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The use of so-called protein scaffolds has recently attracted considerable attention in biochemistry in the context of generating novel types of ligand receptors for various applications in research and medicine. This development started with the notion that immunoglobulins owe their function to the composition of a conserved framework region and a spatially well-defined antigen-binding site made of peptide segments that are hypervariable both in sequence and in conformation. After the application of antibody engineering methods along with library techniques had resulted in first successes in the selection of functional antibody fragments, several laboratories began to exploit other types of protein architectures for the construction of practically useful binding proteins. Properties like small size of the receptor protein, stability and ease of production were the focus of this work. Hence, among others, single domains of antibodies or of the immunoglobulin superfamily, protease inhibitors, helix-bundle proteins, disulphide-knotted peptides and lipocalins were investigated. Recently, the scaffold concept has even been adopted for the construction of enzymes. However, it appears that not all kinds of polypeptide fold which may appear attractive for the engineering of loop regions at a first glance will indeed permit the construction of independent ligand-binding sites with high affinities and specificities. This review will therefore concentrate on the critical description of the structural properties of experimentally tested protein scaffolds and of the novel functions that have been achieved on their basis, rather than on the methodology of how to best select a particular mutant with a certain activity. An overview will be provided about the current approaches, and some emerging trends will be identified. (c) 2000 John Wiley & Sons, Ltd. Abbreviations used: ABD albumin-binding domain of protein G APPI Alzheimer's amyloid beta-protein precursor inhibitor BBP bilin-binding protein BPTI bovine (or basic) pancreatic trypsin inhibitor BSA bovine serum albumin CBD cellulose-binding domain of cellobiohydrolase I CD circular dichroism Cdk2 human cyclin-dependent kinase 2 CDR complementarity-determining region CTLA-4 human cytotoxic T-lymphocyte associated protein-4 FN3 fibronectin type III domain GSH glutathione GST glutathione S-transferase hIL-6 human interleukin-6 HSA human serum albumin IC(50) half-maximal inhibitory concentration Ig immunoglobulin IMAC immobilized metal affinity chromatography K(D) equilibrium constant of dissociation K(i) equilibrium dissociation constant of enzyme inhibitor LACI-D1 human lipoprotein-associated coagulation inhibitor pIII gene III minor coat protein from filamentous bacteriophage f1 PCR polymerase-chain reaction PDB Protein Data Bank PSTI human pancreatic secretory trypsin inhibitor RBP retinol-binding protein SPR surface plasmon resonance TrxA E. coli thioredoxin
J Mol Recognit
PMID:Engineered protein scaffolds for molecular recognition. 1093 55

The structure of a protein is dictated by a large number of weak interactions that cooperatively stabilize the native state. Usually, excised fragments smaller than a domain have little if any residual structure. When autonomous units of structure are found within domains, this challenges common assumptions about the cooperativity of protein structure. Such autonomous folding units (AFUs) are of wide interest and have applications in protein engineering and as simple model systems for studying the determinants of stability and specificity. A new method of identifying AFUs within proteins is presented here. The rapid autonomous fragment test (RAFT) identifies AFUs based on analysis of inter-residue contacts present in the three-dimensional structure of a protein. RAFT is fast enough to mine the entire PDB for AFUs and provide a library of potential small stable folds. We show that RAFT is able to predict whether a protein fragment will be structured if isolated from its parent domain.
J Mol Biol 2000 Sep 22
PMID:A rapid test for identification of autonomous folding units in proteins. 1098 28

We present the results of a large-scale testing of the ROSETTA method for ab initio protein structure prediction. Models were generated for two independently generated lists of small proteins (up to 150 amino acid residues), and the results were evaluated using traditional rmsd based measures and a novel measure based on the structure-based comparison of the models to the structures in the PDB using DALI. For 111 of 136 all alpha and alpha/beta proteins 50 to 150 residues in length, the method produced at least one model within 7 A rmsd of the native structure in 1000 attempts. For 60 of these proteins, the closest structure match in the PDB to at least one of the ten most frequently generated conformations was found to be structurally related (four standard deviations above background) to the native protein. These results suggest that ab initio structure prediction approaches may soon be useful for generating low resolution models and identifying distantly related proteins with similar structures and perhaps functions for these classes of proteins on the genome scale.
J Mol Biol 2001 Mar 09
PMID:Prospects for ab initio protein structural genomics. 1123 27

The recent growth in protein databases has revealed the functional diversity of many protein superfamilies. We have assessed the functional variation of homologous enzyme superfamilies containing two or more enzymes, as defined by the CATH protein structure classification, by way of the Enzyme Commission (EC) scheme. Combining sequence and structure information to identify relatives, the majority of superfamilies display variation in enzyme function, with 25 % of superfamilies in the PDB having members of different enzyme types. We determined the extent of functional similarity at different levels of sequence identity for 486,000 homologous pairs (enzyme/enzyme and enzyme/non-enzyme), with structural and sequence relatives included. For single and multi-domain proteins, variation in EC number is rare above 40 % sequence identity, and above 30 %, the first three digits may be predicted with an accuracy of at least 90 %. For more distantly related proteins sharing less than 30 % sequence identity, functional variation is significant, and below this threshold, structural data are essential for understanding the molecular basis of observed functional differences. To explore the mechanisms for generating functional diversity during evolution, we have studied in detail 31 diverse structural enzyme superfamilies for which structural data are available. A large number of variations and peculiarities are observed, at the atomic level through to gross structural rearrangements. Almost all superfamilies exhibit functional diversity generated by local sequence variation and domain shuffling. Commonly, substrate specificity is diverse across a superfamily, whilst the reaction chemistry is maintained. In many superfamilies, the position of catalytic residues may vary despite playing equivalent functional roles in related proteins. The implications of functional diversity within supefamilies for the structural genomics projects are discussed. More detailed information on these superfamilies is available at http://www.biochem.ucl.ac.uk/bsm/FAM-EC/.
J Mol Biol 2001 Apr 06
PMID:Evolution of function in protein superfamilies, from a structural perspective. 1128 60

Side-chain or even backbone adjustments upon docking of different ligands to the same protein structure, a phenomenon known as induced fit, are frequently observed. Sometimes point mutations within the active site influence the ligand binding of proteins. Furthermore, for homology derived protein structures there are often ambiguities in side-chain placement and uncertainties in loop modeling which may be critical for docking applications. Nevertheless, only very few molecular docking approaches have taken into account such variations in protein structures. We present the new software tool FlexE which addresses the problem of protein structure variations during docking calculations. FlexE can dock flexible ligands into an ensemble of protein structures which represents the flexibility, point mutations, or alternative models of a protein. The FlexE approach is based on a united protein description generated from the superimposed structures of the ensemble. For varying parts of the protein, discrete alternative conformations are explicitly taken into account, which can be combinatorially joined to create new valid protein structures.FlexE was evaluated using ten protein structure ensembles containing 105 crystal structures from the PDB and one modeled structure with 60 ligands in total. For 50 ligands (83 %) FlexE finds a placement with an RMSD to the crystal structure below 2.0 A. In all cases our results are of similar quality to the best solution obtained by sequentially docking the ligands into all protein structures (cross docking). In most cases the computing time is significantly lower than the accumulated run times for the single structures. FlexE takes about five and a half minutes on average for placing one ligand into the united protein description on a common workstation. The example of the aldose reductase demonstrates the necessity of considering protein structure variations for docking calculations. We docked three potent inhibitors into four protein structures with substantial conformational changes within the active site. Using only one rigid protein structure for screening would have missed potential inhibitors whereas all inhibitors can be docked taking all protein structures into account.
J Mol Biol 2001 Apr 27
PMID:FlexE: efficient molecular docking considering protein structure variations. 1132 74

A new computer program to annotate DNA and RNA three-dimensional structures, MC-Annotate, is introduced. The goals of annotation are to efficiently extract and manipulate structural information, to simplify further structural analyses and searches, and to objectively represent structural knowledge. The input of MC-Annotate is a PDB formatted DNA or RNA three-dimensional structure. The output of MC-Annotate is composed of a structural graph that contains the annotations, and a series of HTML documents, one for each nucleotide conformation and base-base interaction present in the input structure. The atomic coordinates of all nucleotides and the homogeneous transformation matrices of all base-base interactions are stored in the structural graph. Symbolic classifications of nucleotide conformations, using sugar puckering modes and nitrogen base orientations around the glycosyl bond, and base-base interactions, using stacking and hydrogen bonding information, are introduced. Peculiarity factors of nucleotide conformations and base-base interactions are defined to indicate their marginalities with all other examples. The peculiarity factors allow us to identify irregular regions and possible stereochemical errors in 3-D structures without interactive visualization. The annotations attached to each nucleotide conformation include its class, its torsion angles, a distribution of the root-mean-square deviations with examples of the same class, the list of examples of the same class, and its peculiarity value. The annotations attached to each base-base interaction include its class, a distribution of distances with examples of the same class, the list of examples of the same class, and its peculiarity value. The distance between two homogeneous transformation matrices is evaluated using a new metric that distinguishes between the rotation and the translation of a transformation matrix in the context of nitrogen bases. MC-Annotate was used to build databases of nucleotide conformations and base-base interactions. It was applied to the ribosomal RNA fragment that binds to protein L11, which annotations revealed peculiar nucleotide conformations and base-base interactions in the regions where the RNA contacts the protein. The question of whether the current database of RNA three-dimensional structures is complete is addressed.
J Mol Biol 2001 May 18
PMID:Quantitative analysis of nucleic acid three-dimensional structures. 1135 82

A common residue numbering scheme for all immunoglobulin variable domains (immunoglobulin light chain lambda (V(lambda)) and kappa (V(kappa)) variable domains, heavy chain variable domains (V(H)) and T-cell receptor alpha (V(alpha)), beta (V(beta)), gamma (V(gamma)) and delta (V(delta)) variable domains) has been devised. Based on the spatial alignment of known three-dimensional structures of immunoglobulin domains, it places the alignment gaps in a way that minimizes the average deviation from the averaged structure of the aligned domains. This residue numbering scheme was applied to the immunoglobulin variable domain structures in the PDB database to automate the extraction of information on structural variations in homologous positions of the different molecules. A number of methods are presented that allow the automated projection of information derived from individual structures or from the comparison of multi-structure alignments onto a graphical representation of the sequence alignment.
J Mol Biol 2001 Jun 08
PMID:Yet another numbering scheme for immunoglobulin variable domains: an automatic modeling and analysis tool. 1139 87

This study reveals that AA and AG oppositions occur frequently at the ends of helices in RNA crystal and NMR structures in the PDB database and in the 16 S and 23 S rRNA comparative structure models, with the G usually 3' to the helix for the AG oppositions. In addition, these oppositions are frequently base-paired and usually in the sheared conformation, although other conformations are present in NMR and crystal structures. These A:A and A:G base-pairs are present in a variety of structural environments, including GNRA tetraloops, E and E-like loops, interfaced between two helices that are coaxially stacked, tandem G:A base-pairs, U-turns, and adenosine platforms. Finally, given structural studies that reveal conformational rearrangements occurring in regions of the RNA with AA and AG oppositions at the ends of helices, we suggest that these conformationally unique helix extensions might be associated with functionally important structural rearrangements.
J Mol Biol 2001 Jul 20
PMID:AA.AG@helix.ends: A:A and A:G base-pairs at the ends of 16 S and 23 S rRNA helices. 1145 84

A homology model of the dopamine D2 receptor was constructed based on the crystal structure of rhodopsin. A putative sodium-binding pocket identified in an earlier model (PDB ) was revised. It is now defined by Asn-419 backbone oxygen at the apex of a pyramid and Asp-80, Ser-121, Asn-419, and Ser-420 at each vertex of the planar base. Asn-423 stabilizes this pocket through hydrogen bonds to two of these residues. Highly conserved Asn-52 is positioned near the sodium pocket, where it hydrogen-bonds with Asp-80 and the backbone carbonyl of Ser-420. Mutation of three of these residues, Asn-52 in helix 1, Ser-121 in helix 3, and Ser-420 in helix 7, profoundly altered the properties of the receptor. Mutants in which Asn-52 was replaced with Ala or Leu or Ser-121 was replaced with Leu exhibited no detectable binding of radioligands, although receptor immunoreactivity in the membrane was similar to that in cells expressing the wild-type D2L receptor. A mutant in which Asn-52 was replaced with Gln, preserving hydrogen-bonding capability, was similar to D2L in affinity for ligands and ability to inhibit cAMP accumulation. Mutants in which either Ser-121 or Ser-420 was replaced with Ala or Asn had decreased affinity for agonists (Ser-121), but increased affinity for the antagonists haloperidol and clozapine. Interestingly, the affinity of these Ser-121 and Ser-420 mutants for substituted benzamide antagonists showed little or no dependence on sodium, consistent with our hypothesis that Ser-121 and Ser-420 contribute to the formation of a sodium-binding pocket.
Mol Pharmacol 2001 Aug
PMID:Modeling and mutational analysis of a putative sodium-binding pocket on the dopamine D2 receptor. 1145 25

Until recently, drawing general conclusions about RNA recognition by proteins has been hindered by the paucity of high-resolution structures. We have analyzed 45 PDB entries of protein-RNA complexes to explore the underlying chemical principles governing both specific and non-sequence specific binding. To facilitate the analysis, we have constructed a database of interactions using ENTANGLE, a JAVA-based program that uses available structural models in their PDB format and searches for appropriate hydrogen bonding, stacking, electrostatic, hydrophobic and van der Waals interactions. The resulting database of interactions reveals correlations that suggest the basis for the discrimination of RNA from DNA and for base-specific recognition. The data illustrate both major and minor interaction strategies employed by families of proteins such as tRNA synthetases, ribosomal proteins, or RNA recognition motifs with their RNA targets. Perhaps most surprisingly, specific RNA recognition appears to be mediated largely by interactions of amide and carbonyl groups in the protein backbone with the edge of the RNA base. In cases where a base accepts a proton, the dominant amino acid donor is arginine, whereas in cases where the base donates a proton, the predominant acceptor is the backbone carbonyl group, not a side-chain group. This is in marked contrast to DNA-protein interactions, which are governed predominantly by amino acid side-chain interactions with functional groups that are presented in the accessible major groove. RNA recognition often proceeds through loops, bulges, kinks and other irregular structures that permit use of all the RNA functional groups and this is seen throughout the protein-RNA interaction database.
J Mol Biol 2001 Aug 03
PMID:Structure-based analysis of protein-RNA interactions using the program ENTANGLE. 1146 58


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