A Unified Conformational Selection and Induced Fit Approach to Protein-Peptide Docking

Protein-peptide interactions are vital for the cell. They mediate, inhibit or serve as structural components in nearly 40% of all macromolecular interactions, and are often associated with diseases, making them interesting leads for protein drug design. In recent years, large-scale technologies have enabled exhaustive studies on the peptide recognition preferences for a number of peptide-binding domain families. Yet, the paucity of data regarding their molecular binding mechanisms together with their inherent flexibility makes the structural prediction of protein-peptide interactions very challenging. This leaves flexible docking as one of the few amenable computational techniques to model these complexes. We present here an ensemble, flexible protein-peptide docking protocol that combines conformational selection and induced fit mechanisms. Starting from an ensemble of three peptide conformations (extended, a-helix, polyproline-II), flexible docking with HADDOCK generates 79.4% of high quality models for bound/unbound and 69.4% for unbound/unbound docking when tested against the largest protein-peptide complexes benchmark dataset available to date. Conformational selection at the rigid-body docking stage successfully recovers the most relevant conformation for a given protein-peptide complex and the subsequent flexible refinement further improves the interface by up to 4.5 Å interface RMSD. Cluster-based scoring of the models results in a selection of near-native solutions in the top three for ∼75% of the successfully predicted cases. This unified conformational selection and induced fit approach to protein-peptide docking should open the route to the modeling of challenging systems such as disorder-order transitions taking place upon binding, significantly expanding the applicability limit of biomolecular interaction modeling by docking.     

All-Atom Monte Carlo Approach to Protein-Peptide Binding

We develop a procedure for exploring the free energy landscape of protein-peptide binding at atomic detail and apply it to PDZ domain-peptide interactions. The procedure involves soft constraints on receptor proteins providing limited chain flexibility, including backbone motions. Peptide chains are left fully flexible and kept in spatial proximity of the protein through periodic boundary conditions. By extensive Monte Carlo simulations, full representative conformational ensembles at temperatures where bound and unbound states coexist are obtained. To make this approach computationally feasible, we develop an effective all-atom energy function centering on hydrophobicity, hydrogen bonding, and electrostatic interactions. Our initial focus is a set of 11 PDZ domain-peptide pairs with experimentally determined complex structures. Minimum-energy conformations are found to be highly similar to the respective native structures in eight of the cases (all-atom peptide RMSDs < 6 Å). Having achieved that, we turn to a more complete characterization of the bound peptide state through a clustering scheme applied on the full ensembles of peptide structures. We find a significant diversity among bound peptide conformations for several PDZ domains, in particular involving the N terminal side of the peptide chains. Our computational model is then tested further on a set of nine PDZ domain-peptide pairs where the peptides are not originally present in the experimentally determined structures. We find a similar success rate in terms of the nativeness of minimum-energy conformations. Finally, we investigate the ability of our approach to capture variations in binding affinities for different peptide sequences. This is done in particular for a set of related sequences binding to the third PDZ domain of PSD-95 with encouraging results.     

Recapitulation and design of protein binding peptide structures and sequences

An important objective of computational protein design is the generation of high affinity peptide inhibitors of protein-peptide interactions, both as a precursor to the development of therapeutics aimed at disrupting disease causing complexes, and as a tool to aid investigators in understanding the role of specific complexes in the cell. We have developed a computational approach to increase the affinity of a protein-peptide complex by designing N or C-terminal extensions which interact with the protein outside the canonical peptide binding pocket. In a first in silico test, we show that by simultaneously optimizing the sequence and structure of three to nine residue peptide extensions starting from short (1-6 residue) peptide stubs in the binding pocket of a peptide binding protein, the approach can recover both the conformations and the sequences of known binding peptides. Comparison with phage display and other experimental data suggests that the peptide extension approach recapitulates naturally occurring peptide binding specificity better than fixed backbone design, and that it should be useful for predicting peptide binding specificities from crystal structures. We then experimentally test the approach by designing extensions for p53 and dystroglycan-based peptides predicted to bind with increased affinity to the Mdm2 oncoprotein and to dystrophin, respectively. The measured increases in affinity are modest, revealing some limitations of the method. Based on these in silico and experimental results, we discuss future applications of the approach to the prediction and design of protein-peptide interactions.     

Pre-existing soft modes of motion uniquely defined by native contact topology facilitate ligand binding to proteins

Modeling protein flexibility constitutes a major challenge in accurate prediction of protein-ligand and protein-protein interactions in docking simulations. The lack of a reliable method for predicting the conformational changes relevant to substrate binding prevents the productive application of computational docking to proteins that undergo large structural rearrangements. Here, we examine how coarse-grained normal mode analysis has been advantageously applied to modeling protein flexibility associated with ligand binding. First, we highlight recent studies that have shown that there is a close agreement between the large-scale collective motions of proteins predicted by elastic network models and the structural changes experimentally observed upon ligand binding. Then, we discuss studies that have exploited the predicted soft modes in docking simulations. Two general strategies are noted: pregeneration of conformational ensembles that are then utilized as input for standard fixed-backbone docking and protein structure deformation along normal modes concurrent to docking. These studies show that the structural changes apparently "induced" upon ligand binding occur selectively along the soft modes accessible to the protein prior to ligand binding. They further suggest that proteins offer suitable means of accommodating/facilitating the recognition and binding of their ligand, presumably acquired by evolutionary selection of the suitable three-dimensional structure.     

Paratope plasticity in diverse modes facilitates molecular mimicry in antibody response

The immune response against methyl-alpha-D-mannopyranoside mimicking 12-mer peptide (DVFYPYPYASGS) was analyzed at the molecular level towards understanding the equivalence of these otherwise disparate Ags. The Ab 7C4 recognized the immunizing peptide and its mimicking carbohydrate Ag with comparable affinities. Thermodynamic analyses of the binding interactions of both molecules suggested that the mAb 7C4 paratope lacks substantial conformational flexibility, an obvious possibility for facilitating binding to chemically dissimilar Ags. Favorable changes in entropy during binding indicated the importance of hydrophobic interactions in recognition of the mimicking carbohydrate Ag. Indeed, the topology of the Ag-combining site was dominated by a cluster of aromatic residues, contributed primarily by the specificity defining CDR H3. Epitope-mapping analysis demonstrated the critical role of three aromatic residues of the 12-mer in binding to the Ab. Our studies delineate a mechanism by which mimicry is manifested in the absence of either structural similarity of the epitopes or conformational flexibility in the paratope. An alternate mode of recognition of dissimilar yet mimicking Ags by the anti-peptide Ab involves plasticity associated with aromatic/hydrophobic and van der Waals interactions. Thus, antigenic mimicry may be a consequence of paratope-specific modulations rather than being dependent only on the properties of the epitope. Such modulations may have evolved toward minimizing the consequences of antigenic variation by invading pathogens.     

Structure-based prediction of protein-peptide specificity in Rosetta

Protein-peptide interactions mediate many of the connections in intracellular signaling networks. A generalized computational framework for atomically precise modeling of protein-peptide specificity may allow for predicting molecular interactions, anticipating the effects of drugs and genetic mutations, and redesigning molecules for new interactions. We have developed an extensible, general algorithm for structure-based prediction of protein-peptide specificity as part of the Rosetta molecular modeling package. The algorithm is not restricted to any one peptide-binding domain family and, at minimum, does not require an experimentally characterized structure of the target protein nor any information about sequence specificity; although known structural data can be incorporated when available to improve performance. We demonstrate substantial success in specificity prediction across a diverse set of peptide-binding proteins, and show how performance is affected when incorporating varying degrees of input structural data. We also illustrate how structure-based approaches can provide atomic-level insight into mechanisms of peptide recognition and can predict the effects of point mutations on peptide specificity. Shortcomings and artifacts of our benchmark predictions are explained and limits on the generality of the method are explored. This work provides a promising foundation upon which further development of completely generalized, de novo prediction of peptide specificity may progress.     

SxIP binding disrupts the constitutive homodimer interface of EB1 and stabilizes EB1 monomer

SxIP is a microtubule tip localizing signal found in many +TIP proteins that bind to the hydrophobic cavity of the C-terminal domain of end binding protein 1 (EB1) and then positively regulate the microtubule plus-end tracking of EBs. However, the exact mechanism of microtubule activation of EBs in the presence of SxIP signaling motif is not known. Here, we studied the effect of SxIP peptide on the native conformation of EB1 in solution. Using various NMR experiments, we found that SxIP peptide promoted the dissociation of natively formed EB1 dimer. We also discovered that I224A mutation of EB1 resulted in an unfolded C-terminal domain, which upon binding with the SxIP motif folded to its native structure. Molecular dynamics simulations also confirmed the relative structural stability of EB1 monomer in the SxIP bound state. Residual dipolar couplings and heteronuclear NOE analysis suggested that the binding of SxIP peptide at the C-terminal domain of EB1 decreased the dynamics and conformational flexibility of the N-terminal domain involved in EB1-microtubule interaction. The SxIP-induced disruption of the dimeric interactions in EB1, coupled with the reduction in conformational flexibility of the N-terminal domain of EB1, might facilitate the microtubule association of EB1.     

Modeling protein-protein,protein-peptide,and protein-oligosaccharide complexes: CAPRI 7th edition

We present the seventh report on the performance of methods for predicting the atomic resolution structures of protein complexes offered as targets to the community-wide initiative on the Critical Assessment of Predicted Interactions. Performance was evaluated on the basis of 36 114 models of protein complexes submitted by 57 groups—including 13 automatic servers—in prediction rounds held during the years 2016 to 2019 for eight protein-protein, three protein-peptide, and five protein-oligosaccharide targets with different length ligands. Six of the protein-protein targets represented challenging hetero-complexes, due to factors such as availability of distantly related templates for the individual subunits, or for the full complex, inter-domain flexibility, conformational adjustments at the binding region, or the multi-component nature of the complex. The main challenge for the protein-peptide and protein-oligosaccharide complexes was to accurately model the ligand conformation and its interactions at the interface. Encouragingly, models of acceptable quality, or better, were obtained for a total of six protein-protein complexes, which included four of the challenging hetero-complexes and a homo-decamer. But fewer of these targets were predicted with medium or higher accuracy. High accuracy models were obtained for two of the three protein-peptide targets, and for one of the protein-oligosaccharide targets. The remaining protein-sugar targets were predicted with medium accuracy. Our analysis indicates that progress in predicting increasingly challenging and diverse types of targets is due to closer integration of template-based modeling techniques with docking, scoring, and model refinement procedures, and to significant incremental improvements in the underlying methodologies.     

Differential peptide dynamics is linked to major histocompatibility complex polymorphism

Peptide presentation by major histocompatibility complex (MHC) molecules is of central importance for immune responses, which are triggered through recognition of peptide-loaded MHC molecules (pMHC) by cellular ligands such as T-cell receptors (TCR). However, a unifying link between structural features of pMHC and cellular responses has not been established. Instead, pMHC/TCR binding studies suggest conformational and/or flexibility changes of the binding partners as a possible cause of differential T-cell stimulation, but information on real-time dynamics is lacking. We therefore probed the real-time dynamics of a MHC-bound nonapeptide (m9), by combining time-resolved fluorescence depolarization and molecular dynamics simulations. Here we show that the nanosecond dynamics of this peptide presented by two human MHC class I subtypes (HLA-B*2705 and HLA-B*2709) with differential autoimmune disease association varies dramatically, despite virtually identical crystal structures. The peptide dynamics is linked to the single, buried polymorphic residue 116 in the peptide binding groove. Pronounced peptide flexibility is seen only for the non-disease-associated subtype HLA-B*2709, suggesting an entropic control of peptide recognition. Thermodynamic data obtained for two additional peptides support this hypothesis.     

Exploring Protein-Peptide Binding Specificity through Computational Peptide Screening

The binding of short disordered peptide stretches to globular protein domains is important for a wide range of cellular processes, including signal transduction, protein transport, and immune response. The often promiscuous nature of these interactions and the conformational flexibility of the peptide chain, sometimes even when bound, make the binding specificity of this type of protein interaction a challenge to understand. Here we develop and test a Monte Carlo-based procedure for calculating protein-peptide binding thermodynamics for many sequences in a single run. The method explores both peptide sequence and conformational space simultaneously by simulating a joint probability distribution which, in particular, makes searching through peptide sequence space computationally efficient. To test our method, we apply it to 3 different peptide-binding protein domains and test its ability to capture the experimentally determined specificity profiles. Insight into the molecular underpinnings of the observed specificities is obtained by analyzing the peptide conformational ensembles of a large number of binding-competent sequences. We also explore the possibility of using our method to discover new peptide-binding pockets on protein structures.     

Specificity of a protein–protein interface: Local dynamics direct substrate recognition of effector caspases

Proteases are prototypes of multispecific protein–protein interfaces. Proteases recognize and cleave protein and peptide substrates at a well‐defined position in a substrate binding groove and a plethora of experimental techniques provide insights into their substrate recognition. We investigate the caspase family of cysteine proteases playing a key role in programmed cell death and inflammation, turning caspases into interesting drug targets. Specific ligand binding to one particular caspase is difficult to achieve, as substrate specificities of caspase isoforms are highly similar. In an effort to rationalize substrate specificity of two closely related caspases, we investigate the substrate promiscuity of the effector Caspases 3 and 7 by data mining (cleavage entropy) and by molecular dynamics simulations. We find a strong correlation between binding site rigidity and substrate readout for individual caspase subpockets explaining more stringent substrate readout of Caspase 7 via its narrower conformational space. Caspase 3 subpockets S3 and S4 show elevated local flexibility explaining the more unspecific substrate readout of that isoform in comparison to Caspase 7. We show by in silico exchange mutations in the S3 pocket of the proteases that a proline residue in Caspase 7 contributes to the narrowed conformational space of the binding site. These findings explain the substrate specificities of caspases via a mechanism of conformational selection and highlight the crucial importance of binding site local dynamics in substrate recognition of proteases. Proteins 2014; 82:546–555. © 2013 Wiley Periodicals, Inc.     

Molecular Basis for Bcl-2 Homology 3 Domain Recognition in the Bcl-2 Protein Family: IDENTIFICATION OF CONSERVED HOT SPOT INTERACTIONS*

The proteins of the Bcl-2 family are important regulators of apoptosis, or programmed cell death. These proteins regulate this fundamental biological process via the formation of heterodimers involving both pro- and anti-apoptotic family members. Disruption of the balance between anti- and pro-apoptotic Bcl-2 proteins is the cause of numerous pathologies. Bcl-xl, an anti-apoptotic protein of this family, is known to form heterodimers with multiple pro-apoptotic proteins, such as Bad, Bim, Bak, and Bid. To elucidate the molecular basis of this recognition process, we used molecular dynamics simulations coupled with the Molecular Mechanics/Poisson-Boltzmann Surface Area approach to identify the amino acids that make significant energetic contributions to the binding free energy of four complexes formed between Bcl-xl and pro-apoptotic Bcl-2 homology 3 peptides. A fifth protein-peptide complex composed of another anti-apoptotic protein, Bcl-w, in complex with the peptide from Bim was also studied. The results identified amino acids of both the anti-apoptotic proteins as well as the Bcl-2 homology 3 (BH3) domains of the pro-apoptotic proteins that make strong, recurrent interactions in the protein complexes. The calculations show that the two anti-apoptotic proteins, Bcl-xl and Bcl-w, share a similar recognition mechanism. Our results provide insight into the molecular basis for the promiscuous nature of this molecular recognition process by members of the Bcl-2 protein family. These amino acids could be targeted in the design of new mimetics that serve as scaffolds for new antitumoral molecules.     

Multi-scaled explorations of binding-induced folding of intrinsically disordered protein inhibitor IA3 to its target enzyme

Biomolecular function is realized by recognition, and increasing evidence shows that recognition is determined not only by structure but also by flexibility and dynamics. We explored a biomolecular recognition process that involves a major conformational change - protein folding. In particular, we explore the binding-induced folding of IA3, an intrinsically disordered protein that blocks the active site cleft of the yeast aspartic proteinase saccharopepsin (YPrA) by folding its own N-terminal residues into an amphipathic alpha helix. We developed a multi-scaled approach that explores the underlying mechanism by combining structure-based molecular dynamics simulations at the residue level with a stochastic path method at the atomic level. Both the free energy profile and the associated kinetic paths reveal a common scheme whereby IA3 binds to its target enzyme prior to folding itself into a helix. This theoretical result is consistent with recent time-resolved experiments. Furthermore, exploration of the detailed trajectories reveals the important roles of non-native interactions in the initial binding that occurs prior to IA3 folding. In contrast to the common view that non-native interactions contribute only to the roughness of landscapes and impede binding, the non-native interactions here facilitate binding by reducing significantly the entropic search space in the landscape. The information gained from multi-scaled simulations of the folding of this intrinsically disordered protein in the presence of its binding target may prove useful in the design of novel inhibitors of aspartic proteinases.     

Peptide Modulation of Class I Major Histocompatibility Complex Protein Molecular Flexibility and the Implications for Immune Recognition

T cells use the αβ T cell receptor (TCR) to recognize antigenic peptides presented by class I major histocompatibility complex proteins (pMHCs) on the surfaces of antigen-presenting cells. Flexibility in both TCRs and peptides plays an important role in antigen recognition and discrimination. Less clear is the role of flexibility in the MHC protein; although recent observations have indicated that mobility in the MHC can impact TCR recognition in a peptide-dependent fashion, the extent of this behavior is unknown. Here, using hydrogen/deuterium exchange, fluorescence anisotropy, and structural analyses, we show that the flexibility of the peptide binding groove of the class I MHC protein HLA-A*0201 varies significantly with different peptides. The variations extend throughout the binding groove, impacting regions contacted by TCRs as well as other activating and inhibitory receptors of the immune system. Our results are consistent with statistical mechanical models of protein structure and dynamics, in which the binding of different peptides alters the populations and exchange kinetics of substates in the MHC conformational ensemble. Altered MHC flexibility will influence receptor engagement, impacting conformational adaptations, entropic penalties associated with receptor recognition, and the populations of binding-competent states. Our results highlight a previously unrecognized aspect of the “altered self” mechanism of immune recognition and have implications for specificity, cross-reactivity, and antigenicity in cellular immunity.     

A role for both conformational selection and induced fit in ligand binding by the LAO protein

Molecular recognition is determined by the structure and dynamics of both a protein and its ligand, but it is difficult to directly assess the role of each of these players. In this study, we use Markov State Models (MSMs) built from atomistic simulations to elucidate the mechanism by which the Lysine-, Arginine-, Ornithine-binding (LAO) protein binds to its ligand. We show that our model can predict the bound state, binding free energy, and association rate with reasonable accuracy and then use the model to dissect the binding mechanism. In the past, this binding event has often been assumed to occur via an induced fit mechanism because the protein's binding site is completely closed in the bound state, making it impossible for the ligand to enter the binding site after the protein has adopted the closed conformation. More complex mechanisms have also been hypothesized, but these have remained controversial. Here, we are able to directly observe roles for both the conformational selection and induced fit mechanisms in LAO binding. First, the LAO protein tends to form a partially closed encounter complex via conformational selection (that is, the apo protein can sample this state), though the induced fit mechanism can also play a role here. Then, interactions with the ligand can induce a transition to the bound state. Based on these results, we propose that MSMs built from atomistic simulations may be a powerful way of dissecting ligand-binding mechanisms and may eventually facilitate a deeper understanding of allostery as well as the prediction of new protein-ligand interactions, an important step in drug discovery.     

Soft docking an L and a D peptide to an anticholera toxin antibody using internal coordinate mechanics.

BACKGROUND: The tremendous increase in sequential and structural information is a challenge for computer-assisted modelling to predict the binding modes of interacting biomolecules. One important area is the structural understanding of protein-peptide interactions, information that is increasingly important for the design of biologically active compounds. RESULTS: We predicted the three-dimensional structure of a complex between the monoclonal antibody TE33 and its cholera-toxin-derived peptide epitope VPGSQHID. Using the internal coordinate mechanics (ICM) method of flexible docking, the bound conformation of the initially extended peptide epitope to the antibody crystal or modelled structure reproduced the known binding conformation to a root mean square deviation of between 1.9 A and 3.1 A. The predicted complexes are in good agreement with binding data obtained from substitutional analyses in which each epitope residue is replaced by all other amino acids. Furthermore, a de novo prediction of the recently discovered TE33-binding D peptide dwGsqhydp (single-letter amino acid code where D amino acids are represented by lower-case letters) explains results obtained from binding studies with 172 peptide analogues. CONCLUSIONS: Despite the difficulties arising from the huge conformational space of a peptide, this approach allowed the prediction of the correct binding orientation and the majority of essential binding features of a peptide-antibody complex.     

Relative solvent accessible surface area predicts protein conformational changes upon binding

Protein interactions are often accompanied by significant changes in conformation. We have analyzed the relationships between protein structures and the conformational changes they undergo upon binding. Based upon this, we introduce a simple measure, the relative solvent accessible surface area, which can be used to predict the magnitude of binding-induced conformational changes from the structures of either monomeric proteins or bound subunits. Applying this to a large set of protein complexes suggests that large conformational changes upon binding are common. In addition, we observe considerable enrichment of intrinsically disordered sequences in proteins predicted to undergo large conformational changes. Finally, we demonstrate that the relative solvent accessible surface area of monomeric proteins can be used as a simple proxy for protein flexibility. This reveals a powerful connection between the flexibility of unbound proteins and their binding-induced conformational changes, consistent with the conformational selection model of molecular recognition.