Comparison of the energy scores versus the affinities of the mutations show how well the programs reproduce the differences.

<p>For each test case with more than two mutations, we plotted the top binding scores of CADDSuite, Vina, and Rosetta designs for each mutation on each scaffold structure together with the logarithm of the affinity. Here we show plots for Carbonic anhydrase II, HIV-1 protease, and Streptavidin test 1. All other plots are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052505#pone.0052505.s001" target="_blank">Information S1</a>. Values are scaled to fit in the same range. Shown on the x-axis of a plot are the mutants in order of affinity to the ligand (the leftmost has the lowest affinity, compare <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052505#pone-0052505-t001" target="_blank">Table 1</a> for the actual values). The y-axis measures predicted binding scores for the designs, and the log affinities, scaled between 0 and 1. Both are proportional to the binding free energy, and can therefore be compared when scaled to the same range. The lowest predicted binding score or log affinity is set to 0, the highest respective value to 1. Each plot contains a line for the affinity logarithm (solid, black no marker). This line represents the goal, if a method predicts binding well, the binding score lines should closely follow the log affinity line. The other markers and lines show the scaled predicted binding scores. One line represents the designs calculated for all available mutants, calculated by using one crystal structure as the scaffold. (Crystal structure 1: dashed, blue, circle markers; structure 2: red, dotted, square markers; structure 3: green, dash-dot pattern, diamond markers; structure 4: cyan, two dashes one dot pattern, star markers). We chose to use lines for representation, because this makes it easy to visually compare the shape of the black log affinity line to the lines representing the design binding scores. Each row has plots for one test case, in parentheses the order of scaffold structures is listed: <i>CA</i>: Carbonic anhydrase II (1ydb, 1yda, 1ydd), <i>HP</i>: HIV-1 protease (1met, 1meu, 1mes), <i>S1</i>: Streptavidin test 1 (1swe, 1n43).</p

Two-dimensional structures of benchmark set ligands.

<p>The ligands of the test cases of our benchmark sets. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052505#pone-0052505-t001" target="_blank">Table 1</a> for which ligand belongs to which test case.</p

Workflow of PocketOptimizer.

<p>The input specific for a design is depicted in circles, parts of the pipeline are shown in pointed rectangles, and output components in rounded rectangles. The output is stored in standard file formats (SDF and PDB for structural data, csv for energy tables). This allows the easy replacement of a component with another that solves the same task (e.g. replacing the binding score function).</p

Differences of the ligand poses and pocket side chains in the benchmark designs compared to the crystal structures.

<p>The upper graph shows the average RMSDs and standard deviation between the ligand pose in the designs and in the crystal structures. The lower graph shows the average RMSD and standard deviation between the binding pocket side chain heavy atoms of designs and the corresponding crystal structure. The RMSDs are calculated after superimposing the structures using the backbone to make sure that the differences come from pocket/ligand pose differences only. RMSD from PocketOptimizer CADDSuite score designs are plotted in blue, from PocketOptimizer vina designs in green, and from Rosetta designs in red. Each point marks the average RMSD for all designs of a test case usign one score. The number of designs that contribute to a value depends on the number of mutations with a crystal structure, it is the square of this number (because each structure is used as a design scaffold for each mutation). Test cases are: <i>CA</i>: Carbonic anhydrase II, <i>ABP</i> D7r4 amine binding protein, <i>ER</i>: Estrogen receptor , <i>HP</i>: HIV-1 protease, <i>KI</i>: Ketosteroid isomerase, <i>L</i>: Lectin, <i>MS</i>: Methylglyoxal synthase, <i>N1</i>: Neuroaminidase test 1, <i>N2</i>: Neuroaminidase test 2, <i>PNP</i>: Purine nucleoside phosphorylase, <i>S1</i>: Streptavidin test 1, <i>S2</i>: Streptavidin test 2, <i>TS</i>: Thymidylate synthase, <i>T</i>: Trypsin.</p

Benchmark set.

<p>Each row lists a test case. Columns <b>Protein</b> and <b>Ligand</b> contain the name of protein or ligand, <b>Positions</b> the indices of the mutable positions (for HIV protease along with the chain identifier, in the other cases the pocket is formed by one chain only), <b>Mutants</b> lists the variants: in subcolumn <b>AA</b> the amino acids at the mutable positions, in <b>aff.</b> the affinities of the variants, and in <b>PDB</b> the PDB identifier of the corresponding crystal structure, should one exist.</p

Molecular Engineering of Organophosphate Hydrolysis Activity from a Weak Promiscuous Lactonase Template

Rapid evolution of enzymes provides unique molecular insights into the remarkable adaptability of proteins and helps to elucidate the relationship between amino acid sequence, structure, and function. We interrogated the evolution of the phospho­triesterase from Pseudomonas diminuta (<i>Pd</i>PTE), which hydrolyzes synthetic organophosphates with remarkable catalytic efficiency. PTE is thought to be an evolutionarily “young” enzyme, and it has been postulated that it has evolved from members of the phospho­triesterase-like lactonase (PLL) family that show promiscuous organophosphate-degrading activity. Starting from a weakly promiscuous PLL scaffold (<i>Dr</i>0930 from Deinococcus radiodurans), we designed an extremely efficient organophosphate hydrolase (OPH) with broad substrate specificity using rational and random mutagenesis in combination with in vitro activity screening. The OPH activity for seven organophosphate substrates was simultaneously enhanced by up to 5 orders of magnitude, achieving absolute values of catalytic efficiencies up to 10⁶ M^–1 s^–1. Structural and computational analyses identified the molecular basis for the enhanced OPH activity of the engineered PLL variants and demonstrated that OPH catalysis in <i>Pd</i>PTE and the engineered PLL differ significantly in the mode of substrate binding