Unbound Conformations Research Articles

Advancing membrane-associated protein docking with improved sampling and scoring in Rosetta.

The oligomerization of protein macromolecules on cell membranes plays a fundamental role in regulating cellular function. From modulating signal transduction to directing immune response, membrane proteins (MPs) play a crucial role in biological processes and are often the target of many pharmaceutical drugs. Despite their biological relevance, the challenges in experimental determination have hampered the structural availability of membrane proteins and their complexes. Computational docking provides a promising alternative to model membrane protein complex structures. Here, we present Rosetta-MPDock, a flexible transmembrane (TM) protein docking protocol that captures binding-induced conformational changes. Rosetta-MPDock samples large conformational ensembles of flexible monomers and docks them within an implicit membrane environment. We benchmarked this method on 29 TM-protein complexes of variable backbone flexibility. These complexes are classified based on the root-mean-square deviation between the unbound and bound states (RMSDUB) as: rigid (RMSDUB <1.2 Å), moderately-flexible (RMSDUB ∈ [1.2, 2.2) Å), and flexible targets (RMSDUB > 2.2 Å). In a local docking scenario, i.e. with membrane protein partners starting ≈10 Å apart embedded in the membrane in their unbound conformations, Rosetta-MPDock successfully predicts the correct interface (success defined as achieving 3 near-native structures in the 5 top-ranked models) for 67% moderately flexible targets and 60% of the highly flexible targets, a substantial improvement from the existing membrane protein docking methods. Further, by integrating AlphaFold2-multimer for structure determination and using Rosetta-MPDock for docking and refinement, we demonstrate improved success rates over the benchmark targets from 64% to 73%. Rosetta-MPDock advances the capabilities for membrane protein complex structure prediction and modeling to tackle key biological questions and elucidate functional mechanisms in the membrane environment. The benchmark set and the code is available for public use at github.com/Graylab/MPDock.

Phosphate uptake in PhoX: Molecular mechanisms

PhoX is a high-affinity phosphate binding protein, present in Xanthomonas citri, a phytopathogen responsible for the citrus canker disease. Performing molecular dynamics simulations and different types of computational analyses, we study the molecular mechanisms at play in relation to phosphate binding, revealing the global functioning of the protein: PhoX naturally oscillates along its global normal modes, which allow it to explore both bound and unbound conformations, eventually attracting a nearby negative phosphate ion to the highly positive electrostatic potential on its surface, particularly close to the binding pocket. There, several hydrogen bonds are formed with the two main domains of the structure. Phosphate creates, in this way, a strong bridge that connects the domains, keeping itself between them, in a tight closed conformation, explaining its high binding affinity.

Dynamics-based network analysis identifies laci rheostats with high accuracy.

One current challenge in protein engineering involves accurately predicting distal mutations which lead towards a desired function. A more complex problem is identifying and characterizing putative “rheostat” sites which result in progressive, amino acid-dependent biophysical changes upon substitution. The E. coli LacI system contains several rheostat positions with respect to the binding of LacI inducer IPTG, which allosterically inhibits DNA binding. Here we employ in-house computational tools for characterizing network dynamics: Site-specific flexibility via the Dynamic Flexibility Index (DFI), residue pair coupling strength via the Dynamic Coupling Index (DCI), and Vibrational Density of States (VDOS) analysis to generate measures of position-specific dynamic-based metrics. Comparison of bound and unbound LacI shows specific regions of each system exhibit fundamentally different flexibilities where critical DNA binding domain positions show increased flexibility when bound to IPTG. Further, we assess asymmetry in dynamic coupling as a cause-effect measure between ligands/ligand binding positions and all other positions within the protein. Additionally, it is possible to calculate positional vibrational entropy through VDOS; this analysis, again, showed distinctions between bound and unbound simulations in both the DNA binding domain as well as the IPTG binding region. Taken together, the drastic differences of these dynamics-based metrics between bound and unbound conformations indicate their strength in construction unique dynamical fingerprints for both systems. Using a machine learning approach, we construct features based off of these dynamic properties from which we extract probabilities of a given position being “neutral”, “toggle” (i.e., abolishing biophysical activity upon mutation) or “rheostat” based upon experimental IPTG EC50 data of IPTG binding. Using a small subset of dynamic network features, we show it is possible to categorize, with accuracy rates of ∼0.9, a given position's probability of falling into one of the three above categories.

Higher Affinity Antibodies Bind With Lower Hydration and Flexibility in Large Scale Simulations.

We have carried out a long-timescale simulation study on crystal structures of nine antibody-antigen pairs, in antigen-bound and antibody-only forms, using molecular dynamics with enhanced sampling and an explicit water model to explore interface conformation and hydration. By combining atomic level simulation and replica exchange to enable full protein flexibility, we find significant numbers of bridging water molecules at the antibody-antigen interface. Additionally, a higher proportion of interactions excluding bulk waters and a lower degree of antigen bound CDR conformational sampling are correlated with higher antibody affinity. The CDR sampling supports enthalpically driven antibody binding, as opposed to entropically driven, in that the difference between antigen bound and unbound conformations do not correlate with affinity. We thus propose that interactions with waters and CDR sampling are aspects of the interface that may moderate antibody-antigen binding, and that explicit hydration and CDR flexibility should be considered to improve antibody affinity prediction and computational design workflows.

From complete cross-docking to partners identification and binding sites predictions.

Proteins ensure their biological functions by interacting with each other. Hence, characterising protein interactions is fundamental for our understanding of the cellular machinery, and for improving medicine and bioengineering. Over the past years, a large body of experimental data has been accumulated on who interacts with whom and in what manner. However, these data are highly heterogeneous and sometimes contradictory, noisy, and biased. Ab initio methods provide a means to a "blind" protein-protein interaction network reconstruction. Here, we report on a molecular cross-docking-based approach for the identification of protein partners. The docking algorithm uses a coarse-grained representation of the protein structures and treats them as rigid bodies. We applied the approach to a few hundred of proteins, in the unbound conformations, and we systematically investigated the influence of several key ingredients, such as the size and quality of the interfaces, and the scoring function. We achieved some significant improvement compared to previous works, and a very high discriminative power on some specific functional classes. We provide a readout of the contributions of shape and physico-chemical complementarity, interface matching, and specificity, in the predictions. In addition, we assessed the ability of the approach to account for protein surface multiple usages, and we compared it with a sequence-based deep learning method. This work may contribute to guiding the exploitation of the large amounts of protein structural models now available toward the discovery of unexpected partners and their complex structure characterisation.

Probing protein flexibility reveals a mechanism for selective promiscuity

Many eukaryotic regulatory proteins adopt distinct bound and unbound conformations, and use this structural flexibility to bind specifically to multiple partners. However, we lack an understanding of how an interface can select some ligands, but not others. Here, we present a molecular dynamics approach to identify and quantitatively evaluate the interactions responsible for this selective promiscuity. We apply this approach to the anticancer target PD-1 and its ligands PD-L1 and PD-L2. We discover that while unbound PD-1 exhibits a hard-to-drug hydrophilic interface, conserved specific triggers encoded in the cognate ligands activate a promiscuous binding pathway that reveals a flexible hydrophobic binding cavity. Specificity is then established by additional contacts that stabilize the PD-1 cavity into distinct bound-like modes. Collectively, our studies provide insight into the structural basis and evolution of multiple binding partners, and also suggest a biophysical approach to exploit innate binding pathways to drug seemingly undruggable targets.

Multi-drug resistance profile of PR20 HIV-1 protease is attributed to distorted conformational and drug binding landscape: molecular dynamics insights

The PR20 HIV-1 protease, a variant with 20 mutations, exhibits high levels of multi-drug resistance; however, to date, there has been no report detailing the impact of these 20 mutations on the conformational and drug binding landscape at a molecular level. In this report, we demonstrate the first account of a comprehensive study designed to elaborate on the impact of these mutations on the dynamic features as well as drug binding and resistance profile, using extensive molecular dynamics analyses. Comparative MD simulations for the wild-type and PR20 HIV proteases, starting from bound and unbound conformations in each case, were performed. Results showed that the apo conformation of the PR20 variant of the HIV protease displayed a tendency to remain in the open conformation for a longer period of time when compared to the wild type. This led to a phenomena in which the inhibitor seated at the active site of PR20 tends to diffuse away from the binding site leading to a significant change in inhibitor–protein association. Calculating the per-residue fluctuation (RMSF) and radius of gyration, further validated these findings. MM/GBSA showed that the occurrence of 20 mutations led to a drop in the calculated binding free energies (ΔGbind) by ~25.17 kcal/mol and ~5 kcal/mol for p2-NC, a natural peptide substrate, and darunavir, respectively, when compared to wild type. Furthermore, the residue interaction network showed a diminished inter-residue hydrogen bond network and changes in inter-residue connections as a result of these mutations. The increased conformational flexibility in PR20 as a result of loss of intra- and inter-molecular hydrogen bond interactions and other prominent binding forces led to a loss of protease grip on ligand. It is interesting to note that the difference in conformational flexibility between PR20 and WT conformations was much higher in the case of substrate-bound conformation as compared to DRV. Thus, developing analogues of DRV by retaining its key pharmacophore features will be the way forward in the search for novel protease inhibitors against multi-drug resistant strains.

Molecular Determinants and Kinetic Parameters of Ligand Binding to G Protein-Coupled Receptors using Markov State Model Analysis

G protein-coupled receptors (GPCRs) continue to hold leading positions as drug targets. However, many GPCR drug candidates fail in clinical trials because of limited in vivo efficacy. While binding affinity, i.e. the strength of association of a drug to its receptor, has traditionally been viewed as an appropriate surrogate for in vivo efficacy, retrospective analyses of marketed drugs suggest that kinetic parameters, such as the rates at which the drug associates with or dissociates from the target, may play a role that is as important as, or even more important than, binding affinity in determining in vivo efficacy. Thus, predictions of both kinetic and thermodynamic parameters of ligand binding to GPCRs are highly desirable because they may inform the rational discovery of improved therapeutics.The main challenge in studying molecular recognition by a GPCR using molecular dynamics (MD) is that ligand binding and unbinding are rare events on microscopic time-scales, and as such, they are difficult to observe using unbiased simulations. In this work, we present a general strategy that employs biased MD simulations to build Markov State Models (MSMs) of the binding of small molecules to a prototypic family A GPCR. Using Perron cluster analysis and transition path theory we were able to identify the kinetic basins of the binding process, and to characterize both the metastable and the transition states between bound and unbound conformations. By investigating the role of hydrophobic interactions, dewetting, and conformational changes in the binding pocket, we were able to characterize the microscopic determinants that influence association and dissociation rates of the ligand. This information has a direct applicability in rational drug design approaches.

Quantitative thermodynamic predication of interactions between nucleic acid and non-nucleic acid species using Microsoft excel

Proper design of nucleic acid sequences is crucial for many applications. We have previously established a thermodynamics-based quantitative model to help design aptamer-based nucleic acid probes by predicting equilibrium concentrations of all interacting species. To facilitate customization of this thermodynamic model for different applications, here we present a generic and easy-to-use platform to implement the algorithm of the model with Microsoft® Excel formulas and VBA (Visual Basic for Applications) macros. Two Excel spreadsheets have been developed: one for the applications involving only nucleic acid species, the other for the applications involving both nucleic acid and non-nucleic acid species. The spreadsheets take the nucleic acid sequences and the initial concentrations of all species as input, guide the user to retrieve the necessary thermodynamic constants, and finally calculate equilibrium concentrations for all species in various bound and unbound conformations. The validity of both spreadsheets has been verified by comparing the modeling results with the experimental results on nucleic acid sequences reported in the literature. This Excel-based platform described here will allow biomedical researchers to rationalize the sequence design of nucleic acid probes using the thermodynamics-based modeling even without relevant theoretical and computational skills.

Combining geometric pocket detection and desolvation properties to detect putative ligand binding sites on proteins

The accurate identification of cavities that can bind ligands on the surface of proteins is of major importance for the characterization of the function of proteins based on its structure. In addition it can be helpful for rational structure-based drug design on target proteins of medical relevance and for evaluating the tendency of proteins to aggregate or oligomerize. A new approach termed dPredGB to detect and evaluate putative binding cavities on protein surfaces has been developed. In contrast to existing prediction methods that are based on purely geometric features of binding sites or on possible direct interactions with a putative binding partner the dPredGB approach combines rapid geometric detection with an evaluation of the desolvation properties of the putative binding pocket. It has been tested on a variety of proteins known to bind ligands in bound and unbound conformations. The approach outperforms most available methods and offers also the spatial characterization of the desolvation properties of a binding region. On a test set of proteins the method identifies in 69% of the unbound cases and 85% of the bound cases the known ligand binding cavity as the top ranking prediction. Possibilities to improve the prediction performance even further are also discussed.

Scoring optimisation of unbound protein–protein docking including protein binding site predictions

The prediction of the structure of the protein-protein complex is of great importance to better understand molecular recognition processes. During systematic protein-protein docking, the surface of a protein molecule is scanned for putative binding sites of a partner protein. The possibility to include external data based on either experiments or bioinformatic predictions on putative binding sites during docking has been systematically explored. The external data were included during docking with a coarse-grained protein model and on the basis of force field weights to bias the docking search towards a predicted or known binding region. The approach was tested on a large set of protein partners in unbound conformations. The significant improvement of the docking performance was found if reliable data on the native binding sites were available. This was possible even if data for single key amino acids at a binding interface are included. In case of binding site predictions with limited accuracy, only modest improvement compared with unbiased docking was found. The optimisation of the protocol to bias the search towards predicted binding sites was found to further improve the docking performance resulting in approximately 40% acceptable solutions within the top 10 docking predictions compared with 22% in case of unbiased docking of unbound protein structures.

Shape complementarity of protein-protein complexes at multiple resolutions.

Biological complexes typically exhibit intermolecular interfaces of high shape complementarity. Many computational docking approaches use this surface complementarity as a guide in the search for predicting the structures of protein-protein complexes. Proteins often undergo conformational changes to create a highly complementary interface when associating. These conformational changes are a major cause of failure for automated docking procedures when predicting binding modes between proteins using their unbound conformations. Low resolution surfaces in which high frequency geometric details are omitted have been used to address this problem. These smoothed, or blurred, surfaces are expected to minimize the differences between free and bound structures, especially those that are due to side chain conformations or small backbone deviations. Despite the fact that this approach has been used in many docking protocols, there has yet to be a systematic study of the effects of such surface smoothing on the shape complementarity of the resulting interfaces. Here we investigate this question by computing shape complementarity of a set of 66 protein-protein complexes represented by multiresolution blurred surfaces. Complexed and unbound structures are available for these protein-protein complexes. They are a subset of complexes from a nonredundant docking benchmark selected for rigidity (i.e. the proteins undergo limited conformational changes between their bound and unbound states). In this work, we construct the surfaces by isocontouring a density map obtained by accumulating the densities of Gaussian functions placed at all atom centers of the molecule. The smoothness or resolution is specified by a Gaussian fall-off coefficient, termed "blobbyness." Shape complementarity is quantified using a histogram of the shortest distances between two proteins' surface mesh vertices for both the crystallographic complexes and the complexes built using the protein structures in their unbound conformation. The histograms calculated for the bound complex structures demonstrate that medium resolution smoothing (blobbyness = -0.9) can reproduce about 88% of the shape complementarity of atomic resolution surfaces. Complexes formed from the free component structures show a partial loss of shape complementarity (more overlaps and gaps) with the atomic resolution surfaces. For surfaces smoothed to low resolution (blobbyness = -0.3), we find more consistency of shape complementarity between the complexed and free cases. To further reduce bad contacts without significantly impacting the good contacts we introduce another blurred surface, in which the Gaussian densities of flexible atoms are reduced. From these results we discuss the use of shape complementarity in protein-protein docking.

Prediction of interaction sites from apo 3D structures when the holo conformation is different.

The predictability of catalytic and binding sites from apo structures is addressed for proteins that undergo significant conformational change upon binding. Theoretical microscopic titration curves (THEMATICS), an electrostatics-based method for the prediction of functional sites, is performed on a test set of 24 proteins with both apo and holo structures available. For 23 of these 24 proteins (96%), THEMATICS predicts the correct catalytic or binding site for both the apo and holo forms. For only one of the 24 proteins, THEMATICS makes the correct prediction for the holo structure but fails for the apo structure. The metrics used by THEMATICS to identify functional residues generally are larger in absolute value for the functional residues in the holo forms compared to the corresponding residues in the apo forms. However, even in the apo forms, these identifying metrics are still statistically significantly larger for functional residues than for residues not involved in catalysis or binding. This indicates that some of the unusual electrostatic properties of functional residues are preserved in the apo conformation. Evidence is presented that certain residues immediately surrounding the active catalytic and binding residues impart functionally important chemical and electrostatic properties to the active residues. At least parts of these microenvironments exist in the unbound conformations, such that THEMATICS is able to distinguish the functional residues even in the apo structures.

Quantitative measurement of protease ligand conformation

The tendency for protease ligands to bind in an extended conformation has been suggested as an important factor for the identification of compounds of medicinal importance. Here we present a novel graph-theoretical method giving a quantitative measure of ligand conformation, and through application of this method to a representative set of protease ligands in bound and unbound conformations, derive the result that protease ligands are more extended in conformation when in their bound state.

Evaluating conformational changes in protein structures binding RNA

Many protein-RNA recognition events are known to exhibit conformational changes from qualitative observations of individual complexes. However, a quantitative estimation of conformational changes is required if protein-RNA docking and template-based methods for RNA binding site prediction are to be developed. This study presents the first quantitative evaluation of conformational changes that occur when proteins bind RNA. The analysis of twelve RNA-binding proteins in the bound and unbound states using error-scaled difference distance matrices is presented. The binding site residues are mapped to each structure, and the conformational changes that affect these residues are evaluated. Of the twelve proteins four exhibit greater movements in nonbinding site residues, and a further four show the greatest movements in binding site residues. The remaining four proteins display no significant conformational change. When interface residues are found to be in conformationally variable regions of the protein they are typically seen to move less than 2 A between the bound and unbound conformations. The current data indicate that conformational changes in the binding site residues of RNA binding proteins may not be as significant as previously suggested, but a larger data set is required before wider conclusions may be drawn. The implications of the observed conformational changes for protein function prediction are discussed.

Simulation of conformational changes occurring when a protein interacts with its receptor

In order to simulate the conformational changes occurring when a protein interacts with its receptor, we firstly evaluated the structural differences between the experimental unbound and bound conformations for selected proteins and created theoretical complexes by replacing, in each experimental complex, the protein-bound with the protein-unbound chain. The theoretical models were then subjected to additional modeling refinements to improve the side chain geometry. Comparing the theoretical and experimental complexes in term of structural and energetic factors is resulted that the refined theoretical complexes became more similar to the experimental ones. We applied the same procedure within an homology modeling experiment, using as templates the experimental structures of human interleukin-1β (IL-1β) unbound and bound with its receptor, to build models of the homologous proteins from mouse and trout in unbound and bound conformations and to simulate the interaction with the related receptors. Our results suggest that homology modeling techniques are sensitive to differences between bound and unbound conformations, and that modeling with accuracy the side chains in the complex improves the interaction and molecular recognition. Moreover, our refinement procedure could be used in protein–protein interaction studies and, also, applied in conjunction with rigid-body docking when is not available the protein-bound conformation.