Tunnels and channels facilitate the transport of small molecules, ions and water solvent in a large variety of proteins. Characteristics of individual transport pathways, including their geometry, physico-chemical properties and dynamics are instrumental for understanding of structure-function relationships of these proteins, for the design of new inhibitors and construction of improved biocatalysts. CAVER is a software tool widely used for the identification and characterization of transport pathways in static macromolecular structures. Herein we present a new version of CAVER enabling automatic analysis of tunnels and channels in large ensembles of protein conformations. CAVER 3.0 implements new algorithms for the calculation and clustering of pathways. A trajectory from a molecular dynamics simulation serves as the typical input, while detailed characteristics and summary statistics of the time evolution of individual pathways are provided in the outputs. To illustrate the capabilities of CAVER 3.0, the tool was applied for the analysis of molecular dynamics simulation of the microbial enzyme haloalkane dehalogenase DhaA. CAVER 3.0 safely identified and reliably estimated the importance of all previously published DhaA tunnels, including the tunnels closed in DhaA crystal structures. Obtained results clearly demonstrate that analysis of molecular dynamics simulation is essential for the estimation of pathway characteristics and elucidation of the structural basis of the tunnel gating. CAVER 3.0 paves the way for the study of important biochemical phenomena in the area of molecular transport, molecular recognition and enzymatic catalysis. The software is freely available as a multiplatform command-line application at http://www.caver.cz.

Virtual screening is becoming an important tool for drug discovery. However, the application of virtual screening has been limited by the lack of accurate scoring functions. Here, we present a novel scoring function, MedusaScore, for evaluating protein-ligand binding. MedusaScore is based on models of physical interactions that include van der Waals, solvation and hydrogen bonding energies. To ensure the best transferability of the scoring function, we do not use any protein-ligand experimental data for parameter training. We then test the MedusaScore for docking decoy recognition and binding affinity prediction and find superior performance compared to other widely used scoring functions. Statistical analysis indicates that one source of inaccuracy of MedusaScore may arise from the unaccounted entropic loss upon ligand binding, which suggests avenues of approach for further MedusaScore improvement.

Allylation of aromatic aldehydes 1a-m with allyl- and crotyl-trichlorosilanes 2- 4, catalyzed by the chiral N-oxide QUINOX (9), has been found to exhibit a significant dependence on the electronics of the aldehyde, with p-(trifluoromethyl)benzaldehyde 1g and its p-methoxy counterpart 1h affording the corresponding homoallylic alcohols 6g, h in 96 and 16% ee, respectively, at -40 degrees C. The kinetic and computational data indicate that the reaction is likely to proceed via an associative pathway involving neutral, octahedral silicon complex 22 with only one molecule of the catalyst involved in the rate- and selectivity-determining step. The crotylation with (E) and (Z)-crotyltrichlorosilanes 3 and 4 is highly diastereoselective, suggesting the chairlike transition state 5, which is supported by computational data. High-level quantum chemical calculations further suggest that attractive aromatic interactions between the catalyst 9 and the aldehyde 1 contribute to the enantiodifferentiation and that the dramatic drop in enantioselectivity, observed with the electron-rich aldehyde 1h, originates from narrowing the energy gap between the (R)- and (S)-reaction channels in the associative mechanism (22). Overall, a good agreement between the theoretically predicted enantioselectivities for 1a and 1h and the experimental data allowed to understand the specific aspects of the reaction mechanism.

Background: Tunnel properties affect ligand passage in enzymes with buried active sites. Results: A tunnel mutation from leucine to tryptophan changes the mechanism of bromide ion release from haloalkane dehalogenase LinB. Conclusion:Interactions of the bromide ion with the tryptophan increase free energy barrier for its passage, causing the reaction mechanism change. Significance: The results provide guidelines for enzyme engineering.

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Proline-tryptophan complexes derived from experimental structures are investigated by quantum chemical procedures known to properly describe the London dispersion energy. We study two geometrical arrangements: the "L-shaped", stabilized by an H-bond, and the "stacked-like", where the two residues are in parallel orientation without any H-bond. Interestingly, the interaction energies in both cases are comparable and very large ( approximately 7 kcal mol(-1)). The strength of stabilization in the stacked arrangement is rather surprising considering the fact that only one partner has an aromatic character. The interaction energy decomposition using the SAPT method further demonstrates the very important role of dispersion energy in such arrangement. To elucidate the structural features responsible for this unexpectedly large stabilization we examined the role of the nitrogen heteroatom and the importance of the cyclicity of the proline residue. We show that the electrostatic interaction due to the presence of the dipole, caused by the nitrogen heteroatom, contributes largely to the strength of the interaction. Nevertheless, the cyclic arrangement of proline, which allows for the largest amount of dispersive contact with the aromatic partner, also has a notable-effect. Geometry optimizations carried out for the "stacked-like" complexes show that the arrangements derived from protein structure are close to their gas phase optimum geometry, suggesting that the environment has only a minor effect on the geometry of the interaction. We conclude that the strength of proline non-covalent interactions, combined with this residue's rigidity, might be the explanation for its prominent role in protein stabilization and recognition processes.

We are proposing an interresidue interaction energy map (IEM)--a new tool for protein structure analysis and protein bioinformatics. This approach employs the sum of pair-wise interaction energies of a particular residue as a measure of its structural importance. We will show that the IEM can serve as a means for identifying key residues responsible for the stability of a protein. Our method can be compared with the interresidue contact map but has the advantage of weighting the contacts by the stabilization energy content which they bring to the protein structure. For the theoretical adjustment of the proposed method, we chose the Trp-cage mini protein as a model system to compare a spectrum of computational methods ranging from the ab initio MP2 level through the DFT method to empirical force-field methods. The IEM method correctly identifies Tryptophane 6 as the key residue in the Trp-cage. The other residues with the highest stabilizing contributions correspond to the structurally important positions in the protein. We have further tested our method on the Trp2Cage miniprotein--a P12W mutant of the Trp-cage and on two proteins from the rubredoxin family that differ in their thermostability. Our method correctly identified the thermodynamically more stable variants in both cases and therefore can also be used as a tool for the relative measurement of protein stability. Finally, we will point out the important role played by dispersion energy, which contributes significantly to the total stabilization energy and whose role in aromatic pairs is clearly dominant. Surprisingly, the dispersion energy plays an even more important role in the interaction of prolines with aromatic systems.

Recombinant ligands derived from small protein scaffolds show promise as robust research and diagnostic reagents and next generation protein therapeutics. Here, we derived high-affinity binders of human interferon gamma (hIFNγ) from the three helix bundle scaffold of the albumin-binding domain (ABD) of protein G from Streptococcus G148. Computational interaction energy mapping, solvent accessibility assessment, and in silico alanine scanning identified 11 residues from the albumin-binding surface of ABD as suitable for randomization. A corresponding combinatorial ABD scaffold library was synthesized and screened for hIFNγ binders using in vitro ribosome display selection, to yield recombinant ligands that exhibited K(d) values for hIFNγ from 0.2 to 10 nM. Molecular modeling, computational docking onto hIFNγ, and in vitro competition for hIFNγ binding revealed that four of the best ABD-derived ligands shared a common binding surface on hIFNγ, which differed from the site of human IFNγ receptor 1 binding. Thus, these hIFNγ ligands provide a proof of concept for design of novel recombinant binding proteins derived from the ABD scaffold.