Probing protein-protein interactions. The ribose-binding protein in bacterial transport and chemotaxis

A Site-Specific Cross-Linker for Visible-Light Control of Proteins

There is a need for photochemical tools that allow precise control of protein structure and function with visible light. We focus here on the s-tetrazine moiety, which can be installed at a specific protein site via the reaction between dichlorotetrazine and two adjacent sulfhydryl groups. Tetrazine's compact size enables structural mimicry of native amino acid linkages, such as an intramolecular salt bridge or disulfide bond. In this study, we investigated tetrazine installation in three different proteins, where it was confirmed that the cross-linking reaction is highly efficient in aqueous conditions and site-specific when two cysteines are located proximally: the S-S distance was 4-10 Å. As shown in maltose binding protein, the tetrazine cross-linker can replace an interdomain salt bridge crucial for xenon binding and serve as a visible-light photoswitch to modulate 129Xe NMR contrast. This work highlights the ease of aqueous tetrazine bioconjugation and its applications for protein photoregulation.

SAXS/WAXS data of conformationally flexible ribose binding protein

Modern artificial intelligence-based protein structure prediction methods, such as Alphafold2, can predict structures of folded proteins with reasonable accuracy. However, Alphafold2 provides a static view of a protein, which does not show the conformational variability of the protein, domain movement in a multi-domain protein, or ligand-induced conformational changes it might undergo in solution. Small-angle X-ay scattering (SAXS) and wide-angle X-ray scattering (WAXS) are solution techniques that can aid in integrative modeling of conformationally flexible proteins, or in validating their predicted ensemble structures. While SAXS is sensitive to global structural features, WAXS can expand the scope of structural modeling by including information about local structural changes. We present SAXS and WAXS datasets obtained from conformationally flexible d-ribose binding protein (RBP) from Escherichia coli in the ribose bound and unbound forms. SAXS/WAXS datasets of RBP provided here may aid in method development efforts for more accurate prediction of structural ensembles of conformationally flexible proteins, and their conformational changes.

Deciphering Cellodextrin and Glucose Uptake in Clostridium thermocellum

Sugar uptake is of great significance in industrially relevant microorganisms. Clostridium thermocellum has extensive potential in lignocellulose biorefineries as an environmentally prominent, thermophilic, cellulolytic bacterium. The bacterium employs five putative ATP-binding cassette transporters which purportedly take up cellulose hydrolysates. Here, we first applied combined genetic manipulations and biophysical titration experiments to decipher the key glucose and cellodextrin transporters. In vivo gene inactivation of each transporter and in vitro calorimetric and nuclear magnetic resonance (NMR) titration of each putative sugar-binding protein with various saccharides supported the conclusion that only transporters A and B play the roles of glucose and cellodextrin transport, respectively. To gain insight into the structural mechanism of the transporter specificities, 11 crystal structures, both alone and in complex with appropriate saccharides, were solved for all 5 putative sugar-binding proteins, thus providing detailed specific interactions between the proteins and the corresponding saccharides. Considering the importance of transporter B as the major cellodextrin transporter, we further identified its cryptic, hitherto unknown ATPase-encoding gene as clo1313_2554, which is located outside the transporter B gene cluster. The crystal structure of the ATPase was solved, showing that it represents a typical nucleotide-binding domain of the ATP-binding cassette (ABC) transporter. Moreover, we determined that the inducing effect of cellobiose (G2) and cellulose on cellulosome production could be eliminated by deletion of transporter B genes, suggesting the coupling of sugar transport and regulation of cellulosome components. This study provides key basic information on the sugar uptake mechanism of C. thermocellum and will promote rational engineering of the bacterium for industrial application.

SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2

The unprecedented performance of Deepmind’s Alphafold2 in predicting protein structure in CASP XIV and the creation of a database of structures for multiple proteomes and protein sequence repositories is reshaping structural biology. However, because this database returns a single structure, it brought into question Alphafold’s ability to capture the intrinsic conformational flexibility of proteins. Here we present a general approach to drive Alphafold2 to model alternate protein conformations through simple manipulation of the multiple sequence alignment via in silico mutagenesis. The approach is grounded in the hypothesis that the multiple sequence alignment must also encode for protein structural heterogeneity, thus its rational manipulation will enable Alphafold2 to sample alternate conformations. A systematic modeling pipeline is benchmarked against canonical examples of protein conformational flexibility and applied to interrogate the conformational landscape of membrane proteins. This work broadens the applicability of Alphafold2 by generating multiple protein conformations to be tested biologically, biochemically, biophysically, and for use in structure-based drug design.

Many questions have arisen with the remarkable protein prediction capability of Alphafold2. One of the main questions is whether Alphafold2’s architecture is amenable to reveal the intrinsic conformational heterogeneity of proteins. The potential to obtain unseen or hidden conformations with high accuracy would greatly advance a broad range of structural biology pursuits. We have devised a method of in silico mutagenesis of the multiple sequence alignments (MSA) that are central to Alphafold2’s prediction capabilities. The approach consistently unveils conformations not seen with the unmodified default MSA. Analysis of the ensembles of predicted conformations relative to experimental structures fully support the biochemical significance of the models.

Structural Validation by the G-Factor Properly Regulates Boost Potentials Imposed in Conformational Sampling of Proteins

Free energy landscapes (FELs) of proteins are indispensable for evaluating thermodynamic properties. Molecular dynamics (MD) simulation is a computational method for calculating FELs; however, conventional MD simulation frequently fails to search a broad conformational subspace due to its accessible timescale, which results in the calculation of an unreliable FEL. To search a broad subspace, an external bias can be imposed on a protein system, and biased sampling tends to cause a strong perturbation that might collapse the protein structures, indicating that the strength of the external bias should be properly regulated. This regulation can be challenging, and empirical parameters are frequently employed to impose an optimal bias. To address this issue, several methods regulate the external bias by referring to system energies. Herein, we focused on protein structural information for this regulation. In this study, a well-established structural indicator (the G-factor) was used to obtain structural information. Based on the G-factor, we proposed a scheme for regulating biased sampling, which is referred to as a G-factor-based external bias limiter (GERBIL). With GERBIL, the configurations were structurally validated by the G-factor during biased sampling. As an example of biased sampling, an accelerated MD (aMD) simulation was adopted in GERBIL (aMD-GERBIL), whereby the aMD simulation was repeatedly performed by increasing the strength of the boost potential. Furthermore, the configurations sampled by the aMD simulation were structurally validated by their G-factor values, and aMD-GERBIL stopped increasing the strength of the boost potential when the sampled configurations were regarded as low-quality (collapsed) structures. This structural validation is regarded as a "Brake" of the boost potential. For demonstrations, aMD-GERBIL was applied to globular proteins (ribose binding and maltose-binding proteins) to promote their large-amplitude open-closed transitions and successfully identify their domain motions.

Tailoring Escherichia coli Chemotactic Sensing towards Cadmium by Computational Redesign of Ribose-Binding Protein

Periplasmic binding proteins such as ribose-binding proteins (RBPs) are involved in the bacterial chemotaxis two-component system. RBP selectively identifies and interacts with ribose to induce a conformational change that leads to chemotaxis. Here, we report the development of an engineered Escherichia coli (E. coli) strain expressing a redesigned RBP that can effectively sense cadmium ions and regulate chemotactic movement of cells toward a cadmium ion gradient. RBP was computationally redesigned to bind cadmium ions and produce the conformational change required for chemoreceptor binding. The successful design, CdRBP1, binds to cadmium ions with a dissociation constant of 268 nM. When CdRBP1 was expressed in the periplasmic space of E. coli, the bacteria became live cadmium ion hunters with high selectivity over other divalent metal ions. This work presents an example of making cadmium ions, which are toxic for most organisms, as an attractant to regulate cells movement. Our approach also demonstrates that RBP can be precisely designed to develop metal-detecting living systems for potential applications in synthetic biology and environmental studies.

IMPORTANCE Cadmium pollution is one of the major environmental problems due to excessive release and accumulation. New technologies that can auto-detect cadmium ions with good biocompatibility are in urgent need. In this study, we engineered the bacterial chemotaxis system to positively sense cadmium ions by redesigning ribose-binding protein (RBP) to tightly bind cadmium ion and produce the right conformational change for receptor binding and signaling. Our engineered E. coli cells can auto-detect and chase cadmium ions with divalent metal ion selectivity. Many attempts have been carried out to redesign RBP at the ribose binding site with little success. Instead of the ribose binding site, we introduced the cadmium binding site in the opening of the ribose binding pocket by a specially developed computational algorithm. Our design strategy can be applied to engineer live bacteria with autonomous detection and remediation abilities for metal ions or other chemicals in the future.

Protein conformational transitions explored by a morphing approach based on normal mode analysis in internal coordinates

Large-scale conformational changes are essential for proteins to function properly. Given that these transition events rarely occur, however, it is challenging to comprehend their underlying mechanisms through experimental and theoretical approaches. In this study, we propose a new computational methodology called internal coordinate normal mode-guided elastic network interpolation (ICONGENI) to predict conformational transition pathways in proteins. Its basic approach is to sample intermediate conformations by interpolating the interatomic distance between two end-point conformations with the degrees of freedom constrained by the low-frequency dynamics afforded by normal mode analysis in internal coordinates. For validation of ICONGENI, it is applied to proteins that undergo open-closed transitions, and the simulation results (i.e., simulated transition pathways) are compared with those of another technique, to demonstrate that ICONGENI can explore highly reliable pathways in terms of thermal and chemical stability. Furthermore, we generate an ensemble of transition pathways through ICONGENI and investigate the possibility of using this method to reveal the transition mechanisms even when there are unknown metastable states on rough energy landscapes.

Multiscale Enhanced Sampling Using Machine Learning

Multiscale enhanced sampling (MSES) allows for an enhanced sampling of all-atom protein structures by coupling with the accelerated dynamics of the associated coarse-grained (CG) model. In this paper, we propose an MSES extension to replace the CG model with the dynamics on the reduced subspace generated by a machine learning approach, the variational autoencoder (VAE). The molecular dynamic (MD) trajectories of the ribose-binding protein (RBP) in both the closed and open forms were used as the input by extracting the inter-residue distances as the structural features in order to train the VAE model, allowing the encoded latent layer to characterize the difference in the structural dynamics of the closed and open forms. The interpolated data characterizing the RBP structural change in between the closed and open forms were thus efficiently generated in the low-dimensional latent space of the VAE, which was then decoded into the time-series data of the inter-residue distances and was useful for driving the structural sampling at an atomistic resolution via the MSES scheme. The free energy surfaces on the latent space demonstrated the refinement of the generated data that had a single basin into the simulated data containing two closed and open basins, thus illustrating the usefulness of the MD simulation together with the molecular mechanics force field in recovering the correct structural ensemble.

Coarse-Grained Modeling of Multiple Pathways in Conformational Transitions of Multi-Domain Proteins

Large-scale conformational transitions in multi-domain proteins are often essential for their functions. To investigate the transitions, it is necessary to explore multiple potential pathways, which involve different intermediate structures. Here, we present a multi-basin (MB) coarse-grained (CG) structure-based Go̅ model for describing transitions in proteins with more than two moving domains. This model is an extension of our dual-basin Go̅ model in which system-dependent parameters are determined systematically using the multistate Bennett acceptance ratio method. In the MB Go̅ model for multi-domain proteins, we assume that intermediate structures may have partial inter-domain native contacts. This approach allows us to search multiple transition pathways that involve distinct intermediate structures using the CG molecular dynamics (MD) simulations. We apply this scheme to an enzyme, adenylate kinase (AdK), which has three major domains and can move along two different pathways. Using the optimized mixing parameters for each pathway, AdK shows frequent transitions between the Open, Closed, and the intermediate basins and samples a wide variety of conformations within each basin. The explored multiple transition pathways could be compared with experimental data and examined in more detail by atomistic MD simulations.

The role of solute binding proteins in signal transduction

The solute binding proteins (SBPs) of prokaryotes are present in the extracytosolic space. Although their primary function is providing substrates to transporters, SBPs also stimulate different signaling proteins, including chemoreceptors, sensor kinases, diguanylate cyclases/phosphodiesterases and Ser/Thr kinases, thereby causing a wide range of responses. While relatively few such systems have been identified, several pieces of evidence suggest that SBP-mediated receptor activation is a widespread mechanism. (1) These systems have been identified in Gram-positive and Gram-negative bacteria and archaea. (2) There is a structural diversity in the receptor domains that bind SBPs. (3) SBPs belonging to thirteen different families interact with receptor ligand binding domains (LBDs). (4) For the two most abundant receptor LBD families, dCache and four-helix-bundle, there are different modes of interaction with SBPs. (5) SBP-stimulated receptors carry out many different functions. The advantage of SBP-mediated receptor stimulation is attributed to a strict control of SBP levels, which allows a precise adjustment of the systeḿs sensitivity. We have compiled information on the effect of ligands on the transcript/protein levels of their cognate SBPs. In 87 % of the cases analysed, ligands altered SBP expression levels. The nature of the regulatory effect depended on the ligand family. Whereas inorganic ligands typically downregulate SBP expression, an upregulation was observed in response to most sugars and organic acids. A major unknown is the role that SBPs play in signaling and in receptor stimulation. This review attempts to summarize what is known and to present new information to narrow this gap in knowledge.

Unraveling the Coupling between Conformational Changes and Ligand Binding in Ribose Binding Protein Using Multiscale Molecular Dynamics and Free-Energy Calculations

Conformational changes of proteins upon ligand binding are usually explained in terms of several mechanisms including the induced fit, conformational selection, or their mixtures. Due to the slow time scales, conventional molecular dynamics (cMD) simulations based on the atomistic models cannot easily simulate the open-to-closed conformational transition in proteins. In our previous study, we have developed an enhanced sampling scheme (generalized replica exchange with solute tempering selected surface charged residues: gREST_SSCR) for multidomain proteins and applied it to ligand-mediated conformational changes in the G134R mutant of ribose-binding protein (RBPG134R) in solution. The free-energy landscape (FEL) of RBPG134R in the presence of a ribose at the binding site included the open and closed states and two intermediates, open-like and closed-like forms. Only the open and open-like forms existed in the FEL without a ribose. In the current study, the coupling between the conformational changes and ligand binding is further investigated using coarse-grained MD, multiple atomistic cMD, and free-energy calculations. The ribose is easily dissociated from the binding site of wild-type RBP and RBPG134R in the cMD simulations starting from the open and open-like forms. In contrast, it is stable at the binding site in the simulations from the closed and closed-like forms. The free-energy calculations provide the binding affinities of different structures, supporting the results of cMD simulations. Importantly, cMD simulations from the closed-like structures reveal transitions toward the closed one in the presence of a bound ribose. On the basis of the computational results, we propose a molecular mechanism in which conformational selection and induced fit happen in the first and second halves of the open-to-closed transition in RBP, respectively.

Multi-Scale Flexible Fitting of Proteins to Cryo-EM Density Maps at Medium Resolution

Structure determination using cryo-electron microscopy (cryo-EM) medium-resolution density maps is often facilitated by flexible fitting. Avoiding overfitting, adjusting force constants driving the structure to the density map, and emulating complex conformational transitions are major concerns in the fitting. To address them, we develop a new method based on a three-step multi-scale protocol. First, flexible fitting molecular dynamics (MD) simulations with coarse-grained structure-based force field and replica-exchange scheme between different force constants replicas are performed. Second, fitted Cα atom positions guide the all-atom structure in targeted MD. Finally, the all-atom flexible fitting refinement in implicit solvent adjusts the positions of the side chains in the density map. Final models obtained via the multi-scale protocol are significantly better resolved and more reliable in comparison with long all-atom flexible fitting simulations. The protocol is useful for multi-domain systems with intricate structural transitions as it preserves the secondary structure of single domains.

Ligand-induced structural changes analysis of ribose-binding protein as studied by molecular dynamics simulations

BACKGROUND

The ribose-binding protein (RBP) from Escherichia coli is one of the representative structures of periplasmic binding proteins. Binding of ribose at the cleft between two domains causes a conformational change corresponding to a closure of two domains around the ligand. The RBP has been crystallized in the open and closed conformations.

OBJECTIVE

With the complex trajectory as a control, our goal was to study the conformation changes induced by the detachment of the ligand, and the results have been revealed from two computational tools, MD simulations and elastic network models.

METHODS

Molecular dynamics (MD) simulations were performed to study the conformation changes of RBP starting from the open-apo, closed-holo and closed-apo conformations.

RESULTS

The evolution of the domain opening angle θ clearly indicates large structural changes. The simulations indicate that the closed states in the absence of ribose are inclined to transition to the open states and that ribose-free RBP exists in a wide range of conformations. The first three dominant principal motions derived from the closed-apo trajectories, consisting of rotating, bending and twisting motions, account for the major rearrangement of the domains from the closed to the open conformation.

CONCLUSIONS

The motions showed a strong one-to-one correspondence with the slowest modes from our previous study of RBP with the anisotropic network model (ANM). The results obtained for RBP contribute to the generalization of robustness for protein domain motion studies using either the ANM or PCA for trajectories obtained from MD.

Exploring Large Domain Motions in Proteins Using Atomistic Molecular Dynamics with Enhanced Conformational Sampling

Conformational transitions in multidomain proteins are essential for biological functions. The Apo conformations are typically open and flexible, while the Holo states form more compact conformations stabilized by protein-ligand interactions. Unfortunately, the atomically detailed mechanisms for such open-closed conformational changes are difficult to be accessed experimentally as well as computationally. To simulate the transitions using atomistic molecular dynamics (MD) simulations, efficient conformational sampling algorithms are required. In this work, we propose a new approach based on generalized replica-exchange with solute tempering (gREST) for exploring the open-closed conformational changes in multidomain proteins. Wherein, selected surface charged residues in a target protein are defined as the solute region in gREST simulation and the solute temperatures are different in replicas and exchanged between them to enhance the domain motions. This approach is called gREST selected surface charged residues (gREST_SSCR) and is applied to the Apo and Holo states of ribose binding protein (RBP) in solution. The conformational spaces sampled with gREST_SSCR are much wider than those with the conventional MD, sampling open-closed conformational changes while maintaining RBP domains’ stability. The free-energy landscapes of RBP in the Apo and Holo states are drawn along with twist and hinge angles of the two moving domains. The inter-domain salt-bridges that are not observed in the experimental structures are also important in the intermediate states during the conformational changes.

FingerprintContacts: Predicting Alternative Conformations of Proteins from Coevolution

Proteins are dynamic molecules which perform diverse molecular functions by adopting different three-dimensional structures. Recent progress in residue-residue contacts prediction opens up new avenues for the de novo protein structure prediction from sequence information. However, it is still difficult to predict more than one conformation from residue-residue contacts alone. This is due to the inability to deconvolve the complex signals of residue-residue contacts, i.e., spatial contacts relevant for protein folding, conformational diversity, and ligand binding. Here, we introduce a machine learning based method, called FingerprintContacts, for extending the capabilities of residue-residue contacts. This algorithm leverages the features of residue-residue contacts, that is, (1) a single conformation outperforms the others in the structural prediction using all the top ranking residue-residue contacts as structural constraints and (2) conformation specific contacts rank lower and constitute a small fraction of residue-residue contacts. We demonstrate the capabilities of FingerprintContacts on eight ligand binding proteins with varying conformational motions. Furthermore, FingerprintContacts identifies small clusters of residue-residue contacts which are preferentially located in the dynamically fluctuating regions. With the rapid growth in protein sequence information, we expect FingerprintContacts to be a powerful first step in structural understanding of protein functional mechanisms.

A Biosensor Strategy for E. coli Based on Ligand-Dependent Stabilization

The engineering of microorganisms to monitor environmental chemicals or to produce desirable bioproducts is often reliant on the availability of a suitable biosensor. However, the conversion of a ligand-binding protein into a biosensor has been difficult. Here, we report a general strategy for generating biosensors in Escherichia coli that act by ligand-dependent stabilization of a transcriptional activator and mediate ligand concentration-dependent expression of a reporter gene. We constructed such a biosensor by using the lac repressor, LacI, as the ligand-binding domain and fusing it to the Zif268 DNA-binding domain and RNA polymerase omega subunit transcription-activating domain. Using error-prone PCR mutagenesis of lacI and selection, we identified a biosensor with multiple mutations, only one of which was essential for biosensor behavior. By tuning parameters of the assay, we obtained a response dependent on the ligand isopropyl β-d-1-thiogalactopyranoside (IPTG) of up to a 7-fold increase in the growth rate of E. coli. The single destabilizing mutation combined with a lacI mutation that expands ligand specificity to d-fucose generated a biosensor with improved response both to d-fucose and to IPTG. However, a mutation equivalent to the one that destabilized LacI in either of two structurally similar periplasmic binding proteins did not confer ligand-dependent stabilization. Finally, we demonstrated the generality of this method by using mutagenesis and selection to engineer another ligand-binding domain, MphR, to function as a biosensor. This strategy may allow many natural proteins that recognize and bind to ligands to be converted into biosensors.

Maintaining and Enhancing Diversity of Sampled Protein Conformations in Robotics-Inspired Methods

The ability to efficiently sample structurally diverse protein conformations allows one to gain a high-level view of a protein's energy landscape. Algorithms from robot motion planning have been used for conformational sampling, and several of these algorithms promote diversity by keeping track of "coverage" in conformational space based on the local sampling density. However, large proteins present special challenges. In particular, larger systems require running many concurrent instances of these algorithms, but these algorithms can quickly become memory intensive because they typically keep previously sampled conformations in memory to maintain coverage estimates. In addition, robotics-inspired algorithms depend on defining useful perturbation strategies for exploring the conformational space, which is a difficult task for large proteins because such systems are typically more constrained and exhibit complex motions. In this article, we introduce two methodologies for maintaining and enhancing diversity in robotics-inspired conformational sampling. The first method addresses algorithms based on coverage estimates and leverages the use of a low-dimensional projection to define a global coverage grid that maintains coverage across concurrent runs of sampling. The second method is an automatic definition of a perturbation strategy through readily available flexibility information derived from B-factors, secondary structure, and rigidity analysis. Our results show a significant increase in the diversity of the conformations sampled for proteins consisting of up to 500 residues when applied to a specific robotics-inspired algorithm for conformational sampling. The methodologies presented in this article may be vital components for the scalability of robotics-inspired approaches.

Complete alanine scanning of the Escherichia coli RbsB ribose binding protein reveals residues important for chemoreceptor signaling and periplasmic abundance

The Escherichia coli RbsB ribose binding protein has been used as a scaffold for predicting new ligand binding functions through in silico modeling, yet with limited success and reproducibility. In order to possibly improve the success of predictive modeling on RbsB, we study here the influence of individual residues on RbsB-mediated signaling in a near complete library of alanine-substituted RbsB mutants. Among a total of 232 tested mutants, we found 10 which no longer activated GFPmut2 reporter expression in E. coli from a ribose-RbsB hybrid receptor signaling chain, and 13 with significantly lower GFPmut2 induction than wild-type. Quantitative mass spectrometry abundance measurements of 25 mutants and wild-type RbsB in periplasmic space showed four categories of effects. Some (such as D89A) seem correctly produced and translocated but fail to be induced with ribose. Others (such as N190A) show lower induction probably as a result of less efficient production, folding and translocation. The third (such as N41A or K29A) have defects in both induction and abundance. The fourth category consists of semi-constitutive mutants with increased periplasmic abundance but maintenance of ribose induction. Our data show how RbsB modeling should include ligand-binding as well as folding, translocation and receptor binding.

Engineering a switch-based biosensor for arginine using a Thermotoga maritima periplasmic binding protein

The Thermotoga maritima arginine-binding protein (TmArgBP) has been modified to create a reagentless fluorescent protein biosensor. Two design methods for biosensor construction are compared: 1) solvent accessibility of environmentally-sensitive probes and 2) fluorescence deactivation due to photo-induced electron transfer (PET). Nine single cysteine TmArgBP mutants were created and labeled with three different environmentally sensitive fluorescent probes. These mutants demonstrated limited changes in fluorescence emission upon the addition of arginine. In contrast, the PET-based biosensor provides significant enhancements over the traditional approach and provides a fluorescence quenching mechanism that was capable of providing quantitative detection of arginine. Site-directed mutagenesis of TmArgBP was used to create attachment points for the fluorescent probe (K145C) and for an internal aromatic residue (D18X) to serve as the PET quencher. Both tyrosine and tryptophan, but not phenylalanine, were able to quench the emission of the fluorescent probe by more than 80% upon the addition of arginine. The dissociation constant for arginine ranged from 0.87 to 1.5 μM across the different sensors. This PET-based strategy provides a simple and broadly applicable approach for the analytical detection of small molecules that may be applied to any protein that exhibits conformational switching in a ligand dependent manner.

CHARMM-GUI MDFF/xMDFF Utilizer for Molecular Dynamics Flexible Fitting Simulations in Various Environments

X-ray crystallography and cryo-electron microscopy are two popular methods for the structure determination of biological molecules. Atomic structures are derived through the fitting and refinement of an initial model into electron density maps constructed by both experiments. Two computational approaches, MDFF and xMDFF, have been developed to facilitate this process by integrating the experimental data with molecular dynamics simulation. However, the setup of an MDFF/xMDFF simulation requires knowledge of both experimental and computational methods, which is not straightforward for nonexpert users. In addition, sometimes it is desirable to include realistic environments, such as explicit solvent and lipid bilayers during the simulation, which poses another challenge even for expert users. To alleviate these difficulties, we have developed MDFF/xMDFF Utilizer in CHARMM-GUI that helps users to set up an MDFF/xMDFF simulation. The capability of MDFF/xMDFF Utilizer is greatly enhanced by integration with other CHARMM-GUI modules, including protein structure manipulation, a diverse set of lipid types, and all-atom CHARMM and coarse-grained PACE force fields. With this integration, various simulation environments are available for MDFF Utilizer (vacuum, implicit/explicit solvent, and bilayers) and xMDFF Utilizer (vacuum and solution). In this work, three examples are shown to demonstrate the usage of MDFF/xMDFF Utilizer.

Defining Low-Dimensional Projections to Guide Protein Conformational Sampling

Exploring the conformational space of proteins is critical to characterize their functions. Numerous methods have been proposed to sample a protein's conformational space, including techniques developed in the field of robotics and known as sampling-based motion-planning algorithms (or sampling-based planners). However, these algorithms suffer from the curse of dimensionality when applied to large proteins. Many sampling-based planners attempt to mitigate this issue by keeping track of sampling density to guide conformational sampling toward unexplored regions of the conformational space. This is often done using low-dimensional projections as an indirect way to reduce the dimensionality of the exploration problem. However, how to choose an appropriate projection and how much it influences the planner's performance are still poorly understood issues. In this article, we introduce two methodologies defining low-dimensional projections that can be used by sampling-based planners for protein conformational sampling. The first method leverages information about a protein's flexibility to construct projections that can efficiently guide conformational sampling, when expert knowledge is available. The second method builds similar projections automatically, without expert intervention. We evaluate the projections produced by both methodologies on two conformational search problems involving three middle-size proteins. Our experiments demonstrate that (i) defining projections based on expert knowledge can benefit conformational sampling and (ii) automatically constructing such projections is a reasonable alternative.

Toward an Enhanced Sampling Molecular Dynamics Method for Studying Ligand-Induced Conformational Changes in Proteins

Current enhanced sampling molecular dynamics methods for studying large conformational changes in proteins suffer from certain limitations. These include, among others, the need for user defined collective variables, the prerequisite of both start and end point structures of the conformational change, and the need for a priori knowledge of the amount by which to boost specific parts of the potential. In this paper, a framework is proposed for a molecular dynamics method for studying ligand-induced conformational changes, in which the nonbonded interactions between the ligand and the protein are used to calculate a biasing force. The method requires only a single input structure, and does not entail the use of collective variables. We provide a proof-of-concept for accelerating conformational changes in three simple test molecules, as well as promising results for two proteins known to undergo domain closure upon ligand binding. For the ribose-binding protein, backbone root-mean-square deviations as low as 0.75 Å compared to the crystal structure of the closed conformation are obtained within 50 ns simulations, whereas no domain closures are observed in unbiased simulations. A skewed closed structure is obtained for the glutamine-binding protein at high bias values, indicating that specific protein-ligand interactions might suppress important protein-protein interactions.

TEMPy: a Python library for assessment of three-dimensional electron microscopy density fits

TEMPy is an object-oriented Python library that provides the means to validate density fits in electron microscopy reconstructions. This article highlights several features of particular interest for this purpose and includes some customized examples.

Three-dimensional electron microscopy is currently one of the most promising techniques used to study macromolecular assemblies. Rigid and flexible fitting of atomic models into density maps is often essential to gain further insights into the assemblies they represent. Currently, tools that facilitate the assessment of fitted atomic models and maps are needed. TEMPy (template and electron microscopy comparison using Python) is a toolkit designed for this purpose. The library includes a set of methods to assess density fits in intermediate-to-low resolution maps, both globally and locally. It also provides procedures for single-fit assessment, ensemble generation of fits, clustering, and multiple and consensus scoring, as well as plots and output files for visualization purposes to help the user in analysing rigid and flexible fits. The modular nature of TEMPy helps the integration of scoring and assessment of fits into large pipelines, making it a tool suitable for both novice and expert structural biologists.

Mechanical unfolding of ribose binding protein and its comparison with other periplasmic binding proteins

Folding and unfolding studies on large, multidomain proteins are still rare despite their high abundance in genomes of prokaryotes and eukaryotes. Here, we investigate the unfolding properties of a 271 residue, two-domain ribose binding protein (RBP) from the bacterial periplasm using single-molecule force spectroscopy. We observe that RBP predominately unfolds via a two-state pathway with an unfolding force of ∼80 pN and an unfolding contour length of ∼95 nm. Only a small population (∼15%) of RBP follows three-state pathways. The ligand binding neither increases the mechanical stability nor influences the unfolding flux of RBP through different pathways. The kinetic partitioning between two-state and three-state pathways, which has been reported earlier for other periplasmic proteins, is also observed in RBP, albeit to a lesser extent. These results provide important insights into the mechanical stability and unfolding processes of large two-domain proteins and highlight the contrasting features upon ligand binding. Protein structural topology diagrams are used to explain the differences in the mechanical unfolding behavior of RBP with other periplasmic binding proteins.

Active-site models for complexes of quinolinate synthase with substrates and intermediates

Quinolinate synthase (QS) catalyzes the condensation of iminoaspartate and dihydroxyacetone phosphate to form quinolinate, the universal precursor for the de novo biosynthesis of nicotinamide adenine dinucleotide. QS has been difficult to characterize owing either to instability or lack of activity when it is overexpressed and purified. Here, the structure of QS from Pyrococcus furiosus has been determined at 2.8 Å resolution. The structure is a homodimer consisting of three domains per protomer. Each domain shows the same topology with a four-stranded parallel β-sheet flanked by four α-helices, suggesting that the domains are the result of gene triplication. Biochemical studies of QS indicate that the enzyme requires a [4Fe-4S] cluster, which is lacking in this crystal structure, for full activity. The organization of domains in the protomer is distinctly different from that of a monomeric structure of QS from P. horikoshii [Sakuraba et al. (2005), J. Biol. Chem. 280, 26645-26648]. The domain arrangement in P. furiosus QS may be related to protection of cysteine side chains, which are required to chelate the [4Fe-4S] cluster, prior to cluster assembly.

SIMS: a hybrid method for rapid conformational analysis

Proteins are at the root of many biological functions, often performing complex tasks as the result of large changes in their structure. Describing the exact details of these conformational changes, however, remains a central challenge for computational biology due the enormous computational requirements of the problem. This has engendered the development of a rich variety of useful methods designed to answer specific questions at different levels of spatial, temporal, and energetic resolution. These methods fall largely into two classes: physically accurate, but computationally demanding methods and fast, approximate methods. We introduce here a new hybrid modeling tool, the Structured Intuitive Move Selector (sims), designed to bridge the divide between these two classes, while allowing the benefits of both to be seamlessly integrated into a single framework. This is achieved by applying a modern motion planning algorithm, borrowed from the field of robotics, in tandem with a well-established protein modeling library. sims can combine precise energy calculations with approximate or specialized conformational sampling routines to produce rapid, yet accurate, analysis of the large-scale conformational variability of protein systems. Several key advancements are shown, including the abstract use of generically defined moves (conformational sampling methods) and an expansive probabilistic conformational exploration. We present three example problems that sims is applied to and demonstrate a rapid solution for each. These include the automatic determination of “active” residues for the hinge-based system Cyanovirin-N, exploring conformational changes involving long-range coordinated motion between non-sequential residues in Ribose-Binding Protein, and the rapid discovery of a transient conformational state of Maltose-Binding Protein, previously only determined by Molecular Dynamics. For all cases we provide energetic validations using well-established energy fields, demonstrating this framework as a fast and accurate tool for the analysis of a wide range of protein flexibility problems.

Analysis of conformational motions and residue fluctuations for Escherichia coli ribose-binding protein revealed with elastic network models

The ribose-binding protein (RBP) is a sugar-binding bacterial periplasmic protein whose function is associated with a large allosteric conformational change from an open to a closed conformation upon binding to ribose. The open (ligand-free) and closed (ligand-bound) forms of RBP have been found. Here we investigate the conformational motions and residue fluctuations of the RBP by analyzing the modes of motion with two coarse-grained elastic network models, the Gaussian Network Model (GNM) and Anisotropic Network Model (ANM). The calculated B-factors in both the calculated models are in good agreement with the experimentally determined B-factors in X-ray crystal structures. The slowest mode analysis by GNM shows that both forms have the same motion hinge axes around residues Ser103, Gln235, Asp264 and the two domains of both structures have similar fluctuation range. The superposition of the first three dominant modes of ANM, consisting of the rotating, bending and twisting motions of the two forms, accounts for large rearrangement of domains from the ligand-free (open) to ligand-bound (closed) conformation and thus constitutes a critical component of the RBP's functions. By analyzing cross-correlations between residue fluctuation and the difference-distance plot, it is revealed that the conformational change can be described as a rigid rotation of the two domains with respect to each other, whereas the internal structure of the two domains remains largely intact. The results directly indicate that the dominant dynamic characteristics of protein structures can be captured from their static native state using coarse-grained models.

myo-inositol and D-ribose ligand discrimination in an ABC periplasmic binding protein

The periplasmic binding protein (PBP) IbpA mediates the uptake of myo-inositol by the IatP-IatA ATP-binding cassette transmembrane transporter. We report a crystal structure of Caulobacter crescentus IbpA bound to myo-inositol at 1.45 Å resolution. This constitutes the first structure of a PBP bound to inositol. IbpA adopts a type I PBP fold consisting of two α-β lobes that surround a central hinge. A pocket positioned between the lobes contains the myo-inositol ligand, which binds with submicromolar affinity (0.76 ± 0.08 μM). IbpA is homologous to ribose-binding proteins and binds D-ribose with low affinity (50.8 ± 3.4 μM). On the basis of IbpA and ribose-binding protein structures, we have designed variants of IbpA with inverted binding specificity for myo-inositol and D-ribose. Five mutations in the ligand-binding pocket are sufficient to increase the affinity of IbpA for D-ribose by 10-fold while completely abolishing binding to myo-inositol. Replacement of ibpA with these mutant alleles unable to bind myo-inositol abolishes C. crescentus growth in medium containing myo-inositol as the sole carbon source. Neither deletion of ibpA nor replacement of ibpA with the high-affinity ribose binding allele affected C. crescentus growth on D-ribose as a carbon source, providing evidence that the IatP-IatA transporter is specific for myo-inositol. This study outlines the evolutionary relationship between ribose- and inositol-binding proteins and provides insight into the molecular basis upon which these two related, but functionally distinct, classes of periplasmic proteins specifically bind carbohydrate ligands.

Agrobacterium tumefaciens recognizes its host environment using ChvE to bind diverse plant sugars as virulence signals

Agrobacterium tumefaciens is a broad host range plant pathogen that combinatorially recognizes diverse host molecules including phenolics, low pH, and aldose monosaccharides to activate its pathogenic pathways. Chromosomal virulence gene E (chvE) encodes a periplasmic-binding protein that binds several neutral sugars and sugar acids, and subsequently interacts with the VirA/VirG regulatory system to stimulate virulence (vir) gene expression. Here, a combination of genetics, X-ray crystallography, and isothermal calorimetry reveals how ChvE binds the different monosaccharides and also shows that binding of sugar acids is pH dependent. Moreover, the potency of a sugar for vir gene expression is modulated by a transport system that also relies on ChvE. These two circuits tune the overall system to respond to sugar concentrations encountered in vivo. Finally, using chvE mutants with restricted sugar specificities, we show that there is host variation in regard to the types of sugars that are limiting for vir induction.

Structure of a periplasmic glucose-binding protein from Thermotoga maritima

ABC transport systems have been characterized in organisms ranging from bacteria to humans. In most bacterial systems, the periplasmic component is the primary determinant of specificity of the transport complex as a whole. Here, the X-ray crystal structure of a periplasmic glucose-binding protein (GBP) from Thermotoga maritima determined at 2.4 Å resolution is reported. The molecule consists of two similar α/β domains connected by a three-stranded hinge region. In the current structure, a ligand (β-D-glucose) is buried between the two domains, which have adopted a closed conformation. Details of the substrate-binding sites revealed features that determine substrate specificity. In toto, ten residues from both domains form eight hydrogen bonds to the bound sugar and four aromatic residues (two from each domain) stabilize the substrate through stacking interactions.

Biophysical limits of protein-ligand binding

In classic work, Kuntz et al. (Proc. Nat. Acad. Sci. USA 1999, 96, 9997-10002) introduced the concept of ligand efficiency. Though that study focused primarily on drug-like molecules, it also showed that metal binding led to the greatest ligand efficiencies. Here, the physical limits of binding are examined across the wide variety of small molecules in the Binding MOAD database. The complexes with the greatest ligand efficiencies share the trait of being small, charged ligands bound in highly charged, well buried binding sites. The limit of ligand efficiency is -1.75 kcal/mol·atom for the protein-ligand complexes within Binding MOAD, and 95% of the set have efficiencies below a "soft limit" of -0.83 kcal/mol·atom. On the basis of buried molecular surface area, the hard limit of ligand efficiency is -117 cal/mol·Å(2), which is in surprising agreement with the limit of macromolecule-protein binding. Close examination of the most efficient systems reveals their incredibly high efficiency is dictated by tight contacts between the charged groups of the ligand and the pocket. In fact, a misfit of 0.24 Å in the average contacts inherently decreases the maximum possible efficiency by at least 0.1 kcal/mol·atom.

Protein conformational switches: from nature to design

Protein conformational switches alter their shape upon receiving an input signal, such as ligand binding, chemical modification, or change in environment. The apparent simplicity of this transformation--which can be carried out by a molecule as small as a thousand atoms or so--belies its critical importance to the life of the cell as well as its capacity for engineering by humans. In the realm of molecular switches, proteins are unique because they are capable of performing a variety of biological functions. Switchable proteins are therefore of high interest to the fields of biology, biotechnology, and medicine. These molecules are beginning to be exploited as the core machinery behind a new generation of biosensors, functionally regulated enzymes, and "smart" biomaterials that react to their surroundings. As inspirations for these designs, researchers continue to analyze existing examples of allosteric proteins. Recent years have also witnessed the development of new methodologies for introducing conformational change into proteins that previously had none. Herein we review examples of both natural and engineered protein switches in the context of four basic modes of conformational change: rigid-body domain movement, limited structural rearrangement, global fold switching, and folding-unfolding. Our purpose is to highlight examples that can potentially serve as platforms for the design of custom switches. Accordingly, we focus on inducible conformational changes that are substantial enough to produce a functional response (e.g., in a second protein to which it is fused), yet are relatively simple, structurally well-characterized, and amenable to protein engineering efforts.

Optimized torsion-angle normal modes reproduce conformational changes more accurately than cartesian modes

We present what to our knowledge is a new method of optimized torsion-angle normal-mode analysis, in which the normal modes move along curved paths in Cartesian space. We show that optimized torsion-angle normal modes reproduce protein conformational changes more accurately than Cartesian normal modes. We also show that orthogonalizing the displacement vectors from torsion-angle normal-mode analysis and projecting them as straight lines in Cartesian space does not lead to better performance than Cartesian normal modes. Clearly, protein motion is more naturally described by curved paths in Cartesian space.

Predicting protein ligand binding motions with the conformation explorer

BACKGROUND

Knowledge of the structure of proteins bound to known or potential ligands is crucial for biological understanding and drug design. Often the 3D structure of the protein is available in some conformation, but binding the ligand of interest may involve a large scale conformational change which is difficult to predict with existing methods.

RESULTS

We describe how to generate ligand binding conformations of proteins that move by hinge bending, the largest class of motions. First, we predict the location of the hinge between domains. Second, we apply an Euler rotation to one of the domains about the hinge point. Third, we compute a short-time dynamical trajectory using Molecular Dynamics to equilibrate the protein and ligand and correct unnatural atomic positions. Fourth, we score the generated structures using a novel fitness function which favors closed or holo structures. By iterating the second through fourth steps we systematically minimize the fitness function, thus predicting the conformational change required for small ligand binding for five well studied proteins.

CONCLUSIONS

We demonstrate that the method in most cases successfully predicts the holo conformation given only an apo structure.

Conformational changes and ligand recognition of Escherichia coli D-xylose binding protein revealed

ATP binding cassette transport systems account for most import of necessary nutrients in bacteria. The periplasmic binding component (or an equivalent membrane-anchored protein) is critical to recognizing cognate ligand and directing it to the appropriate membrane permease. Here we report the X-ray structures of D-xylose binding protein from Escherichia coli in ligand-free open form, ligand-bound open form, and ligand-bound closed form at 2.15 Å, 2.2 Å, and 2.2 Å resolutions, respectively. The ligand-bound open form is the first such structure to be reported at high resolution; the combination of the three different forms from the same protein furthermore gives unprecedented details concerning the conformational changes involved in binding protein function. As is typical of the structural family, the protein has two similar globular domains, which are connected by a three-stranded hinge region. The open liganded structure shows that xylose binds first to the C-terminal domain, with only very small conformational changes resulting. After a 34° closing motion, additional interactions are formed with the N-terminal domain; changes in this domain are larger and serve to make the structure more ordered near the ligand. An analysis of the interactions suggests why xylose is the preferred ligand. Furthermore, a comparison with the most closely related proteins in the structural family shows that the conformational changes are distinct in each type of binding protein, which may have implications for how the individual proteins act in concert with their respective membrane permeases.

Furanose-specific sugar transport: characterization of a bacterial galactofuranose-binding protein

The widespread utilization of sugars by microbes is reflected in the diversity and multiplicity of cellular transporters used to acquire these compounds from the environment. The model bacterium Escherichia coli has numerous transporters that allow it to take up hexoses and pentoses, which recognize the more abundant pyranose forms of these sugars. Here we report the biochemical and structural characterization of a transporter protein YtfQ from E. coli that forms part of an uncharacterized ABC transporter system. Remarkably the crystal structure of this protein, solved to 1.2 A using x-ray crystallography, revealed that YtfQ binds a single molecule of galactofuranose in its ligand binding pocket. Selective binding of galactofuranose over galactopyranose was also observed using NMR methods that determined the form of the sugar released from the protein. The pattern of expression of the ytfQRTyjfF operon encoding this transporter mirrors that of the high affinity galactopyranose transporter of E. coli, suggesting that this bacterium has evolved complementary transporters that enable it to use all the available galactose present during carbon limiting conditions.

Structural analysis of the choline-binding protein ChoX in a semi-closed and ligand-free conformation

The periplasmic ligand-binding protein ChoX is part of the ABC transport system ChoVWX that imports choline as a nutrient into the soil bacteriumSinorhizobium meliloti. We have recently reported the crystal structures of ChoX in complex with its ligands choline and acetylcholine and the structure of a fully closed but substrate-free state of ChoX. This latter structure revealed an architecture of the ligand-binding site that is superimposable to the closed, ligand-bound form of ChoX. We report here the crystal structure of ChoX in an unusual, ligand-free conformation that represents a semi-closed form of ChoX. The analysis revealed a subdomain movement in the N-lobe of ChoX. Comparison with the two well-characterized substrate binding proteins, MBP and HisJ, suggests the presence of a similar subdomain in these proteins.

StoneHinge: hinge prediction by network analysis of individual protein structures

Hinge motions are important for molecular recognition, and knowledge of their location can guide the sampling of protein conformations for docking. Predicting domains and intervening hinges is also important for identifying structurally self-determinate units and anticipating the influence of mutations on protein flexibility and stability. Here we present StoneHinge, a novel approach for predicting hinges between domains using input from two complementary analyses of noncovalent bond networks: StoneHingeP, which identifies domain-hinge-domain signatures in ProFlex constraint counting results, and StoneHingeD, which does the same for DomDecomp Gaussian network analyses. Predictions for the two methods are compared to hinges defined in the literature and by visual inspection of interpolated motions between conformations in a series of proteins. For StoneHingeP, all the predicted hinges agree with hinge sites reported in the literature or observed visually, although some predictions include extra residues. Furthermore, no hinges are predicted in six hinge-free proteins. On the other hand, StoneHingeD tends to overpredict the number of hinges, while accurately pinpointing hinge locations. By determining the consensus of their results, StoneHinge improves the specificity, predicting 11 of 13 hinges found both visually and in the literature for nine different open protein structures, and making no false-positive predictions. By comparison, a popular hinge detection method that requires knowledge of both the open and closed conformations finds 10 of the 13 known hinges, while predicting four additional, false hinges.

Rapid sampling of molecular motions with prior information constraints

Proteins are active, flexible machines that perform a range of different functions. Innovative experimental approaches may now provide limited partial information about conformational changes along motion pathways of proteins. There is therefore a need for computational approaches that can efficiently incorporate prior information into motion prediction schemes. In this paper, we present PathRover, a general setup designed for the integration of prior information into the motion planning algorithm of rapidly exploring random trees (RRT). Each suggested motion pathway comprises a sequence of low-energy clash-free conformations that satisfy an arbitrary number of prior information constraints. These constraints can be derived from experimental data or from expert intuition about the motion. The incorporation of prior information is very straightforward and significantly narrows down the vast search in the typically high-dimensional conformational space, leading to dramatic reduction in running time. To allow the use of state-of-the-art energy functions and conformational sampling, we have integrated this framework into Rosetta, an accurate protocol for diverse types of structural modeling. The suggested framework can serve as an effective complementary tool for molecular dynamics, Normal Mode Analysis, and other prevalent techniques for predicting motion in proteins. We applied our framework to three different model systems. We show that a limited set of experimentally motivated constraints may effectively bias the simulations toward diverse predicates in an outright fashion, from distance constraints to enforcement of loop closure. In particular, our analysis sheds light on mechanisms of protein domain swapping and on the role of different residues in the motion.

Can morphing methods predict intermediate structures?

Movement is crucial to the biological function of many proteins, yet crystallographic structures of proteins can give us only a static snapshot. The protein dynamics that are important to biological function often happen on a timescale that is unattainable through detailed simulation methods such as molecular dynamics as they often involve crossing high-energy barriers. To address this coarse-grained motion, several methods have been implemented as web servers in which a set of coordinates is usually linearly interpolated from an initial crystallographic structure to a final crystallographic structure. We present a new morphing method that does not extrapolate linearly and can therefore go around high-energy barriers and which can produce different trajectories between the same two starting points. In this work, we evaluate our method and other established coarse-grained methods according to an objective measure: how close a coarse-grained dynamics method comes to a crystallographically determined intermediate structure when calculating a trajectory between the initial and final crystal protein structure. We test this with a set of five proteins with at least three crystallographically determined on-pathway high-resolution intermediate structures from the Protein Data Bank. For simple hinging motions involving a small conformational change, segmentation of the protein into two rigid sections outperforms other more computationally involved methods. However, large-scale conformational change is best addressed using a nonlinear approach and we suggest that there is merit in further developing such methods.

Ligand-induced conformational changes in a thermophilic ribose-binding protein

BACKGROUND

Members of the periplasmic binding protein (PBP) superfamily are involved in transport and signaling processes in both prokaryotes and eukaryotes. Biological responses are typically mediated by ligand-induced conformational changes in which the binding event is coupled to a hinge-bending motion that brings together two domains in a closed form. In all PBP-mediated biological processes, downstream partners recognize the closed form of the protein. This motion has also been exploited in protein engineering experiments to construct biosensors that transduce ligand binding to a variety of physical signals. Understanding the mechanistic details of PBP conformational changes, both global (hinge bending, twisting, shear movements) and local (rotamer changes, backbone motion), therefore is not only important for understanding their biological function but also for protein engineering experiments.

RESULTS

Here we present biochemical characterization and crystal structure determination of the periplasmic ribose-binding protein (RBP) from the hyperthermophile Thermotoga maritima in its ribose-bound and unliganded state. The T. maritima RBP (tmRBP) has 39% sequence identity and is considerably more resistant to thermal denaturation (app Tm value is 108 degrees C) than the mesophilic Escherichia coli homolog (ecRBP) (app Tm value is 56 degrees C). Polar ligand interactions and ligand-induced global conformational changes are conserved among ecRBP and tmRBP; however local structural rearrangements involving side-chain motions in the ligand-binding site are not conserved.

CONCLUSION

Although the large-scale ligand-induced changes are mediated through similar regions, and are produced by similar backbone movements in tmRBP and ecRBP, the small-scale ligand-induced structural rearrangements differentiate the mesophile and thermophile. This suggests there are mechanistic differences in the manner by which these two proteins bind their ligands and are an example of how two structurally similar proteins utilize different mechanisms to form a ligand-bound state.

A Gibbs free energy correlation for automated docking of carbohydrates

Thermodynamic information can be inferred from static atomic configurations. To model the thermodynamics of carbohydrate binding to proteins accurately, a large binding data set has been assembled from the literature. The data set contains information from 262 unique protein-carbohydrate crystal structures for which experimental binding information is known. Hydrogen atoms were added to the structures and training conformations were generated with the automated docking program AutoDock 3.06, resulting in a training set of 225,920 all-atom conformations. In all, 288 formulations of the AutoDock 3.0 free energy model were trained against the data set, testing each of four alternate methods of computing the van der Waals, solvation, and hydrogen-bonding energetic components. The van der Waals parameters from AutoDock 1 produced the lowest errors, and an entropic model derived from statistical mechanics produced the only models with five physically and statistically significant coefficients. Eight models predict the Gibbs free energy of binding with an error of less than 40% of the error of any similar models previously published.