Additional file 1 of Iterative machine learning-based chemical similarity search to identify novel chemical inhibitors

Additional file 1: Figure S1. The duplicated dose response curves to determine Kd values of chemical compounds are shown for MEK1, MEK2, and MEK5. X-axis represents ligand concentration (nM) and Y-axis relative inhibitory activity by KdELECT service. Figure S2. Structurally similar molecules were identified via substructure search in Reaxys database. Figure S3. Molecular docking conformations of ZINC5814210 for MEK1, MEK2, and MEK5 are superimposed with ATP found in the MEK1 structure (PDB ID: 3V01). Figure S4. Two-dimensional interaction diagram of previously reported MEK1 inhibitors retrieved by 2D fingerprint similarity. Figure S5. Two-dimensional interaction diagram of MEK-ZINC5479148 docking models. Figure S6. Two-dimensional interaction diagram of MEK-ZINC32911363 docking models. Except MEK2, ZINC32911363 has better Kd binding affinity to other two MEKs. Figure S7. The molecules selected based on binding free energy scores from either MM/GBSA or MM/PBSA or both of the methods. Table S1. Experimental chemical activity data and cross-validation results (AUC of Precision-Recall curve) for each test target protein. Table S2. Comparison of the prediction performance of the standard single chemical-based Random Forest model with the ECBS model trained with PP-NP-NN data. Table S3. Estimation of chemical pair data size. Table S4. LogP values for the tested compounds. Table S5. GNINA docking scores for MEKs are shown with biochemical binding affinity data in Table 3. Table S6. The target prediction results for ZINC5814210 from Swiss target prediction server. Table S7. The target prediction results for ZINC5814210 from Structure Ensemble Approach (SEA) server

Additional file 2 of Iterative machine learning-based chemical similarity search to identify novel chemical inhibitors

Additional file 2: Table S1. SMILES for the tested compounds

Binding affinities of double mutants for MD2.

<p>Fold-increase indicates the ratio of binding affinities between the mutants and wild-type decoy receptor.</p

Crystal structure of the F63W mutant in complex with MD2.

<p>(A) Superimposed backbone structure of F63W mutant/MD2 complex into the wild-type decoy receptor/MD2 complex structure. The F63W showed a slight movement toward the N-terminal direction by 0.4 Å compared to the wild-type decoy receptor in complex with MD2. (B) The complex structure of F63W mutant/MD2. The mutated Tyr-63 was closely located to Arg-68 on MD2, creating cation-π interaction. The F63W mutant in complex structure is indicated as red and MD2 of F63W/MD2 complex is colored in yellow. The electron density map of mutant complex is shown as blue. (C) Comparison of the wild-type decoy receptor/MD2 and F63W/MD2 complex structures. The key residues and backbone structure of wild-type decoy receptor/MD2 complex are shown in grey. In F63W/MD2 complex, the F63W mutant is shown in red, and MD2 in yellow, respectively.</p

Changes in the interaction energies and hydrogen bond numbers.

<p>*Differences in the interaction energies between the mutants and wild-type decoy receptor (E_mut-E_wild, kJ/mol) were calculated using Gromacs4 package with an Amber03 force field. Each energy value represents the average of 3-ns molecular dynamics trajectories.</p>†<p>The number of hydrogen bonds was analyzed for each 3-ns molecular dynamics trajectory snapshot, and differences in their average values between the mutants and wild-type decoy receptor are shown as ΔHB.</p><p>Values in parenthesis were obtained by molecular dynamics simulations with the crystal structures of the single mutants (M41E, F63W, and V134L).</p

Interface structures of single variants.

<p>(A) Superimposed backbone structures of the wild-type decoy receptor and three single mutants (M41E, F63W, and V134L). (B) Interface structure of the M41E mutant obtained by superimposing the crystal structure of apo M41E mutant into the wild-type decoy receptor/MD2 complex structure. Glu-41 was positioned to be able to form a salt-bridge with Lys-109 on MD2. The crystal structure of the M41E is shown in blue, and the wild-type decoy receptor and MD2 are colored in green and yellow, respectively. (C) Interface structure of the V134L mutant obtained by superimposing the crystal structure of apo V134L mutant into the wild-type decoy receptor/MD2 complex structure. The larger Leu-134 is possible to make closer contact with Leu-108 on MD2. The crystal structure of the V134L is colored in orange (D) Interface structure of the F63W mutant obtained by superimposing the crystal structure of apo F63W mutant into the wild-type decoy receptor/MD2 complex structure. The mutated Trp-63 was expected to create the amino-aromatic (cation-π) interaction of Tyr-42 and Arg-68. The crystal structure of the F63W is shown in red.</p

Binding affinities of single mutants for MD2.

<p>Binding affinities (K_D) of the single mutants for MD2 were measured from the association rate constants (K_a) and dissociation rate constants (K_d) using surface plasmon resonance. Fold-increase represents the ratio of binding affinities between the mutants and wild-type decoy receptor.</p

Identification of the mutation sites on the decoy receptor, TV3, for constructing single variants.

<p>(A) Crystal structure of the decoy receptor in complex with MD2 (PDB ID 2Z65). The decoy receptor and MD2 are shown as green and yellow, respectively. (B) Structure of the interaction interface. 14 identified residues in TV3 are indicated in green, and potential interaction residues in MD2 are represented in yellow.</p

Snapshots of molecular dynamics simulation trajectories.

<p>Each snapshot was chosen to represent the changes in interaction energies. (A) Modeled structure of the double mutant, M41E/F63W. Increased hydrophobic environment resulting from the F63W mutation induced strong charge interactions between Glu-41 of the decoy receptor and Arg-68, Arg-69, and Lys-109 on MD2. (B) Modeled structure of the double mutant, V134L/H159Q. The V134L mutation led to increased hydrophobic interaction, strengthening the charge interaction between Gln-159 on decoy receptor and Glu-111 on MD2.</p

Dissecting the Critical Factors for Thermodynamic Stability of Modular Proteins Using Molecular Modeling Approach

<div><p>Repeat proteins have recently attracted much attention as alternative scaffolds to immunoglobulin antibodies due to their unique structural and biophysical features. In particular, repeat proteins show high stability against temperature and chaotic agents. Despite many studies, structural features for the stability of repeat proteins remain poorly understood. Here we present an interesting result from <i>in silico</i> analyses pursuing the factors which affect the stability of repeat proteins. Previously developed repebody structure based on variable lymphocytes receptors (VLRs) which consists of leucine-rich repeat (LRR) modules was used as initial structure for the present study. We constructed extra six repebody structures with varying numbers of repeat modules and those structures were used for molecular dynamics simulations. For the structures, the intramolecular interactions including backbone H-bonds, van der Waals energy, and hydrophobicity were investigated and then the radius of gyration, solvent-accessible surface area, ratio of secondary structure, and hydration free energy were also calculated to find out the relationship between the number of LRR modules and stability of the protein. Our results show that the intramolecular interactions lead to more compact structure and smaller surface area of the repebodies, which are critical for the stability of repeat proteins. The other features were also well compatible with the experimental results. Based on our observations, the repebody-5 was proposed as the best structure from the all repebodies in structure optimization process. The present study successfully demonstrated that our computer-based molecular modeling approach can significantly contribute to the experiment-based protein engineering challenge.</p></div