Dataset Information

Modeling antibody hypervariable loops: a combined algorithm.

ABSTRACT: 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-Gill, S. J., Finzel, B. C. & Davies, D. R. (1987) Proc. Natl. Acad. Sci. USA 84, 8075-8079], and the free antigen binding fragment (Fab) of the anti-lysozyme peptide antibody, Gloop2. This protocol may be used with a high degree of confidence to model single-loop replacements, insertions, deletions, and side-chain replacements. In addition, it may be used in conjunction with other modeling protocols as a method by which to model particular loops whose conformations are predicted poorly by these methods.

SUBMITTER: Martin AC

PROVIDER: S-EPMC298475 | biostudies-other | 1989 Dec

REPOSITORIES: biostudies-other

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Publications

Modeling antibody hypervariable loops: a combined algorithm.

Martin A C AC Cheetham J C JC Rees A R AR

Proceedings of the National Academy of Sciences of the United States of America 19891201 23

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 ...[more]

PMID: 2594766

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Project description:We present a fragment-search based method for predicting loop conformations in protein models. A hierarchical and multidimensional database has been set up that currently classifies 105,950 loop fragments and loop flanking secondary structures. Besides the length of the loops and types of bracing secondary structures the database is organized along four internal coordinates, a distance and three types of angles characterizing the geometry of stem regions. Candidate fragments are selected from this library by matching the length, the types of bracing secondary structures of the query and satisfying the geometrical restraints of the stems and subsequently inserted in the query protein framework where their fit is assessed by the root mean square deviation (r.m.s.d.) of stem regions and by the number of rigid body clashes with the environment. In the final step remaining candidate loops are ranked by a Z-score that combines information on sequence similarity and fit of predicted and observed phi/psi main chain dihedral angle propensities. Confidence Z-score cut-offs were determined for each loop length that identify those predicted fragments that outperform a competitive ab initio method. A web server implements the method, regularly updates the fragment library and performs prediction. Predicted segments are returned, or optionally, these can be completed with side chain reconstruction and subsequently annealed in the environment of the query protein by conjugate gradient minimization. The prediction method was tested on artificially prepared search datasets where all trivial sequence similarities on the SCOP superfamily level were removed. Under these conditions it is possible to predict loops of length 4, 8 and 12 with coverage of 98, 78 and 28% with at least of 0.22, 1.38 and 2.47 A of r.m.s.d. accuracy, respectively. In a head-to-head comparison on loops extracted from freshly deposited new protein folds the current method outperformed in a approximately 5:1 ratio an earlier developed database search method.

Project description:The purposes was to establish optimal modeling of single-chain antibody molecules based on similarity algorithm and seek the connecting peptides that had the minimal effect on the structure and bioactivity of the variable region of heavy chain (VH) and that of light chain (VL) in a single-chain antibody against liver cancer. After the Linker with different lengths (n=0~7) had been added into single chain fragment variable (ScFv), modeling of the overall sequences of VH, VL and ScFv were conducted respectively. Meanwhile, the peptide chain structure of (Gly4Ser)n was adopted for the connecting peptide. Then the spatial spherical shell layer alignment algorithm based on spherical polar coordinates was utilized for comparing the structural similarity of VH and VL before and after adding connecting peptide. Equally, in order to determine the stability of VH and VL, MATLAB was applied for analysis of the fore and aft distances and the diffusion radius. Indirect ELISA method was used to detect single-chain antibody immunological activity of Linker with different lengths. The MTT assay was utilized for the examination of the inhibition rate of single-chain antibody with different lengths of Linker to liver cancer cell. When n=4, the structural similarity between VH together with VL and their original ones was the highest. When n=3, the influence of connecting peptide on the stability of VH and VL was minimum. When n>3, the fore and aft distances changed little due to the increase and fold of the length of peptide chain. The results of ELISA detection showed that when n=4, affinity of single chain antibody to liver cancer cells was much higher. The MTT test also indicated that when n=4, the inhibition rate of the connecting peptide on hepatoma carcinoma cell reached the highest, and that came second when n=3. When n=4, the structural stability and biological functions of anti-hepatoma single-chain antibody were both favorable. This study has provided a basis for the design and construction of single-chain antibody.

Project description:Comparative protein structure prediction is limited mostly by the errors in alignment and loop modeling. We describe here a new automated modeling technique that significantly improves the accuracy of loop predictions in protein structures. The positions of all nonhydrogen atoms of the loop are optimized in a fixed environment with respect to a pseudo energy function. The energy is a sum of many spatial restraints that include the bond length, bond angle, and improper dihedral angle terms from the CHARMM-22 force field, statistical preferences for the main-chain and side-chain dihedral angles, and statistical preferences for nonbonded atomic contacts that depend on the two atom types, their distance through space, and separation in sequence. The energy function is optimized with the method of conjugate gradients combined with molecular dynamics and simulated annealing. Typically, the predicted loop conformation corresponds to the lowest energy conformation among 500 independent optimizations. Predictions were made for 40 loops of known structure at each length from 1 to 14 residues. The accuracy of loop predictions is evaluated as a function of thoroughness of conformational sampling, loop length, and structural properties of native loops. When accuracy is measured by local superposition of the model on the native loop, 100, 90, and 30% of 4-, 8-, and 12-residue loop predictions, respectively, had <2 A RMSD error for the mainchain N, C(alpha), C, and O atoms; the average accuracies were 0.59 +/- 0.05, 1.16 +/- 0.10, and 2.61 +/- 0.16 A, respectively. To simulate real comparative modeling problems, the method was also evaluated by predicting loops of known structure in only approximately correct environments with errors typical of comparative modeling without misalignment. When the RMSD distortion of the main-chain stem atoms is 2.5 A, the average loop prediction error increased by 180, 25, and 3% for 4-, 8-, and 12-residue loops, respectively. The accuracy of the lowest energy prediction for a given loop can be estimated from the structural variability among a number of low energy predictions. The relative value of the present method is gauged by (1) comparing it with one of the most successful previously described methods, and (2) describing its accuracy in recent blind predictions of protein structure. Finally, it is shown that the average accuracy of prediction is limited primarily by the accuracy of the energy function rather than by the extent of conformational sampling.

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Modeling antibody hypervariable loops: a combined algorithm.

Publications

Modeling antibody hypervariable loops: a combined algorithm.

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