Evidence of Conformational Selection Driving the Formation of Ligand Binding Sites in Protein-Protein Interfaces

Many protein-protein interactions (PPIs) are compelling targets for drug discovery, and in a number of cases can be disrupted by small molecules. The main goal of this study is to examine the mechanism of binding site formation in the interface region of proteins that are PPI targets by comparing ligand-free and ligand-bound structures. To avoid any potential bias, we focus on ensembles of ligand-free protein conformations obtained by nuclear magnetic resonance (NMR) techniques and deposited in the Protein Data Bank, rather than on ensembles specifically generated for this study. The measures used for structure comparison are based on detecting binding hot spots, i.e., protein regions that are major contributors to the binding free energy. The main tool of the analysis is computational solvent mapping, which explores the surface of proteins by docking a large number of small “probe” molecules. Although we consider conformational ensembles obtained by NMR techniques, the analysis is independent of the method used for generating the structures. Finding the energetically most important regions, mapping can identify binding site residues using ligand-free models based on NMR data. In addition, the method selects conformations that are similar to some peptide-bound or ligand-bound structure in terms of the properties of the binding site. This agrees with the conformational selection model of molecular recognition, which assumes such pre-existing conformations. The analysis also shows the maximum level of similarity between unbound and bound states that is achieved without any influence from a ligand. Further shift toward the bound structure assumes protein-peptide or protein-ligand interactions, either selecting higher energy conformations that are not part of the NMR ensemble, or leading to induced fit. Thus, forming the sites in protein-protein interfaces that bind peptides and can be targeted by small ligands always includes conformational selection, although other recognition mechanisms may also be involved.     

Flexible ligand docking using conformational ensembles.

Molecular docking algorithms suggest possible structures for molecular complexes. They are used to model biological function and to discover potential ligands. A present challenge for docking algorithms is the treatment of molecular flexibility. Here, the rigid body program, DOCK, is modified to allow it to rapidly fit multiple conformations of ligands. Conformations of a given molecule are pre-calculated in the same frame of reference, so that each conformer shares a common rigid fragment with all other conformations. The ligand conformers are then docked together, as an ensemble, into a receptor binding site. This takes advantage of the redundancy present in differing conformers of the same molecule. The algorithm was tested using three organic ligand protein systems and two protein-protein systems. Both the bound and unbound conformations of the receptors were used. The ligand ensemble method found conformations that resembled those determined in X-ray crystal structures (RMS values typically less than 1.5 A). To test the method's usefulness for inhibitor discovery, multi-compound and multi-conformer databases were screened for compounds known to bind to dihydrofolate reductase and compounds known to bind to thymidylate synthase. In both cases, known inhibitors and substrates were identified in conformations resembling those observed experimentally. The ligand ensemble method was 100-fold faster than docking a single conformation at a time and was able to screen a database of over 34 million conformations from 117,000 molecules in one to four CPU days on a workstation.     

Converting a protein into a switch for biosensing and functional regulation

Proteins that switch conformations in response to a signaling event (e.g., ligand binding or chemical modification) present a unique solution to the design of reagent-free biosensors as well as molecules whose biological functions are regulated in useful ways. The principal roadblock in the path to develop such molecules is that the majority of natural proteins do not change conformation upon binding their cognate ligands or becoming chemically modified. Herein, we review recent protein engineering efforts to introduce switching properties into binding proteins. By co-opting natural allosteric coupling, joining proteins in creative ways and formulating altogether new switching mechanisms, researchers are learning how to coax conformational changes from proteins that previously had none. These studies are providing some answers to the challenging question: how can one convert a lock-and-key binding protein into a molecular switch?     

Conformational selection in protein binding and function

Protein binding and function often involves conformational changes. Advanced nuclear magnetic resonance (NMR) experiments indicate that these conformational changes can occur in the absence of ligand molecules (or with bound ligands), and that the ligands may “select” protein conformations for binding (or unbinding). In this review, we argue that this conformational selection requires transition times for ligand binding and unbinding that are small compared to the dwell times of proteins in different conformations, which is plausible for small ligand molecules. Such a separation of timescales leads to a decoupling and temporal ordering of binding/unbinding events and conformational changes. We propose that conformational‐selection and induced‐change processes (such as induced fit) are two sides of the same coin, because the temporal ordering is reversed in binding and unbinding direction. Conformational‐selection processes can be characterized by a conformational excitation that occurs prior to a binding or unbinding event, while induced‐change processes exhibit a characteristic conformational relaxation that occurs after a binding or unbinding event. We discuss how the ordering of events can be determined from relaxation rates and effective on‐ and off‐rates determined in mixing experiments, and from the conformational exchange rates measured in advanced NMR or single‐molecule fluorescence resonance energy transfer experiments. For larger ligand molecules such as peptides, conformational changes and binding events can be intricately coupled and exhibit aspects of conformational‐selection and induced‐change processes in both binding and unbinding direction.     

How different are structurally flexible and rigid binding sites? Sequence and structural features discriminating proteins that do and do not undergo conformational change upon ligand binding

Proteins are dynamic molecules and often undergo conformational change upon ligand binding. It is widely accepted that flexible loop regions have a critical functional role in enzymes. Lack of consideration of binding site flexibility has led to failures in predicting protein functions and in successfully docking ligands with protein receptors. Here we address the question: which sequence and structural features distinguish the structurally flexible and rigid binding sites? We analyze high-resolution crystal structures of ligand bound (holo) and free (apo) forms of 41 proteins where no conformational change takes place upon ligand binding, 35 examples with moderate conformational change, and 22 cases where a large conformational change has been observed. We find that the number of residue-residue contacts observed per-residue (contact density) does not distinguish flexible and rigid binding sites, suggesting a role for specific interactions and amino acids in modulating the conformational changes. Examination of hydrogen bonding and hydrophobic interactions reveals that cases that do not undergo conformational change have high polar interactions constituting the binding pockets. Intriguingly, the large, aromatic amino acid tryptophan has a high propensity to occur at the binding sites of examples where a large conformational change has been noted. Further, in large conformational change examples, hydrophobic-hydrophobic, aromatic-aromatic, and hydrophobic-polar residue pair interactions are dominant. Further analysis of the Ramachandran dihedral angles (phi, psi) reveals that the residues adopting disallowed conformations are found in both rigid and flexible cases. More importantly, the binding site residues adopting disallowed conformations clustered narrowly into two specific regions of the L-Ala Ramachandran map. Examination of the dihedral angles changes upon ligand binding shows that the magnitude of phi, psi changes are in general minimal, although some large changes particularly between right-handed alpha-helical and extended conformations are seen. Our work further provides an account of conformational changes in the dihedral angles space. The findings reported here are expected to assist in providing a framework for predicting protein-ligand complexes and for template-based prediction of protein function.     

Shape variation in protein binding pockets and their ligands

A common assumption about the shape of protein binding pockets is that they are related to the shape of the small ligand molecules that can bind there. But to what extent is that assumption true? Here we use a recently developed shape matching method to compare the shapes of protein binding pockets to the shapes of their ligands. We find that pockets binding the same ligand show greater variation in their shapes than can be accounted for by the conformational variability of the ligand. This suggests that geometrical complementarity in general is not sufficient to drive molecular recognition. Nevertheless, we show when considering only shape and size that a significant proportion of the recognition power of a binding pocket for its ligand resides in its shape. Additionally, we observe a "buffer zone" or a region of free space between the ligand and protein, which results in binding pockets being on average three times larger than the ligand that they bind.     

Protein dynamics and conformational selection in bidirectional signal transduction

Protein conformational dynamics simultaneously allow promiscuity and specificity in binding. The multiple conformations of the free EphA4 ligand-binding domain observed in two new EphA4 crystal structures provide a unique insight into the conformational dynamics of EphA4 and its signaling pathways. The heterogeneous ensemble and loop dynamics explain how the EphA4 receptor is able to bind multiple A- and B-ephrin ligands and small molecules via conformational selection, which helps to fine-tune cellular signal response in both receptor and ligand cells.     

Crystal structure of an RNA aptamer bound to thrombin

Aptamers, an emerging class of therapeutics, are DNA or RNA molecules that are selected to bind molecular targets that range from small organic compounds to large proteins. All of the determined structures of aptamers in complex with small molecule targets show that aptamers cage such ligands. In structures of aptamers in complex with proteins that naturally bind nucleic acid, the aptamers occupy the nucleic acid binding site and often mimic the natural interactions. Here we present a crystal structure of an RNA aptamer bound to human thrombin, a protein that does not naturally bind nucleic acid, at 1.9 A resolution. The aptamer, which adheres to thrombin at the binding site for heparin, presents an extended molecular surface that is complementary to the protein. Protein recognition involves the stacking of single-stranded adenine bases at the core of the tertiary fold with arginine side chains. These results exemplify how RNA aptamers can fold into intricate conformations that allow them to interact closely with extended surfaces on non-RNA binding proteins.     

A Fast Flexible Docking Method using an Incremental Construction Algorithm

We present an automatic method for docking organic ligands into protein binding sites. The method can be used in the design process of specific protein ligands. It combines an appropriate model of the physico-chemical properties of the docked molecules with efficient methods for sampling the conformational space of the ligand. If the ligand is flexible, it can adopt a large variety of different conformations. Each such minimum in conformational space presents a potential candidate for the conformation of the ligand in the complexed state. Our docking method samples the conformation space of the ligand on the basis of a discrete model and uses a tree-search technique for placing the ligand incrementally into the active site. For placing the first fragment of the ligand into the protein, we use hashing techniques adapted from computer vision. The incremental construction algorithm is based on a greedy strategy combined with efficient methods for overlap detection and for the search of new interactions. We present results on 19 complexes of which the binding geometry has been crystallographically determined. All considered ligands are docked in at most three minutes on a current workstation. The experimentally observed binding mode of the ligand is reproduced with 0.5 to 1.2 Å rms deviation. It is almost always found among the highest-ranking conformations computed.     

Computer-based screening of functional conformers of proteins

A long-standing goal in biology is to establish the link between function, structure, and dynamics of proteins. Considering that protein function at the molecular level is understood by the ability of proteins to bind to other molecules, the limited structural data of proteins in association with other bio-molecules represents a major hurdle to understanding protein function at the structural level. Recent reports show that protein function can be linked to protein structure and dynamics through network centrality analysis, suggesting that the structures of proteins bound to natural ligands may be inferred computationally. In the present work, a new method is described to discriminate protein conformations relevant to the specific recognition of a ligand. The method relies on a scoring system that matches critical residues with central residues in different structures of a given protein. Central residues are the most traversed residues with the same frequency in networks derived from protein structures. We tested our method in a set of 24 different proteins and more than 260,000 structures of these in the absence of a ligand or bound to it. To illustrate the usefulness of our method in the study of the structure/dynamics/function relationship of proteins, we analyzed mutants of the yeast TATA-binding protein with impaired DNA binding. Our results indicate that critical residues for an interaction are preferentially found as central residues of protein structures in complex with a ligand. Thus, our scoring system effectively distinguishes protein conformations relevant to the function of interest.     

Conformational and dynamics changes induced by bile acids binding to chicken liver bile acid binding protein

The correlation between protein motions and function is a central problem in protein science. Several studies have demonstrated that ligand binding and protein dynamics are strongly correlated in intracellular lipid binding proteins (iLBPs), in which the high degree of flexibility, principally occurring at the level of helix-II, CD, and EF loops (the so-called portal area), is significantly reduced upon ligand binding. We have recently investigated by NMR the dynamic properties of a member of the iLBP family, chicken liver bile acid binding protein (cL-BABP), in its apo and holo form, as a complex with two bile salts molecules. Binding was found to be regulated by a dynamic process and a conformational rearrangement was associated with this event. We report here the results of molecular dynamics (MD) simulations performed on apo and holo cL-BABP with the aim of further characterizing the protein regions involved in motion propagation and of evaluating the main molecular interactions stabilizing bound ligands. Upon binding, the root mean square fluctuation values substantially decrease for CD and EF loops while increase for the helix-loop-helix region, thus indicating that the portal area is the region mostly affected by complex formation. These results nicely correlate with backbone dynamics data derived from NMR experiments. Essential dynamics analysis of the MD trajectories indicates that the major concerted motions involve the three contiguous structural elements of the portal area, which however are dynamically coupled in different ways whether in the presence or in the absence of the ligands. Motions of the EF loop and of the helical region are part of the essential space of both apo and holo-BABP and sample a much wider conformational space in the apo form. Together with NMR results, these data support the view that, in the apo protein, the flexible EF loop visits many conformational states including those typical of the holo state and that the ligand acts stabilizing one of these pre-existing conformations. The present results, in agreement with data reported for other iLBPs, sharpen our knowledge on the binding mechanism for this protein family.     

Sampling-based exploration of folded state of a protein under kinematic and geometric constraints

Flexibility is critical for a folded protein to bind to other molecules (ligands) and achieve its functions. The conformational selection theory suggests that a folded protein deforms continuously and its ligand selects the most favorable conformations to bind to. Therefore, one of the best options to study protein-ligand binding is to sample conformations broadly distributed over the protein-folded state. This article presents a new sampler, called kino-geometric sampler (KGS). This sampler encodes dominant energy terms implicitly by simple kinematic and geometric constraints. Two key technical contributions of KGS are (1) a robotics-inspired Jacobian-based method to simultaneously deform a large number of interdependent kinematic cycles without any significant break-up of the closure constraints, and (2) a diffusive strategy to generate conformation distributions that diffuse quickly throughout the protein folded state. Experiments on four very different test proteins demonstrate that KGS can efficiently compute distributions containing conformations close to target (e.g., functional) conformations. These targets are not given to KGS, hence are not used to bias the sampling process. In particular, for a lysine-binding protein, KGS was able to sample conformations in both the intermediate and functional states without the ligand, while previous work using molecular dynamics simulation had required the ligand to be taken into account in the potential function. Overall, KGS demonstrates that kino-geometric constraints characterize the folded subset of a protein conformation space and that this subset is small enough to be approximated by a relatively small distribution of conformations.     

Cofactor‐binding sites in proteins of deviating sequence: Comparative analysis and clustering in torsion angle,cavity, and fold space

Small molecules are recognized in protein‐binding pockets through surface‐exposed physicochemical properties. To optimize binding, they have to adopt a conformation corresponding to a local energy minimum within the formed protein–ligand complex. However, their conformational flexibility makes them competent to bind not only to homologous proteins of the same family but also to proteins of remote similarity with respect to the shape of the binding pockets and folding pattern. Considering drug action, such observations can give rise tounexpected and undesired cross reactivity. In this study, datasets of six different cofactors (ADP, ATP, NAD(P)(H), FAD, and acetyl CoA, sharing an adenosine diphosphate moiety as common substructure), observed in multiple crystal structures of protein–cofactor complexes exhibiting sequence identity below 25%, have been analyzed for the conformational properties of the bound ligands, the distribution of physicochemical properties in the accommodating protein‐binding pockets, and the local folding patterns next to the cofactor‐binding site. State‐of‐the‐art clustering techniques have been applied to group the different protein–cofactor complexes in the different spaces. Interestingly, clustering in cavity (Cavbase) and fold space (DALI) reveals virtually the same data structuring. Remarkable relationships can be found among the different spaces. They provide information on how conformations are conserved across the host proteins and which distinct local cavity and fold motifs recognize the different portions of the cofactors. In those cases, where different cofactors are found to be accommodated in a similar fashion to the same fold motifs, only a commonly shared substructure of the cofactors is used for the recognition process. Proteins 2012. © 2011 Wiley Periodicals, Inc.     

Two-dimensional NMR and All-atom Molecular Dynamics of Cytochrome P450 CYP119 Reveal Hidden Conformational Substates

Cytochrome P450 enzymes are versatile catalysts involved in a wide variety of biological processes from hormonal regulation and antibiotic synthesis to drug metabolism. A hallmark of their versatility is their promiscuous nature, allowing them to recognize a wide variety of chemically diverse substrates. However, the molecular details of this promiscuity have remained elusive. Here, we have utilized two-dimensional heteronuclear single quantum coherence NMR spectroscopy to examine a series of mutants site-specific labeled with the unnatural amino acid, [¹³C]p-methoxyphenylalanine, in conjunction with all-atom molecular dynamics simulations to examine substrate and inhibitor binding to CYP119, a P450 from Sulfolobus acidocaldarius. The results suggest that tight binding hydrophobic ligands tend to lock the enzyme into a single conformational substate, whereas weak binding low affinity ligands bind loosely in the active site, resulting in a distribution of localized conformers. Furthermore, the molecular dynamics simulations suggest that the ligand-free enzyme samples ligand-bound conformations of the enzyme and, therefore, that ligand binding may proceed largely through a process of conformational selection rather than induced fit.     

Applying conformational selection theory to improve crossdocking efficiency in 3‐phosphoinositide dependent protein kinase‐1

The emerging picture of biomolecular recognition is that of conformational selection followed by induced‐fit. Conformational selection theory states that binding partners exist in various conformations in solution, with binding involving a “selection” between complementary conformers. In this study, we devise a docking protocol that mimics conformational selection in protein–ligand binding and demonstrate that it significantly enhances crossdocking accuracy over Glide's flexible docking protocol, which is widely used in the pharmaceutical industry. Our protocol uses a pregenerated conformational ensemble to simulate ligand flexibility. The ensemble was generated by thorough conformational sampling coupled with conformer minimization. The generated conformers were then rigidly docked in the active site of the protein along with a postdocking minimization step that allows limited induced fit effects to be modeled for the ligand. We illustrate the improved performance of our protocol through crossdocking of 31 ligands to cocomplexed proteins of the kinase 3‐phosphoinositide dependent protein kinase‐1 extracted from the crystal structures 1H1W (ATP bound), 1OKY (staurosporine bound) and 3QD0 (bound to a potent inhibitor). Consistent with conformational selection theory, the performance of our protocol was the best for crossdocking to the cognate protein bound to the natural ligand, ATP. Proteins 2014; 82:436–451. © 2013 Wiley Periodicals, Inc.     

Conformational selection in silico: loop latching motions and ligand binding in enzymes

Ligand binding frequently induces significant conformational changes in a protein receptor. Understanding and predicting such conformational changes represent an important challenge for computational biology, including applications to structure-based drug design. We describe an approach to this problem based on the assumption that the holo state is at least transiently populated in the absence of a ligand; this hypothesis has been referred to as "conformational selection." Here, we apply a method that tests this hypothesis on a challenging class of ligand-induced conformational changes, which we refer to as loop latching: the closing of a loop around an active site that sequesters the ligand from solvent. The method uses a combination of replica exchange molecular dynamics and a loop prediction algorithm to generate low-energy loop structures, and docking to select the conformation appropriate for binding a particular ligand. On a test set of six proteins, it yields loop structures including hololike conformations, generally below 2 A RMSD from the liganded structure, for loops that span up to 15 residues. Docking serves as a stringent test of the predictions. In five of the six cases, the predicted loop conformations improve the ranks of cognate ligands relative to using the apo structure, although the results remain, in most cases, significantly worse than using a holo structure. The poses of the cognate ligands are correct in four of the six test cases, while they are correct for five of the six using a holo structure.     

FLIPDock: docking flexible ligands into flexible receptors

Conformational changes of biological macromolecules when binding with ligands have long been observed and remain a challenge for automated docking methods. Here we present a novel protein-ligand docking software called FLIPDock (Flexible LIgand-Protein Docking) allowing the automated docking of flexible ligand molecules into active sites of flexible receptor molecules. In FLIPDock, conformational spaces of molecules are encoded using a data structure that we have developed recently called the Flexibility Tree (FT). While the FT can represent fully flexible ligands, it was initially designed as a hierarchical and multiresolution data structure for the selective encoding of conformational subspaces of large biological macromolecules. These conformational subspaces can be built to span a range of conformations important for the biological activity of a protein. A variety of motions can be combined, ranging from domains moving as rigid bodies or backbone atoms undergoing normal mode-based deformations, to side chains assuming rotameric conformations. In addition, these conformational subspaces are parameterized by a small number of variables which can be searched during the docking process, thus effectively modeling the conformational changes in a flexible receptor. FLIPDock searches the variables using genetic algorithm-based search techniques and evaluates putative docking complexes with a scoring function based on the AutoDock3.05 force-field. In this paper, we describe the concepts behind FLIPDock and the overall architecture of the program. We demonstrate FLIPDock's ability to solve docking problems in which the assumption of a rigid receptor previously prevented the successful docking of known ligands. In particular, we repeat an earlier cross docking experiment and demonstrate an increased success rate of 93.5%, compared to original 72% success rate achieved by AutoDock over the 400 cross-docking calculations. We also demonstrate FLIPDock's ability to handle conformational changes involving backbone motion by docking balanol to an adenosine-binding pocket of protein kinase A.