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Cite this: DOI: 10.1039/c5mb00800j

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A two-step binding mechanism for the selfbinding peptide recognition of target domains Chao Yang,ab Shilei Zhang,ab Zhengya Bai,ab Shasha Hou,ab Di Wu,a Jian Huangabc and Peng Zhou*abc Self-binding peptides (SBPs) represent a novel biomolecular phenomenon spanning between folding and binding, where a short peptide segment within a monomeric protein fulfills biological functions by dynamically binding to/unbinding from its target domain in the same monomer. Here, we were able to quantitatively reconstruct the complete structural dynamics picture of binding of free SBPs to their target domains for five representative SBP systems by carrying out the state-of-the-art molecular dynamics (MD) simulations. In the picture, a two-step binding mechanism for SBP–domain recognition and association was proposed, which includes a fast, nonspecific diffusive phase and a slow, specific organizational phase. The electrostatic interactions and desolvation effects play a predominant role in the first phase that leads to the formation of a metastable encounter complex, while conformational rearrangement is observed in the second phase to optimize the exquisite network of nonbonded chemical forces such as hydrogen bonds and salt bridges across the complex interface. From an energetic point of view, a funnel-shape enthalpy landscape steers these SBPs towards their native bound state and thus facilitates the binding process. However, the binding exhibits typical enthalpy–entropy compensation due

Received 20th November 2015, Accepted 28th January 2016

to the high flexibility of peptides that results in a relatively low affinity for SBP–domain binding and forces

DOI: 10.1039/c5mb00800j

tional changes in the target domain and/or in the polypeptide linker between the domain and peptide can

www.rsc.org/molecularbiosystems

significantly affect the energetic properties and dynamic behavior of the fine-tuned binding process of SBP–domain recognition.

the SBP systems to rapidly switch between the bound and unbound states. In addition, slight conforma-

Introduction Protein–protein interactions (PPIs) are essential for most of the processes that occur in living cells. Many protein interactions in cell signaling are mediated by interactions between globular domains and short linear motifs (SLiMs).1,2 SLiMs represent short peptide segments in disordered protein regions that often become folded in the bound state to interact with their biological partners. Russell and co-workers suggested that 15–40% of all PPIs in the cell are mediated by short peptide segments located at the interaction sites.3,4 Due to their cardinal role in the regulation and modulation of the crowded protein network in the cells, peptides are in many cases implicated in human disease and can provide an excellent starting point for drug discovery. a

Center of Bioinformatics (COBI), School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China. E-mail: [email protected] b Key Laboratory for Neuroinformation of the Ministry of Education, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China c Center for Information in BioMedicine, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China

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Peptides as drug candidates also have the potential to combine the advantages of strong specificity, high biocompatibility and low toxicity profiles over small-molecule inhibitors.5–7 In the past few decades, much work has been devoted to unravel the mechanistic details of peptide–protein recognition. The X-ray crystal structures of peptide–protein complexes as well as the apo structures of their members provide a straightforward fashion to impart how the recognition and binding work at the atomic level, what kinds of chemical forces are involved with, and whether the binding is in a specific manner.7,8 Moreover, dynamic information of the complexes could also be partially captured from the solution NMR structures directly. For example, SH3 domains bind sequences with polyproline type II (PPII) helix configuration,9 and PDZ domains target short sequence patterns occurring at the extreme C-terminal end of proteins by adding the sequence as a single strand to their b-sheet groove.10 The relatively few classes of solved peptide–protein complex structures can be complemented by an outburst of modeling approaches that have been introduced in recent years. Currently available high-resolution peptide docking protocols such as Rosetta FlexPepDock11,12 and HADDOCK13 are based on prior knowledge about the local binding site to guide modeling and

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also pave a molecular recognition pathway in exploring the biophysical nature of peptide–protein interactions. Previously, by using a long-term molecular dynamics (MD) simulation with explicit water representation, Ahmad et al. proposed a two-state model to explain the binding of a ultra-fast peptide to its cognate SH3 receptor: the first one is a relatively fast diffusive phase, leading to the formation of nonspecific encounter complexes stabilized by long-range electrostatic force, and the secondary one is a slowly adjusting phase during which the water molecules are drained out from the SH3–peptide interface.14 The twostate model was later confirmed by Demers and Mittermaier who applied NMR together with ITC to characterize the interaction of the SH3 domain with a 12-residue peptide at temperatures between 10 and 50 1C.15 These technological advances and intensive studies enlighten researchers to make use of computational and theoretical approaches to study peptides with novel structure architectures. A new concept of peptide recognition called self-binding peptides (SBPs) was introduced in our recent work.16 SBPs represent the short peptide segments within monomeric proteins to fulfill their biological functions by dynamically binding to/unbinding from their target domains in the same monomers (Fig. 1). The sequence pattern of SBPs is very similar to those of SLiMs found in many protein-binding peptides where few key residues contribute predominantly to peptide–protein interactions. Since SBPs are a portion of their parent monomer and connected to their target via a flexible linker, it is supposed that SBPs adopt a so-called ‘‘binding-on-swinging’’ manner to interact with their target weakly and reversibly. This feature makes SBPs ideal for mediating biological mechanisms that are required as fast responses to stimuli. In addition to acting as auto-inhibitory peptides for competitively blocking the binding of native ligands,17,18 SBPs exhibit a broad spectrum of biological functions such as improving protein thermostability,19 acting as molecular switches to regulate enzyme activity20–22 and plugging in the pores of ion channels.23 Considering that the SBP– target binding is transient, reversible and governed by weak

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nonbonded interactions, it is suggested that the SBPs would be promising as a new class of therapeutic targets that can be readily interfered with small-molecule agents and peptidic drugs.24,25 However, the atomic details about the binding event as well as the chemical forces that drive the binding have not yet been explored. Elucidating the dynamic behavior and chemical forces involved in SBP recognition by vicinal target domains is fundamentally important to understand the molecular mechanism and the biological implication of SBPs. Since SBP–target binding/ unbinding can be triggered by various biological events such as protein modification (i.e. phosphonation, methylation and acetylation), changes in physical conditions (i.e. pH value, temperature and pressure), and ligand regulation (i.e. binding of small-molecule ligands),16 we herein selected five representative SBP systems to perform a systematic investigation of the molecular mechanism of SBP–target binding, including two phosphonation-mediated SBPs,26,27 two peptide-competing SBPs,28,29 and one ligand-regulated SBP (Table 1).30 In addition, the SBP–target interaction is governed by diffusive, noncovalent chemical forces associated with significant conformational flexibility, which can only be reconstructed and dissected at the atomic level by using long-term molecular dynamics (MD) simulations. Here, using the state-of-the-art simulations we were able to reproducibly recover the complete structural dynamics of SBP–target recognition and association. Based on the simulations we will focus on the structural basis, energetic properties and the dynamic pathway of SBP transition from unbound to bound states.

Experimental Data set The high-resolution crystal structures of five monomeric proteins, i.e. Cryptosporidium parvum 14-3-3 protein (Cp14b), artificial scaffold protein (INAD), human retinoic acid receptor (hRARg),

Fig. 1 Schematic representation of a fine-tuned SBP system. In a monomeric protein, the SBP segment is integrated into the monomer in the primary sequence via a flexible polypeptide linker and modulates the biological activity of the protein by binding to/unbinding from its target domain in the same protein. As shown here, the SBP (red) is located at the terminus of its parent monomer containing three structured domains, in which domain 2 (yellow) is the cognate target of the SBP. Under certain events, such as protein modification or physical condition change, domain 2 can be specifically recognized and bound by the SBP to form their complex. The flexible linker (blue) that connects between the SBP and domain 2 facilitates the fast transition between the unbound state (left) and bound state (right), and thus makes the SBP ideal for mediating biological events that are required as fast responses to stimuli.

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Table 1

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Five representative SBP systems

PDB

Protein

SBP sequenceb

3EFZ

Cryptosporidium parvum 14-3-3 protein (Cp14b) Artificial scaffold protein (INAD)

259

Human retinoic acid receptor (hRARg)

407

2LA8 2LBD

2MDJ Htt yeast two-hybrid protein B [wild type] (HYPB) 2MDI [mutant] a Bacterial TCA cycle regulator 2KB4 (OdhI)

Target domain

CSGLLTpSAFF268

100

RRNQYWV106 PMPPLIREMLENP419

1

GSDLPPPSPP10

10

PQVETpTSVFR19c

Type

14-3-3 domain Phosphonationmediated PDZ5 domain Peptidecompeting LBD domain Ligand-regulated WW domain FHA domain

Modeling the unbound state of SBP–target structures The distance between a SBP and its target domain was separated artificially by using steered molecular dynamics (SMD) simulations with a constant velocity.32 In the procedure, the Ca atoms of the domain were fixed, while external steering forces were applied to the center of mass of Ca atoms of the SBP and the polypeptide linker. The direction of the force was defined by the vector that links the center of mass of Ca atoms between the SBP and the domain. During SMD simulations, the force was only applied along the pulling direction with a pulling speed of 0.05 Å ps 1. The simulations were addressed on the four SBP–target bound crystal structures, i.e. Cp14b, INAD, hRARg and HYPB, for 0.6, 0.3, 0.4 and 0.2 ns, respectively, during which the time step was set to be 1 fs and the harmonic spring constant was 7 kcal mol 1 Å 2. The final unbound structures yielded from SMD were used as the starting point to perform routine MD simulations (Fig. 2). Molecular dynamics simulations Molecular dynamics (MD) simulations of SBP–target binding were carried out using the AMBER ff14SB force field implemented in the Amber 14 suite of programs.33,34 AmberTools package was

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Distancec (Å)

120

30

100 [reduced form] 15 100 [oxidized form] 50 [ligand-bound 20 form] 50 [ligand-free form] Peptide-competing 1020 [wild type] 10 100 [mutant] 830

Phosphonationmediated

a 2KB4 is the crystal structure in SBP-unbound state and its crystal counterpart in SBP-bound state is 2KB3. phosphorylated threonine. c The initial distance between the SBP and target domain.

Htt yeast two-hybrid protein B (HYPB), and bacterial TCA cycle regulator (OdhI), were retrieved from the PDB database,31 from which five typical SBP segments were extracted according to the three basic features described previously for SBPs (Table 1).16 The SBPs investigated here are located at either the terminus or the middle of their parent proteins. We visually examined these crystal structures and found that four SBPs (Cp14b, INAD, hRARg and HYPB) are crystallized only in the bound state, while the SBP of OdhI protein has been solved in both bound (PDB: 2KB3) and unbound states (PDB: 2KB4).26 For the Cp14b, INAD, hRARg and HYPB, we artificially separated the SBP segments and their target domains in crystal structures by a sufficient distance to define corresponding unbound structures (vide post). For the OdhI protein, we directly adopted the unbound state of the crystal structure as the starting point of MD simulations; prior to the simulations the structure was artificially phosphorylated at the Thr15 residue in the SBP sequence.

Simulation time (ns)

b

39

pS, phosphorylated serine; pT,

employed to create topology and coordinate files for the initial unbound structures of SBP systems. The force field parameters for phosphorylated amino acids in two monomeric proteins Cp14b and OdhI (PDB: 3EFZ and 2KB4, respectively) were obtained from the AMBER parameter database.35 The LBD domain of hRARg (PDB: 2LBD) was co-crystallized with a small-molecule ligand, i.e. all-trans retinoic acid, which was found to be responsible for receptor stability.30 Thus, the ligand was kept during the MD simulations and parameterized using the general AMBER force field (GAFF).36 Each structure was solvated in a rectangular box full of TIP3P water molecules so that the boundary of the box is at least 12 Å away from any solute atom. Counter-ions of Na+ or Cl were placed based on the Columbic potential to keep the whole system electroneutral. The cutoff distance was set to be 10 Å for shortrange Coulomb and van der Waals interactions. The particle mesh Ewald (PME) method37 and the SHAKE algorithm38 were utilized to treat long-range electrostatic interactions and to constrain all covalent bonds involving hydrogen atoms, respectively. The system was first relaxed by 500 steps of the steepest descent minimization followed by up to 1000 steps of conjugate gradient minimization. Harmonic restraints with a force constant of 10 kcal mol 1 Å 2 were applied to all solute atoms during the minimization. These restraints were maintained through 40 ps constant volume ensemble (NVT) MD simulations, during which the system was heated from 0 to 300 K. The restraints were also addressed on subsequent 100 ps MD simulations with the isothermal isobaric ensemble (NPT) to adjust the solvent density. Finally, production simulations with a time step of 2 fs were performed to equilibrate the whole system at a constant temperature of 300 K and a constant pressure of 1 atm; the temperature and pressure were maintained using Langevin thermostat and Berendsen barostat methods, respectively. The Langevin thermostat was applied using a collision frequency time of 1.0 ps 1 to maintain the production simulations at a constant temperature of 300 K, and the pressure was maintained by coupling to the reference pressure of 1 bar. The atomic coordinates of production were saved every 5 ps to obtain a large number of dynamic snapshots during the SBP–target binding simulations for post structural and energetic analysis.

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Fig. 2 The five SBP systems in unbound (before simulations) and bound (after simulations) states as well as, for comparison, crystal structures in bound state. The initial (unbound) structures of the five systems were modeled artificially from crystal (bound) structures (PDB: 3EFZ, 2LA8, 2LBD and 2MDI) by manually separating SBP segments and target domains for a sufficient distance (A–D) or directly using the available crystal (unbound) structure (PDB: 2KB4) (E). The initial distances between SBPs and target domains are shown in Table 1.

Linear interaction energy analysis The electrostatic and van der Waals potentials between a SBP and its target domain were calculated using the linear interaction energy (LIE) approach39 implemented using the cpptraj program.33 For each snapshot extracted from the MD trajectory, the nonbonded interactions of all atom-pairs between the SBP and domain were calculated systematically. The cutoff distance was set to be 40 Å for both electrostatic and van der Waals interactions. Water density analysis In order to investigate the solvent effect on SBP binding, we calculated the water density in the SBP–target interfacial gap for several selected transient states, which were divided by means of the time interval during the binding process. To ensure equilibrium of water density in the interfacial gap, each selected transient state was subjected to 10 ns independent, unbiased MD simulations. In the procedure, 2000-step energy minimizations were carried out on the system under harmonic restraints with a force constant of 100 kcal mol 1 Å 2 applied to

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all heavy atoms, followed by 40 ps NVT-MD and 10 ns NPT-MD simulations. The gap volume was defined using the SURFNET program.40 Water density in the gap was calculated through the snapshots from the last 5 ns of each simulation. Binding free energy analysis The free energy change upon the binding of a SBP to its target domain was calculated over the dynamic snapshots extracted from the MD trajectory via the post molecular mechanics/Poisson– Boltzmann surface area (MM/PBSA) approach41 implemented using the mmpbsa program.33 The binding free energy can be computed from the absolute free energies of SBP–target bound states (Gcomplex) as well as the unbound states of the separated target domain (GTD) and SBP (GSBP) as follows: DGtotal = Gcomplex = DEmm + DGdslv

(GTD + GSBP) T  DS

(1)

where DEmm is the molecular mechanics interaction energy between the SBP and domain, DGdslv is the desolvation free energy upon SBP–target binding, and DS is the conformational

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entropy change due to the binding. The DEmm consists of three components, i.e. the internal energy term (DEint), the electrostatic energy term (DEelc) and the van der Waals energy term (DEvdw). The DGdslv is a co-contribution from polar and nonpolar effects (DGsol_pol and DGsol_npol) due to draining out of water molecules from the complex interface upon SBP–target binding; the DGsol_pol and DGsol_npol were described using the Poisson– Boltzmann (PB) approach and surface area (SA) model, respectively. The entropic penalty (DS) was estimated by normal mode analysis (NMA)42 using the nmode program.33 For each SBP system, every 50 consecutive snapshots extracted from the selected time point over the binding process were used to perform binding free energy analysis, which was divided by means of the time interval. Due to the high computational demand, only 25 snapshots taken from the above 50 consecutive snapshots were used to estimate entropy.

Results Structural dynamics reveal a two-step binding mechanism of SBP–target recognition To dissect the molecular mechanism of SBP binding to the target domain, five representative SBP systems (Cp14b, INAD, hRARg, HYPB and OdhI proteins) were subjected to long-term MD simulations in an explicit water environment to reconstruct their structural dynamics. The initial (unbound) structures of the five systems were modeled artificially from crystal (bound) structures (PDB: 3EFZ, 2LA8, 2LBD and 2MDI) by manually separating SBP segments and target domains for a sufficient distance or directly using the available crystal (unbound) structure (PDB 2KB4). First, the five protein systems were subjected to long-term MD simulations varying between 50 and 830 ns to recover their bound state (Fig. 2). Then, three additional simulations were carried out for the modified forms of INAD, hRARg and OdhI. Therefore, totally eight independent simulations were run as follows: (i) One with Cp14b. 120 ns simulations were performed for the system; the SBP segment can correctly bind to the 14-3-3 domain at B40 ns. (ii) Two with INAD in reduced and oxidized forms. 100 ns simulations were performed for the two systems; the SBP segment can correctly bind to the PDZ5 domain at B12 ns for the reduced form and at B30 ns for the oxidized form. (iii) Two with hRARg in ligand-bound and ligand-free forms. 50 ns simulations were performed for the two systems; the SBP segment can correctly bind to the LBD domain very quick (B1.2 ns) in ligand-bound form, but appears never to stably interact with the domain in ligand-free form. (iv) Two with wild-type and mutant HYPB. 1.02 ms simulation was performed for wild type due to its complicated binding pathway, while 100 ns simulations of mutant were sufficient to reach at its equilibrium state. (v) One with OdhI. Due to the SBP preceded by an extended, intrinsically disordered N-terminal tail, the journey of its binding to the FHA domain became very rugged that may involve random

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Fig. 3 RMSD fluctuation profiles as a function of time over the whole MD simulations of Cp14b (A), INAD (B), hRARg (C), HYPB (D) and OdhI (E).

perturbation caused by the additional tail. Thus, totally 830 ns simulations cannot reproduce the SBP–FHA bound state as that in the crystal structure. Here, the root-mean-square deviation (RMSD) fluctuations of backbone heavy atoms as a function of time over the simulations were calculated to characterize the dynamics profile of simulated SBP–target systems and to gauge SBP motion during the binding process (Fig. 3). It is seen that all the simulations exhibited an initial rapid rise in RMSD values over the first 1–40 ns, after which a relatively stable state is maintained. In addition, a few representative snapshots were extracted from the MD trajectories at different times for the five systems (Fig. 4). As can be seen, during the initial stage of MD progression the SBP segments undergo a considerable conformational change to approach to target domains, and then the segments are swinging on the domain surface to find out their binding sites. During the simulations the SBP segment of Cryptosporidium parvum 14-3-3 protein Cp14b (PDB: 3EFZ) gradually associated with the 14-3-3 domain in the first 40 ns and finally achieved to a correct binding mode (Fig. 4A). More specifically, the phosphorylated Ser265 (pSer265) residue of the SBP serves as an anchor that can form an initial encounter complex with the positively charged Lys91 of the 14-3-3 domain at B20 ns, and then the metastable complex was fast broken to release pSer265, which subsequently jumped into a highly positively charged pocket defined by three domain residues Lys83, Arg79 and Arg153 to obtain the correct binding mode. The SBP segment

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Fig. 4 Few representative snapshots extracted from MD simulations of Cp14b (A), INAD (B), hRARg (C), HYPB (D) and OdhI (E).

of INAD (PDB: 2LA8) can swiftly fold into bound conformation in the active site of the PDZ5 domain after a short period of random contacts with the domain (Fig. 4B). A binding process that combined conformational selection and induced fit13,43 was observed for hRARg (PDB: 2LBD), where the SBP was first structured as an a-helix and then fast touched onto the surface of the LBD domain, which was finally optimized to the native bound state (Fig. 4C). The SBP of Htt yeast two-hybrid protein B HYPB is a proline-rich sequence, which was first anchored at a region nearby the active site of the WW domain and then underwent a fast conformational change to form a poly-proline II (PPII) helix that can further be fitted into the site (Fig. 4D). The unbound state of SBP in bacterial TCA cycle regulator OdhI was retrieved from the solution NMR structure directly (PDB: 2KB4),26 from which the MD simulations can well recover the binding process and native bound structure of the SBP to the FHA domain. Two stages were observed during the simulations of SBP–FHA binding: first, the SBP and the polypeptide linker that connects between the SBP and FHA domain were swiftly packed against the domain to form an unspecific encounter complex; second, the SBP segment was slowly folded onto the phosphopeptide-binding surface of the domain to form a partially helical conformation (Fig. 4E). However, the N-terminal tail preceding the SBP exhibited

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intrinsically disordered dynamics (Fig. 5), which made it hard for the SBP to overcome the energy barriers before reaching its final bound state. Thus, the journey of SBP binding to the FHA domain became very rugged that may involve random perturbation from the additional tail. Therefore, even up to 830 ns simulations seem not to reproduce the SBP–FHA bound state in the crystal structure. Instead, a stable, tightly packed complex where the SBP is partially structured can be derived from the exhaustive simulations, which is supposed to be an alternative binding mode of the SBP system.

Fig. 5 Top-view (left) and side-view (right) of the superposed representative structures in the second stage of 830 ns MD simulations of OdhI. The N-terminal tail is shown to be intrinsically disordered.

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The structural dynamics reconstructed by MD simulations revealed a common two-step binding mechanism in SBP–target recognition, that is, the recognition includes a fast, nonspecific diffusive phase and a slow, specific organizational phase. The former results in a transient state of SBP–target encounter, which is further refined in the latter to obtain the final native structure of the stable SBP–target complex. Role of nonbonded chemical forces in SBP–target binding To quantify the energetic properties of SBP–target binding the changes in nonbonded potentials of electrostatic and van der Waals interactions between the five SBPs and their cognate domains during MD simulations were calculated using the linear interaction energy (LIE) approach.39 As can be seen in Fig. 6, a general tendency of decrease in different nonbonded energies can be observed with the binding progression; the primary chemical force that drives the formation of metastable SBP–target encounter in the first diffusive phase is long-range electrostatic attraction, which contributes dominantly to the binding as compared to the van der Waals effect. For the four SBP systems of Cp14b, hRARg, HYPB and OdhI, the potential profiles go through a rapid decline followed by a stable period. In particular, the electrostatic effect is much significant in Cp14b and OdhI. In fact, the binding events of the two systems are triggered by SBP phosphonation, which can constitute a number of salt-bridging interactions with the positively charged residues of target domains. For example, as shown by the electrostatic potential profile of Cp14b (Fig. 6A) the system is largely electrostatically stabilized over the 25–40 ns of MD simulations, after which the metastable SBP–target encounter is readily defined (Fig. 4A). However, due to the relatively small distance (B10 Å) and weak affinity between the SBP and WW domain in the initial unbound structure of the HYPB mutant, the nonbonded potentials of the system exhibit a distinct profile compared to other four systems, that is, the system in beginning and ending of MD simulations appears to be more stable than others. It is seen from Fig. 6D that both the electrostatic and van der Waals potentials first go up to a plateau over 16–91 ns, and then rapidly decline in the final stage to reach a equilibrium state, suggesting that the system would undergo significant conformational rearrangement and large energetic variation upon SBP binding. Desolvation effect serves as a driving force for SBP–target binding Similar to classical peptide–protein interactions,14,44 the desolvation effect may play an important role in SBP–target recognition and binding. In order to investigate the solvent behavior at the SBP–target interface prior to binding, we herein calculated water density in the intermolecular gap of several transient states extracted from the binding process, which were divided by means of the time interval during the first step of the binding. Here, the interfacial gaps were defined using the SURFNET program,40 and each extracted transient system was subjected to 10 ns MD simulations with position harmonic restraints to estimate water density in the SBP–target gap.

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Due to the high flexibility of the SBP segment displayed in initial binding phase, the intermolecular gap between the SBP and target domain sometimes may not correspond to the final binding site. Thus, water density in the gap of some random contacts was also calculated. As can be seen (Fig. 7A), ten transient states were extracted from the first 45 ns MD simulations of Cp14b protein at an interval of 5 ns. The SBP–target interfacial gaps at 10 and 15 ns were supposed to be random contacts at which the water density suddenly decreases (Fig. 7B), suggesting that the desolvation has no long-range effect on SBP binding. After the random contacts, SBP–target interfacial gaps held in a stable state of water density (B742 g L 1) until 40 ns. After that a large decrease in water density (B647 g L 1) was observed, indicating a fast draining out of water from the interface before the final binding. In this respect, the solvent effect increases considerably with SBP approaching its target domain, indicating that hydrophobic contribution would play a predominant role in the later stage of SBP–target binding. This is similar to the fact that dewetting has been found to exhibit a distance-dependent behavior to help specific peptide–protein recognition.14,45,46 For the INAD system totally 12 snapshots of transient binding states were extracted from the first 11 ns MD simulations at an interval of 1 ns. Due to the short initial distance between the SBP and PDZ5 domain in the system (B15 Å), the interfacial gap exhibits a fast decrease in water density after starting the simulations (Fig. 8B). The system snapshots at 1, 2 and 3 ns were considered as random contacts (Fig. 8A) so that the water densities at these points did not reflect the dewetting effect at the correct binding site. During 9–11 ns simulations the SBP C-terminus was observed to spatially rearrange to fit in the binding pocket of the INAD aB/bB groove, and thus the water density of the interfacial gap slightly increased in this period. The desolvation effect was particularly significant in the hRARg system, in which the modeled initial distance was set to be 20 Å between the SBP segment and the LBD domain. During MD simulations the interfacial gap exhibited a significant degree of reduced water density and extracted transient states converged very fast to the final structure (Fig. 9). Due to the fact that the SBP segment should first form a structured a-helix conformation in the binding, the hydrophobic residues in the helix, such as Leu, Ile and Met, can be forced to the binding site of the LBD domain and thus the desolvation effect would accelerate SBP association with the pocket. Moreover, as a result of rapid conformational changes and reversible interfacial contacts in the protein system, it is difficult to characterize quantitatively the reduced water density profile at the SBP–target binding interface of the mutant HYPB protein. Therefore, no direct desolvation effect was calculated for the system. However, several transient snapshots extracted from MD simulations showed a low water density in interfacial gap, suggesting that interface dewetting may already start before the binding. In addition, the transient snapshots extracted at 5, 10 and 15 ns of the MD trajectory also exhibit a low water density of B0.779, 0.614 and 0.575 g L 1, respectively, during SBP binding to the FHA domain in the OdhI protein.

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Fig. 6 Fluctuation profiles of electrostatic potentials and van der Waals energies over MD simulations of Cp14b (A), INAD (B), hRARg (C), HYPB (D) and OdhI (E).

Conformational entropy in SBP–target binding Given the fact that the SBP would convert from a flexible segment to a rigid, well-defined structure upon its binding to the target domain, conformational entropy loss (DS)47–49 due to the binding was considered here and calculated via the normal mode analysis (NMA) approach42 based on the transient snapshots extracted

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from the MD trajectory. The estimation of entropy penalty to SBP binding is important because it allows a detailed understanding of thermodynamic components underlying the binding. Here, entropy penalty at each extracted transient state was calculated using the NMA method. As can be seen in Fig. 10A, the conformational entropy penalty decreased gradually with the binding of the phosphorylated SBP to the 14-3-3 domain in

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Fig. 7 Change in water density of the Cp14b SBP–target interfacial gap during the binding process. (A) The transient snapshots of ten interfacial gaps between the phosphorylated SBP and 14-3-3 domain extracted evenly from the first 45 ns MD simulations of the Cp14b protein at an interval of 5 ns. (B) Water density in interfacial gaps in the 10 extracted transient snapshots.

Fig. 8 Change in water density of the INAD SBP–target interfacial gap during the binding process. (A) The transient snapshots of twelve interfacial gaps between the C-terminal SBP and PDZ5 domain extracted evenly from the first 11 ns MD simulations of the INAD protein at an interval of 1 ns. (B) Water density in interfacial gaps in the 12 extracted transient snapshots.

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Fig. 10 Conformational entropy changes during the SBP–target binding processes (T = 300 K) of Cp14b (A), INAD (B), hRARg (C) and the oxidized form of INAD (D).

the Cp14b system, suggesting that the SBP segment was trapped into the 14-3-3 pocket and possessed low mobility during the binding. A decreasing trend in entropy loss is shown in Fig. 10B for the INAD protein, with a moderate difference of 7.11 kcal mol 1 between the two end points. In line with the random contacts at 1, 2 and 3 ns, the entropy penalty of these transient states was increased during the binding process. The conformational entropy loss of the hRARg protein is contributed by two aspects: SBP folding to form an a-helix configuration and SBP–target binding to form a complex; entropy penalty resulted primarily from the backbone rearrangement associated with the folding over the first 0.8 ns simulations, and after then the conformational entropy of the system showed a clear decrease upon the subsequent binding (Fig. 10C). The observation of large changes in conformational entropy supports the notion that the entropic effect is an important aspect of SBP–target binding thermodynamics and may play an essential role in contribution to the system affinity.50,51 We found that the conformational entropy of the SBP–target system reduced gradually during the simulations to ultimately reach the final complex as observed in the crystal structure. The formation of the SBP–target bound state would accompany diverse intramolecular interactions involved in a large contact area, which provide a favorable enthalpy contribution to compensate the associated unfavorable entropy penalty. Due to the fast, reversible binding/unbinding in the SBP-mediated biological mechanism, conformational entropy should play a lubricant role in enthalpy driving SBP–target binding.

Discussion Fig. 9 Change in water density of the hRARgSBP–target interfacial gap during the binding process. (A) The transient snapshots of twelve interfacial gaps between the SBP and LBD domain extracted evenly from the first 2.8 ns MD simulations of hRARg protein at an interval of 0.4 ns. (B) Water density in interfacial gaps in the 8 extracted transient snapshots.

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The structural basis, energetic properties and dynamics behavior of five representative SBP systems were explored in detail by using atomistic MD simulations; the simulations were from completely unbound to final bound states. Based on the simulations, we suggested a two-step mechanism for the progression of SBP–target binding: first, the SBP associates swiftly with its

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target domain to form a nonspecific encounter complex; second, the complex is gradually optimized converging to the final specific structure. The two-step binding mechanism of SBP is analogous to the two-state model proposed by Ahmad et al. to describe domain–peptide recognition.14 However, some distinct features are presented for the SBP. For example, during the first phase of SBP–target binding the SBP can readily touch its target domain to form a metastable encounter complex between them (Fig. 3), during which the electrostatic potential acts as the primary chemical force driving the complex formation. When the SBP approaches its target domain closely, the desolvation effect also guides the SBP search for the correct binding site in the target, thus accelerating the binding progression. After the encounter complex formation, the contributions from other physicochemical factors such as hydrogen bonding and van der Waals contacts as well as conformation changes can slowly optimize the system to reach its final bound state. The two-state model reveals a bimodal binding mechanism, where the electrostatic interactions and hydrophobic forces work in a synergistic manner to promote and guide protein–peptide recognition. From an energetic point of view, SBP–target binding is a spontaneous process associated with the free energy decrease. As can be seen in Fig. 11A, during the first stage of binding enthalpy decreases significantly upon SBP binding to the 14-3-3 domain of the Cp14b protein (first 40 ns), which is in line with the changing trends of both electrostatic potential (Fig. 6A) and water density in interfacial gaps (Fig. 7). Thus, the SBP–target binding is enthalpy-driven where noncovalent chemical forces such as electrostatic attraction and desolvation effect are primarily responsible for the formation of a metastable encounter complex. The INAD and hRARg proteins also have a clear decrease in enthalpy upon the encounter complex formation (Fig. 11B and C), in which the SBP–target system can readily occupy a low-energy state with a short distance. In general, a funnelshape energy landscape of enthalpy change is depicted, which guides SBP binding in a spontaneous and specific way. Moreover, the conformational entropy seems to decrease in a slow, gradual manner during the binding, which can partially offset the predominant enthalpy effect. Consequently, SBP–target association involves with the appropriate location of molecular fragments with respect to each other, a gradual decrease in conformational entropy, and simultaneous lowering of the free energy by solvent exclusion and nonbonded formation. In order to examine the conclusions and findings obtained above, we herein performed three additional MD simulation studies on several modified SBP systems to further explore the molecular mechanism of SBP–target recognition and association: (i) The oxidized form of the INAD protein29 was constructed by manually bridging an intramolecular Cys27–Cys66 disulfide bond in the PDZ5 domain of this protein, which was then subjected to 100 ns MD simulations and compared to its reduced form. A dramatic oscillation of the RMSD profile of oxidized INAD can be observed in the first 30 ns simulations (Fig. 12A), which sustains longer than that (B12 ns) of the reduced form, suggesting that the INAD protein in reduced state is inclined to form an encounter complex. Compared with its reduced form,

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Fig. 11 Enthalpy (DH) and free energy (DG) changes during the SBP–target binding processes (T = 300 K) of Cp14b (A and E), INAD (B and F), hRARg (C and G) and the oxidized form of INAD (D and H).

Fig. 12 100 ns MD simulations of the oxidized form of INAD. (A) RMSD fluctuation profile. (B) Electrostatic potential profile. (C) van der Waals energy profile. (D) Change in water density in SBP–target interfacial gap during the first 30 ns simulations that cover the first phase of SBP binding.

the oxidized protein possesses much higher barriers in the funnel-like energy landscape of enthalpy change before reaching a lower energy state of the encounter complex (Fig. 11D). However, the protein in both reduced and oxidized states exhibits a similar profile of electrostatic energy during the second phase

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of binding, while the van der Waals potential of the reduced form is markedly stabilized relative to the oxidized form (Fig. 6B and 12C). Thus, it is indicated that the conformational arrangement of the INAD protein from reduced to oxidized states would impair SBP–target affinity considerably, which can be supported by the experimental evidence that a B20-fold decrease in binding affinity of the Kon peptide (SBP) to the PDZ5 domain (target) is induced by INAD oxidation.29,52 In a similar manner, the associated desolvation was observed to drain out of water molecules from the SBP–target interfacial gap in the oxidized INAD protein (Fig. 12D). (ii) The SBP–target binding of the hRARg protein is triggered by ligand regulation, that is, the conformation of the LBD domain changes dramatically when bound with an all-trans retinoic acid to facilitate the binding.30 Here, both the apo (ligand-free) and holo (ligand-bound) structures of the hRARg protein were separately subjected to 50 ns MD simulations and compared with each other. As can be seen in Fig. 13, the SBP segment of the unliganded hRARg protein cannot bind stably to the LBD domain, indicating that the presence of the retinoic acid ligand in the hRARg protein is the prerequisite for the correct SBP binding. In line with a previous prediction,30 the conformational change of the LBD domain induced by ligand regulation would reshape the physicochemical properties of the LBD active site, thus altering SBP–target binding behavior significantly.

Moreover, the hRARg protein converting between apo and holo states was found to largely shift SBP binding affinity, primarily contributed by the van der Waals effect (B6 kcal mol 1 difference) (Table 2). (iii) While the HYPB mutant is locked in a closed conformation, the wild-type protein (PDB: 2MDJ) exhibits two conformations in equilibrated state (the ratio of closed to open is about 6 : 1).28 Although both the wild-type and mutant proteins have a well-structured WW domain that can form intramolecular interaction with its preceding poly-proline stretch (SBP segment), a difference between them is that the 11KPK13 sequence in the linker region of the wild-type protein is substituted by 11AAA13 in the mutant. We herein carried out 1.02 ms MD simulations of SBP–target binding for the wild-type HYPB protein to reconstruct its conformational conversion from open to closed states (Fig. 14). Apparently, the simulations took much more time than the mutant (100 ns), suggesting a very complicated pathway of SBP–target binding for the wild type compared to the mutant. In addition, the simulations revealed that the 5PPP7 region of the SBP poly-proline stretch can directly contact the WW domain in a conventional manner53 for both wild-type and mutant proteins. However, the 9PP10 residues of the stretch were found to interact with the WW domain only in the mutant, which was supposed to confer additional stabilization for the mutant in closed state. Consequently, the polypeptide linker

Fig. 13 50 ns MD simulations of unliganded hRARg. (A) Representative snapshots of unliganded hRARg extracted from the simulations, where the SBP segment can fold into an a-helix configuration but cannot bind correctly to its cognate target domain. (B) RMSD fluctuation profile. (C) Superposition between the MD-equilibrated structures of the ligand-bound protein (silver) and its ligand-free counterpart (cyan). Top-view (left) and side-view (right) of the superposed structures, in which the small-molecule ligand all-trans retinoic acid is shown in green stick.

Table 2

Free energy decomposition of SBP–target binding for ligand-free and ligand-bound hRARg

DEint

DEnbd System Ligand-bound Ligand-free a

DEelc 103.66 103.44

DEvdw 63.44 57.55

DEbond

DEangle

DEtors

0.51 0.51

0.85 0.85

10.80 10.80

DEgas 154.95 148.83

DGsol_pol 121.32 120.54

DGsol_npol 7.13 7.07

DGdslv

DGpolara

114.19 113.47

17.66 17.10

DH 40.76 35.36

TDS

DGtotal

39.12 35.33

1.64 0.03

DGpolar is the polar contribution (DEelc + DGsol_pol) to the total binding free energy.

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a funnel-shape energy landscape of enthalpy change is depicted for SBP binding, indicating that the SBP–target recognition and association are spontaneous and walk in a specific binding trajectory. However, the unfavorable entropy penalty upon SBP–target binding would largely impair the favorable enthalpy contribution, thus rendering a typical entropy–enthalpy compensation that generally gives rise to a weak binding affinity, which enables SBPs to promptly switch between the unbound and bound states. Furthermore, the additional simulations for some non-typical systems revealed that conformation changes in either the target domain or the polypeptide linker can substantially affect the SBP binding event, which can be regulated through diverse mechanisms such as chemical modification, condition alteration and ligand binding. Fig. 14 1.02 ms MD simulations of wild-type HYPB. (A) RMSD fluctuation profile. (B) Electrostatic potential profile. (C) van der Waals energy profile. (D) Representative snapshots extracted from the simulations as well as superposition between the closed conformations derived from MD simulations (silver) and crystal structure (cyan) (RMSD = 3.64 Å).

between the SBP and WW domain became more flexible due to introduction of three small, nonpolar Ala residues into the region, thus largely facilitating the binding. The modifications in the target domain or poly-peptide linker were clearly demonstrated to regulate SBP–target binding efficiently, suggesting that the finely tuned features of SBP specificity and affinity can be involved in diverse biological functions. In fact, the dynamic equilibrium between SBP–target binding and unbinding generally relies on protein modifications (i.e. phosphonation, methylation and acetylation), physical conditions (i.e. pH, temperature and pressure) and ligand regulation.16 In addition, conformational changes in both the SBP segment and target domain can also affect their specific binding behavior. A variety of transitions of the binding are usually associated with large conformational rearrangement that requires an additional flexibility in the polypeptide linker connecting between the SBP and domain.

Conclusions Here, we have employed long-term, atomistic MD simulations to elucidate the molecular mechanism of SBP binding and recognition by its target domain within the same monomer. Several SBP systems were investigated in detail to reconstruct the complete structural dynamics from their unbound state to the final bound state. Despite the limited systems studied, SBPs were demonstrated to play an important role in the structural and functional regulation of biomolecules. The simulations revealed a two-step binding mechanism for SBPs; the electrostatic interactions and the desolvation effect are the predominant chemical forces in the first phase of SBP binding that results in a nonspecific, metastable encounter complex, and the second phase involves a slow process of conformational rearrangement and optimization by refining the complicated network of nonbonded interactions involved in the encounter complex, which finally recovers the specific, native bound state. Moreover,

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Acknowledgements This work was supported by the Science and Technology Project of Sichuan Province (No. 2015JY0252), the National Natural Science Foundation of China (No. 31200993), the Young Teacher Doctoral Discipline Fund of Ministry of Education of China (No. 20120185120025), the Fundamental Research Funds for the Central Universities of China (No. ZYGX2015J144) and the New Academic Researcher Award of UESTC.

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A two-step binding mechanism for the self-binding peptide recognition of target domains.

Self-binding peptides (SBPs) represent a novel biomolecular phenomenon spanning between folding and binding, where a short peptide segment within a mo...
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