Redesigning protein pKa values

Acid-resistant enzymes: the acquisition strategies and applications

Enzymes have promising applications in chemicals, food, pharmaceuticals, and other variety products because of their high efficiency, specificity, and environmentally friendly properties. However, due to the complexity of raw materials, pH, temperature, solvents, etc., the application range of enzymes is greatly limited in the industry. Protein engineering and enzyme immobilization are classical strategies to overcome the limitations of industrial applications. Although the pH tendency of enzymes has been extensively researched, the mechanism underlying enzyme acid resistance is unclear, and a less practical strategy for altering the pH propensity of enzymes has been suggested. This review proposes that the optimum pH of enzyme is determined by the pKa values of active center ionizable amino acid residues. Three levels of acquiring acid-resistant enzymes are summarized: mining from extreme environments and enzyme databases, modification with protein engineering and enzyme microenvironment engineering, and de novo synthesis. The industrial applications of acid-resistant enzymes in chemicals, food, and pharmaceuticals are also summarized. KEY POINTS: • The mechanism of enzyme acid resistance is fundamentally determined. • The three aspects of the method for acquiring acid-resistant enzymes are summarized. • Computer-aided strategies and artificial intelligence are used to obtain acid-resistant enzymes.

Strategies for tailoring pH performances of glycoside hydrolases

Glycoside hydrolases (GHs) exhibit high activity and stability under harsh conditions, such as high temperatures and extreme pHs, given their wide use in industrial biotechnology. However, strategies for improving the acidophilic and alkalophilic adaptations of GHs are poorly summarized due to the complexity of the mechanisms of these adaptations. This review not only highlights the adaptation mechanisms of acidophilic and alkalophilic GHs under extreme pH conditions, but also summarizes the recent advances in engineering the pH performances of GHs with a focus on four strategies of protein engineering, enzyme immobilization, chemical modification, and medium engineering (additives). The examples described here summarize the methods used in modulating the pH performances of GHs and indicate that methods integrated in different protein engineering techniques or methods are efficient to generate industrial biocatalysts with the desired pH performance and other adapted enzyme properties.

Modulating the pH profile of the pullulanase from Pyrococcus yayanosii CH1 by synergistically engineering the active center and surface

A preferable pullulanase with high thermostability and catalytic activity at pH 4.5-5 is desired to match with glucoamylase in the starch-saccharification process. However, most of them exhibit low activity under such low pH conditions. Here, the optimal pH of the hyperthermostable pullulanase from Pyrococcus yayanosii (Pul_PY2) was successfully shifted from 6.4 to 5 with a 2-fold increase in the specific activity based on synergistic engineering of the active center and surface. Synergistic engineering was performed by introducing histidine within 6 Å of the active sites, and by enhancing negative charges on the enzymatic surface. Two single-site mutants of Pul_PY2-Q13H and Pul_PY2-I25E with higher hydrolytic activity were obtained, the optimal pH of which was shifted to pH 5 and 5.4, respectively; the combined mutant Pul_PY2-Q13H/I25E exhibited the optimal pH of 5, 3.2-fold increasing catalytic efficiency at pH 5, and high thermostability compared to Pul_PY2. These results not only obtained an applicable pullulanase for industrial application, but also provided a strategy for shifting the optimal pH of the enzyme based on synergistic engineering of the active center and surface.

Enhanced acidic resistance ability and catalytic properties of Bacillus 1,3-1,4-β-glucanases by sequence alignment and surface charge engineering

High stability at acidic environment is required for 1,3-1,4-β-glucanase to function in biofuel, brewing and animal feed industries. In this study, a mesophilic β-glucanase from Bacillus terquilensis CGX 5-1 was rationally engineered through sequence alignment and surface charge engineering to improve its acidic resistance ability. Nineteen singly-site variants were constructed and Q1E, I133L and V134A variants showed better acidic stability without the compromise of catalytic property and thermostability. Furthermore, four multi-site variants were constructed and one double-site variant Q1E/I133L with better stability at acidic environment and higher catalytic property was obtained. The fluorescence spectroscopy and structural analysis showed that more surface negative charge, decreased exposure degree of residue No.1, shifted side chain direction of residue No.133 and the lower total and folding free energy might be the reason for the improvement of acidic stability of Q1E/I133L variant. The obtained Q1E/I133L variant has potential applications in industries.

Current prospective in using cold-active enzymes as eco-friendly detergent additive

Advanced developments in the field of enzyme technology have increased the use of enzymes in industrial applications, especially in detergents. Enzymes as detergent additives have been extensively studied and the demand is considerably increasing due to its distinct properties and potential applications. Enzymes from microorganisms colonized at various geographical locations ranging from extreme hot to cold are explored for compatibility studies as detergent additives. Especially psychrophiles growing at cold conditions have cold-active enzymes with high catalytic activity and their stability under extreme conditions makes it as an appropriate eco-friendly and cost-effective additive in detergents. Adequate number of reports are available on cold-active enzymes such as proteases, lipases, amylases, and cellulases with high efficiency and exceptional features. These enzymes with increased thermostability and alkaline stability have become the premier choice as detergent additives. Modern approaches in genomics and proteomics paved the way to understand the compatibility of cold-active enzymes as detergent additives in broader dimensions. The molecular techniques such as gene coding, amino acid sequencing, and protein engineering studies helped to solve the mysteries related to alkaline stability of these enzymes and their chemical compatibility with oxidizing agents. The present review provides an overview of cold-active enzymes used as detergent additives and molecular approaches that resulted in development of these enzymes as commercial hit in detergent industries. The scope and challenges in using cold-active enzymes as eco-friendly and sustainable detergent additive are also discussed.

Computational design of enhanced detoxification activity of a zearalenone lactonase from Clonostachys rosea in acidic medium

Computational design of pH-activity profiles for enzymes is of great importance in industrial applications. In this research, a computational strategy was developed to engineer the pH-activity profile of a zearalenone lactonase (ZHD101) from Clonostachys rosea to promote its activity in acidic medium. The active site pK a values of ZHD101 were computationally designed by introducing positively charged lysine mutations on the enzyme surface, and the experimental results showed that two variants, M2(D157K) and M9(E171K), increased the catalytic efficiencies of ZHD101 modestly under acidic conditions. Moreover, two variants, M8(D133K) and M9(E171K), were shown to increase the turnover numbers by 2.73 and 2.06-fold with respect to wild type, respectively, though their apparent Michaelis constants were concomitantly increased. These results imply that the active site pK a value change might affect the pH-activity profile of the enzyme. Our computational strategy for pH-activity profile engineering considers protein stability; therefore, limited experimental validation is needed to discover beneficial mutations under shifted pH conditions.

Rational and Semirational Protein Design

This mini review gives an overview over different design approaches and methodologies applied in rational and semirational enzyme engineering. The underlying principles for engineering novel activities, enantioselectivity, substrate specificity, stability, and pH optimum are summarized.

Sequence homolog-based molecular engineering for shifting the enzymatic pH optimum

Cell-free synthetic biology system organizes multiple enzymes (parts) from different sources to implement unnatural catalytic functions. Highly adaption between the catalytic parts is crucial for building up efficient artificial biosynthetic systems. Protein engineering is a powerful technology to tailor various enzymatic properties including catalytic efficiency, substrate specificity, temperature adaptation and even achieve new catalytic functions. However, altering enzymatic pH optimum still remains a challenging task. In this study, we proposed a novel sequence homolog-based protein engineering strategy for shifting the enzymatic pH optimum based on statistical analyses of sequence-function relationship data of enzyme family. By two statistical procedures, artificial neural networks (ANNs) and least absolute shrinkage and selection operator (Lasso), five amino acids in GH11 xylanase family were identified to be related to the evolution of enzymatic pH optimum. Site-directed mutagenesis of a thermophilic xylanase from Caldicellulosiruptor bescii revealed that four out of five mutations could alter the enzymatic pH optima toward acidic condition without compromising the catalytic activity and thermostability. Combination of the positive mutants resulted in the best mutant M31 that decreased its pH optimum for 1.5 units and showed increased catalytic activity at pH < 5.0 compared to the wild-type enzyme. Structure analysis revealed that all the mutations are distant from the active center, which may be difficult to be identified by conventional rational design strategy. Interestingly, the four mutation sites are clustered at a certain region of the enzyme, suggesting a potential "hot zone" for regulating the pH optima of xylanases. This study provides an efficient method of modulating enzymatic pH optima based on statistical sequence analyses, which can facilitate the design and optimization of suitable catalytic parts for the construction of complicated cell-free synthetic biology systems.

protonation behavior of histidine during HSF1 activation by physiological acidification

The expression of eukaryotic molecular chaperones (heat shock proteins, HSPs) is triggered in response to a wide range of environmental stresses, including: heat shock, hydrogen peroxide, heavy metal, low-pH, or virus infection. Biochemical and genetic studies have clearly shown the fundamental roles of heat shock factor 1 (HSF1) in stress-inducible HSP gene expression, resistance to stress-induced cell death, carcinogenesis, and other biological phenomena. Previous studies show that acidic pH changes within the physiological range directly activate the HSF1 function in vitro. However, the detailed mechanism is unclear. Though computational pKa-predications of the amino acid side-chain, acidic-pH induced protonation of a histidine residue was found to be most-likely involved in this process. The histidine 83 (His83) residue, which could be protonated by mild decrease in pH, causes mild acidic-induced HSF1 activation (including in-vitro trimerization, DNA binding, in-vivo nuclear accumulation, and HSPs expression). His83, which is located in the loop region of the HSF1 DNA binding domain, was suggested to enhance the intermolecular force with Arginine 79, which helps HSF1 form a DNA-binding competent. Therefore, low-pH-induced activation of HSF1 by the protonation of histidine can help us better to understand the HSF1 mechanism and develop more therapeutic applications (particularly in cancer therapy). J. Cell. Biochem. 116: 977-984, 2015. © 2015 Wiley Periodicals, Inc.

Advances in protease engineering for laundry detergents

Proteases are essential ingredients in modern laundry detergents. Over the past 30 years, subtilisin proteases employed in the laundry detergent industry have been engineered by directed evolution and rational design to tailor their properties towards industrial demands. This comprehensive review discusses recent success stories in subtilisin protease engineering. Advances in protease engineering for laundry detergents comprise simultaneous improvement of thermal resistance and activity at low temperatures, a rational strategy to modulate pH profiles, and a general hypothesis for how to increase promiscuous activity towards the production of peroxycarboxylic acids as mild bleaching agents. The three protease engineering campaigns presented provide in-depth analysis of protease properties and have identified principles that can be applied to improve or generate enzyme variants for industrial applications beyond laundry detergents.

Effect of Calcium Ion Removal, Ionic Strength, and Temperature on the Conformation Change in Calmodulin Protein at Physiological pH

The response of the calmodulin (CaM) protein as a function of calcium ion removal, ionic strength, and temperature at physiological pH condition was investigated using classical molecular dynamics simulations. Changing the ionic strength and temperature came out to be two of the possible routes for observing a conformation change in the protein. This behavior is similar to the conformation change observed in our previous study where a change in the pH was observed to trigger a conformation change in this protein. In the present study, as the calcium ions are removed from the protein, the protein is observed to acquire more flexibility. This flexibility is observed to be more prominent at a higher ionic strength. At a lower ionic strength of 150 mM with all the four calcium ions intact, the N- and C-lobes are observed to come close to a distance of 30 Å starting from an initial separation distance of 48 Å. This conformation change is observed to take place around 50 ns in a simulation of 100 ns. As a second parameter, temperature is observed to play a key role in the conformation change of the protein. With an increase in the temperature, the protein is observed to acquire a more compact form with the formation of different salt bridges between the residues of the N- and the C-lobes. The salt bridge formation leads to an overall lowering of the energy of the protein thus favoring the bending of the two lobes towards each other. The improper and dihedral terms show a significant shift thus leading to a more compact form on increasing the temperature. Another set of simulations is also performed at an increased temperature of 500 K to verify the reproducibility of the results. Thus a set of three possible alterations in the environmental conditions of the protein CaM are studied, with two of them giving rise to a conformation change and one adding flexibility to the protein.

Protein dielectric constants determined from NMR chemical shift perturbations

Understanding the connection between protein structure and function requires a quantitative understanding of electrostatic effects. Structure-based electrostatic calculations are essential for this purpose, but their use has been limited by a long-standing discussion on which value to use for the dielectric constants (ε(eff) and ε(p)) required in Coulombic and Poisson-Boltzmann models. The currently used values for ε(eff) and ε(p) are essentially empirical parameters calibrated against thermodynamic properties that are indirect measurements of protein electric fields. We determine optimal values for ε(eff) and ε(p) by measuring protein electric fields in solution using direct detection of NMR chemical shift perturbations (CSPs). We measured CSPs in 14 proteins to get a broad and general characterization of electric fields. Coulomb's law reproduces the measured CSPs optimally with a protein dielectric constant (ε(eff)) from 3 to 13, with an optimal value across all proteins of 6.5. However, when the water-protein interface is treated with finite difference Poisson-Boltzmann calculations, the optimal protein dielectric constant (ε(p)) ranged from 2 to 5 with an optimum of 3. It is striking how similar this value is to the dielectric constant of 2-4 measured for protein powders and how different it is from the ε(p) of 6-20 used in models based on the Poisson-Boltzmann equation when calculating thermodynamic parameters. Because the value of ε(p) = 3 is obtained by analysis of NMR chemical shift perturbations instead of thermodynamic parameters such as pK(a) values, it is likely to describe only the electric field and thus represent a more general, intrinsic, and transferable ε(p) common to most folded proteins.

In silico investigation of pH-dependence of prolactin and human growth hormone binding to human prolactin receptor

Experimental data shows that the binding of human prolactin (hPRL) to human prolactin receptor (hPRLr-ECD) is strongly pH-dependent, while the binding of the same receptor to human growth hormone (hGH) is pH-independent. Here we carry in silico analysis of the molecular effects causing such a difference and reveal the role of individual amino acids. It is shown that the computational modeling correctly predicts experimentally determined pKa's of histidine residues in an unbound state in the majority of the cases and the pH-dependence of the binding free energy. Structural analysis carried in conjunction with calculated pH-dependence of the binding revealed that the main reason for pH-dependence of the binding of hPRL-hPRLr-ECD is a number of salt- bridges across the interface of the complex, while no salt-bridges are formed in the hGH-hPRlr-ECD. Specifically, most of the salt-bridges involve histidine residues and this is the reason for the pH-dependence across a physiological range of pH. The analysis not only revealed the molecular mechanism of the pH-dependence of the hPRL-hPRLr-ECD, but also provided critical insight into the underlying physic-chemical mechanism.

Computational approaches for rational design of proteins with novel functionalities

Proteins are the most multifaceted macromolecules in living systems and have various important functions, including structural, catalytic, sensory, and regulatory functions. Rational design of enzymes is a great challenge to our understanding of protein structure and physical chemistry and has numerous potential applications. Protein design algorithms have been applied to design or engineer proteins that fold, fold faster, catalyze, catalyze faster, signal, and adopt preferred conformational states. The field of de novo protein design, although only a few decades old, is beginning to produce exciting results. Developments in this field are already having a significant impact on biotechnology and chemical biology. The application of powerful computational methods for functional protein designing has recently succeeded at engineering target activities. Here, we review recently reported de novo functional proteins that were developed using various protein design approaches, including rational design, computational optimization, and selection from combinatorial libraries, highlighting recent advances and successes.

Calmodulin readily switches conformation upon protonating high pKa acidic residues

We investigate protonation as a possible route for triggering conformational change in proteins by focusing on the calmodulin (CaM) example. Two hundred nanosecond molecular dynamics (MD) simulations are performed on both the extended and compact forms of calcium loaded CaM. The stability of both structures is confirmed under prevailing conditions. Protonation of nine acidic residues with upshifted pK(a) values leads to a large conformational change in less than 100 ns. The structure attained is consistent with fluorescence resonance energy transfer experimental results as well as structures from an ensemble compatible with NMR data. Analysis of the MD trajectories summing up to one microsecond implies that the key events leading to the completion of the conformational change begins with an initial formation of a salt bridge between the N-lobe and the linker, followed by the bending of the C-lobe and the organization of a stabilizing hydrophobic patch between the lobes. We find that CaM utilizes its Ca(2+) ions to harden/soften different regions so as to achieve various conformations. Thus, barrier crossing between extended and compact forms of CaM which is normally a rare event due to the repulsive electrostatic interactions between the two lobes is facilitated by protonation of high pK(a) residues. The results delineate how pH changes might be utilized in the cell to achieve different conformation-related functions.

pKa modulation of the acid/base catalyst within GH32 and GH68: a role in substrate/inhibitor specificity?

Glycoside hydrolases of families 32 (GH32) and 68 (GH68) belong to clan GH-J, containing hydrolytic enzymes (sucrose/fructans as donor substrates) and fructosyltransferases (sucrose/fructans as donor and acceptor substrates). In GH32 members, some of the sugar substrates can also function as inhibitors, this regulatory aspect further adding to the complexity in enzyme functionalities within this family. Although 3D structural information becomes increasingly available within this clan and huge progress has been made on structure-function relationships, it is not clear why some sugars bind as inhibitors without being catalyzed. Conserved aspartate and glutamate residues are well known to act as nucleophile and acid/bases within this clan. Based on the available 3D structures of enzymes and enzyme-ligand complexes as well as docking simulations, we calculated the pKa of the acid-base before and after substrate binding. The obtained results strongly suggest that most GH-J members show an acid-base catalyst that is not sufficiently protonated before ligand entrance, while the acid-base can be fully protonated when a substrate, but not an inhibitor, enters the catalytic pocket. This provides a new mechanistic insight aiming at understanding the complex substrate and inhibitor specificities observed within the GH-J clan. Moreover, besides the effect of substrate entrance on its own, we strongly suggest that a highly conserved arginine residue (in the RDP motif) rather than the previously proposed Tyr motif (not conserved) provides the proton to increase the pKa of the acid-base catalyst.

Gene cloning, expression, and biochemical characterization of an alkali-tolerant β-mannanase from Humicola insolens Y1

In this article, we firstly report a highly alkali-tolerant fungal β-mannanase from Humicola insolens Y1. The full-length cDNA of the β-mannanase, designated as man5A, has an open reading frame of 1,233 bp that encodes a 411-amino acid polypeptide (Man5A) with a calculated molecular mass of 42.3 kDa. The deduced sequence of Man5A comprises a putative 20-residue signal peptide and a catalytic domain belonging to glycoside hydrolase family 5, and displays 61–85% identities with hypothetical proteins and 32–39% with experimentally verified fungal β-mannanases. Purified recombinant Man5A produced by Pichia pastoris has a specific activity of 1,122 U mg−1 and exhibits optimal activity at pH 5.5 and 70°C. Distinct from other reported fungal β-mannanases, Man5A is highly alkali tolerant, exhibiting 45 and 36% of the maximal activity at pH 8.0 and 9.0, respectively, and more than 10% activity even at pH 10.0. Moreover, Man5A has excellent pH stability at pH 5.0–12.0 and is highly thermostable at 50°C. The higher frequency of alkaline amino acids (Arg and Lys), greater pKa values of the catalytic residues, and more positively charged residues on the surface of Man5A might be the causes. Man5A has strong resistance to various neutral and alkaline proteases, retaining more than 97% of the activity after proteolytic treatment for 1 h. The superior characteristics of Man5A make it more advantageous for the application in the kraft pulp industry.

Calculating pH-dependent free energy of proteins by using Monte Carlo protonation probabilities of ionizable residues

Protein folding, stability, and function are usually influenced by pH. And free energy plays a fundamental role in analysis of such pH-dependent properties. Electrostatics-based theoretical framework using dielectric solvent continuum model and solving Poisson-Boltzmann equation numerically has been shown to be very successful in understanding the pH-dependent properties. However, in this approach the exact computation of pH-dependent free energy becomes impractical for proteins possessing more than several tens of ionizable sites (e.g. > 30), because exact evaluation of the partition function requires a summation over a vast number of possible protonation microstates. Here we present a method which computes the free energy using the average energy and the protonation probabilities of ionizable sites obtained by the well-established Monte Carlo sampling procedure. The key feature is to calculate the entropy by using the protonation probabilities. We used this method to examine a well-studied protein (lysozyme) and produced results which agree very well with the exact calculations. Applications to the optimum pH of maximal stability of proteins and protein-DNA interactions have also resulted in good agreement with experimental data. These examples recommend our method for application to the elucidation of the pH-dependent properties of proteins.

Creation of aggregation-defective α-synuclein variants by engineering the sequence connecting β-strand-forming domains

The aggregation of α-synuclein (αS), which is implicated in the pathology of Parkinson's disease, produces fibrils in which layers of parallel, in-register β-sheet-loop-β-sheets are formed. The effects of sequence variation in the loop-forming region (referred to as the linker region) on αS aggregation have yet to be systematically studied. In the study described here, we created and characterized αS variants containing mutations in the linker regions. Our results indicate that although the physicochemical properties of the linker region, evaluated based on an intrinsic property of a single amino acid, still play a significant role in aggregation, additional factors can also determine aggregation of αS linker mutants. Our analyses suggest that these factors include a pairwise potential for parallel in-register β-sheet formation. A linker variant displaying significantly reduced self-aggregation interfered with αS aggregation by inhibiting the conversion of αS soluble species to αS insoluble fibrils. We anticipate that linker mutations could serve as a novel method of creating αS variants that are aggregation-defective and/or inhibit αS aggregation.

On the development of protein pKa calculation algorithms

Protein pK(a) calculation methods are developed partly to provide fast non-experimental estimates of the ionization constants of protein side chains. However, the most significant reason for developing such methods is that a good pK(a) calculation method is presumed to provide an accurate physical model of protein electrostatics, which can be applied in methods for drug design, protein design, and other structure-based energy calculation methods. We explore the validity of this presumption by simulating the development of a pK(a) calculation method using artificial experimental data derived from a human-defined physical reality. We examine the ability of an RMSD-guided development protocol to retrieve the correct (artificial) physical reality and find that a rugged optimization landscape and a huge parameter space prevent the identification of the correct physical reality. We examine the importance of the training set in developing pK(a) calculation methods and investigate the effect of experimental noise on our ability to identify the correct physical reality, and find that both effects have a significant and detrimental impact on the physical reality of the optimal model identified. Our findings are of relevance to all structure-based methods for protein energy calculations and simulation, and have large implications for all types of current pK(a) calculation methods. Our analysis furthermore suggests that careful and extensive validation on many types of experimental data can go some way in making current models more realistic.

Electrostatics in proteins and protein-ligand complexes

Accurate computational methods for predicting electrostatic energies are of major importance for our understanding of protein energetics in general for computer-aided drug design as well as for the design of novel biocatalysts and protein therapeutics. Electrostatic energies are of particular importance in such applications as virtual screening, drug design and protein-protein docking due to the high charge density of protein ligands and small-molecule drugs, and the frequent protonation state changes observed when drugs bind to their protein targets. Therefore, the development of a reliable and fast algorithm for the evaluation of electrostatic free energies, as an important contributor to the overall protein energy function, has been the focus for many scientists over the past three decades. In this review we describe the current state-of-the-art in modeling electrostatic effects in proteins and protein-ligand complexes. We focus mainly on the merits and drawbacks of the continuum methodology, and speculate on future directions in refining algorithms for calculating electrostatic energies in proteins using experimental data.

Integrated prediction of the effect of mutations on multiple protein characteristics

Site-directed mutagenesis is routinely used in modern biology to elucidate the functional or biophysical roles of protein residues, and plays an important role in the field of rational protein design. Over the past decade, a number of computational tools have been developed that can predict the effect of point mutations on a protein's biophysical characteristics. However, these programs usually provide predictions for only a single characteristic. Furthermore, online versions of these tools are often impractical to use for examination of large and diverse sets of mutants. We have created a new web application, (http://enzyme.ucd.ie/PEAT_SA), that can simultaneously predict the effect of mutations on stability, ligand affinity and pK(a) values. PEAT-SA also provides an expanded feature-set with respect to other online tools which includes the ability to obtain predictions for multiple mutants in one submission. As a result, researchers who use site-directed mutagenesis can access state-of-the-art protein design methods with a fraction of the effort previously required. The results of benchmarking PEAT-SA on standard test-sets demonstrate that its accuracy for all three prediction types compares well to currently available tools. We illustrate PEAT-SA's potential by using it to investigate the influence of mutations on the activity of Subtilisin BPN'. This example demonstrates how the ability to obtain a wide range of information from one source, that can be combined to obtain deeper insight into the influence of mutations, makes PEAT-SA a valuable service to both experimental and computational biologists.

On the role of electrostatics in protein-protein interactions

The role of electrostatics in protein-protein interactions and binding is reviewed in this paper. A brief outline of the computational modeling, in the framework of continuum electrostatics, is presented and the basic electrostatic effects occurring upon the formation of the complex are discussed. The effect of the salt concentration and pH of the water phase on protein-protein binding free energy is demonstrated which indicates that the increase of the salt concentration tends to weaken the binding, an observation that is attributed to the optimization of the charge-charge interactions across the interface. It is pointed out that the pH-optimum (pH of optimal binding affinity) varies among the protein-protein complexes, and perhaps is a result of their adaptation to particular subcellular compartments. The similarities and differences between hetero- and homo-complexes are outlined and discussed with respect to the binding mode and charge complementarity.

Computational protein design with a generalized Born solvent model: application to Asparaginyl-tRNA synthetase

Computational Protein Design (CPD) is a promising method for high throughput protein and ligand mutagenesis. Recently, we developed a CPD method that used a polar-hydrogen energy function for protein interactions and a Coulomb/Accessible Surface Area (CASA) model for solvent effects. We applied this method to engineer aspartyl-adenylate (AspAMP) specificity into Asparaginyl-tRNA synthetase (AsnRS), whose substrate is asparaginyl-adenylate (AsnAMP). Here, we implement a more accurate function, with an all-atom energy for protein interactions and a residue-pairwise generalized Born model for solvent effects. As a first test, we compute aminoacid affinities for several point mutants of Aspartyl-tRNA synthetase (AspRS) and Tyrosyl-tRNA synthetase and stability changes for three helical peptides and compare with experiment. As a second test, we readdress the problem of AsnRS aminoacid engineering. We compare three design criteria, which optimize the folding free-energy, the absolute AspAMP affinity, and the relative (AspAMP-AsnAMP) affinity. The sequences and conformations are improved with respect to our previous, polar-hydrogen/CASA study: For several designed complexes, the AspAMP carboxylate forms three interactions with a conserved arginine and a designed lysine, as in the active site of the AspRS:AspAMP complex. The conformations and interactions are well maintained in molecular dynamics simulations and the sequences have an inverted specificity, favoring AspAMP over AsnAMP. The method is not fully successful, since experimental measurements with the seven most promising sequences show that they do not catalyze at a detectable level the adenylation of Asp (or Asn) with ATP. This may be due to weak AspAMP binding and/or disruption of transition-state stabilization.

Calculating pKa values in the cAMP-dependent protein kinase: the effect of conformational change and ligand binding

The conformational change observed upon ligand binding and phosphorylation for the cAMP-dependent protein kinase (protein kinase A-PKA) is of high importance for the regulation of its activity. We calculate pKa values and net charges for 18 3D structures of PKA in various conformations and liganded states to examine the role of electrostatics in ligand binding and activation. We find that the conformational change of PKA takes place without any significant net proton uptake/release at all pH values, thus indicating that PKA has evolved to reduce any pH-dependent barriers to the conformational motion. We furthermore find that the binding of ligands induces large changes in the net charge of PKA at most pH values, but significantly, we find that the net charge difference at physiological pH is close to zero, thus indicating that the active-site pKa values have been preorganized for substrate binding. We are unable to unequivocally resolve the identity of the groups responsible for determining the pH-activity profile of PKA but speculate that the titration of Lys 168 or the titration of ATP itself could be responsible for the loss of activity at high pH values. Finally, we examine the effect of point mutations on the pKa values of the PKA catalytic residues and find these to be relatively insensitive to both noncharge-altering and charge-altering mutations.

Remeasuring HEWL pK(a) values by NMR spectroscopy: methods, analysis, accuracy, and implications for theoretical pK(a) calculations

Site-specific pK(a) values measured by NMR spectroscopy provide essential information on protein electrostatics, the pH-dependence of protein structure, dynamics and function, and constitute an important benchmark for protein pK(a) calculation algorithms. Titration curves can be measured by tracking the NMR chemical shifts of several reporter nuclei versus sample pH. However, careful analysis of these curves is needed to extract residue-specific pK(a) values since pH-dependent chemical shift changes can arise from many sources, including through-bond inductive effects, through-space electric field effects, and conformational changes. We have re-measured titration curves for all carboxylates and His 15 in Hen Egg White Lysozyme (HEWL) by recording the pH-dependent chemical shifts of all backbone amide nitrogens and protons, Asp/Glu side chain protons and carboxyl carbons, and imidazole protonated carbons and protons in this protein. We extracted pK(a) values from the resulting titration curves using standard fitting methods, and compared these values to each other, and with those measured previously by ¹H NMR (Bartik et al., Biophys J 1994;66:1180–1184). This analysis gives insights into the true accuracy associated with experimentally measured pK(a) values. We find that apparent pK(a) values frequently differ by 0.5–1.0 units depending upon the nuclei monitored, and that larger differences occasionally can be observed. The variation in measured pK(a) values, which reflects the difficulty in fitting and assigning pH-dependent chemical shifts to specific ionization equilibria, has significant implications for the experimental procedures used for measuring protein pK(a) values, for the benchmarking of protein pK(a) calculation algorithms, and for the understanding of protein electrostatics in general.

Early aggregation steps in alpha-synuclein as measured by FCS and FRET: evidence for a contagious conformational change

The kinetics of aggregation of alpha-synuclein are usually studied by turbidity or Thio-T fluorescence. Here we follow the disappearance of monomers and the formation of early oligomers using fluorescence correlation spectroscopy. Alexa488-labeled A140C-synuclein was used as a fluorescent probe in trace amounts in the presence of excess unlabeled alpha-synuclein. Repeated short measurements produce a distribution of diffusion coefficients. Initially, a sharp peak is obtained corresponding to monomers, followed by a distinct transient population and the gradual formation of broader-sized distributions of higher oligomers. The kinetics of aggregation can be followed by the decreasing number of fast-diffusing species. Both the disappearance of fast-diffusing species and the appearance of turbidity can be fitted to the Finke-Watzky equation, but the apparent rate constants obtained are different. This reflects the fact that the disappearance of fast species occurs largely during the lag phase of turbidity development, due to the limited sensitivity of turbidity to the early aggregation process. The nucleation of the early oligomers is concentration-dependent and accompanied by a conformational change that precedes beta-structure formation, and can be visualized using fluorescence resonance energy transfer between the donor-labeled N-terminus and the acceptor-labeled cysteine in the mutant A140C.

Automated site preparation in physics-based rescoring of receptor ligand complexes

Hydrogen atoms are not typically observable in X-ray crystal structures, but inferring their locations is often important in structure-based drug design. In addition, protonation states of the protein can change in response to ligand binding, as can the orientations of OH groups, a subtle form of "induced fit." We implement and evaluate an automated procedure for optimizing polar hydrogens in protein-binding sites in complex with ligands. Specifically, we apply the previously described Independent Cluster Decomposition Algorithm (ICDA) algorithm (Li et al., Proteins 2007;66:824-837), which assigns the ionization states of titratable residues, the amide orientations of Asn/Gln side chains, the imidazole ring orientation in His, and the orientations of OH/SH groups, in a unified algorithm. We test the utility of this method for identifying nativelike ligand poses using 247 protein-ligand complexes from an established database of docked decoys. Pose selection is performed with a physics-based scoring function based on a molecular mechanics energy function and a Generalized Born implicit solvent model. The use of the ICDA receptor preparation protocol, implemented with no knowledge of the native ligand pose, increases the accuracy of pose selection significantly, with the average RMSD over all complexes decreasing from 2.7 to 1.5 A when applying ICDA. Large improvements are seen for specific classes of binding sites with titratable groups, such as aspartyl proteases.

Computational tools for designing and engineering biocatalysts

Current computational tools to assist experimentalists for the design and engineering of proteins with desired catalytic properties are reviewed. The applications of these tools for de novo design of protein active sites, optimization of substrate access and product exit pathways, redesign of protein-protein interfaces, identification of neutral/advantageous/deleterious mutations in the libraries from directed evolution and stabilization of protein structures are described. Remarkable progress is seen in de novo design of enzymes catalyzing a chemical reaction for which a natural biocatalyst does not exist. Yet, constructed biocatalysts do not match natural enzymes in their efficiency, suggesting that more research is needed to capture all the important features of natural biocatalysts in theoretical designs.

Solution structure and catalytic mechanism of human protein histidine phosphatase 1

Protein histidine phosphorylation exists widely in vertebrates, and it plays important roles in signal transduction and other cellular functions. However, knowledge about eukaryotic PHPT (protein histidine phosphatase) is still very limited. To date, only one vertebrate PHPT has been discovered, and two crystal structures of hPHPT1 (human PHPT1) have been solved. However, these two structures gave different ligand-binding sites and co-ordination patterns. In the present paper, we have solved the solution structures of hPHPT1 in both Pi-free and Pi-bound states. Through comparison of the structures, along with a mutagenesis study, we have determined the active site of hPHPT1. In contrast with previous results, our results indicate that the active site is located between helix α1 and loop L5. His53 was identified to be the catalytic residue, and the NH groups of residues His53, Ala54 and Ala96 and the OH group of Ser94 should act as anchors of Pi or substrate by forming H-bonds with Pi. On the basis of our results, a catalytic mechanism is proposed for hPHPT1: the imidazole ring of His53 serves as a general base to activate a water molecule, and the activated water would attack the substrate as a nucleophile in the catalysis; the positively charged side chain of Lys21 can help stabilize the transition state. No similar catalytic mechanism can be found in the EzCatDB database.

Solvent reaction field potential inside an uncharged globular protein: a bridge between implicit and explicit solvent models?

The solvent reaction field potential of an uncharged protein immersed in simple point charge/extended explicit solvent was computed over a series of molecular dynamics trajectories, in total 1560 ns of simulation time. A finite, positive potential of 13-24 kbTec(-1) (where T=300 K), dependent on the geometry of the solvent-accessible surface, was observed inside the biomolecule. The primary contribution to this potential arose from a layer of positive charge density 1.0 A from the solute surface, on average 0.008 ec/A3, which we found to be the product of a highly ordered first solvation shell. Significant second solvation shell effects, including additional layers of charge density and a slight decrease in the short-range solvent-solvent interaction strength, were also observed. The impact of these findings on implicit solvent models was assessed by running similar explicit solvent simulations on the fully charged protein system. When the energy due to the solvent reaction field in the uncharged system is accounted for, correlation between per-atom electrostatic energies for the explicit solvent model and a simple implicit (Poisson) calculation is 0.97, and correlation between per-atom energies for the explicit solvent model and a previously published, optimized Poisson model is 0.99.