Biomedical subjects

Jeffrey J Gray

Publications and source records attributed to Jeffrey J Gray.

7 recordsLinked to original sources

PubMed2006

High-resolution protein-protein docking.

The high-resolution prediction of protein-protein docking can now create structures with atomic-level accuracy. This progress arises from both improvements in the rapid sampling of conformations and increased accuracy of binding free energy calculations. Consequently, the quality of models submitted to the blind prediction challenge CAPRI (Critical Assessment of PRedicted Interactions) has steadily increased, including complexes predicted from homology structures of one binding partner and complexes with atomic accuracy at the interface. By exploiting experimental information, docking has created model structures for real applications, even when confronted with challenges such as moving backbones and uncertain monomer structures. Work remains to be done in docking large or flexible proteins, ranking models consistently, and producing models accurate enough to allow computational design of higher affinities or specificities.

Animals↗

PubMed2006

Structural model of the mAb 806-EGFR complex using computational docking followed by computational and experimental mutagenesis.

In this work, we combined computational protein-protein docking with computational and experimental mutagenesis to predict the structure of the complex formed by monoclonal antibody 806 (mAb 806) and the epidermal growth factor receptor (EGFR). We docked mAb 806, an antitumor antibody, to its epitope of EGFR residues 287-302. Potential mAb 806-EGFR orientations were generated, and computational mutagenesis was used to filter them according to their agreement with experimental mutagenesis data. Further computational mutagenesis suggested additional mutations, which were tested to arrive at a final structure that was most consistent with experimental mutagenesis data. We propose that this is the EGFR-mAb 806 structure, in which mAb 806 binds to an untethered form of the receptor, consistent with published experimental results. The steric hindrance created by the antibody near the EGFR dimer interface interferes with receptor dimerization, and we postulate this as the structural origin for the antitumor effect of mAb 806.

Antibodies, Monoclonal↗

PubMed2005

CAPRI rounds 3-5 reveal promising successes and future challenges for RosettaDock.

CAPRI Rounds 3, 4, and 5 are the first public test of the published RosettaDock algorithm. The targets cover a wide range of sizes and shapes. For most targets, published biological information indicated the region of the binding site on at least one docking partner. The RosettaDock algorithm produced high accuracy predictions for three targets, medium-accuracy predictions for two targets, and an acceptable prediction for one target. RosettaDock predicted all five targets with less than 450 residues to high or medium accuracy, but it predicted only one of seven targets with above 450 residues to acceptable accuracy. RosettaDock's high-accuracy predictions for small to moderately large targets reveal the predictive power and fidelity of the algorithm, especially the high-resolution refinement and scoring protocol. In addition, RosettaDock can predict complexes from at least one homology-modeled docking partner with comparable accuracy to unbound cases of similar size. Larger targets present a more intensive sampling problem, and some large targets present repulsive barriers to entering the binding site. Ongoing improvements to RosettaDock's low-resolution search may alleviate this problem. This first public test suggests that RosettaDock can be useful in a significant range of applications in biochemistry and cell biology.

Algorithms↗

PubMed2005

Applying linear interaction energy method for rational design of noncompetitive allosteric inhibitors of the sarco- and endoplasmic reticulum calcium-ATPase.

Noncompetitive inhibitors of sarco- and endoplasmic reticulum calcium-ATPase (SERCA) have important therapeutic value in the treatment of cancer, due to their ability to induce apoptosis in cancer cells in a proliferation-independent manner. Thapsigargin (TG) and its analogues are one such class of inhibitors that bind to a hydrophobic pocket located in the transmembrane region of SERCA near the biomembrane surface and interfere with calcium transport. The binding free energies of thapsigargin-based inhibitors of SERCA were computed using a novel linear interaction energy (LIE) method with a surface generalized Born (SGB) continuum solvation model. A training set of 20 TG analogues was used to build a binding affinity model for estimating the free energy of binding for 18 new inhibitors with a root-mean-square (rms) error of 1.36 kcal/mol with respect to experimental data. For 15 out of the 18 inhibitors in the test set, the rms error was 1.02 kcal/mol, which is on the order of the accuracy level achieved by highly rigorous free energy of perturbation (FEP) or thermodynamic integration (TI) methods. On the basis of the analysis of the binding cavity at the interface of the membrane surface and the cytoplasmic region, we propose that side chains of TG derivatives at the O-8 position orient toward the cytoplasmic region through a hydrophobic channel. On the basis of this insight, four analogues of varying side chain length at the O-8 position with a charged moiety at the end were designed, tested with LIE methodology, and then validated experimentally for their SERCA inhibition activity. Low levels of rms error for the majority of inhibitors establish the structure-based LIE method as an efficient tool for generating more potent and specific inhibitors of SERCA by testing rationally designed lead compounds based on thapsigargin derivatization.

Allosteric Regulation↗

PubMed2004

The interaction of proteins with solid surfaces.

The interaction of proteins with solid surfaces is a fundamental phenomenon with implications for nanotechnology, biomaterials and biotechnological processes. Kinetic and thermodynamic studies have long indicated that significant conformational changes may occur as a protein encounters a surface; new techniques are measuring and modeling these changes. Combinatorial and directed evolution techniques have created new peptide sequences that bind specifically to solid surfaces, similar to the natural proteins that regulate crystal growth. Modeling efforts capture kinetics and thermodynamics on the colloidal scale, but detailed treatments of atomic structure are still in development and face the usual challenges of protein modeling. Opportunities abound for fundamental discovery, as well as breakthroughs in biomaterials, biotechnology and nanotechnology.

Adsorption↗

PubMed2003

Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations.

Protein-protein docking algorithms provide a means to elucidate structural details for presently unknown complexes. Here, we present and evaluate a new method to predict protein-protein complexes from the coordinates of the unbound monomer components. The method employs a low-resolution, rigid-body, Monte Carlo search followed by simultaneous optimization of backbone displacement and side-chain conformations using Monte Carlo minimization. Up to 10(5) independent simulations are carried out, and the resulting "decoys" are ranked using an energy function dominated by van der Waals interactions, an implicit solvation model, and an orientation-dependent hydrogen bonding potential. Top-ranking decoys are clustered to select the final predictions. Small-perturbation studies reveal the formation of binding funnels in 42 of 54 cases using coordinates derived from the bound complexes and in 32 of 54 cases using independently determined coordinates of one or both monomers. Experimental binding affinities correlate with the calculated score function and explain the predictive success or failure of many targets. Global searches using one or both unbound components predict at least 25% of the native residue-residue contacts in 28 of the 32 cases where binding funnels exist. The results suggest that the method may soon be useful for generating models of biologically important complexes from the structures of the isolated components, but they also highlight the challenges that must be met to achieve consistent and accurate prediction of protein-protein interactions.

Algorithms↗

PubMed2003

Protein-protein docking predictions for the CAPRI experiment.

We predicted structures for all seven targets in the CAPRI experiment using a new method in development at the time of the challenge. The technique includes a low-resolution rigid body Monte Carlo search followed by high-resolution refinement with side-chain conformational changes and rigid body minimization. Decoys (approximately 10(6) per target) were discriminated using a scoring function including van der Waals and solvation interactions, hydrogen bonding, residue-residue pair statistics, and rotamer probabilities. Decoys were ranked, clustered, manually inspected, and selected. The top ranked model for target 6 predicted the experimental structure to 1.5 A RMSD and included 48 of 65 correct residue-residue contacts. Target 7 was predicted at 5.3 A RMSD with 22 of 37 correct residue-residue contacts using a homology model from a known complex structure. Using a preliminary version of the protocol in round 1, target 1 was predicted within 8.8 A although few contacts were correct. For targets 2 and 3, the interface locations and a small fraction of the contacts were correctly identified.

Algorithms↗