Proteolytic activity of Co(III) complex of 1-oxa-4,7,10-triazacyclododecane: a new catalytic center for peptide-cleavage agents

Catalytic drugs based on target-selective artificial proteases have been proposed as a new paradigm in drug design. Peptide-cleavage agents selective for pathogenic proteins of Alzheimer’s disease, type 2 diabetes mellitus or Parkinson’s disease have been prepared using the Co(III) aqua complex (Co(III)cyclen) of 1,4,7,10-tetraazacyclododecane as the catalytic center. In the present study, the Co(III) aqua complex (Co(III)oxacyclen) of 1-oxa-4,7,10-triazacyclododecane was examined in search of an improved catalytic center for peptide-cleavage agents. An X-ray crystallographic study of [Co(oxacyclen)(CO₃)](ClO₄), titration of Co(III)oxacyclen, and kinetic studies on the cleavage of albumin, γ-globulin, lysozyme, and myoglobin by Co(III)oxacyclen were carried out. Considerably higher proteolytic activity was observed for Co(III)oxacyclen in comparison with Co(III)cyclen, indicating that better target-selective artificial metalloproteases would be obtained using Co(III)oxacyclen as the catalytic center. The improved proteolytic activity was attributed to either steric effects or the increased Lewis acidity of the Co(III) center. The kinetic data also predicted that side effects due to the cleavage of nontarget proteins by a catalytic drug based on Co(III)oxacyclen would be insignificant.     

Using a library of structural templates to recognise catalytic sites and explore their evolution in homologous families

Catalytic site structure is normally highly conserved between distantly related enzymes. As a consequence, templates representing catalytic sites have the potential to succeed at function prediction in cases where methods based on sequence or overall structure fail. There are many methods for searching protein structures for matches to structural templates, but few validated template libraries to use with these methods. We present a library of structural templates representing catalytic sites, based on information from the scientific literature. Furthermore, we analyse homologous template families to discover the diversity within families and the utility of templates for active site recognition. Templates representing the catalytic sites of homologous proteins mostly differ by less than 1A root mean square deviation, even when the sequence similarity between the two proteins is low. Within these sets of homologues there is usually no discernible relationship between catalytic site structure similarity and sequence similarity. Because of this structural conservation of catalytic sites, the templates can discriminate between matches to related proteins and random matches with over 85% sensitivity and predictive accuracy. Templates based on protein backbone positions are more discriminating than those based on side-chain atoms. These analyses show encouraging prospects for prediction of functional sites in structural genomics structures of unknown function, and will be of use in analyses of convergent evolution and exploring relationships between active site geometry and chemistry. The template library can be queried via a web server at and is available for download.     

Ionizable side chains at catalytic active sites of enzymes

Catalytic active sites of enzymes of known structure can be well defined by a modern program of computational geometry. The CASTp program was used to define and measure the volume of the catalytic active sites of 573 enzymes in the Catalytic Site Atlas database. The active sites are identified as catalytic because the amino acids they contain are known to participate in the chemical reaction catalyzed by the enzyme. Acid and base side chains are reliable markers of catalytic active sites. The catalytic active sites have 4 acid and 5 base side chains, in an average volume of 1,072 Å³. The number density of acid side chains is 8.3 M (in chemical units); the number density of basic side chains is 10.6 M. The catalytic active site of these enzymes is an unusual electrostatic and steric environment in which side chains and reactants are crowded together in a mixture more like an ionic liquid than an ideal infinitely dilute solution. The electrostatics and crowding of reactants and side chains seems likely to be important for catalytic function. In three types of analogous ion channels, simulation of crowded charges accounts for the main properties of selectivity measured in a wide range of solutions and concentrations. It seems wise to use mathematics designed to study interacting complex fluids when making models of the catalytic active sites of enzymes.     

Cell-penetration by Co(III)cyclen-based peptide-cleaving catalysts selective for pathogenic proteins of amyloidoses

Derivatives of the Co(III) complex of 1,4,7,10-tetraazacyclododecane (cyclen) with various organic pendants have been reported as target-selective peptide-cleaving catalysts, which can be exploited as catalytic drugs. In order to provide a firm basis for the catalytic drugs based on Co(III)cyclen, the ability of the Co(III)cyclen-containing peptide-cleaving catalysts to penetrate animal cells such as mouse fibroblast NIH-3T 3 or human embryonic kidney (HEK) 293 cells is demonstrated in the present study. Since the catalysts destroy pathogenic proteins for amyloidoses, results of the present study are expected to initiate extensive efforts to obtain therapeutically safe catalytic drugs for amyloidoses such as Alzheimer’s disease, type 2 diabetes mellitus, Parkinson’s disease, Huntington’s disease, mad cow disease, and so on.     

Identification of catalytic residues from protein structure using support vector machine with sequence and structural features

Identification of catalytic residues can provide valuable insights into protein function. With the increasing number of protein 3D structures having been solved by X-ray crystallography and NMR techniques, it is highly desirable to develop an efficient method to identify their catalytic sites. In this paper, we present an SVM method for the identification of catalytic residues using sequence and structural features. The algorithm was applied to the 2096 catalytic residues derived from Catalytic Site Atlas database. We obtained overall prediction accuracy of 88.6% from 10-fold cross validation and 95.76% from resubstitution test. Testing on the 254 catalytic residues shows our method can correctly predict all 254 residues. This result suggests the usefulness of our approach for facilitating the identification of catalytic residues from protein structures.     

Knowledge-based modeling of the serine protease triad into non-proteases.

The Asp-His-Ser triad of serine proteases has been regarded, in the present study, as an independent catalytic motif, because in nature it has been incorporated at the active sites of enzymes as diverse as the serine proteases and the lipases. Incorporating this motif into non-protease scaffolds, by rational design and mutagenesis, might lead to the generation of novel catalysts. As an aid to such experiments, a knowledge-based computer modeling procedure has been developed to model the protease Asp-His-Ser triad into non-proteases. Catalytic triads from a set of trypsin family proteases have been analyzed and criteria that characterize the geometry of the triads have been obtained. Using these criteria, the modeling procedure first identifies sites in non-proteases that are suitable for modeling the protease triad. H-bonded Asp-His-Ser triads, that mimic the protease catalytic triad in geometry, are then modeled in at these sites, provided it is stereochemically possible to do so. Thus non-protease sites at which H-bonded Asp-His-Ser triads are successfully modeled in may be considered for mutagenesis experiments that aim at introducing the protease triad into non-proteases. The triad modeling procedure has been used to identify sites for introducing the protease triad in three binding proteins and an immunoglobulin. A scoring function, depending on inter-residue distances, solvent accessibility and the substitution potential of amino acid residues at the modeling sites in the host proteins, has been used to assess the quality of the model triads.     

SABER: a computational method for identifying active sites for new reactions

A software suite, SABER (Selection of Active/Binding sites for Enzyme Redesign), has been developed for the analysis of atomic geometries in protein structures, using a geometric hashing algorithm (Barker and Thornton, Bioinformatics 2003;19:1644–1649). SABER is used to explore the Protein Data Bank (PDB) to locate proteins with a specific 3D arrangement of catalytic groups to identify active sites that might be redesigned to catalyze new reactions. As a proof‐of‐principle test, SABER was used to identify enzymes that have the same catalytic group arrangement present in o‐succinyl benzoate synthase (OSBS). Among the highest‐scoring scaffolds identified by the SABER search for enzymes with the same catalytic group arrangement as OSBS were L ‐Ala D/L ‐Glu epimerase (AEE) and muconate lactonizing enzyme II (MLE), both of which have been redesigned to become effective OSBS catalysts, demonstrated by experiments. Next, we used SABER to search for naturally existing active sites in the PDB with catalytic groups similar to those present in the designed Kemp elimination enzyme KE07. From over 2000 geometric matches to the KE07 active site, SABER identified 23 matches that corresponded to residues from known active sites. The best of these matches, with a 0.28 Å catalytic atom RMSD to KE07, was then redesigned to be compatible with the Kemp elimination using RosettaDesign. We also used SABER to search for potential Kemp eliminases using a theozyme predicted to provide a greater rate acceleration than the active site of KE07, and used Rosetta to create a design based on the proteins identified.     

Enhanced performance in prediction of protein active sites with THEMATICS and support vector machines

Theoretical microscopic titration curves (THEMATICS) is a computational method for the identification of active sites in proteins through deviations in computed titration behavior of ionizable residues. While the sensitivity to catalytic sites is high, the previously reported sensitivity to catalytic residues was not as high, about 50%. Here THEMATICS is combined with support vector machines (SVM) to improve sensitivity for catalytic residue prediction from protein 3D structure alone. For a test set of 64 proteins taken from the Catalytic Site Atlas (CSA), the average recall rate for annotated catalytic residues is 61%; good precision is maintained selecting only 4% of all residues. The average false positive rate, using the CSA annotations is only 3.2%, far lower than other 3D-structure-based methods. THEMATICS-SVM returns higher precision, lower false positive rate, and better overall performance, compared with other 3D-structure-based methods. Comparison is also made with the latest machine learning methods that are based on both sequence alignments and 3D structures. For annotated sets of well-characterized enzymes, THEMATICS-SVM performance compares very favorably with methods that utilize sequence homology. However, since THEMATICS depends only on the 3D structure of the query protein, no decline in performance is expected when applied to novel folds, proteins with few sequence homologues, or even orphan sequences. An extension of the method to predict non-ionizable catalytic residues is also presented. THEMATICS-SVM predicts a local network of ionizable residues with strong interactions between protonation events; this appears to be a special feature of enzyme active sites.     

Biomolecular engineering and drug development

Biomolecular engineering is a technology to create novel structures of high-value biomolecules for use in medicine and industry, through the directed alteration of proteins and/or biologically active molecules in living cells to produce a novel biometabolites as well as engineered protein itself. For the development of new drugs by biomolecular engineering, desired biomolecules have to be rationally designed based on their structure-stability/structure-activity relationship, and then screened through well-established mutation and selection program. Over the past decade, there has been significant progress in mutation and selection methodology; DNA shuffling technology mimicking natural evolution for artificial DNA recombination and phage-displayed combinatorial peptide library for rapid selection of proteins expressed from mutated genes. Bioinformatic tools including functional genomics and proteomics have been also developed for the ready access to the information related to the protein-function and genome-protein, leading to the design and identification of new drug targets. Throughout the use of an enormous amount of bioinformatic databases, many protein/peptide drugs and biometabolite molecules have been designed. The candidates of new drugs are monoclonal antibodies, vaccines, enzymes, antibiotics, therapeutic peptides, and so on. Two humanized monoclonal antibodies approved by FDA became the first line of drugs designed by biomolecular engineering approach. They are Herceptin and Synagis, for the treatment of breast cancer and pediatric respiratory syncytial viral infection, respectively. Many more newly engineered biomolecules are under developing for medicinal application. Some clinical trials for therapeutic applications are now in progress, and very positive results are already anticipated.     

Catalytic antibodies: balancing between Dr. Jekyll and Mr. Hyde

The immunoglobulin molecule is a perfect template for the de novo generation of biocatalytic functions. Catalytic antibodies, or abzymes, obtained by the structural mimicking of enzyme active sites have been shown to catalyze numerous chemical reactions. Natural enzyme analogs for some of these reactions have not yet been found or possibly do not exist at all. Nowadays, the dramatic breakthrough in antibody engineering and expression technologies has promoted a considerable expansion of immunoglobulin's medical applications and is offering abzymes a unique chance to become a promising source of high‐precision “catalytic vaccines.” At the same time, the discovery of natural abzymes on the background of autoimmune disease revealed their beneficial and pathogenic roles in the disease progression. Thus, the conflicting Dr. Jekyll and Mr. Hyde protective and destructive essences of catalytic antibodies should be carefully considered in the development of therapeutic abzyme applications.     

Analysis of water patterns in protein kinase binding sites

Deregulation of protein kinases is associated with numerous diseases, making them important targets for drug discovery. The majority of drugs target the catalytic site of these proteins, but due to the high level of similarity within the ATP binding sites of protein kinases, it is often difficult to achieve the required pharmacological selectivity. In this study, we describe the identification and subsequent analysis of water patterns in the ATP binding sites of 171 protein kinase structures, comprising 19 different kinases from various branches of the kinome, and demonstrate that structurally similar binding sites often have significantly different water patterns. We show that the observed variations in water patterns of different, but structurally similar kinases can be exploited in the structure-based design of potent and selective kinase inhibitors.     

Analysis of substructural variation in families of enzymatic proteins with applications to protein function prediction

Background  

Structural variations caused by a wide range of physico-chemical and biological sources directly influence the function of a protein. For enzymatic proteins, the structure and chemistry of the catalytic binding site residues can be loosely defined as a substructure of the protein. Comparative analysis of drug-receptor substructures across and within species has been used for lead evaluation. Substructure-level similarity between the binding sites of functionally similar proteins has also been used to identify instances of convergent evolution among proteins. In functionally homologous protein families, shared chemistry and geometry at catalytic sites provide a common, local point of comparison among proteins that may differ significantly at the sequence, fold, or domain topology levels.     

Folding and activity of cAMP-dependent protein kinase mutants

The catalytic subunit of cAMP-dependent protein kinase (PKA) can easily be expressed in Escherichia coli and is catalytically active. Four phosphorylation sites are known in PKA (S10, S139, T197 and S338), and the isolated recombinant protein is a mixture of different phosphorylated forms. Obtaining uniformly phosphorylated protein requires separation of the protein preparation leading to significant loss in protein yield. It is found that the mutant S10A/S139D/S338D has similar properties as the wild-type protein, whereas additional replacement of T197 with either E or D reduces protein expression yield as well as folding propensity of the protein. Due to its high sequence homology to Akt/PKB, which cannot easily be expressed in E. coli, PKA has been used as a surrogate kinase for drug design. Several mutations within the ATP binding site have been described to make PKA even more similar to Akt/PKB. Two proteins with Akt/PKB-like mutations in the ATP binding site were made (PKAB6 and PKAB8), and in addition S10, S139 and S338 phosphorylation sites have been removed. These proteins can be expressed in high yields but have reduced activity compared to the wild-type. Proper folding of all proteins was analyzed by 2D 1H, 15N-TROSY NMR experiments.     

TRMP: a database of therapeutically relevant multiple pathways

SUMMARY: Disease processes often involve crosstalks between proteins in different pathways. Different proteins have been used as separate therapeutic targets for the same disease. Synergetic targeting of multiple targets has been explored in combination therapy of a number of diseases. Potential harmful interactions of multiple targeting have also been closely studied. To facilitate mechanistic study of drug actions and a more comprehensive understanding the relationship between different targets of the same disease, it is useful to develop a database of known therapeutically relevant multiple pathways (TRMPs). Information about non-target proteins and natural small molecules involved in these pathways also provides useful hint for searching new therapeutic targets and facilitate the understanding of how therapeutic targets interact with other molecules in performing specific tasks. The TRMPs database is designed to provide information about such multiple pathways along with related therapeutic targets, corresponding drugs/ligands, targeted disease conditions, constituent individual pathways, structural and functional information about each protein in the pathways. Cross links to other databases are also introduced to facilitate the access of information about individual pathways and proteins. AVAILABILITY: This database can be accessed at http://bidd.nus.edu.sg/group/trmp/trmp.asp and it currently contains 11 entries of multiple pathways, 97 entries of individual pathways, 120 targets covering 72 disease conditions together with 120 sets of drugs directed at each of these targets. Each entry can be retrieved through multiple methods including multiple pathway name, individual pathway name and disease name. SUPPLEMENTARY INFORMATION: http://bidd.nus.edu.sg/group/trmp/sm.pdf     

锌指蛋白及人工锌指蛋白对微生物代谢影响的研究进展

锌指蛋白由于锌指结构域序列相对保守,识别DNA序列具有高度特异性,所以成为研究较广泛的DNA结合蛋白,但目前对锌指蛋白的研究多集中在真核细胞,而对微生物锌指蛋白,尤其是原核微生物锌指蛋白的研究相对较少。本文综述了近年来微生物锌指蛋白,尤其是原核微生物锌指蛋白的发现及功能的最新研究进展,以及人工锌指蛋白技术在微生物菌株改造中的应用。特定人工锌指蛋白不仅可调控微生物细胞中多基因控制的复杂性状,例如耐热性、乙醇和丁醇耐性、渗透胁迫耐受性等,还可以利用锌指结构域构建DNA脚手架系统,进而构建复合酶系统,从而提高催化效率和代谢物产量。目前报道的用于微生物代谢调控的人工锌指蛋白利用的都是哺乳动物的基因,未来根据不同微生物中天然锌指蛋白的序列进行人工锌指的设计,将拓展人工转录因子技术在微生物全局基因表达调控中的应用。     

Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties

Background  

The number of protein sequences deriving from genome sequencing projects is outpacing our knowledge about the function of these proteins. With the gap between experimentally characterized and uncharacterized proteins continuing to widen, it is necessary to develop new computational methods and tools for functional prediction. Knowledge of catalytic sites provides a valuable insight into protein function. Although many computational methods have been developed to predict catalytic residues and active sites, their accuracy remains low, with a significant number of false positives. In this paper, we present a novel method for the prediction of catalytic sites, using a carefully selected, supervised machine learning algorithm coupled with an optimal discriminative set of protein sequence conservation and structural properties.     

Structural Analysis of Metal Sites in Proteins: Non-heme Iron Sites as a Case Study

In metalloproteins, the protein environment modulates metal properties to achieve the required goal, which can be protein stabilization or function. The analysis of metal sites at the atomic level of detail provided by protein structures can thus be of benefit in functional and evolutionary studies of proteins. In this work, we propose a structural bioinformatics approach to the study of metalloproteins based on structural templates of metal sites that include the PDB coordinates of protein residues forming the first and the second coordination sphere of the metal. We have applied this approach to non-heme iron sites, which have been analyzed at various levels. Templates of sites located in different protein domains have been compared, showing that similar sites can be found in unrelated proteins as the result of convergent evolution. Templates of sites located in proteins of a large superfamily have been compared, showing possible mechanisms of divergent evolution of proteins to achieve different functions. Furthermore, template comparisons have been used to predict the function of uncharacterized proteins, showing that similarity searches focused on metal sites can be advantageously combined with typical whole-domain comparisons. Structural templates of metal sites, finally, may constitute the basis for a systematic classification of metalloproteins in databases.     

Design and evolution of artificial M13 coat proteins

Using simple design and selective pressure, we have evolved an artificial M13 bacteriophage coat protein. M13 coat proteins first reside in the bacterial inner membrane and subsequently surround the DNA core of the assembled virus. The artificial coat protein (ACP) was designed and evolved to mimic both functions of the natural M13 coat proteins, but with an inverted orientation. ACP is a non-functional coat protein because it is not required for the production of phage particles. Instead, it incorporates into a phage coat which still requires all the natural coat proteins for structural integrity. In contrast with other M13 coat proteins, which can display polypeptides as amino-terminal fusions, ACP permits the carboxy-terminal display of large polypeptides. The results suggest that viruses can co-opt host membrane proteins to acquire new coat proteins and thus new functions. In particular, M13 bacteriophage can be engineered for new functions, such as carboxy-terminal phage display.     

Engineering M13 for phage display

Phage display is achieved by fusing polypeptide libraries to phage coat proteins. The resulting phage particles display the polypeptides on their surfaces and they also contain the encoding DNA. Library members with particular functions can be isolated with simple selections and polypeptide sequences can be decoded from the encapsulated DNA. The technology's success depends on the efficiency with which polypeptides can be displayed on the phage surface, and significant progress has been made in engineering M13 bacteriophage coat proteins as improved phage display platforms. Functional display has been achieved with all five M13 coat proteins, with both N- and C-terminal fusions. Also, coat protein mutants have been designed and selected to improve the efficiency of heterologous protein display, and in the extreme case, completely artificial coat proteins have been evolved specifically as display platforms. These studies demonstrate that the M13 phage coat is extremely malleable, and this property can be used to engineer the phage particle specifically for phage display. These improvements expand the utility of phage display as a powerful tool in modern biotechnology.     

The reaction mechanism of FokI excludes the possibility of targeting zinc finger nucleases to unique DNA sites

The FokI endonuclease is a monomeric protein with discrete DNA-recognition and catalytic domains. The latter has only one active site so, to cut both strands, the catalytic domains from two monomers associate to form a dimer. The dimer involving a monomer at the recognition site and another from free solution is less stable than that from two proteins tethered to the same DNA. FokI thus cleaves DNA with two sites better than one-site DNA. The two sites can be immediately adjacent, but they can alternatively be many hundreds of base pairs apart, in either inverted or repeated orientations. The catalytic domain of FokI is often a component of zinc finger nucleases. Typically, the zinc finger domains of two such nucleases are designed to recognize two neighbouring DNA sequences, with the objective of cutting the DNA exclusively between the target sequences. However, this strategy fails to take account of the fact that the catalytic domains of FokI can dimerize across distant sites or even at a solitary site. Additional copies of either target sequence elsewhere in the chromosome must elicit off-target cleavages.