Geometrical Conversion of the EGFR Extracellular Domain by Adiabatic Mapping Combining Normal Mode Analysis of the Elastic Network Model and Energy Optimization

The activation of epidermal growth factor receptor (EGFR) involves the geometrical conversion of the extracellular domain (ECD) from the tethered to the extended forms with the dynamic rearrangement of the relative positions of four subdomains (SDs); however, this conversion process has not yet been thoroughly understood. We compare the two different forms of the X-ray crystal structures of ECD and simulate the ECD conversion process using adiabatic mapping that combines normal mode analysis of the elastic network model (ENM-NMA) and energy optimization. A comparison of the crystal structures reveals the rigidity of the intradomain geometry of the SD-I and -III backbone regardless of the form. The forward mapping from the tethered to the extended forms retains the intradomain geometry of the SD-I and -III backbone and reveals the trends to rearrange the relative positions of SD-I and -III and to dissociate the C-terminal tail of SD-IV from the hairpin loop in SD-II. The reverse mapping from the extended to the tethered forms complements the promotion of ECD conversion in the presence of epidermal growth factor (EGF).

ΔΔPT: a comprehensive toolbox for the analysis of protein motion

Background

Normal Mode Analysis is one of the most successful techniques for studying motions in proteins and macromolecules. It can provide information on the mechanism of protein functions, used to aid crystallography and NMR data reconstruction, and calculate protein free energies.

Results

ΔΔPT is a toolbox allowing calculation of elastic network models and principle component analysis. It allows the analysis of pdb files or trajectories taken from; Gromacs, Amber, and DL_POLY. As well as calculation of the normal modes it also allows comparison of the modes with experimental protein motion, variation of modes with mutation or ligand binding, and calculation of molecular dynamic entropies.

Conclusions

This toolbox makes the respective tools available to a wide community of potential NMA users, and allows them unrivalled ability to analyse normal modes using a variety of techniques and current software.

Computational analysis on the binding of epitope peptide to human leukocyte antigen class I molecule A*2402 subtype

Immunological response induced by small amino peptide has attracted much recent attention in the field of immunotherapy. Wilms' tumor (WT1) protein is one of the potent tumor antigens inducing immunological response in mouse and human, because WT1 is over expressed in many types of leukemia and various kinds of solid tumors. A 9-mer peptide encoded in WT1 protein (CMTWNQMNL; amino acid 235-243) is known to serve as antigenic peptide for human leukocyte antigen (HLA)-A*2402 molecule. It was reported that the replacement of the second amino residue, which is deeply responsible for the peptide binding to HLA, induced strong immunological response compared to the natural peptide. In this study, 19 kinds of single amino substitutions were introduced at position 2 of this 9-mer WT1 peptide. We performed molecular dynamics simulation on the complex of each of WT1 epitope peptides and HLA-β2 micro globulin (β2m) molecule, and subsequently estimated the binding affinity using molecular mechanics/generalized-Born surface area method combined with normal mode analysis. Our computation indicated that the peptide containing M2Y or M2W mutation showed high binding affinity to the HLA-β2m molecule as well as the natural peptide. We have also examined the role of the residue at position 2 in peptide binding to HLA-β2m. The calculation showed that van der Waals interaction between the side chain of the residue at position 2 and hydrophobic residues inside B-pocket of HLA are important. These findings will be helpful to search other potent peptides that will enhance strong immunological response specific to HLA-A*2402 molecule.

New protein structure model evaluation methods that include a side-chain consensus score for the protein modeling

Selecting the best quality model from a set of predicted structures is one of the most important aspects of protein structure prediction. We have developed model quality assessment programs that select high quality models which account for both the Calpha backbone and side-chain atom positions. The new methods are based on the consensus method with consideration of the side-chain environment of a protein structure and the secondary structure agreement. This Side-chain Environment Consensus (SEC) method is compared with the conventional consensus method, 3D-Jury (Ginalski K. et al., Bioinformatics, 19, 1015-1018 (2003)), which takes into account only the Calpha backbone atoms of the protein model. As the result, it was found that the SEC method selects the models with more accurate positioning of the side-chain atoms than the 3D-Jury method. When the SEC method was used in combination with the 3D-Jury method (3DJ+SEC), models were selected with improved quality both in the Calpha backbone and side-chain atom positions. Moreover, the CIRCLE (CCL) method (Terashi G. et al., Proteins, 69 (Suppl. 8), 98-107 (2007)) based on the 3D-1D profile score has been shown to select the best possible models that are the closest to the native structures from candidate models. Accordingly, the 3DJ+SEC+CCL method, in which CIRCLE is used after reducing the number of candidates by the 3DJ+SEC consensus method, was found to be very effective in selecting high quality models. Thus, the best method (the 3DJ+SEC+CCL method) includes the consensus approaches of the Calpha backbone and the side-chains, the secondary structure agreement and the 3D-1D profile score which corresponds to the free energy-like score in the residues of the protein model. In short, new algorithms are introduced in protein structure evaluation methods that are based on a side-chain consensus score. Additionally, in order to apply the 3DJ+SEC+CCL method and indicate the usefulness of this method, a model of human Cabin1, a protein associated with p53 function and cancer, is created using various internet modeling and alignment servers.

Dynamic influence of the two membrane-proximal immunoglobulin-like domains upon the peptide-binding platform domain in class I and class II major histocompatibility complexes: normal mode analysis

Major histocompatibility complexes (MHCs) mainly fall into class I and class II. The two classes have similar structures, with two membrane-proximal immunoglobulin-like domains and a peptide-binding platform domain, though their organizations are different. We simulated the dynamics of a whole and partial model deficient in either of the two membrane-proximal domains for class I and class II using normal mode analysis. Our study showed that the influence of the two membrane-proximal domains upon the dynamics of the platform domain were decisively different between class II and class I. Both membrane-proximal domains (the alpha2 and beta2 domains) of class II MHC, especially the alpha2 domain, influenced the most important pocket that accommodates a large hydrophobic anchor side chain of the N-terminal side of the bound peptide, though the pocket was not in the alpha2 domain neighborhood. By contrast, the two membrane-proximal domains (the alpha3 and beta2m domains) of class I MHC had little influence upon the most important pocket that accommodates the N-terminal residue of the bound peptide. These results suggest that the two membrane-proximal domains of class II MHC have a greater influence upon peptide-binding than those of class I MHC.