Exploring the molecular design of protein interaction sites with molecular dynamics simulations and free energy calculations

Abstract

The significant work that has been invested toward understanding protein-protein interaction has not translated into significant advances in structure-based predictions. In particular redesigning protein surfaces to bind to unrelated receptors remains a challenge, partly due to receptor flexibility, which is often neglected in these efforts. In this work, we computationally graft the binding epitope of various small proteins obtained from the RCSB database to bind to barnase, lysozyme, and trypsin using a previously derived and validated algorithm. In an effort to probe the protein complexes in a realistic environment, all native and designer complexes were subjected to a total of nearly 400 ns of explicit-solvent molecular dynamics (MD) simulation. The MD data led to an unexpected observation: some of the designer complexes were highly unstable and decomposed during the trajectories. In contrast, the native and a number of designer complexes remained consistently stable. The unstable conformers provided us with a unique opportunity to define the structural and energetic factors that lead to unproductive protein-protein complexes. To that end we used free energy calculations following the MM-PBSA approach to determine the role of nonpolar effects, electrostatics and entropy in binding. Remarkably, we found that a majority of unstable complexes exhibited more favorable electrostatics than native or stable designer complexes, suggesting that favorable electrostatic interactions are not prerequisite for complex formation between proteins. However, nonpolar effects remained consistently more favorable in native and stable designer complexes reinforcing the importance of hydrophobic effects in protein-protein binding. While entropy systematically opposed binding in all cases, there was no observed trend in the entropy difference between native and designer complexes. A series of alanine scanning mutations of hot-spot residues at the interface of native and designer complexes showed less than optimal contacts of hot-spot residues with their surroundings in the unstable conformers, resulting in more favorable entropy for these complexes. Finally, disorder predictions revealed that secondary structures at the interface of unstable complexes exhibited greater disorder than the stable complexes.

FIGURE 1

Schematic representation of the grafting process. (A) Scanning of a large database for scaffold proteins with minimal binding epitope (MBE); (B) transfer of MBE from bound ligand to scaffold; (C) superimposition of scaffold to ligand using atoms in MBE; (D) grafting of surface of scaffold to complement receptor binding surface. (E) Final complex of receptor with grafted scaffold.

FIGURE 2

A comparison of observed and designed interface sequences. Highlighted are the conserved residues. Hot-spot residues are shown in bold red. A residue is considered hot-spot if the binding free energy change is greater than 2.5 kcal · mol⁻¹ upon mutation to alanine as reported for barnase–barstar (43), for BPTI (44), and for lysozyme–antibody D1.3 (47), respectively. The hot-spot residues of trypsin are not marked since the mutational information is not available.

FIGURE 3

Comparison of trypsin–BPTI and trypsin-mutated scaffold interfaces. (A) trypsin–BPTI; (B) trypsin–CheY. Proteins are shown in ribbon representation (marine blue, orange, and red for trypsin, BPTI and CheY respectively). Several residues at the interface are shown in capped stick representation color-coded according to atom types (yellow, red and blue for C, O, and N respectively).

FIGURE 4

Comparison of the interface between native and designer complex of the antibody D1.3. (A) Native antibody D1.3/lysozyme; (B) lysozyme/α-amylase inhibitor. Complexes are shown in ribbon representation with lysozyme shown in orange and antibody D1.3 and α-amylase shown in deep teal. Hot-spot residues are depicted in capped-stick representation and colored according to atom types (yellow, red and blue correspond to C, O, and N).

FIGURE 5

Root-mean-square deviation with respect to time for (A) five trajectories involving the barnase receptor, where red, blue, yellow, pink and cyan correspond to the native complex of barnase–barstar, the designer complexes of barnase–barstar NMR, barnase–NSF1 RNA binding domain, barnase–IF-3 C-terminal domain, and barnase–allergen PHL P2 respectively; (B) four trajectories of complexes that involve lysozyme where the red, yellow, blue, and pink lines correspond to the lysozyme–antibody D1.3 native complex, the designer complexes of lysozyme with α-amylase inhibitor, scorpion toxin BJXTR-IT, MPB1 DNA binding domain, respectively; (C) four trajectories of complexes involving trypsin where red corresponds to the native trypsin–BPTI complex and blue, yellow, and pink correspond to the trypsin–CheY, trypsin–allergen, and trypsin–tenascin designer complexes, respectively.

FIGURE 6

Stereoview of the three-dimensional structure of (A) native barnase–barstar complex, (B) banase–barstar NMR stable designer complex, and (C) barnase–allergen PHL P2 unstable designer complex. The receptor (marine blue) and ligand (yellow) are shown in ribbon representation.

FIGURE 7

Stereoview of the three-dimensional structure of (A) native lysozyme–antibody D1.3, (B) lysozyme–scorpion toxin BJXTR-IT stable designer complex, and (C) lysozyme–α-amylase unstable designer complex. The receptor (teal) and ligand (orange) are shown in ribbon representation.

FIGURE 8

Electrostatic (solid lines) and nonpolar (dashed lines) components of the free energy plotted for (A) trajectories involving barnase as a receptor (the red lines correspond to the native barnase–barstar complex, the yellow lines to the stable designer complex barnase–allergen PHL P2, the blue lines to the unstable designer complex barnase–NS1 RNA binding domain) and (B) trajectories involving antibody D1.3 as a receptor (the green line corresponds to the native complex between antibody D1.3 and lysozyme, the cyan line to the antibody D1.3–α-amylase inhibitor).

FIGURE 9

PONDR VSL1 score distributions for proteins in the barnase–barstar-analogue complexes. Localizations of the barstar-binding regions are indicated as gray shaded areas. The values of the mean distance from the 0.5 boundary for each binding region are indicated in the corresponding plot. In PONDR plot, segments with scores above 0.5 correspond to the disordered regions, whereas those below 0.5 correspond to the ordered regions.

FIGURE 10

PONDR VSL1 score distributions for proteins in the antibody–antigene-analogue complexes. Localizations of antibody-binding regions are indicated as gray shaded areas. The values of the mean distance from the 0.5 boundary for each binding region are indicated in the corresponding plot. In PONDR plot, segments with scores above 0.5 correspond to the disordered regions, whereas those below 0.5 correspond to the ordered regions.

FIGURE 11

PONDR VSL1 score distributions for proteins in the trypsin–inhibitor-analogue complexes. Localizations of trypsin-binding regions are indicated as gray shaded areas. The values of the mean distance from the 0.5 boundary for each binding region are indicated in the corresponding plot. In PONDR plot, segments with scores above 0.5 correspond to the disordered regions, whereas those below 0.5 correspond to the ordered regions.