able to divide gatekeeper mutations into three classes (i.e. classes I, II and III) and to divide Staralogs into two groups (i.e. groups 1 and 2) using heuristic clustering. The class I and II mutations confer consistent sensitivity and resistance for all Staralogs, respectively, while the class III mutations address divergent effects on different Staralogs. The mutations to Ile residue can generally reduce Staralog affinity by inducing unfavorable steric hindrance, whereas the mutations to Met and Leu residues would improve Staralog affinity by establishing favorable S···π interaction, van der Waals packing and/or hydrophobic contact. The group 1 and 2 Staralogs are primarily determined by carbonyl or hydroxyl substitution state at the position 7 of Staralog core, where points to kinase gatekeeper residue and can thus be directly influenced by gatekeeper mutation.

Systematic analysis and identification of unexpected interactions from the neuroprotein drug interactome in hydrocephalus pharmacological intervention

Hydrocephalus is a neurological condition caused by an abnormal accumulation of cerebrospinal fluid; pharmacological intervention of the disease has been found to elicit a variety of adverse drug reactions (ADRs) in central nervous system (CNS) by unexpectedly targeting certain functional neuroproteins. Here, a systematic neuroprotein drug interactome (SNDI) is created for 11 hydrocephalus drugs/metabolites plus 20 control drugs across 518 druggable pockets on the surface of 472 CNS neuroproteins via a large-scale molecular docking approach. Heuristic clustering analysis of the SNDI profile divides the 31 investigated drug ligands into a distinct panel and a background panel; the former consists of two hydrocephalus drugs (Furosemide and Triamterene) and their respective metabolites (Furosemide glucuronide and Hydroxytriamterene) that are inferred to have generally high affinity towards the whole array of neuroprotein pockets. A total of 13 neuroproteins are enriched in gene ontology semantic mining as putative unexpected targets of the distinct panel, and their intermolecular interactions with hydrocephalus drugs/metabolites are investigated in detail using dynamics simulation and energetics analysis. We also perform kinase assay and viability test to substantiate the interactome analysis. It is found that the Furosemide and Triamterene have significant cytotoxic effects on normal human astrocytes, in which the Triamterene can inhibit the neurokinase ROCK2, a representative of putative unexpected targets, with a high activity, which is comparable with the sophisticated ROCK2 inhibitor Fasudil.

Rational cyclization-based minimization of entropy penalty upon the binding of Nrf2-derived linear peptides to Keap1: A new strategy to improve therapeutic peptide activity against sepsis

Nrf2 is a critical regulator of innate immune response and survival during sepsis, which is constitutively degraded through binding to the Keap1 adapter protein of E3 ubiquitin ligase. Two linear peptides DLG and ETG derived from, respectively, the low-affinity and high-affinity motifs of Nrf2 binding site exhibit self-binding affinity to Keap1 central hole (active pocket); they can be exploited as therapeutic self-inhibitory peptides to disrupt the Nrf2-Keap1 interaction. Molecular dynamics simulation and binding energetics decomposition reveal that the two peptides possess large flexibility and intrinsic disorder in unbound free state, and thus would incur a considerable entropy penalty upon binding to Keap1. In order to improve Keap1-peptide binding affinity (or free energy ΔG), instead of traditionally increasing favorable enthalpy contribution (ΔH) we herein describe a rational peptide cyclization strategy to minimize unfavorable entropy penalty (ΔS) upon the binding of Nrf2-derived linear peptides to Keap1. Crystal structure analysis impart that the native active conformations of DLG and ETG peptides bound with Keap1 are folded into U-shape and hairpin configurations, respectively, and adopt their turning head to insert into the central hole of Keap1. Here, cyclization is designed by adding a disulfide bond across the two arms of DLG U-shape or ETG hairpin, which would not influence the direct intermolecular interaction between Keap1 and peptide as well as desolvation effect involved in the interaction, but can effectively constrain the conformational flexibility and disorder of the two peptides in free state, thus largely minimizing entropy penalty upon the binding. Both free energy calculation and binding affinity assay substantiate that the cyclization, as might be expected, can moderately or considerably enhance peptide binding potency to Keap1, with affinity (dissociation constant K_d) increase by 1.4-7.5-fold for designed cyclic peptides relative to their linear counterparts.

Systematic analysis and integrative discovery of active-site subpocket-specific dehydroquinate synthase inhibitors combating antibiotic-resistant Staphylococcus aureus infection

Shikimate pathway plays an essential role in the biosynthesis of aromatic amino acids in various plants and bacteria, which consists of seven key enzymes and they are all attractive targets for antibacterial agent development due to their absence in humans. The Staphylococcus aureus dehydroquinate synthase (SaDHQS) is involved in the second step of shikimate pathway, which catalyzes the NAD <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow/><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:msup></mml:math> -dependent conversion of 3-deoxy-D-arabino-heptulosonate-7-phosphate to dehydroquinate via multiple steps. The enzyme active site can be characterized by two spatially separated subpockets 1 and 2, which represent the reaction center of substrate adduct with NAD <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow/><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:msup></mml:math> nicotinamide moiety and the assistant binding site of NAD <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow/><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:msup></mml:math> adenine moiety, respectively. In silico virtual screening is performed against a biogenic compound library to discover SaDHQS subpocket-specific inhibitors, which were then tested against both antibiotic-sensitive and antibiotic-resistant S. aureus strains by using in vitro susceptibility test. The activity profile of hit compounds has no considerable difference between the antibiotic-sensitive and -resistant strains. The subpocket 1-specific inhibitors exhibit a generally higher activity than subpocket 2-specific inhibitors, and they also hold a strong selectivity between their cognate and noncognate subpockets. Dynamics and energetics analyses reveal that the SaDHQS active site prefers to interact with amphipathic and polar inhibitors by forming multiple hydrogen bonds and van der Waals packing at the complex interfaces of the two subpockets with their cognate inhibitors.

Design, cyclization, and optimization of MMP13-TIMP1 interaction-derived self-inhibitory peptides against chondrocyte senescence in osteoarthritis

The matrix metallopeptidase 13 (MMP13) is a central regulator of chondrocyte senescence that contributes to the development and progression of osteoarthritis (OA). In the present study, the native inhibitory structure of MMP13 in complex with its natural cognate inhibitor, the tissue inhibitor of metalloproteinases 1 (TIMP1), was modeled at atomic level using a grafting-based structural bioinformatics method with existing crystal structures. The modeled complex structure was then examined in detail, from which a TIMP1 inhibitory site that directly inserts into the active site of MMP13 enzyme was identified. The inhibitory site contains a coiled inhibitory loop (ILP) and a stretched N-terminal tail (NTT); they are highly structured in the intact MMP13-TIMP1 complex interface, but exhibit a large flexibility and intrinsic disorder when split from the interface context. In vitro binding assays demonstrated that the isolated ILP and NTT peptides cannot effectively rebind at the MMP13 active site (K_d > ~100 μM or = n.d.), although they have all key interacting residues in the enzyme inhibition. In silico simulations revealed that splitting of the peptide segments from TIMP1 inhibitory site does not influence the direct intermolecular interaction between MMP13 and the peptides substantially; instead, the large conformational flexibility of these isolated peptides in absence of interface context is primarily responsible for the affinity impairment, which would incur a considerable entropy penalty upon the peptide binding to MMP13. An extended version of ILP peptide, namely eILP (⁶³TPAMESVCGY⁷²), was redesigned with a rational strategy to derive a number of its cyclized counterparts by introducing a disulfide bridge across the peptide two-termini; the redesign reduces the peptide flexibility in free state and constrains the peptide pre-folding to a native-like conformation, which would help the peptide binding with minimized entropy penalty. Binding assays substantiated that the affinity K_d values of four designed cyclic peptides (, , and ) were improved to 23, 67, 42 and 18 μM, respectively, from the 96 μM of linear eILP peptide.

Structural Identification and Systematic Comparison of Phorbol Ester, Dioleoylglycerol, Alcohol and Sevoflurane Binding Sites in PKCδ C1A Domain

Protein kinase C (PKC) is a family of signal transducing enzymes that have been implicated in anesthetic preconditioning signaling cascade. Evidences are emerging that certain exogenous neuromodulators such as n-alkanols and general anesthetics can stimulate PKC activity by binding to regulatory C1A domain of the enzyme. However, the accurate binding sites in C1A domain as well as the molecular mechanism underlying binding-stimulated PKC activation still remain unelucidated. Here, we report a systematic investigation of the intermolecular interaction of human PKCδ C1A domain with its natural activator phorbol ester (PE) and co-activator dioleoylglycerol (DOG) as well as exogenous stimulators butanol, octanol and sevoflurane. The domain is computationally identified to potentially have three spatially vicinal ligand-binding pockets 1, 2 and 3, in which the pockets 1 and 2 have previously been determined as the binding sites of PE and DOG, respectively. Systematic cross-binding analysis reveals that long-chain octanol and DOG are well compatible with the flat, nonpolar pocket 2, where the nonspecific hydrophobic contacts and van der Waals packing are primarily responsible for the binding, while the general anesthetic sevoflurane prefer to interact with the rugged, polar pocket 3 through specific hydrogen bonds and electrostatic forces. Short-chain butanol appears to bind effectively none of the three pockets. In addition, the pocket 1 consists of two angled arms 1 and 2 that are also involved in pockets 2 and 3, respectively. Dynamics characterization imparts that binding of long-chain octanol and DOG to pocket 2 or binding of sevoflurane to pocket 3 can induce a conformational displacement in arm 1 or 2, thus further opening the included angle and enlarging pocket 1, which can improve the pocket 1-PE affinity via an allosteric mechanism, consequently stimulating the PE-induced PKCδ activation.

Integration of virtual screening and susceptibility test to discover active-site subpocket-specific biogenic inhibitors of Helicobacter pylori shikimate dehydrogenase

Shikimate dehydrogenase (HpSDH) (EC 1.1.1.25) is a key enzyme in the shikimate pathway of Helicobacter pylori (H. pylori), which catalyzes the NADPH-dependent reversible reduction of 3-dehydroshikimate to shikimate. Targeting HpSDH has been recognized as an attractive therapeutic strategy against H. pylori infection. Here, the catalytic active site in the crystal structure of HpSDH in complex with its substrate NADPH and product shikimate was examined in detail; the site can be divided into three spatially separated subpockets that separately correspond to the binding regions of shikimate, NADPH dihydronicotinamide moiety, and NADPH adenine moiety. Subsequently, a cascading protocol that integrated virtual screening and antibacterial test was performed against a biogenic compound library to identify biologically active, subpocket-specific inhibitors. Consequently, five, eight, and six promising compounds for, respectively, subpockets 1, 2, and 3 were selected from the top-100 docking-ranked hits, from which 11 compounds were determined to have high or moderate antibacterial potencies against two reference H. pylori strains, with MIC range between 8 and 93 μg/mL. It is found that the HpSDH active site prefers to accommodate amphipathic and polar inhibitors that consist of an aromatic core as well as a number of oxygen-rich polar/charged substituents such as hydroxyl, carbonyl, and carboxyl groups. Subpockets 1- and 2-specific inhibitors exhibit a generally higher activity than subpocket 3-specific inhibitors. Molecular dynamics simulations revealed an intense nonbonded network of hydrogen bonds, π-π stacking, and van der Waals contacts at the tightly packed complex interfaces of active-site subpockets with their cognate inhibitors, conferring strong stability and specificity to these complex systems. Binding energetic analysis demonstrated that the identified potent inhibitors can target their cognate subpockets with an effective selectivity over noncognate ones.

Application of Rigidity Theory to the Thermostabilization of Lipase A from Bacillus subtilis

Protein thermostability is a crucial factor for biotechnological enzyme applications. Protein engineering studies aimed at improving thermostability have successfully applied both directed evolution and rational design. However, for rational approaches, the major challenge remains the prediction of mutation sites and optimal amino acid substitutions. Recently, we showed that such mutation sites can be identified as structural weak spots by rigidity theory-based thermal unfolding simulations of proteins. Here, we describe and validate a unique, ensemble-based, yet highly efficient strategy to predict optimal amino acid substitutions at structural weak spots for improving a protein’s thermostability. For this, we exploit the fact that in the majority of cases an increased structural rigidity of the folded state has been found as the cause for thermostability. When applied prospectively to lipase A from Bacillus subtilis, we achieved both a high success rate (25% over all experimentally tested mutations, which raises to 60% if small-to-large residue mutations and mutations in the active site are excluded) in predicting significantly thermostabilized lipase variants and a remarkably large increase in those variants’ thermostability (up to 6.6°C) based on single amino acid mutations. When considering negative controls in addition and evaluating the performance of our approach as a binary classifier, the accuracy is 63% and increases to 83% if small-to-large residue mutations and mutations in the active site are excluded. The gain in precision (predictive value for increased thermostability) over random classification is 1.6-fold (2.4-fold). Furthermore, an increase in thermostability predicted by our approach significantly points to increased experimental thermostability (p < 0.05). These results suggest that our strategy is a valuable complement to existing methods for rational protein design aimed at improving thermostability.

Protein thermostability is a crucial factor for biotechnological enzyme applications. However, performance studies of computational approaches for predicting effects of mutations on protein (thermo)stability have suggested that there is still room for improvement. We describe and validate a novel and unique strategy to predict optimal amino acid substitutions at structural weak spots. At variance with other rational approaches, we exploit the fact that in the majority of cases an increased structural rigidity of the folded state is the underlying cause for thermostability. When applied prospectively on lipase LipA from Bacillus subtilis, a high success rate in predicting thermostabilized lipase variants and a remarkably large increase in their thermostability is achieved. This demonstrates the value of the novel strategy, which extends the existing portfolio of methods for rational protein design.

Structural features determining thermal adaptation of esterases

The adaptation of microorganisms to extreme living temperatures requires the evolution of enzymes with a high catalytic efficiency under these conditions. Such extremophilic enzymes represent valuable tools to study the relationship between protein stability, dynamics and function. Nevertheless, the multiple effects of temperature on the structure and function of enzymes are still poorly understood at the molecular level. Our analysis of four homologous esterases isolated from bacteria living at temperatures ranging from 10°C to 70°C suggested an adaptation route for the modulation of protein thermal properties through the optimization of local flexibility at the protein surface. While the biochemical properties of the recombinant esterases are conserved, their thermal properties have evolved to resemble those of the respective bacterial habitats. Molecular dynamics simulations at temperatures around the optimal temperatures for enzyme catalysis revealed temperature-dependent flexibility of four surface-exposed loops. While the flexibility of some loops increased with raising the temperature and decreased with lowering the temperature, as expected for those loops contributing to the protein stability, other loops showed an increment of flexibility upon lowering and raising the temperature. Preserved flexibility in these regions seems to be important for proper enzyme function. The structural differences of these four loops, distant from the active site, are substantially larger than for the overall protein structure, indicating that amino acid exchanges within these loops occurred more frequently thereby allowing the bacteria to tune atomic interactions for different temperature requirements without interfering with the overall enzyme function.