Updates to the Integrated Protein–Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2

We present an updated and integrated version of our widely used protein–protein docking and binding affinity benchmarks. The benchmarks consist of non-redundant, high-quality structures of protein–protein complexes along with the unbound structures of their components. Fifty-five new complexes were added to the docking benchmark, 35 of which have experimentally measured binding affinities. These updated docking and affinity benchmarks now contain 230 and 179 entries, respectively. In particular, the number of antibody–antigen complexes has increased significantly, by 67% and 74% in the docking and affinity benchmarks, respectively. We tested previously developed docking and affinity prediction algorithms on the new cases. Considering only the top 10 docking predictions per benchmark case, a prediction accuracy of 38% is achieved on all 55 cases and up to 50% for the 32 rigid-body cases only. Predicted affinity scores are found to correlate with experimental binding energies up to r = 0.52 overall and r = 0.72 for the rigid complexes.Peer ReviewedPostprint (author's final draft

Molecular association of glucose-6- phosphate isomerase and pyruvate kinase M2 with glyceraldehyde-3-phosphate dehydrogenase in cancer cells

Background: For a long time cancer cells are known for increased uptake of glucose and its metabolization through glycolysis. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is a key regulatory enzyme of this pathway and can produce ATP through oxidative level of phosphorylation. Previously, we reported that GAPDH purified from a variety of malignant tissues, but not from normal tissues, was strongly inactivated by a normal metabolite, methylglyoxal (MG).Molecular mechanism behind MG mediated GAPDH inhibition in cancer cells is not well understood. Methods: GAPDH was purified from Ehrlich ascites carcinoma (EAC) cells based on its enzymatic activity. GAPDH associated proteins in EAC cells and 3-methylcholanthrene (3MC) induced mouse tumor tissue were detected by mass spectrometry analysis and immunoprecipitation (IP) experiment, respectively. Interacting domains of GAPDH and its associated proteins were assessed by in silico molecular docking analysis. Mechanism of MG mediated GAPDH inactivation in cancer cells was evaluated by measuring enzyme activity, Circular dichroism (CD) spectroscopy, IP and mass spectrometry analyses. Result: Here, we report that GAPDH is associated with glucose-6-phosphate isomerase (GPI) and pyruvate kinase M2 (PKM2) in Ehrlich ascites carcinoma (EAC) cells and also in 3-methylcholanthrene (3MC) induced mouse tumor tissue. Molecular docking analyses suggest C-terminal domain preference for the interaction between GAPDH and GPI. However, both C and N termini of PKM2 might be interacting with the C terminal domain of GAPDH. Expression of both PKM2 and GPI is increased in 3MC induced tumor compared with the normal tissue. In presence of 1 mM MG,association of GAPDH with PKM2 or GPI is not perturbed, but the enzymatic activity of GAPDH is reduced to 26.8 ± 5 % in 3MC induced tumor and 57.8 ± 2.3 % in EAC cells. Treatment of MG to purified GAPDH complex leads to glycation at R399 residue of PKM2 only, and changes the secondary structure of the protein complex. Conclusion: PKM2 may regulate the enzymatic activity of GAPDH. Increased enzymatic activity of GAPDH in tumor cells may be attributed to its association with PKM2 and GPI. Association of GAPDH with PKM2 and GPI could be a signature for cancer cells. Glycation at R399 of PKM2 and changes in the secondary structure of GAPDH complex could be one of the mechanisms by which GAPDH activity is inhibited in tumor cells by MG

Weak temperature dependence of P (+) H A (-) recombination in mutant Rhodobacter sphaeroides reaction centers

International audienceIn contrast with findings on the wild-type Rhodobacter sphaeroides reaction center, biexponential P (+) H A (-) → PH A charge recombination is shown to be weakly dependent on temperature between 78 and 298 K in three variants with single amino acids exchanged in the vicinity of primary electron acceptors. These mutated reaction centers have diverse overall kinetics of charge recombination, spanning an average lifetime from ~2 to ~20 ns. Despite these differences a protein relaxation model applied previously to wild-type reaction centers was successfully used to relate the observed kinetics to the temporal evolution of the free energy level of the state P (+) H A (-) relative to P (+) B A (-) . We conclude that the observed variety in the kinetics of charge recombination, together with their weak temperature dependence, is caused by a combination of factors that are each affected to a different extent by the point mutations in a particular mutant complex. These are as follows: (1) the initial free energy gap between the states P (+) B A (-) and P (+) H A (-) , (2) the intrinsic rate of P (+) B A (-) → PB A charge recombination, and (3) the rate of protein relaxation in response to the appearance of the charge separated states. In the case of a mutant which displays rapid P (+) H A (-) recombination (ELL), most of this recombination occurs in an unrelaxed protein in which P (+) B A (-) and P (+) H A (-) are almost isoenergetic. In contrast, in a mutant in which P (+) H A (-) recombination is relatively slow (GML), most of the recombination occurs in a relaxed protein in which P (+) H A (-) is much lower in energy than P (+) H A (-) . The weak temperature dependence in the ELL reaction center and a YLH mutant was modeled in two ways: (1) by assuming that the initial P (+) B A (-) and P (+) H A (-) states in an unrelaxed protein are isoenergetic, whereas the final free energy gap between these states following the protein relaxation is large (~250 meV or more), independent of temperature and (2) by assuming that the initial and final free energy gaps between P (+) B A (-) and P (+) H A (-) are moderate and temperature dependent. In the case of the GML mutant, it was concluded that the free energy gap between P (+) B A (-) and P (+) H A (-) is large at all times

Mean first-passage time calculations: comparison of the deterministic Hill’s algorithm with Monte Carlo simulations

Accurate determination of mean first-passage times (MFPTs) between any two states of a complex network still attracts considerable attention. Appropriate methods should take into account the discrepancy in MFPTs when a random walker moves first from a source to a target and then in the opposite direction. In addition, it is desirable to allow fast evaluation of mean first-passage times when transition probabilities are allowed to vary over time. For our calculations we make use of Hill’s algorithm, which enables the exact calculation of MFPTs. As we show in this work, when given a fixed distance to travel, the calculation of a particular MFPT depends on the choice of source and target points and their relative position on a lattice. We also demonstrate, when the network contains a relatively low number of cycles, that this deterministic technique provides exact results much faster in comparison to the more standard, but computationally demanding, stochastic Monte Carlo simulation method, where only approximate results can be obtained that are highly dependent on the number of walkers. Therefore, our specific implementation of Hill’s algorithm should facilitate efficient and accurate computation of MFPTs on a variety of network topologies of practical interest to the broad scientific community

Updates to the Integrated Protein-Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2

We present an updated and integrated version of our widely used protein-protein docking and binding affinity benchmarks. The benchmarks consist of non-redundant, high-quality structures of protein-protein complexes along with the unbound structures of their components. Fifty-five new complexes were added to the docking benchmark, 35 of which have experimentally measured binding affinities. These updated docking and affinity benchmarks now contain 230 and 179 entries, respectively. In particular, the number of antibody-antigen complexes has increased significantly, by 67% and 74% in the docking and affinity benchmarks, respectively. We tested previously developed docking and affinity prediction algorithms on the new cases. Considering only the top 10 docking predictions per benchmark case, a prediction accuracy of 38% is achieved on all 55 cases and up to 50% for the 32 rigid-body cases only. Predicted affinity scores are found to correlate with experimental binding energies up to r = 0.52 overall and r = 0.72 for the rigid complexes

Updates to the Integrated Protein–Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2

We present an updated and integrated version of our widely used protein–protein docking and binding affinity benchmarks. The benchmarks consist of non-redundant, high-quality structures of protein–protein complexes along with the unbound structures of their components. Fifty-five new complexes were added to the docking benchmark, 35 of which have experimentally measured binding affinities. These updated docking and affinity benchmarks now contain 230 and 179 entries, respectively. In particular, the number of antibody–antigen complexes has increased significantly, by 67% and 74% in the docking and affinity benchmarks, respectively. We tested previously developed docking and affinity prediction algorithms on the new cases. Considering only the top 10 docking predictions per benchmark case, a prediction accuracy of 38% is achieved on all 55 cases and up to 50% for the 32 rigid-body cases only. Predicted affinity scores are found to correlate with experimental binding energies up to r = 0.52 overall and r = 0.72 for the rigid complexes.Peer Reviewe

The γδTCR combines innate immunity with adaptive immunity by utilizing spatially distinct regions for agonist selection and antigen responsiveness

T lymphocytes expressing γδ T cell antigen receptors (TCRs) comprise evolutionarily conserved cells with paradoxical features. On the one hand, clonally expanded γδ T cells with unique specificities typify adaptive immunity. Conversely, large compartments of γδTCR+ intraepithelial lymphocytes (γδ IELs) exhibit limited TCR diversity and effect rapid, innate-like tissue surveillance. The development of several γδ IEL compartments depends on epithelial expression of genes encoding butyrophilin-like (Btnl (mouse) or BTNL (human)) members of the B7 superfamily of T cell co-stimulators. Here we found that responsiveness to Btnl or BTNL proteins was mediated by germline-encoded motifs within the cognate TCR variable γ-chains (Vγ chains) of mouse and human γδ IELs. This was in contrast to diverse antigen recognition by clonally restricted complementarity-determining regions CDR1-CDR3 of the same γδTCRs. Hence, the γδTCR intrinsically combines innate immunity and adaptive immunity by using spatially distinct regions to discriminate non-clonal agonist-selecting elements from clone-specific ligands. The broader implications for antigen-receptor biology are considered.</p