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Every cell in the body secretes extracellular vesicles (EVs) possibly as cellular signaling components and these cell-derivatives can be found in multiple numbers in biological fluids. EVs have in the scientific field received great attention in relation to pathophysiology and disease diagnostics. Altered protein expressions associated with circulating EVs in diseased individuals can serve as biomarkers for different disease states. This capacity paves the way for non-invasive screening tools and early diagnostic markers. However, no isolation method of EVs has been acknowledged as the "golden standard," thus reproducibility of the studies remains inadequate. Increasing interest in EV proteins as disease biomarkers could give rise to more scientific knowledge with diagnostic applicability. In this chapter, studies of proteins believed to be associated with EVs within cancer, autoimmunity, metabolic and neurodegenerative diseases have been outlined., (© 2020 Elsevier Inc. All rights reserved.)

Study Question: What is the differentiation stage of human testicular interstitial cells, in particular Leydig cells (LC), within micronodules found in patients with infertility, testicular cancer and Klinefelter syndrome?, Summary Answer: The Leydig- and peritubular-cell populations in testes with dysgenesis contain an increased proportion of undifferentiated cells when compared with control samples, as demonstrated by increased delta-like homolog 1 (DLK1) and decreased insulin-like peptide 3 (INSL3) expression., What Is Known Already: Normal LC function is essential for male development and reproduction. Signs of LC failure, including LC micronodules, are often observed in patients with reproductive disorders., Study Design, Size, Participants: In this retrospective study, a panel of markers and factors linked to the differentiation of LCs was investigated in 33 fetal and prepubertal human specimens and in 58 adult testis samples from patients with testicular germ cell tumours, including precursor carcinoma in situ (CIS), infertility or Klinefelter syndrome., Participants/materials, Setting, Methods: The expression patterns of DLK1, INSL3, chicken ovalbumin upstream promoter transcription factor 2 (COUP-TFII), cytochrome P450, family 11, subfamily A, polypeptide 1 (CYP11A1) and smooth muscle actin (SMA) were investigated by immunohistochemistry and quantitative RT-PCR. The percentage of positive LCs was estimated and correlated to total LC numbers and serum levels of reproductive hormones., Main Results and the Role of Chance: DLK1, INSL3 and COUP-TFII expression changed during normal development and was linked to different stages of LC differentiation: DLK1 was expressed in all fetal LCs, but only in spindle-shaped progenitor cells and in a small subset of polygonal LCs in the normal adult testis; INSL3 was expressed in a subset of fetal LCs, but in the majority of adult LCs; and COUP-TFII was expressed in peritubular and mesenchymal stroma cells at all ages, in fetal LCs early in gestation and in a subset of adult LCs. CYP11A1 was expressed in the majority of LCs regardless of age and pathology and was the best general LC marker examined here. SMA was weakly expressed in peritubular cells in the fetal and infantile testis, but strongly expressed in the adult testis. In pathological testes, the numbers of DLK1-positive interstitial cells were increased. The proportion of DLK1-positive LCs correlated with total LC numbers (R = 0.53; P < 0.001) and was higher in testis with enlargement of the peritubular layers (P < 0.01), which was also highly associated with DLK1 expression in the peritubular compartment (P < 0.001). INSL3 expression was absent in some, but not all LC micronodules, and in the majority of LCs, it was mutually exclusive of DLK1., Limitations, Reasons for Caution: The number of samples was relatively small and no true normal adult controls were available. True stereology was not used for LC counting, instead LCs were counted in three fields of 0.5 µm(2) surface for each sample., Wider Implications of the Findings: The population of LCs, especially those clustered in large nodules, are heterogeneous and comprise cells at different stages of differentiation. The study demonstrated that the differentiation and function of LCs, and possibly also peritubular cells, are impaired in adult men with testicular pathologies including testis cancer and Klinefelter syndrome., Study Funding/competing Interests: This work was funded by Rigshospitalet's research funds, the Danish Cancer Society and Kirsten and Freddy Johansen's foundation. The authors have no conflicts of interest., (© The Author 2014. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Objectives: Hereditary spastic paraplegia (HSP) is a heterogeneous group of neurodegenerative disorders characterized by a progressive gait disorder, lower limb spasticity, hyper-reflexia, weakness and extensor plantar responses. Recently, large intronic hexanucleotide repeat expansions (GGGGCC) in C9ORF72 have been found to cause frontotemporal dementia (FTD), amyotrophic lateral sclerosis and FTD with motor neuron disease. Owing to the overlapping phenotypes among HSP, amyotrophic lateral sclerosis and FTD with motor neuron disease along with shared pathological findings, we hypothesized that C9ORF72 expansions might be a genetic risk factor or modifier of HSP., Methods: Clinically characterized HSP patients were investigated for elongations in the hexanucleotide repeat of C9ORF72., Results: Upon analyses of the repeat lengths in the C9ORF72 gene in a Danish cohort of HSP patients, we found no expansions., Conclusion: We conclude that HSP is most likely not associated with repeat expansions in C9ORF72.

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.

Recently, a hexanucleotide (GGGGCC) repeat expansion in the first intron of C9ORF72 was reported as the cause of chromosome 9p21-linked frontotemporal dementia-amyotrophic lateral sclerosis (FTD-ALS). We here report the prevalence of the expansion in a hospital-based cohort and associated clinical features indicating a wider clinical spectrum of C9ORF72 disease than previously described. We studied 280 patients previously screened for mutations in genes involved in early onset autosomal dominant inherited dementia disorders. A repeat-primed polymerase chain reaction amplification assay was used to identify pathogenic GGGGCC expansions. As a potential modifier, confirmed cases were further investigated for abnormal CAG expansions in ATXN2. A pathogenic GGGGCC expansion was identified in a total of 14 probands. Three of these presented with atypical clinical features and were previously diagnosed with clinical olivopontocerebellar degeneration (OPCD), atypical Parkinsonian syndrome (APS) and a corticobasal syndrome (CBS). Further, the pathogenic expansion was identified in six FTD patients, four patients with FTD-ALS and one ALS patient. All confirmed cases had normal ATXN2 repeat sizes. Our study widens the clinical spectrum of C9ORF72 related disease and confirms the hexanucleotide expansion as a prevalent cause of FTD-ALS disorders. There was no indication of a modifying effect of the ATXN2 gene., (© 2012 John Wiley & Sons A/S.)

The exchange of information between experimentalists and theoreticians is crucial to improving the predictive ability of theoretical methods and hence our understanding of the related biology. However many barriers exist which prevent the flow of information between the two disciplines. Enabling effective collaboration requires that experimentalists can easily apply computational tools to their data, share their data with theoreticians, and that both the experimental data and computational results are accessible to the wider community. We present a prototype collaborative environment for developing and validating predictive tools for protein biophysical characteristics. The environment is built on two central components; a new python-based integration module which allows theoreticians to provide and manage remote access to their programs; and PEATDB, a program for storing and sharing experimental data from protein biophysical characterisation studies. We demonstrate our approach by integrating PEATSA, a web-based service for predicting changes in protein biophysical characteristics, into PEATDB. Furthermore, we illustrate how the resulting environment aids method development using the Potapov dataset of experimentally measured ΔΔGfold values, previously employed to validate and train protein stability prediction algorithms.

The pK(a) Cooperative (http://www.pkacoop.org) was organized to advance development of accurate and useful computational methods for structure-based calculation of pK(a) values and electrostatic energies in proteins. The Cooperative brings together laboratories with expertise and interest in theoretical, computational, and experimental studies of protein electrostatics. To improve structure-based energy calculations, it is necessary to better understand the physical character and molecular determinants of electrostatic effects. Thus, the Cooperative intends to foment experimental research into fundamental aspects of proteins that depend on electrostatic interactions. It will maintain a depository for experimental data useful for critical assessment of methods for structure-based electrostatics calculations. To help guide the development of computational methods, the Cooperative will organize blind prediction exercises. As a first step, computational laboratories were invited to reproduce an unpublished set of experimental pK(a) values of acidic and basic residues introduced in the interior of staphylococcal nuclease by site-directed mutagenesis. The pK(a) values of these groups are unique and challenging to simulate owing to the large magnitude of their shifts relative to normal pK(a) values in water. Many computational methods were tested in this first Blind Prediction Challenge and critical assessment exercise. A workshop was organized in the Telluride Science Research Center to objectively assess the performance of many computational methods tested on this one extensive data set. This volume of Proteins: Structure, Function, and Bioinformatics introduces the pK(a) Cooperative, presents reports submitted by participants in the Blind Prediction Challenge, and highlights some of the problems in structure-based calculations identified during this exercise., (Copyright © 2011 Wiley-Liss, Inc.)

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., (Copyright © 2011 Wiley-Liss, Inc.)

The pK(a) -cooperative aims to provide a forum for experimental and theoretical researchers interested in protein pK(a) values and protein electrostatics in general. The first round of the pK(a) -cooperative, which challenged computational labs to carry out blind predictions against pK(a) s experimentally determined in the laboratory of Bertrand Garcia-Moreno, was completed and results discussed at the Telluride meeting (July 6-10, 2009). This article serves as an introduction to the reports submitted by the blind prediction participants that will be published in a special issue of PROTEINS: Structure, Function and Bioinformatics. Here, we briefly outline existing approaches for pK(a) calculations, emphasizing methods that were used by the participants in calculating the blind pK(a) values in the first round of the cooperative. We then point out some of the difficulties encountered by the participating groups in making their blind predictions, and finally try to provide some insights for future developments aimed at improving the accuracy of pK(a) calculations., (Copyright © 2011 Wiley-Liss, Inc.)

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&#95;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., (© 2010 Wiley-Liss, Inc.)

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.

NMR-monitored pH titration experiments are routinely used to measure site-specific protein pKa values. Accurate experimental pKa values are essential in dissecting enzyme catalysis, in studying the pH-dependence of protein stability and ligand binding, in benchmarking pKa prediction algorithms, and ultimately in understanding electrostatic effects in proteins. However, due to the complex ways in which pH-dependent electrostatic and structural changes manifest themselves in NMR spectra, reported apparent pKa values are often dependent on the way that NMR pH-titration curves are analyzed. It is therefore important to retain the raw NMR spectroscopic data to allow for documentation and possible re-interpretation. We have constructed a database of primary NMR pH-titration data, which is accessible via a web interface. Here, we report statistics of the database contents and analyze the data with a global perspective to provide guidelines on best practice for fitting NMR titration curves. Titration&#95;DB is available at http://enzyme.ucd.ie/Titration&#95;DB. Proteins 2010. (c) 2009 Wiley-Liss, Inc.

The development of docking scoring functions requires high-resolution 3D structures of protein-ligand complexes for which the binding affinity of the ligand has been measured experimentally. Protein-ligand binding affinities are measured in solution experiments, and high resolution protein-ligand structures can be determined only by X-ray crystallography. Protein-ligand scoring functions must therefore reproduce solution binding energies using analyses of proteins in a crystal environment. We present an analysis of the prevalence of crystal-induced artifacts and water-mediated contacts in protein-ligand complexes and demonstrate the effect that these can have on the performance of protein-ligand scoring functions. We find 36% of ligands in the PDBBind 2007 refined data set to be influenced by crystal contacts and find the performance of a scoring function to be affected by these. A Web server for detecting crystal contacts in protein-ligand complexes is available at http://enzyme.ucd.ie/LIGCRYST .

The pH dependence of protein biophysical characteristics is often analyzed to gain an improved understanding of protein stability, enzyme activity, and protein-ligand-binding processes. Indeed, much of our understanding of the catalytic mechanisms of enzymes derives from studies of the pH dependence of catalytic activity, and the ability to redesign the pH-dependent properties of enzymes continues to be of high relevance for both industrial and medical applications of proteins. This chapter discusses current theoretical methods for calculating protein pK(a) values and illustrates how one can analyze protein pK(a) calculation results to study calculation accuracy, pH stability profiles, and enzymatic pH activity profiles. A description of how one can analyze the importance of individual titratable groups is presented along with details on methods for redesigning protein pK(a) values and enzymatic pH activity profiles. Finally, I discuss novel methods for fitting experimental nuclear magnetic resonance titration curves and enzymatic pH activity profiles that can be used to derive information on electrostatic interaction energies in proteins.

Many testis-specific genes from the sex chromosomes are subject to rapid evolution, which can make it difficult to identify murine genes in the human genome. The murine CYPT gene family includes 15 members, but orthologs were undetectable in the human genome. However, using refined homology search, sequences corresponding to the shared promoter region of the CYPT family were identified at 39 loci. Most loci were located immediately upstream of genes belonging to the VCX/Y, SPANX, or CSAG gene families. Sequence comparison of the loci revealed a conserved CYPT promoter-like (CPL) element featuring TATA and CCAAT boxes. The expression of members of the three families harboring the CPL resembled the murine expression of the CYPT family, with weak expression in late pachytene spermatocytes and predominant expression in spermatids, but some genes were also weakly expressed in somatic cells and in other germ cell types. The genomic regions harboring the gene families were rich in direct and inverted segmental duplications (SD), which may facilitate gene conversion and rapid evolution. The conserved CPL and the common expression profiles suggest that the human VCX/Y, SPANX, and CSAG2 gene families together with the murine SPANX gene and the CYPT family may share a common ancestor. Finally, we present evidence that VCX/Y and SPANX may be paralogs with a similar protein structure consisting of C terminal acidic repeats of variable lengths., (2007 Wiley-Liss, Inc)

Real-world observable physical and chemical characteristics are increasingly being calculated from the 3D structures of biomolecules. Methods for calculating pK(a) values, binding constants of ligands, and changes in protein stability are readily available, but often the limiting step in computational biology is the conversion of PDB structures into formats ready for use with biomolecular simulation software. The continued sophistication and integration of biomolecular simulation methods for systems- and genome-wide studies requires a fast, robust, physically realistic and standardized protocol for preparing macromolecular structures for biophysical algorithms. As described previously, the PDB2PQR web server addresses this need for electrostatic field calculations (Dolinsky et al., Nucleic Acids Research, 32, W665-W667, 2004). Here we report the significantly expanded PDB2PQR that includes the following features: robust standalone command line support, improved pK(a) estimation via the PROPKA framework, ligand parameterization via PEOE&#95;PB charge methodology, expanded set of force fields and easily incorporated user-defined parameters via XML input files, and improvement of atom addition and optimization code. These features are available through a new web interface (http://pdb2pqr.sourceforge.net/), which offers users a wide range of options for PDB file conversion, modification and parameterization.

The ability to re-engineer enzymatic pH-activity profiles is of importance for industrial applications of enzymes. We theoretically explore the feasibility of re-engineering enzymatic pH-activity profiles by changing active site pK(a) values using point mutations. We calculate the maximum achievable DeltapK(a) values for 141 target titratable groups in seven enzymes by introducing conservative net-charge altering point mutations. We examine the importance of the number of mutations introduced, their distance from the target titratable group, and the characteristics of the target group itself. The results show that multiple mutations at 10A can change pK(a) values up to two units, but that the introduction of a requirement to keep other pK(a) values constant reduces the magnitude of the achievable DeltapK(a). The algorithm presented shows a good correlation with existing experimental data and is available for download and via a web server at http://enzyme.ucd.ie/pKD.

The results of protein pKa calculations are routinely being analysed to understand the pH-dependence of protein characteristics such as stability and catalysis. Systems of functionally important titratable groups are identified from protein from pKa calculations, but the rationalisation of the behaviour of such systems is inherently problematic due to a lack of theoretical tools and methods. I present a number of novel methods for analysing the results of protein pKa calculations which have been embedded in a graphical user interface (pKaTool). In the present paper I present novel methods for assessing the reliability of protein pKa calculations and for analysing the roles of individual residues in determining active site pKa values and the pH-dependence of protein stability. The methods presented are freely available to academic researchers at http://enzyme.ucd.ie/Science/pKa/pKaTool .

pK(a) calculations for macromolecules are normally performed by solving the Poisson-Boltzmann equation, accounting for the different dielectric constants of solvent and solute, as well as the ionic strength. Despite the large number of successful applications, there are some situations where the current algorithms are not suitable: (1) large scale, high-throughput analysis which requires calculations to be completed within a fraction of a second, e.g. when permanently monitoring pK(a) shifts during a molecular dynamics simulation; (2) prediction of pK(a)s in periodic boundaries, e.g. when reconstructing entire protein crystal unit cells from PDB files, including the correct protonation patterns at experimental pH. Such in silico crystals are needed by 'self-parameterizing' molecular dynamics force fields like YASARA YAMBER, that optimize their parameters while energy-minimizing high-resolution protein crystals. To address both problems, we define an empirical equation that expresses the pK(a) as a function of electrostatic potential, hydrogen bonds and accessible surface area. The electrostatic potential is evaluated by Ewald summation, which captures periodic crystal environments and the uncertainty in atom positions using Gaussian charge densities. The empirical proportionality constants are derived from 217 experimentally determined pK(a)s, and despite its simplicity, this pK(a) calculation method reaches a high overall jack-knifed accuracy, and is fast enough to be used during a molecular dynamics simulation. A reliable null-model to judge pK(a) prediction accuracies is also presented.

The pK(a) values in proteins govern the pH-dependence of protein stability and enzymatic activity. A large number of mutagenesis experiments have been carried out in the last three decades to re-engineer the pH-activity and pH-stability profile of enzymes and proteins. We have developed the pKD webserver (http://polymerase.ucd.ie/pKa&#95;Design), which predicts sets of point mutations that will change the pK(a) values of a set of target residues in a given direction, thus allowing for targeted re-design of the pH-dependent characteristics of proteins. The server provides the user with an interactive experience for re-designing pK(a) values by pre-calculating DeltapK(a) values from all feasible point mutations. Design solutions are found in less than 10 min for a typical design job for a medium-sized protein. Mutant DeltapK(a) values calculated by the pKD web server are in close agreement with those produced by comparing results from full-fledged pK(a) calculation methods.

To identify candidate genes for poor sperm morphology, we have screened for genes expressed during spermiogenesis. We identified 10 new members of the cysteine-rich perinuclear theca (CYPT) family showing that this family contains at least 15 members, which also includes the casein kinase II target genes. Based on similarity the CYPT sequences could be divided into two groups, Cypt1-10 and the novel members Cypt12-15. The 5'-end of the CYPT family is highly similar to exon1A and part of the first intron of Zfy2. Seven CYPT genes mapped to the X chromosome; six contained an intron and one was intron-less. One CYPT gene mapped to chromosome 3 and one mapped to chromosome 9 which were both intron-less. The upstream region of the CYPT family and Zfy2 genes is conserved. For some the conservation extended over a large region, however, only about 150 nucleotides is conserved among all CYPT members and Zfy2. Nevertheless, the short conserved promoter leads to essentially identical expression profiles for the CYPT family members and Zfy2, which was clearly different from the profile of Zfy1. Expression of the CYPT family and Zfy2 preceded the expression of other spermatid-specific genes such as the transition proteins and the protamines. In situ hybridization revealed a low expression in pachytene spermatocytes from stages IX-X followed by a strong upregulation in spermatids from stage VI with maximum expression in spermatids in stages VII-VIII. The CYPT family may function in the remodeling of the spermatid nucleus before condensation of the DNA., (Mol. Reprod. Dev. (c) 2006 Wiley-Liss, Inc.)

Continuum solvation models, such as Poisson-Boltzmann and Generalized Born methods, have become increasingly popular tools for investigating the influence of electrostatics on biomolecular structure, energetics and dynamics. However, the use of such methods requires accurate and complete structural data as well as force field parameters such as atomic charges and radii. Unfortunately, the limiting step in continuum electrostatics calculations is often the addition of missing atomic coordinates to molecular structures from the Protein Data Bank and the assignment of parameters to biomolecular structures. To address this problem, we have developed the PDB2PQR web service (http://agave.wustl.edu/pdb2pqr/). This server automates many of the common tasks of preparing structures for continuum electrostatics calculations, including adding a limited number of missing heavy atoms to biomolecular structures, estimating titration states and protonating biomolecules in a manner consistent with favorable hydrogen bonding, assigning charge and radius parameters from a variety of force fields, and finally generating 'PQR' output compatible with several popular computational biology packages. This service is intended to facilitate the setup and execution of electrostatics calculations for both experts and non-experts and thereby broaden the accessibility to the biological community of continuum electrostatics analyses of biomolecular systems.

The calculation of the physical properties of a protein from its X-ray structure is of importance in virtually every aspect of modern biology. Although computational algorithms have been developed for calculating everything from the dynamics of a protein to its binding specificity, only limited information is available on the ability of these methods to give accurate results when used with a particular X-ray structure. We examine the ability of a pKa calculation algorithm to predict the proton-donating residue in the catalytic mechanism of hen egg white lysozyme. We examine the correlation between the ability of the pKa calculation method to obtain the correct result and the overall characteristics of 41 X-ray structures such as crystallization conditions, resolution, and the output of structure validation software. We furthermore examine the ability of energy minimizations (EM), molecular dynamics (MD) simulations, and structure-perturbation methods to optimize the X-ray structures such that these give correct results with the pKa calculation algorithm. We propose a set of criteria for identifying the proton donor in a catalytic mechanism, and demonstrate that the application of these criteria give highly accurate prediction results when using unmodified X-ray structures. More specifically, we are able to successfully identify the proton donor in 85% of the X-ray structures when excluding structures with crystal contacts near the active site. Neither the use of the overall characteristics of the X-ray structures nor the optimization of the structure by EM, MD, or other methods improves the results of the pKa calculation algorithm. We discuss these results and their implications for the design of structure-based energy calculation algorithms in general.

We have developed a computer algorithm, FOLDEF (for FOLD-X energy function), to provide a fast and quantitative estimation of the importance of the interactions contributing to the stability of proteins and protein complexes. The predictive power of FOLDEF was tested on a very large set of point mutants (1088 mutants) spanning most of the structural environments found in proteins. FOLDEF uses a full atomic description of the structure of the proteins. The different energy terms taken into account in FOLDEF have been weighted using empirical data obtained from protein engineering experiments. First, we considered a training database of 339 mutants in nine different proteins and optimised the set of parameters and weighting factors that best accounted for the changes in stability of the mutants. The predictive power of the method was then tested using a blind test mutant database of 667 mutants, as well as a database of 82 protein-protein complex mutants. The global correlation obtained for 95 % of the entire mutant database (1030 mutants) is 0.83 with a standard deviation of 0.81 kcal mol(-1) and a slope of 0.76. The present energy function uses a minimum of computational resources and can therefore easily be used in protein design algorithms, and in the field of protein structure and folding pathways prediction where one requires a fast and accurate energy function. FOLDEF is available via a web-interface at http://fold-x.embl-heidelberg.de

Electrostatic interactions play a key role in many aspects of protein engineering. Consequently, much effort has been put into the design of software for calculating electrostatic fields around macromolecules. We show that optimization of hydrogen bonding networks can improve both the results of pK(a) calculations and the results of electrostatic calculations performed by commonly used programs such as DelPhi. Further optimization can often be achieved by flipping the side chains of asparagine, histidine and glutamine around their chi2, chi2 and chi3 torsion angles, respectively, when this improves the local hydrogen bonding network. These optimizations are applied to some well characterized proteins: BPTI, hen egg white lysozyme and superoxide dismutase. A search for flipped residues in the PDB revealed that significant improvements in electrostatic calculations in or near the active site of enzymes can be expected for about one quarter of all enzymes in the PDB.

AX2 is a 46-amino-acid cysteine-rich peptide isolated from sugar beet leaves infected with the fungus Cercospora beticola (Sacc.). AX2 strongly inhibits the growth of C. beticola and other filamentous fungi, but has little or no effect against bacteria. AX2 is produced in very low amounts in sugar beet leaves, and to study the protein in greater detail with respect to biological function and protein structural analysis, the methylotrophic yeast Pichia pastoris was used for large-scale production. The amino acid sequence, processing of the signal peptide, disulfide bridges, and biological activity of the recombinant protein were determined and compared with that of the authentic AX2. In P. pastoris, the protein was expressed with an additional N-terminal arginine. The disulfide bonding was found to be identical to that of the authentic AX2. However, when tested in in vitro bioassay, the biological activity of the recombinant protein was slightly lower than that measured for the authentic protein. Furthermore, the recombinant protein was significantly more sensitive to Ca(2+) than the authentic protein. This is most probably due to the extra arginine, since no other differences between the two proteins have been found., (Copyright 1999 Academic Press.)