Thermostabilizing mutations preferentially occur at structural weak spots with a high mutation ratio

Simultaneously Enhanced Thermostability and Catalytic Activity of Xylanase from Streptomyces rameus L2001 by Rigidifying Flexible Regions in Loop Regions of the N-Terminus

The GH11 xylanase XynA from Streptomyces rameus L2001 has favorable hydrolytic properties. However, its poor thermal stability hinders its widespread application in industry. In this study, mutants Mut1 and Mut2 were constructed by rationally combining the mutations 11YHDGYF16, 23AP24/23SP24, and 32GP33. The residual enzyme activity of these combinational mutants was more than 85% when incubated at 80 and 90 °C for 12 h, and thus are the most thermotolerant xylanases known to date. The reduced flexibility of the N-terminus, increased overall rigidity, as well as the surface net charge of Mut1 and Mut2 may be partially responsible for the improved thermal stability. In addition, the specific activity and catalytic efficiency of Mut1 and Mut2 were improved compared with those of wild-type XynA. The broader catalytic cleft and enhanced flexibility of the "thumb" of Mut1 and Mut2 may be partially responsible for the improved specific activity and catalytic efficiency.

Combining structural and coevolution information to unveil allosteric sites

Understanding allosteric regulation in biomolecules is of great interest to pharmaceutical research and computational methods emerged during the last decades to characterize allosteric coupling. However, the prediction of allosteric sites in a protein structure remains a challenging task. Here, we integrate local binding site information, coevolutionary information, and information on dynamic allostery into a structure-based three-parameter model to identify potentially hidden allosteric sites in ensembles of protein structures with orthosteric ligands. When tested on five allosteric proteins (LFA-1, p38-α, GR, MAT2A, and BCKDK), the model successfully ranked all known allosteric pockets in the top three positions. Finally, we identified a novel druggable site in MAT2A confirmed by X-ray crystallography and SPR and a hitherto unknown druggable allosteric site in BCKDK validated by biochemical and X-ray crystallography analyses. Our model can be applied in drug discovery to identify allosteric pockets.

Enzyme adaptation to habitat thermal legacy shapes the thermal plasticity of marine microbiomes

Microbial communities respond to temperature with physiological adaptation and compositional turnover. Whether thermal selection of enzymes explains marine microbiome plasticity in response to temperature remains unresolved. By quantifying the thermal behaviour of seven functionally-independent enzyme classes (esterase, extradiol dioxygenase, phosphatase, beta-galactosidase, nuclease, transaminase, and aldo-keto reductase) in native proteomes of marine sediment microbiomes from the Irish Sea to the southern Red Sea, we record a significant effect of the mean annual temperature (MAT) on enzyme response in all cases. Activity and stability profiles of 228 esterases and 5 extradiol dioxygenases from sediment and seawater across 70 locations worldwide validate this thermal pattern. Modelling the esterase phase transition temperature as a measure of structural flexibility confirms the observed relationship with MAT. Furthermore, when considering temperature variability in sites with non-significantly different MATs, the broadest range of enzyme thermal behaviour and the highest growth plasticity of the enriched heterotrophic bacteria occur in samples with the widest annual thermal variability. These results indicate that temperature-driven enzyme selection shapes microbiome thermal plasticity and that thermal variability finely tunes such processes and should be considered alongside MAT in forecasting microbial community thermal response.

Structure and Function of Redox-Sensitive Superfolder Green Fluorescent Protein Variant

Aims: Genetically encoded green fluorescent protein (GFP)-based redox biosensors are widely used to monitor specific and dynamic redox processes in living cells. Over the last few years, various biosensors for a variety of applications were engineered and enhanced to match the organism and cellular environments, which should be investigated. In this context, the unicellular intraerythrocytic parasite Plasmodium, the causative agent of malaria, represents a challenge, as the small size of the organism results in weak fluorescence signals that complicate precise measurements, especially for cell compartment-specific observations. To address this, we have functionally and structurally characterized an enhanced redox biosensor superfolder roGFP2 (sfroGFP2). Results: SfroGFP2 retains roGFP2-like behavior, yet with improved fluorescence intensity (FI) in cellulo. SfroGFP2-based redox biosensors are pH insensitive in a physiological pH range and show midpoint potentials comparable with roGFP2-based redox biosensors. Using crystallography and rigidity theory, we identified the superfolding mutations as being responsible for improved structural stability of the biosensor in a redox-sensitive environment, thus explaining the improved FI in cellulo. Innovation: This work provides insight into the structure and function of GFP-based redox biosensors. It describes an improved redox biosensor (sfroGFP2) suitable for measuring oxidizing effects within small cells where applicability of other redox sensor variants is limited. Conclusion: Improved structural stability of sfroGFP2 gives rise to increased FI in cellulo. Fusion to hGrx1 (human glutaredoxin-1) provides the hitherto most suitable biosensor for measuring oxidizing effects in Plasmodium. This sensor is of major interest for studying glutathione redox changes in small cells, as well as subcellular compartments in general. Antioxid. Redox Signal. 37, 1-18.

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.

Molecular Insights into Site-Specific Interferon-α2a Bioconjugates Originated from PEG, LPG, and PEtOx

Conjugation of biologics with polymers modulates their pharmacokinetics, with polyethylene glycol (PEG) as the gold standard. We compared alternative polymers and two types of cyclooctyne linkers (BCN/DBCO) for bioconjugation of interferon-α2a (IFN-α2a) using 10 kDa polymers including linear mPEG, poly(2-ethyl-2-oxazoline) (PEtOx), and linear polyglycerol (LPG). IFN-α2a was azide functionalized via amber codon expansion and bioorthogonally conjugated to all cyclooctyne linked polymers. Polymer conjugation did not impact IFN-α2a's secondary structure and only marginally reduced IFN-α2a's bioactivity. In comparison to PEtOx, the LPG polymer attached via the less rigid cyclooctyne linker BCN was found to stabilize IFN-α2a against thermal stress. These findings were further detailed by molecular modeling studies which showed a modulation of protein flexibility upon PEtOx conjugation and a reduced amount of protein native contacts as compared to PEG and LPG originated bioconjugates. Polymer interactions with IFN-α2a were further assessed via a limited proteolysis (LIP) assay, which resulted in comparable proteolytic cleavage patterns suggesting weak interactions with the protein's surface. In conclusion, both PEtOx and LPG bioconjugates resulted in a similar biological outcome and may become promising PEG alternatives for bioconjugation.

F/G Region Rigidity is Inversely Correlated to Substrate Promiscuity of Human CYP Isoforms Involved in Metabolism

Of 57 human cytochrome P450 (CYP) enzymes, 12 metabolize 90% of xenobiotics. To our knowledge, no study has addressed the relation between enzyme dynamics and substrate promiscuity for more than three CYPs. Here, we show by constraint dilution simulations with the Constraint Network Analysis for the 12 isoforms that structural rigidity of the F/G region is significantly inversely correlated to the enzymes' substrate promiscuity. This highlights the functional importance of structural dynamics of the substrate tunnel.

Aqueous ionic liquids redistribute local enzyme stability via long-range perturbation pathways

Ionic liquids (IL) and aqueous ionic liquids (aIL) are attractive (co-)solvents for biocatalysis due to their unique properties. On the other hand, the incubation of enzymes in IL or aIL often reduces enzyme activity. Recent studies proposed various aIL-induced effects to explain the reduction, classified as direct effects, e.g., local dehydration or competitive inhibition, and indirect effects, e.g., structural perturbations or disturbed catalytic site integrity. However, the molecular origin of indirect effects has largely remained elusive. Here we show by multi-μs long molecular dynamics simulations, free energy computations, and rigidity analyses that aIL favorably interact with specific residues of Bacillus subtilis Lipase A (BsLipA) and modify the local structural stability of this model enzyme by inducing long-range perturbations of noncovalent interactions. The perturbations percolate over neighboring residues and eventually affect the catalytic site and the buried protein core. Validation against a complete experimental site saturation mutagenesis library of BsLipA (3620 variants) reveals that the residues of the perturbation pathways are distinguished sequence positions where substitutions highly likely yield significantly improved residual activity. Our results demonstrate that identifying these perturbation pathways and specific IL ion-residue interactions there effectively predicts focused variant libraries with improved aIL tolerance.

Promiscuous Esterases Counterintuitively Are Less Flexible than Specific Ones

Understanding mechanisms of promiscuity is increasingly important from a fundamental and application point of view. As to enzyme structural dynamics, more promiscuous enzymes generally have been recognized to also be more flexible. However, examples for the opposite received much less attention. Here, we exploit comprehensive experimental information on the substrate promiscuity of 147 esterases tested against 96 esters together with computationally efficient rigidity analyses to understand the molecular origin of the observed promiscuity range. Unexpectedly, our data reveal that promiscuous esterases are significantly less flexible than specific ones, are significantly more thermostable, and have a significantly increased specific activity. These results may be reconciled with a model according to which structural flexibility in the case of specific esterases serves for conformational proofreading. Our results signify that an esterase sequence space can be screened by rigidity analyses for promiscuous esterases as starting points for further exploration in biotechnology and synthetic chemistry.

Can constraint network analysis guide the identification phase of KnowVolution? A case study on improved thermostability of an endo-β-glucanase

Cellulases are industrially important enzymes, e.g., in the production of bioethanol, in pulp and paper industry, feedstock, and textile. Thermostability is often a prerequisite for high process stability and improving thermostability without affecting specific activities at lower temperatures is challenging and often time-consuming. Protein engineering strategies that combine experimental and computational are emerging in order to reduce experimental screening efforts and speed up enzyme engineering campaigns. Constraint Network Analysis (CNA) is a promising computational method that identifies beneficial positions in enzymes to improve thermostability. In this study, we compare CNA and directed evolution in the identification of beneficial positions in order to evaluate the potential of CNA in protein engineering campaigns (e.g., in the identification phase of KnowVolution). We engineered the industrially relevant endoglucanase EGLII from Penicillium verruculosum towards increased thermostability. From the CNA approach, six variants were obtained with an up to 2-fold improvement in thermostability. The overall experimental burden was reduced to 40% utilizing the CNA method in comparison to directed evolution. On a variant level, the success rate was similar for both strategies, with 0.27% and 0.18% improved variants in the epPCR and CNA-guided library, respectively. In essence, CNA is an effective method for identification of positions that improve thermostability.

Systematically Scrutinizing the Impact of Substitution Sites on Thermostability and Detergent Tolerance for Bacillus subtilis Lipase A

Improving an enzyme's (thermo-)stability or tolerance against solvents and detergents is highly relevant in protein engineering and biotechnology. Recent developments have tended toward data-driven approaches, where available knowledge about the protein is used to identify substitution sites with high potential to yield protein variants with improved stability, and subsequently, substitutions are engineered by site-directed or site-saturation (SSM) mutagenesis. However, the development and validation of algorithms for data-driven approaches have been hampered by the lack of availability of large-scale data measured in a uniform way and being unbiased with respect to substitution types and locations. Here, we extend our knowledge on guidelines for protein engineering following a data-driven approach by scrutinizing the impact of substitution sites on thermostability or/and detergent tolerance for Bacillus subtilis lipase A (BsLipA) at very large scale. We systematically analyze a complete experimental SSM library of BsLipA containing all 3439 possible single variants, which was evaluated as to thermostability and tolerances against four detergents under respectively uniform conditions. Our results provide systematic and unbiased reference data at unprecedented scale for a biotechnologically important protein, identify consistently defined hot spot types for evaluating the performance of data-driven protein-engineering approaches, and show that the rigidity theory and ensemble-based approach Constraint Network Analysis yields hot spot predictions with an up to ninefold gain in precision over random classification.

Molecular dynamics simulation of metal free structure of Lmb, a laminin-binding adhesin of Streptococcus agalactiae: metal removal and its structural implications

Metal-binding receptors are one of the extracellular components of ATP-binding cassette transporters that are essential for regulation of metal homeostasis in bacteria. Laminin-binding adhesin (Lmb) of Streptococcus agalactiae falls under this class of solute binding proteins. It binds to zinc with a high affinity. Crystal structure of Lmb solved previously by our group reveals that the zinc is tetrahedrally coordinated by three histidines and a glutamate at the interdomain cleft. Lmb contains a long disordered loop close to the metal-binding site whose precise function is unknown. Several experimental attempts to produce apo-Lmb failed and this prompted us to carry out in silico studies to analyse the structural importance of the metal in Lmb. Here, we present the results of the molecular dynamics (MD) simulation studies of native, apo-(metal removed) and the long loop truncated Lmb models along with a homologous protein, TroA from Treponema pallidum that was taken up for validating the MD results of Lmb. Absence of a metal results in significant structural changes in Lmb, particularly at the metal-binding pocket and with the long loop, although the overall fold is retained. This study thus revealed that the Lmb can exist in different conformational states with subtle differences in the overall fold based on the presence or absence of the metal. This could be functionally important for a putative metal uptake and release and also for the adhesive function of Lmb in recognizing laminin, which contains a high number of zinc finger motifs.

Ensemble- and Rigidity Theory-Based Perturbation Approach To Analyze Dynamic Allostery

Allostery describes the functional coupling between sites in biomolecules. Recently, the role of changes in protein dynamics for allosteric communication has been highlighted. A quantitative and predictive description of allostery is fundamental for understanding biological processes. Here, we integrate an ensemble-based perturbation approach with the analysis of biomolecular rigidity and flexibility to construct a model of dynamic allostery. Our model, by definition, excludes the possibility of conformational changes, evaluates static, not dynamic, properties of molecular systems, and describes allosteric effects due to ligand binding in terms of a novel free-energy measure. We validated our model on three distinct biomolecular systems: eglin c, protein tyrosine phosphatase 1B, and the lymphocyte function-associated antigen 1 domain. In all cases, it successfully identified key residues for signal transmission in very good agreement with the experiment. It correctly and quantitatively discriminated between positively or negatively cooperative effects for one of the systems. Our model should be a promising tool for the rational discovery of novel allosteric drugs.

Ensemble-based modeling and rigidity decomposition of allosteric interaction networks and communication pathways in cyclin-dependent kinases: Differentiating kinase clients of the Hsp90-Cdc37 chaperone

The overarching goal of delineating molecular principles underlying differentiation of protein kinase clients and chaperone-based modulation of kinase activity is fundamental to understanding activity of many oncogenic kinases that require chaperoning of Hsp70 and Hsp90 systems to attain a functionally competent active form. Despite structural similarities and common activation mechanisms shared by cyclin-dependent kinase (CDK) proteins, members of this family can exhibit vastly different chaperone preferences. The molecular determinants underlying chaperone dependencies of protein kinases are not fully understood as structurally similar kinases may often elicit distinct regulatory responses to the chaperone. The regulatory divergences observed for members of CDK family are of particular interest as functional diversification among these kinases may be related to variations in chaperone dependencies and can be exploited in drug discovery of personalized therapeutic agents. In this work, we report the results of a computational investigation of several members of CDK family (CDK5, CDK6, CDK9) that represented a broad repertoire of chaperone dependencies—from nonclient CDK5, to weak client CDK6, and strong client CDK9. By using molecular simulations of multiple crystal structures we characterized conformational ensembles and collective dynamics of CDK proteins. We found that the elevated dynamics of CDK9 can trigger imbalances in cooperative collective motions and reduce stability of the active fold, thus creating a cascade of favorable conditions for chaperone intervention. The ensemble-based modeling of residue interaction networks and community analysis determined how differences in modularity of allosteric networks and topography of communication pathways can be linked with the client status of CDK proteins. This analysis unveiled depleted modularity of the allosteric network in CDK9 that alters distribution of communication pathways and leads to impaired signaling in the client kinase. According to our results, these network features may uniquely define chaperone dependencies of CDK clients. The perturbation response scanning and rigidity decomposition approaches identified regulatory hotspots that mediate differences in stability and cooperativity of allosteric interaction networks in the CDK structures. By combining these synergistic approaches, our study revealed dynamic and network signatures that can differentiate kinase clients and rationalize subtle divergences in the activation mechanisms of CDK family members. The therapeutic implications of these results are illustrated by identifying structural hotspots of pathogenic mutations that preferentially target regions of the increased flexibility to enable modulation of activation changes. Our study offers a network-based perspective on dynamic kinase mechanisms and drug design by unravelling relationships between protein kinase dynamics, allosteric communications and chaperone dependencies.

Ligand-mediated and tertiary interactions cooperatively stabilize the P1 region in the guanine-sensing riboswitch

Riboswitches are genetic regulatory elements that control gene expression depending on ligand binding. The guanine-sensing riboswitch (Gsw) binds ligands at a three-way junction formed by paired regions P1, P2, and P3. Loops L2 and L3 cap the P2 and P3 helices and form tertiary interactions. Part of P1 belongs to the switching sequence dictating the fate of the mRNA. Previous studies revealed an intricate relationship between ligand binding and presence of the tertiary interactions, and between ligand binding and influence on the P1 region. However, no information is available on the interplay among these three main regions in Gsw. Here we show that stabilization of the L2-L3 region by tertiary interactions, and the ligand binding site by ligand binding, cooperatively influences the structural stability of terminal base pairs in the P1 region in the presence of Mg2+ ions. The results are based on molecular dynamics simulations with an aggregate simulation time of ~10 μs across multiple systems of the unbound state of the Gsw aptamer and a G37A/C61U mutant, and rigidity analyses. The results could explain why the three-way junction is a central structural element also in other riboswitches and how the cooperative effect could become contextual with respect to intracellular Mg2+ concentration. The results suggest that the transmission of allosteric information to P1 can be entropy-dominated.

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.

Trading off stability against activity in extremophilic aldolases

Understanding enzyme stability and activity in extremophilic organisms is of great biotechnological interest, but many questions are still unsolved. Using 2-deoxy-D-ribose-5-phosphate aldolase (DERA) as model enzyme, we have evaluated structural and functional characteristics of different orthologs from psychrophilic, mesophilic and hyperthermophilic organisms. We present the first crystal structures of psychrophilic DERAs, revealing a dimeric organization resembling their mesophilic but not their thermophilic counterparts. Conversion into monomeric proteins showed that the native dimer interface contributes to stability only in the hyperthermophilic enzymes. Nevertheless, introduction of a disulfide bridge in the interface of a psychrophilic DERA did confer increased thermostability, suggesting a strategy for rational design of more durable enzyme variants. Constraint network analysis revealed particularly sparse interactions between the substrate pocket and its surrounding α-helices in psychrophilic DERAs, which indicates that a more flexible active center underlies their high turnover numbers.

Protein thermostability engineering

Using structure and sequence based analysis we can engineer proteins to increase their thermal stability.

Rigidity versus flexibility: the dilemma of understanding protein thermal stability

The role of fluctuations in protein thermostability has recently received considerable attention. In the current literature a dualistic picture can be found: thermostability seems to be associated with enhanced rigidity of the protein scaffold in parallel with the reduction of flexible parts of the structure. In contradiction to such arguments it has been shown by experimental studies and computer simulation that thermal tolerance of a protein is not necessarily correlated with the suppression of internal fluctuations and mobility. Both concepts, rigidity and flexibility, are derived from mechanical engineering and represent temporally insensitive features describing static properties, neglecting that relative motion at certain time scales is possible in structurally stable regions of a protein. This suggests that a strict separation of rigid and flexible parts of a protein molecule does not describe the reality correctly. In this work the concepts of mobility/flexibility versus rigidity will be critically reconsidered by taking into account molecular dynamics calculations of heat capacity and conformational entropy, salt bridge networks, electrostatic interactions in folded and unfolded states, and the emerging picture of protein thermostability in view of recently developed network theories. Last, but not least, the influence of high temperature on the active site and activity of enzymes will be considered.

Structural Rigidity and Protein Thermostability in Variants of Lipase A from Bacillus subtilis

Understanding the origin of thermostability is of fundamental importance in protein biochemistry. Opposing views on increased or decreased structural rigidity of the folded state have been put forward in this context. They have been related to differences in the temporal resolution of experiments and computations that probe atomic mobility. Here, we find a significant (p = 0.004) and fair (R² = 0.46) correlation between the structural rigidity of a well-characterized set of 16 mutants of lipase A from Bacillus subtilis (BsLipA) and their thermodynamic thermostability. We apply the rigidity theory-based Constraint Network Analysis (CNA) approach, analyzing directly and in a time-independent manner the statics of the BsLipA mutants. We carefully validate the CNA results on macroscopic and microscopic experimental observables and probe for their sensitivity with respect to input structures. Furthermore, we introduce a robust, local stability measure for predicting thermodynamic thermostability. Our results complement work that showed for pairs of homologous proteins that raising the structural stability is the most common way to obtain a higher thermostability. Furthermore, they demonstrate that related series of mutants with only a small number of mutations can be successfully analyzed by CNA, which suggests that CNA can be applied prospectively in rational protein design aimed at higher thermodynamic thermostability.

Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the 'search space' of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (Kd) and catalytic (kcat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving kcat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the 'best' amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust.

Quality matters: extension of clusters of residues with good hydrophobic contacts stabilize (hyper)thermophilic proteins

Identifying determinant(s) of protein thermostability is key for rational and data-driven protein engineering. By analyzing more than 130 pairs of mesophilic/(hyper)thermophilic proteins, we identified the quality (residue-wise energy) of hydrophobic interactions as a key factor for protein thermostability. This distinguishes our study from previous ones that investigated predominantly structural determinants. Considering this key factor, we successfully discriminated between pairs of mesophilic/(hyper)thermophilic proteins (discrimination accuracy: ∼80%) and searched for structural weak spots in E. coli dihydrofolate reductase (classification accuracy: 70%).

Computational tools for designing and engineering enzymes

Protein engineering strategies aimed at constructing enzymes with novel or improved activities, specificities, and stabilities greatly benefit from in silico methods. Computational methods can be principally grouped into three main categories: bioinformatics; molecular modelling; and de novo design. Particularly de novo protein design is experiencing rapid development, resulting in more robust and reliable predictions. A recent trend in the field is to combine several computational approaches in an interactive manner and to complement them with structural analysis and directed evolution. A detailed investigation of designed catalysts provides valuable information on the structural basis of molecular recognition, biochemical catalysis, and natural protein evolution.

Engineering proteins for thermostability through rigidifying flexible sites

Engineering proteins for thermostability is an exciting and challenging field since it is critical for broadening the industrial use of recombinant proteins. Thermostability of proteins arises from the simultaneous effect of several forces such as hydrophobic interactions, disulfide bonds, salt bridges and hydrogen bonds. All of these interactions lead to decreased flexibility of polypeptide chain. Structural studies of mesophilic and thermophilic proteins showed that the latter need more rigid structures to compensate for increased thermal fluctuations. Hence flexibility can be an indicator to pinpoint weak spots for enhancing thermostability of enzymes. A strategy has been proven effective in enhancing proteins' thermostability with two steps: predict flexible sites of proteins firstly and then rigidify these sites. We refer to this approach as rigidify flexible sites (RFS) and give an overview of such a method through summarizing the methods to predict flexibility of a protein, the methods to rigidify residues with high flexibility and successful cases regarding enhancing thermostability of proteins using RFS.

Efficient and robust analysis of biomacromolecular flexibility using ensembles of network topologies based on fuzzy noncovalent constraints

We describe an approach (ENT(FNC)) for performing rigidity analyses of biomacromolecules on ensembles of network topologies (ENT) generated from a single input structure. The ENT is based on fuzzy noncovalent constraints, which considers thermal fluctuations of biomacromolecules without actually sampling conformations. Definitions for fuzzy noncovalent constraints were derived from persistency data from molecular dynamics (MD) simulations. A very good agreement between local flexibility and rigidity characteristics from ENT(FNC) and MD simulations-generated ensembles is found. Regarding global characteristics, convincing results were obtained when relative thermostabilities of citrate synthase and lipase A structures were computed. The ENT(FNC) approach significantly improves the robustness of rigidity analyses, is highly efficient, and does not require a protein-specific parameterization. Its low computational demand makes it especially valuable for the analysis of large data sets, e.g., for data-driven protein engineering.

Structure- and sequence-analysis inspired engineering of proteins for enhanced thermostability

Protein engineering strategies for increasing stability can be improved by replacing random mutagenesis and high-throughput screening by approaches that include bioinformatics and computational design. Mutations can be focused on regions in the structure that are most flexible and involved in the early steps of thermal unfolding. Sequence analysis can often predict the position and nature of stabilizing mutations, and may allow the reconstruction of thermostable ancestral sequences. Various computational tools make it possible to design stabilizing features, such as hydrophobic clusters and surface charges. Different methods for designing chimeric enzymes can also support the engineering of more stable proteins without the need of high-throughput screening.

CNA web server: rigidity theory-based thermal unfolding simulations of proteins for linking structure, (thermo-)stability, and function

The Constraint Network Analysis (CNA) web server provides a user-friendly interface to the CNA approach developed in our laboratory for linking results from rigidity analyses to biologically relevant characteristics of a biomolecular structure. The CNA web server provides a refined modeling of thermal unfolding simulations that considers the temperature dependence of hydrophobic tethers and computes a set of global and local indices for quantifying biomacromolecular stability. From the global indices, phase transition points are identified where the structure switches from a rigid to a floppy state; these phase transition points can be related to a protein’s (thermo-)stability. Structural weak spots (unfolding nuclei) are automatically identified, too; this knowledge can be exploited in data-driven protein engineering. The local indices are useful in linking flexibility and function and to understand the impact of ligand binding on protein flexibility. The CNA web server robustly handles small-molecule ligands in general. To overcome issues of sensitivity with respect to the input structure, the CNA web server allows performing two ensemble-based variants of thermal unfolding simulations. The web server output is provided as raw data, plots and/or Jmol representations. The CNA web server, accessible at http://cpclab.uni-duesseldorf.de/cna or http://www.cnanalysis.de, is free and open to all users with no login requirement.

Constraint Network Analysis (CNA): a Python software package for efficiently linking biomacromolecular structure, flexibility, (thermo-)stability, and function

For deriving maximal advantage from information on biomacromolecular flexibility and rigidity, results from rigidity analyses must be linked to biologically relevant characteristics of a structure. Here, we describe the Python-based software package Constraint Network Analysis (CNA) developed for this task. CNA functions as a front- and backend to the graph-based rigidity analysis software FIRST. CNA goes beyond the mere identification of flexible and rigid regions in a biomacromolecule in that it (I) provides a refined modeling of thermal unfolding simulations that also considers the temperature-dependence of hydrophobic tethers, (II) allows performing rigidity analyses on ensembles of network topologies, either generated from structural ensembles or by using the concept of fuzzy noncovalent constraints, and (III) computes a set of global and local indices for quantifying biomacromolecular stability. This leads to more robust results from rigidity analyses and extends the application domain of rigidity analyses in that phase transition points ("melting points") and unfolding nuclei ("structural weak spots") are determined automatically. Furthermore, CNA robustly handles small-molecule ligands in general. Such advancements are important for applying rigidity analysis to data-driven protein engineering and for estimating the influence of ligand molecules on biomacromolecular stability. CNA maintains the efficiency of FIRST such that the analysis of a single protein structure takes a few seconds for systems of several hundred residues on a single core. These features make CNA an interesting tool for linking biomacromolecular structure, flexibility, (thermo-)stability, and function. CNA is available from http://cpclab.uni-duesseldorf.de/software for nonprofit organizations.

Global and local indices for characterizing biomolecular flexibility and rigidity

Understanding flexibility and rigidity characteristics of biomolecules is a prerequisite for understanding biomolecular structural stability and function. Computational methods have been implemented that directly characterize biomolecular flexibility and rigidity by constraint network analysis. For deriving maximal advantage from these analyses, their results need to be linked to biologically relevant characteristics of a structure. Such links are provided by global and local measures ("indices") of biomolecular flexibility and rigidity. To date, more than 14 indices are available with sometimes overlapping or only vague definitions. We present concise definitions of these indices, analyze the relation between, and the scope and limitations of them, and compare their informative value. For this, we probe the structural stability of the calcium binding protein α-lactalbumin as a showcase, both in the "ground state" and after perturbing the system by changing the network topology. In addition, we introduce three indices for the first time that extend the application domain of flexibility and rigidity analyses. The results allow us to provide guidelines for future studies suggesting which of these indices could best be used for analyzing, understanding, and quantifying structural features that are important for biomolecular stability and function. Finally, we make suggestions for proper index notations in future studies to prevent the misinterpretation and to facilitate the comparison of results obtained from flexibility and rigidity analyses.