Discovery of Novel Allosteric Effectors Based on the Predicted Allosteric Sites for <i>Escherichia coli</i> D-3-Phosphoglycerate Dehydrogenase

<div><p>D-3-phosphoglycerate dehydrogenase (PGDH) from <i>Escherichia coli</i> catalyzes the first critical step in serine biosynthesis, and can be allosterically inhibited by serine. In a previous study, we developed a computational method for allosteric site prediction using a coarse-grained two-state Gō Model and perturbation. Two potential allosteric sites were predicted for <i>E. coli</i> PGDH, one close to the active site and the nucleotide binding site (Site I) and the other near the regulatory domain (Site II). In the present study, we discovered allosteric inhibitors and activators based on site I, using a high-throughput virtual screen, and followed by using surface plasmon resonance (SPR) to eliminate false positives. Compounds 1 and 2 demonstrated a low-concentration activation and high-concentration inhibition phenomenon, with IC₅₀ values of 34.8 and 58.0 µM in enzymatic bioassays, respectively, comparable to that of the endogenous allosteric effector, L-serine. For its activation activity, compound 2 exhibited an AC₅₀ value of 34.7 nM. The novel allosteric site discovered in PGDH was L-serine- and substrate-independent. Enzyme kinetics studies showed that these compounds influenced K_m, k_cat, and k_cat/K_m. We have also performed structure-activity relationship studies to discover high potency allosteric effectors. Compound 2-2, an analog of compound 2, showed the best <i>in vitro</i> activity with an IC₅₀ of 22.3 µM. Compounds targeting this site can be used as new chemical probes to study metabolic regulation in <i>E. coli.</i> Our study not only identified a novel allosteric site and effectors for PGDH, but also provided a general strategy for designing new regulators for metabolic enzymes.</p></div

Binding site verification.

<p>(A–B) The complex structure model of compounds <b>1</b> and <b>2</b> binding to site I in PGDH. Compound <b>1</b> (A), compound <b>2</b> (B), and the mutation sites are shown in stick representation, while site I is in surface representation. (C–D) SPR direct binding curves of WT, Y410A, F147A and E129AK256A. WT PGDH and its mutants were immobilized on the sensor chip. Compounds <b>1</b> (C) and <b>2</b> (D) were injected over the chip at a fixed concentration of 50 and 25 µM, respectively.</p

Identifying Allosteric Binding Sites in Proteins with a Two-State Go̅ Model for Novel Allosteric Effector Discovery

Allostery is a common mechanism of controlling many biological processes such as enzyme catalysis, signal transduction, and metabolic regulation. The use of allostery to regulate protein activity is an important and promising strategy in drug discovery and biological network regulation. In order to modulate protein activity by allostery, predictive methods need to be developed to discover allosteric binding sites. In the present study, we developed a new approach to identify allosteric sites in proteins based on the coarse-grained two-state Go̅ model. Starting from the concept that allostery is a conformation population shift process, we first constructed an ensemble of two functional states of a protein and tuned the energy landscape to bias one state. We then added perturbations to a binding site and monitored the population distribution of the new ensemble. If population redistribution occurred, then the binding perturbed site was predicted as a potential allosteric site. Our approach successfully identified all the known allosteric sites in a set of test proteins. Several new allosteric sites in the test proteins were also predicted. By use of one of the new allosteric sites predicted from Escherichia coli phosphoglycerate dehydrogenase (PGDH), novel allosteric regulating molecules were screened by molecular docking and enzymatic assay. Three novel allosteric inhibitors were discovered and their binding modes were confirmed by mutation experiments and competitive assay. The IC₅₀ of the strongest inhibitor discovered was 21 μM, which is comparable to that of the native allosteric inhibitor l-serine. The novel allosteric site discovered in PGDH is l-serine-independent, and inhibitors targeting this site can be used as novel regulators of the E. coli serine synthesis pathway. Our approach for allosteric site prediction is generally applicable and the predicted sites can be used in discovering novel allosteric regulating molecules

Structure of site I in PGDH (PDB code: 1YBA).

<p>Site I is represented by the green surface, the active site is indicated by orange spheres, and the cofactor NAD⁺ and the endogenous allosteric L-serine are illustrated in stick and sphere, respectively.</p

Mutation effects on the inhibition rates of compounds.

a<p>IC₅₀ values for wild type (µM).</p>b<p>ND, not determined, based on the docking results.</p

Dose-response curves of compounds 1-3.

<p>(A) Residual enzyme activity versus compound concentration. The IC₅₀ values were 34.8±1.3 for compound <b>1</b>, and 58.0±9.0 µM for compound <b>2</b>. The AC₅₀ value was 34.7±4.5 nM for compound <b>2.</b> (B) Enzyme inhibition dose-response curves of compound <b>3.</b> The fitted IC₅₀ value was 131±12 µM. (C–D) SPR dose-response curves of compound <b>1</b>, compound <b>2</b> with immobilized PGDH, respectively. The K_D values were 42.6±2.1 for compound <b>1</b> (C), and 19.0±1.9 µM for compound <b>2</b> (D). K_D values of the compounds <b>1</b> and <b>2</b> were obtained by fitting the data sets to 1:1 Langmuir binding model using Biacore T200 Evaluation Software. Residuals for all SPR sensorgrams were less than 2 RU. Chi-square values were 2.4 for C and 1.2 for D.</p

Competitive assay of compounds 1-3 with the substrate.

<p>(A) Compounds <b>1</b> and <b>2</b> and the substrate do not competitively bind to the same site. Increasing the substrate concentration led to higher inhibition rates of the compounds in contrast to lowered inhibition as expected for competitive inhibitors, indicating that these compounds do not bind to the substrate-binding site. (B) Substrate competition curve of compound <b>3</b>. The percentage inhibition did not change along with the increase of substrate concentration, indicating that there are no significant interactions between compound <b>3</b> and the substrate binding site.</p

Structures of the compounds 1-3.

<p>The SPECS IDs of compounds <b>1</b>-<b>3</b> are AN-698/40677526, AN-023/41981714 and AG-205/07681005.</p

Preferred Orientations of Phosphoinositides in Bilayers and Their Implications in Protein Recognition Mechanisms

Phosphoinositides (PIPs), phosphorylated derivatives of phosphatidylinositol (PI), are essential regulatory lipids involved in various cellular processes, including signal transduction, membrane trafficking, and cytoskeletal remodeling. To gain insight into the protein-PIPs recognition process, it is necessary to study the inositol ring orientation (with respect to the membrane) of PIPs with different phosphorylation states. In this study, 8 PIPs (3 PIP, 2 PIP₂, and 3 PIP₃) with different phosphorylation and protonation sites have been separately simulated in two mixed bilayers (one with 20% phosphatidylserine (PS) lipids and another with PS lipids switched to phosphatidylcholine (PC) lipids), which roughly correspond to yeast membranes. Uniformity of the bilayer properties including hydrophobic thickness, acyl chain order parameters, and heavy atom density profiles is observed in both PS-contained and PC-enriched membranes due to the same hydrophobic core composition. The relationship between the inositol ring orientation (tilt and rotation angles) and its solvent-accessible surface area indicates that the orientation is mainly determined by its solvation energy. Different PIPs exhibit a clear preference in the inositol ring rotation angle. Surprisingly, a larger proportion of PIPs inositol rings stay closer to the surface of PS-contained membranes compared to PC-enriched ones. Such a difference is rationalized with the formation of more hydrogen bonds between the PS/PI headgroups and the PIPs inositol rings in PS-contained membranes. This hydrogen bond network could be functionally important; thus, the present results can potentially add important and detailed features into the existing protein-PIPs recognition mechanism

CHARMM-GUI MDFF/xMDFF Utilizer for Molecular Dynamics Flexible Fitting Simulations in Various Environments

X-ray crystallography and cryo-electron microscopy are two popular methods for the structure determination of biological molecules. Atomic structures are derived through the fitting and refinement of an initial model into electron density maps constructed by both experiments. Two computational approaches, MDFF and xMDFF, have been developed to facilitate this process by integrating the experimental data with molecular dynamics simulation. However, the setup of an MDFF/xMDFF simulation requires knowledge of both experimental and computational methods, which is not straightforward for nonexpert users. In addition, sometimes it is desirable to include realistic environments, such as explicit solvent and lipid bilayers during the simulation, which poses another challenge even for expert users. To alleviate these difficulties, we have developed MDFF/xMDFF Utilizer in CHARMM-GUI that helps users to set up an MDFF/xMDFF simulation. The capability of MDFF/xMDFF Utilizer is greatly enhanced by integration with other CHARMM-GUI modules, including protein structure manipulation, a diverse set of lipid types, and all-atom CHARMM and coarse-grained PACE force fields. With this integration, various simulation environments are available for MDFF Utilizer (vacuum, implicit/explicit solvent, and bilayers) and xMDFF Utilizer (vacuum and solution). In this work, three examples are shown to demonstrate the usage of MDFF/xMDFF Utilizer