Modelling of the combining sites of three anti-lysozyme monoclonal antibodies and of the complex between one of the antibodies and its epitope.

Paz, Patricia de la; Sutton, Brian J.; Darsley, Michael J.; Rees, Anthony R.

doi:10.1002/j.1460-2075.1986.tb04227.x

Cited by 108 publications

(26 citation statements)

References 30 publications

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“…The frequent insertions and deletions in the exposed loop regions are the main reason for loop structural variability between members of the same protein family. As a result, dedicated methods have been developed to predict loop regions either by hand using molecular graphics [47], through database searching [48], or by ab initio methods [49].…”

Section: Model Building and Refinementmentioning

confidence: 99%

Comparative Modeling: The State of the Art and Protein Drug Target Structure Prediction

Liu¹,

Tang²,

Capriotti³

2011

CCHTS

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Abstract:The goal of computational protein structure prediction is to provide three-dimensional (3D) structures with resolution comparable to experimental results. Comparative modeling, which predicts the 3D structure of a protein based on its sequence similarity to homologous structures, is the most accurate computational method for structure prediction. In the last two decades, significant progress has been made on comparative modeling methods. Using the large number of protein structures deposited in the Protein Data Bank (~65,000), automatic prediction pipelines are generating a tremendous number of models (~1.9 million) for sequences whose structures have not been experimentally determined. Accurate models are suitable for a wide range of applications, such as prediction of protein binding sites, prediction of the effect of protein mutations, and structure-guided virtual screening. In particular, comparative modeling has enabled structure-based drug design against protein targets with unknown structures. In this review, we describe the theoretical basis of comparative modeling, the available automatic methods and databases, and the algorithms to evaluate the accuracy of predicted structures. Finally, we discuss relevant applications in the prediction of important drug target proteins, focusing on the G protein-coupled receptor (GPCR) and protein kinase families.

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Section: Model Building and Refinementmentioning

confidence: 99%

Comparative Modeling: The State of the Art and Protein Drug Target Structure Prediction

Liu¹,

Tang²,

Capriotti³

2011

CCHTS

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“…The structural and functional diversity of antibodies arises from sequence diversity on only about 10% of the molecule (Wu & Kabat, 1970). For this reason homology modeling is a logical choice for antibodies and several model building studies have been attempted (Kabat & Wu, 1972;Chothia et al, 1986;de la Paz et al, 1986;Fine et al, 1986;Snow & Amzel, 1986;Shenkin et al, 1987;Bruccoleri et al, 1988;Kussie et al, 1991;Anchin et al, 1992;Davies et al, 1990, and references therein;Ne11 et al, 1992). It has become apparent that the tertiary structures of the nonhypervariable or framework 1465 regions are very similar.…”

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confidence: 99%

Modeling the antigen combining site of an anti‐dinitrophenyl antibody, ANO2

1992

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A model structure has been constructed for a monoclonal anti-dinitrophenyl antibody. The antibody, AN02, has been sequenced and cloned (Anglister, J., Frey ) was used to model the six hypervariable loops that contain the antigen-combining site. All the possible conformations of the loop backbones were constructed and the best loop structures were selected using a combination of the CHARMM potential energy function and evaluation of the solvent-accessible surface area of the conformers. The order in which the loops were searched was carried out based on the relative locations of the loops with reference to the framework of the 6-barrel, namely, L2-Hl-L3-H2-H3-L1. The model structures thus obtained were compared to the high resolution X-ray structure (Briinger, A.T., Leahy, D.J., Hynes, T.R., & Fox, R.O., 1991, J. Mol. Bioi. 221, 239-256).Keywords: antibody modeling; canonical structures; conformational search; homology modeling; hypervariable loops Antibodies of the IgG class consist of heavy and light polypeptide chains linked by disulfide bonds and folded into independent @-sheet domains. The light chain is approximately 220 residues and contains a variable N-terminal domain (VL) and a constant C-terminal region. The heavy chain is made up of an N-terminal variable domain (VH) and three or four constant domains. The heavy chain can contain from 450 to 575 residues. The fragment containing the noncovalent dimer of VL and the VH domains is called the Fv. The VL and VH are each antiparallel @-barrels, and the contact between them forms an eightstranded @-barrel with four strands from each domain (Richardson, 1981;Novotny et al., 1983). In each variable region, three hypervariable loops are attached to a constant &sheet framework region (FR). The specificity ~ . .~

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“…However, sequence information vastly exceeds structural information from x-ray crystallography and, until crystallographic structure determination becomes no less routine than sequencing, modeling of structures is necessary. Since the framework region is conserved, it has proved relatively easy to model, whereas the CDRs, by their very nature (5), present a more challenging problem since accuracy in their modeling is of paramount importance.…”

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confidence: 99%

Modeling antibody hypervariable loops: a combined algorithm.

Martin

Cheetham²,

Rees³

1989

Proc. Natl. Acad. Sci. U.S.A.

182

105

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To be of any value, a predicted model of an antibody combining site should have an accuracy approaching that of antibody structures determined by x-ray crystallography (1.6-2.7 A). A number of modeling protocols have been proposed, which fall into two main categories-those that adopt a knowledge-based approach and those that attempt to construct the hypervariable loop regions of the antibody ab initio. Here we present a combined algorithm requiring no arbitrary decisions on the part of the user, which has been successfully applied to the modeling of the individual loops in two systems: the anti-lysozyme antibody HyHel-5, the crystal structure of which is as a complex with lysozyme [Sheriff, S., Silverton, E. W., Padlan, E. A., Cohen, G. H., Smith-GM, S. The wide range of specificities exhibited by antibodies is a function of the sequence and length variability of six hypervariable loops or complementarity-determining regions (CDRs) (1), which form the antigen combining site. These six CDRs supported on a highly conserved framework region constitute the variable region of the antigen binding fragment (Fab). A knowledge of antibody structure is essential for intelligent design ofantibody enzymes (2), tailoring of affinity (3), and CDR replacement strategies (4). However, sequence information vastly exceeds structural information from x-ray crystallography and, until crystallographic structure determination becomes no less routine than sequencing, modeling of structures is necessary. Since the framework region is conserved, it has proved relatively easy to model, whereas the CDRs, by their very nature (5), present a more challenging problem since accuracy in their modeling is of paramount importance.The approaches taken to modeling the antibody combining site, so far, fall into two groups: knowledge based and ab initio. Knowledge-based approaches have been used to model a number of antibodies, including J539 (6), GLOOP1-5 (5), HyHel-10 (7), and D1.3 (8). Although the methods differ in their detail, the common feature of all the approaches has been to examine only the known antibody crystal structures and select CDRs from these on the basis of length and/or sequence. Although most methods use simple sequence homology to select model loop conformations, Chothia and Lesk (9) have obtained better results by selecting conformations on the basis of the conservation of "key" residues that affect loop packing or conformation. However, the chief problem with any such method is the limited size of the knowledge base: while this has been extended to include all protein loops in the broader protein modeling field (10), a general data base has not previously been used in antibody modeling.The second approach has been to use ab initio conformational search algorithms to saturate the conformational space available to a loop and select an appropriate structure on the basis of its energy, calculated by using an empirical energy function (11). Whereas this overcomes the limited size of the knowledge base, it fails to make us...

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Modelling of the combining sites of three anti-lysozyme monoclonal antibodies and of the complex between one of the antibodies and its epitope.

Cited by 108 publications

References 30 publications

Comparative Modeling: The State of the Art and Protein Drug Target Structure Prediction

Comparative Modeling: The State of the Art and Protein Drug Target Structure Prediction

Modeling the antigen combining site of an anti‐dinitrophenyl antibody, ANO2

Modeling antibody hypervariable loops: a combined algorithm.

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