Near-native Conformations Research Articles

Protein-Peptide Complex Prediction through Fragment Interaction Patterns

The number of protein-peptide interactions in a cell isso large that experimental determination of all these complex structures would be a daunting task. Although homology modeling and refinement protocols have vastly improved the number and quality of predicted structural models, ab initio methods are still challenged by both the large number of possible docking sites and the conformational space accessible to flexible peptides. We present a method that addresses these challenges by sampling the entire accessible surface of a protein with a reduced conformational space of interacting backbone fragment pairs from unrelated structures. We demonstrate its potential by predicting ab initio the bound structure for a variety of protein-peptide complexes. In addition, we show the potential of our method forthe discovery of domain interaction sites and domain-domain docking.

Structure refinement of protein model decoys requires accurate side-chain placement

In this study, the application of temperature-based replica-exchange (T-ReX) simulations for structure refinement of decoys taken from the I-TASSER dataset was examined. A set of eight nonredundant proteins was investigated using self-guided Langevin dynamics (SGLD) with a generalized Born implicit solvent model to sample conformational space. For two of the protein test cases, a comparison of the SGLD/T-ReX method with that of a hybrid explicit/implicit solvent molecular dynamics T-ReX simulation model is provided. Additionally, the effect of side-chain placement among the starting decoy structures, using alternative rotamer conformations taken from the SCWRL4 modeling program, was investigated. The simulation results showed that, despite having near-native backbone conformations among the starting decoys, the determinant of their refinement is side-chain packing to a level that satisfies a minimum threshold of native contacts to allow efficient excursions toward the downhill refinement regime on the energy landscape. By repacking using SCWRL4 and by applying the RWplus statistical potential for structure identification, the SGLD/T-ReX simulations achieved refinement to an average of 38% increase in the number of native contacts relative to the original I-TASSER decoy sets and a 25% reduction in values of C(α) root-mean-square deviation. The hybrid model succeeded in obtaining a sharper funnel to low-energy states for a modeled target than the implicit solvent SGLD model; yet, structure identification remained roughly the same. Without meeting a threshold of near-native packing of side chains, the T-ReX simulations degrade the accuracy of the decoys, and subsequently, refinement becomes tantamount to the protein folding problem.

Small Molecules That Target Protein Misfolding

Protein misfolding is a process in which proteins are unable to attain or maintain their biologically active conformation. Factors contributing to protein misfolding include missense mutations and intracellular factors such as pH changes, oxidative stress, or metal ions. Protein misfolding is linked to a large number of diseases such as cystic fibrosis, Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and less familiar diseases such as Gaucher's disease, nephrogenic diabetes insipidus, and Creutzfeldt-Jakob disease. In this Perspective, we report on small molecules that bind to and stabilize the aberrant protein, thereby helping it to attain a native or near-native conformation and restoring its function. The following targets will be specifically discussed: transthyretin, p53, superoxide dismutase 1, lysozyme, serum amyloid A, prions, vasopressin receptor 2, and α-1-antitrypsin.

Effect of conformation sampling strategies in genetic algorithm for multiple protein docking

BackgroundMacromolecular protein complexes play important roles in a cell and their tertiary structure can help understand key biological processes of their functions. Multiple protein docking is a valuable computational tool for providing structure information of multimeric protein complexes. In a previous study we developed and implemented an algorithm for this purpose, named Multi-LZerD. This method represents a conformation of a multimeric protein complex as a graph, where nodes denote subunits and each edge connecting nodes denotes a pairwise docking conformation of the two subunits. Multi-LZerD employs a genetic algorithm to sample different topologies of the graph and pairwise transformations between subunits, seeking for the conformation of the optimal (lowest) energy. In this study we explore different configurations of the genetic algorithm, namely, the population size, whether to include a crossover operation, as well as the threshold for structural clustering, to find the optimal experimental setup.MethodsMulti-LZerD was executed to predict the structures of three multimeric protein complexes, using different population sizes, clustering thresholds, and configurations of mutation and crossover. We analyzed the impact of varying these parameters on the computational time and the prediction accuracy.Results and conclusionsGiven that computational resources is a key for handling complexes with a large number of subunits and also for computing a large number of protein complexes in a genome-scale study, finding a proper setting for sampling the conformation space is of the utmost importance. Our results show that an excessive sampling of the conformational space by increasing the population size or by introducing the crossover operation is not necessary for improving accuracy for predicting structures of small complexes. The clustering is effective in reducing redundant pairwise predictions, which leads to successful identification of near-native conformations.

Application of Binding Free Energy Calculations to Prediction of Binding Modes and Affinities of MDM2 and MDMX Inhibitors

Molecular docking is widely used to obtain binding modes and binding affinities of a molecule to a given target protein. Despite considerable efforts, however, prediction of both properties by docking remains challenging mainly due to protein's structural flexibility and inaccuracy of scoring functions. Here, an integrated approach has been developed to improve the accuracy of binding mode and affinity prediction and tested for small molecule MDM2 and MDMX antagonists. In this approach, initial candidate models selected from docking are subjected to equilibration MD simulations to further filter the models. Free energy perturbation molecular dynamics (FEP/MD) simulations are then applied to the filtered ligand models to enhance the ability in predicting the near-native ligand conformation. The calculated binding free energies for MDM2 complexes are overestimated compared to experimental measurements mainly due to the difficulties in sampling highly flexible apo-MDM2. Nonetheless, the FEP/MD binding free energy calculations are more promising for discriminating binders from nonbinders than docking scores. In particular, the comparison between the MDM2 and MDMX results suggests that apo-MDMX has lower flexibility than apo-MDM2. In addition, the FEP/MD calculations provide detailed information on the different energetic contributions to ligand binding, leading to a better understanding of the sensitivity and specificity of protein-ligand interactions.

GUIDING PROBABILISTIC SEARCH OF THE PROTEIN CONFORMATIONAL SPACE WITH STRUCTURAL PROFILES

The roughness of the protein energy surface poses a significant challenge to search algorithms that seek to obtain a structural characterization of the native state. Recent research seeks to bias search toward near-native conformations through one-dimensional structural profiles of the protein native state. Here we investigate the effectiveness of such profiles in a structure prediction setting for proteins of various sizes and folds. We pursue two directions. We first investigate the contribution of structural profiles in comparison to or in conjunction with physics-based energy functions in providing an effective energy bias. We conduct this investigation in the context of Metropolis Monte Carlo with fragment-based assembly. Second, we explore the effectiveness of structural profiles in providing projection coordinates through which to organize the conformational space. We do so in the context of a robotics-inspired search framework proposed in our lab that employs projections of the conformational space to guide search. Our findings indicate that structural profiles are most effective in obtaining physically realistic near-native conformations when employed in conjunction with physics-based energy functions. Our findings also show that these profiles are very effective when employed instead as projection coordinates to guide probabilistic search toward undersampled regions of the conformational space.

Multi‐LZerD: Multiple protein docking for asymmetric complexes

The tertiary structures of protein complexes provide a crucial insight about the molecular mechanisms that regulate their functions and assembly. However, solving protein complex structures by experimental methods is often more difficult than single protein structures. Here, we have developed a novel computational multiple protein docking algorithm, Multi-LZerD, that builds models of multimeric complexes by effectively reusing pairwise docking predictions of component proteins. A genetic algorithm is applied to explore the conformational space followed by a structure refinement procedure. Benchmark on eleven hetero-multimeric complexes resulted in near-native conformations for all but one of them (a root mean square deviation smaller than 2.5Å). We also show that our method copes with unbound docking cases well, outperforming the methodology that can be directly compared with our approach. Multi-LZerD was able to predict near-native structures for multimeric complexes of various topologies.

Absolute Binding Free Energy Calculations to Improve the Accuracy of Near-Native Ligand Pose Predictions

When the high-resolution structure of a protein target is available, molecular docking experiments are commonly used for computer-aided drug discovery. Molecular docking experiments are advantageous since they can provide the binding mode of a molecule in a given target protein as well as the binding affinity. However, despite considerable efforts, accurate prediction of ligand poses bound to a target is still challenging due to the protein's structural flexibility. Recent free energy perturbation molecular dynamics simulation (FEP/MD) calculations have shown that the calculated binding free energies are in good agreement with experimental data for co-crystal benchmark targets. Here, we present an integrated methodology in which initial candidate selection from pose decoys obtained by docking is followed by FEP/MD calculations to improve the accuracy of near-native ligand pose prediction. Our approach is evaluated for the small molecule α-helix mimetics inhibiting protein-protein interactions such as p53-MDMX/MDM2 and BAK-MCL-1. The results demonstrate that using the centroid models of the most populated clusters of docking decoys is an efficient approach to select a small set of ligand conformations in which a near-native pose may be included and applying the FEP/MD method enhances ability in discriminating the near-native ligand conformation from the candidates.

Analysis of Amino Acid Specific Energy Contributions to Native Conformations in High-Resolution Protein Structures

Because most proteins are marginally stable, each amino acid must contribute a near-optimal stabilization energy to the overall protein-structure. In this study we have analysed the specific energy contribution of each amino acid found in the native conformation of globular proteins. Using our free-energy forcefield PFF02 we devise a local energy measure on a per-amino acid basis and relax a population of 50 high resolution experimental globular protein structures on POEM@HOME with an evolutionary Monte Carlo energy minimization algorithm. The obtained set of relaxed structures is screened for locally stable segments, ruling out intrinsical disorder, and datasets of the energy ranges are assembled correlated with the position of the amino acid (interior, exterior, solvent-exposed).POEM (Protein Optimization using Energy Methods) identifies the native conformation of the protein as the global minimum of the protein free-energy forcefield PFF02, which stabilized the native conformation of all 32 monomeric proteins (without cofactors) against all decoys in the Rosetta decoy set. In addition we could fold a set of 13 proteins with helical, sheet and mixed secondary structure from completely unfolded conformations to near-native conformations, to an average 2.87 A resolution.The simulations we have conducted, were run on the POEM@HOME (http://boinc.fzk.de) volunteer computing architecture using a multiple population evolutionary strategy, which explores the free-energy surface in many parallel Monte-Carlo random walks. Various distinct temperature populations are evolved to the global free-energy minimum by balancing energy improvement and population diversity.

Optimized grid‐based protein–protein docking as a global search tool followed by incorporating experimentally derivable restraints

Unbound protein docking, or the computational prediction of the structure of a protein complex from the structures of its separated components, is of importance but still challenging. A practical approach toward reliable results for unbound docking is to incorporate experimentally derived information with computation. To this end, truly systematic search of the global docking space is desirable. The fast Fourier transform (FFT) docking is a systematic search method with high computational efficiency. However, by using FFT to perform unbound docking, possible conformational changes upon binding must be treated implicitly. To better accommodate the implicit treatment of conformational flexibility, we develop a rational approach to optimize "softened" parameters for FFT docking. In connection with the increased "softness" of the parameters in this global search step, we use a revised rule to select candidate models from the search results. For complexes designated as of low and medium difficulty for unbound docking, these adaptations of the original FTDOCK program lead to substantial improvements of the global search results. Finally, we show that models resulted from FFT-based global search can be further filtered with restraints derivable from nuclear magnetic resonance (NMR) chemical shift perturbation or mutagenesis experiments, leading to a small set of models that can be feasibly refined and evaluated using computationally more expensive methods and that still include high-ranking near-native conformations.

Ranking Docked Models of Protein-Protein Complexes Using Predicted Partner-Specific Protein-Protein Interfaces: A Preliminary Study.

Computational protein-protein docking is a valuable tool for determining the conformation of complexes formed by interacting proteins. Selecting near-native conformations from the large number of possible models generated by docking software presents a significant challenge in practice. We introduce a novel method for ranking docked conformations based on the degree of overlap between the interface residues of a docked conformation formed by a pair of proteins with the set of predicted interface residues between them. Our approach relies on a method, called PS-HomPPI, for reliably predicting protein-protein interface residues by taking into account information derived from both interacting proteins. PS-HomPPI infers the residues of a query protein that are likely to interact with a partner protein based on known interface residues of the homo-interologs of the query-partner protein pair, i.e., pairs of interacting proteins that are homologous to the query protein and partner protein. Our results on Docking Benchmark 3.0 show that the quality of the ranking of docked conformations using our method is consistently superior to that produced using ClusPro cluster-size-based and energy-based criteria for 61 out of the 64 docking complexes for which PS-HomPPI produces interface predictions. An implementation of our method for ranking docked models is freely available at: http://einstein.cs.iastate.edu/DockRank/.

Rosetta FlexPepDock ab-initio: simultaneous folding, docking and refinement of peptides onto their receptors.

Flexible peptides that fold upon binding to another protein molecule mediate a large number of regulatory interactions in the living cell and may provide highly specific recognition modules. We present Rosetta FlexPepDock ab-initio, a protocol for simultaneous docking and de-novo folding of peptides, starting from an approximate specification of the peptide binding site. Using the Rosetta fragments library and a coarse-grained structural representation of the peptide and the receptor, FlexPepDock ab-initio samples efficiently and simultaneously the space of possible peptide backbone conformations and rigid-body orientations over the receptor surface of a given binding site. The subsequent all-atom refinement of the coarse-grained models includes full side-chain modeling of both the receptor and the peptide, resulting in high-resolution models in which key side-chain interactions are recapitulated. The protocol was applied to a benchmark in which peptides were modeled over receptors in either their bound backbone conformations or in their free, unbound form. Near-native peptide conformations were identified in 18/26 of the bound cases and 7/14 of the unbound cases. The protocol performs well on peptides from various classes of secondary structures, including coiled peptides with unusual turns and kinks. The results presented here significantly extend the scope of state-of-the-art methods for high-resolution peptide modeling, which can now be applied to a wide variety of peptide-protein interactions where no prior information about the peptide backbone conformation is available, enabling detailed structure-based studies and manipulation of those interactions.

Fully differentiable coarse-grained and all-atom knowledge-based potentials for RNA structure evaluation

RNA molecules play integral roles in gene regulation, and understanding their structures gives us important insights into their biological functions. Despite recent developments in template-based and parameterized energy functions, the structure of RNA--in particular the nonhelical regions--is still difficult to predict. Knowledge-based potentials have proven efficient in protein structure prediction. In this work, we describe two differentiable knowledge-based potentials derived from a curated data set of RNA structures, with all-atom or coarse-grained representation, respectively. We focus on one aspect of the prediction problem: the identification of native-like RNA conformations from a set of near-native models. Using a variety of near-native RNA models generated from three independent methods, we show that our potential is able to distinguish the native structure and identify native-like conformations, even at the coarse-grained level. The all-atom version of our knowledge-based potential performs better and appears to be more effective at discriminating near-native RNA conformations than one of the most highly regarded parameterized potential. The fully differentiable form of our potentials will additionally likely be useful for structure refinement and/or molecular dynamics simulations.

A Collaborative Filtering Approach for Protein-Protein Docking Scoring Functions

A protein-protein docking procedure traditionally consists in two successive tasks: a search algorithm generates a large number of candidate conformations mimicking the complex existing in vivo between two proteins, and a scoring function is used to rank them in order to extract a native-like one. We have already shown that using Voronoi constructions and a well chosen set of parameters, an accurate scoring function could be designed and optimized. However to be able to perform large-scale in silico exploration of the interactome, a near-native solution has to be found in the ten best-ranked solutions. This cannot yet be guaranteed by any of the existing scoring functions.In this work, we introduce a new procedure for conformation ranking. We previously developed a set of scoring functions where learning was performed using a genetic algorithm. These functions were used to assign a rank to each possible conformation. We now have a refined rank using different classifiers (decision trees, rules and support vector machines) in a collaborative filtering scheme. The scoring function newly obtained is evaluated using 10 fold cross-validation, and compared to the functions obtained using either genetic algorithms or collaborative filtering taken separately.This new approach was successfully applied to the CAPRI scoring ensembles. We show that for 10 targets out of 12, we are able to find a near-native conformation in the 10 best ranked solutions. Moreover, for 6 of them, the near-native conformation selected is of high accuracy. Finally, we show that this function dramatically enriches the 100 best-ranking conformations in near-native structures.

Antibodies as a model system for comparative model refinement

Predicting the conformations of loops is a critical aspect of protein comparative (homology) modeling. Despite considerable advances in developing loop prediction algorithms, refining loops in homology models remains challenging. In this work, we use antibodies as a model system to investigate strategies for more robustly predicting loop conformations when the protein model contains errors in the conformations of side chains and protein backbone surrounding the loop in question. Specifically, our test system consists of partial models of antibodies in which the "scaffold" (i.e., the portion other than the complementarity determining region, CDR, loops) retains native backbone conformation, whereas the CDR loops are predicted using a combination of knowledge-based modeling (H1, H2, L1, L2, and L3) and ab initio loop prediction (H3). H3 is the most variable of the CDRs. Using a previously published method, a test set of 10 shorter H3 loops (5-7 residues) are predicted to an average backbone (N-C alpha-C-O) RMSD of 2.7 A while 11 longer loops (8-9 residues) are predicted to 5.1 A, thus recapitulating the difficulties in refining loops in models. By contrast, in control calculations predicting the same loops in crystal structures, the same method reconstructs the loops to an average of 0.5 and 1.4 A for the shorter and longer loops, respectively. We modify the loop prediction method to improve the ability to sample near-native loop conformations in the models, primarily by reducing the sensitivity of the sampling to the loop surroundings, and allowing the other CDR loops to optimize with the H3 loop. The new method improves the average accuracy significantly to 1.3 A RMSD and 3.1 A RMSD for the shorter and longer loops, respectively. Finally, we present results predicting 8-10 residue loops within complete comparative models of five nonantibody proteins. While anecdotal, these mixed, full-model results suggest our approach is a promising step toward more accurately predicting loops in homology models. Furthermore, while significant challenges remain, our method is a potentially useful tool for predicting antibody structures based on a known Fv scaffold.

An interaction-motif-based scoring function for protein-ligand docking

BackgroundA good scoring function is essential for molecular docking computations. In conventional scoring functions, energy terms modeling pairwise interactions are cumulatively summed, and the best docking solution is selected. Here, we propose to transform protein-ligand interactions into three-dimensional geometric networks, from which recurring network substructures, or network motifs, are selected and used to provide probability-ranked interaction templates with which to score docking solutions.ResultsA novel scoring function for protein-ligand docking, MotifScore, was developed. It is non-energy-based, and docking is, instead, scored by counting the occurrences of motifs of protein-ligand interaction networks constructed using structures of protein-ligand complexes. MotifScore has been tested on a benchmark set established by others to assess its ability to identify near-native complex conformations among a set of decoys. In this benchmark test, 84% of the highest-scored docking conformations had root-mean-square deviations (rmsds) below 2.0 Å from the native conformation, which is comparable with the best of several energy-based docking scoring functions. Many of the top motifs, which comprise a multitude of chemical groups that interact simultaneously and make a highly significant contribution to MotifScore, capture recurrent interacting patterns beyond pairwise interactions.ConclusionsWhile providing quite good docking scores, MotifScore is quite different from conventional energy-based functions. MotifScore thus represents a new, network-based approach for exploring problems associated with molecular docking.

Denaturant-Dependent Conformational Changes in a β-Trefoil Protein: Global and Residue-Specific Aspects of an Equilibrium Denaturation Process

Conformational properties of the folded and unfolded ensembles of human interleukin-1 receptor antagonist (IL-1ra) are strongly denaturant-dependent as evidenced by high-resolution two-dimensional nuclear magnetic resonance (NMR), limited proteolysis, and small-angle X-ray scattering (SAXS). The folded ensemble was characterized in detail in the presence of different urea concentrations by (1)H-(15)N HSQC NMR. The beta-trefoil fold characteristic of native IL-1ra was preserved until the unfolding transition region beginning at 4 M urea. At the same time, a subset of native resonances disappeared gradually starting at low denaturant concentrations, indicating noncooperative changes in the folded state. Additional evidence of structural perturbations came from the chemical shift analysis, nonuniform and bell-shaped peak intensity profiles, and limited proteolysis. In particular, the following nearby regions of the tertiary structure became progressively destabilized with increasing urea concentrations: the beta-hairpin interface of trefoils 1 and 2 and the H2a-H2 helical region. These regions underwent small-scale perturbations within the native baseline region in the absence of populated molten globule-like states. Similar regions were affected by elevated temperatures known to induce irreversible aggregation of IL-1ra. Further evidence of structural transitions invoking near-native conformations came from an optical spectroscopy analysis of its single-tryptophan variant W17A. The increase in the radius of gyration was associated with a single equilibrium unfolding transition in the case of two different denaturants, urea and guanidine hydrochloride (GuHCl). However, the compactness of urea- and GuHCl-unfolded molecules was comparable only at high denaturant concentrations and deviated under less denaturing conditions. Our results identified the role of conformational flexibility in IL-1ra aggregation and shed light on the nature of structural transitions within the folded ensembles of other beta-trefoil proteins, such as IL-1beta and hFGF-1.

Restriction versus guidance in protein structure prediction

Conformational restriction by fragment assembly and guidance in molecular dynamics are alternate conformational search strategies in protein structure prediction. We examine both approaches using a version of the associative memory Hamiltonian that incorporates the influence of water-mediated interactions (AMW). For short proteins (<70 residues), fragment assembly, while searching a restricted space, compares well to molecular dynamics and is often sufficient to fold such proteins to near-native conformations (4A) via simulated annealing. Longer proteins encounter kinetic sampling limitations in fragment assembly not seen in molecular dynamics which generally samples more native-like conformations. We also present a fragment enriched version of the standard AMW energy function, AMW-FME, which incorporates the local sequence alignment derived fragment libraries from fragment assembly directly into the energy function. This energy function, in which fragment information acts as a guide not a restriction, is found by molecular dynamics to improve on both previous approaches.

Direct electron transfer and electrocatalysis of hemoglobin in ZnO coated multiwalled carbon nanotubes and Nafion composite matrix

ZnO coated multiwalled carbon nanotubes (ZnO-MWCNTs) nano-composite was synthesized by the hydrothermal method and expected to offer a promising template for biosensor fabrication due to satisfying biocompatibility and improved properties. The ZnO-MWCNTs nano-composite was mixed with Nafion solution to form a composite matrix for the fabrication of hemoglobin (Hb) biosensor. This composite matrix combined the advantages of inorganic composite (ZnO-MWCNTs) and organic polymer (Nafion) and could promote the direct electron transfer of Hb. The Hb/ZnO-MWCNTs/Nafion film exhibited a pair of well-defined, quasi-reversible redox peaks with a formal potential of − 0.353 V (vs. SCE), characteristic of heme redox couple (Fe(III)/Fe(II)) of Hb. Hb retained its near-native conformation in the composite film. The immobilized Hb showed fast and excellent electrocatalytic activity to H 2O 2 with a linear range from 2 × 10 − 7 to 1.2 × 10 − 5 M, and the detection limit was 8.4 × 10 − 8 M. The sensitivity and apparent Michaelis–Menten constant were 1.31 A/(M·cm 2) and 82.8 μM, respectively, which indicated that Hb had a high affinity to H 2O 2. This biosensor also showed excellent electrocatalytic activity towards trichloroacetic acid.

Direct electrochemistry and electrocatalysis of hemoglobin immobilized in poly(ethylene glycol) grafted multi-walled carbon nanotubes

The novel poly(ethylene glycol) (PEG) grafted multi-walled carbon nanotubes (PEG-g-MWCNTs) were synthesized by the covalent functionalization of MWCNTs with hydroxyl-terminated PEG chains, exhibited excellent hydrophilicity and biocompatibility. The PEG-g-MWCNTs were characterized by Fourier transform infrared spectra, transmission electron microscopy, and thermogravimetric analysis, which verified that PEG chains were grafted onto the surface of the MWCNTs. The PEG-g-MWCNTs were then used as substrates for the immobilization of hemoglobin (Hb) and their bioelectrochemical behaviors were studied. Electrochemical impedance spectroscopy was used to confirm the adsorption of Hb onto the surface of PEG-g-MWCNTs. The Hb immobilized in PEG-g-MWCNTs retained its near-native conformations as characterized by the UV–vis spectroscopy. The cyclic voltammetry results of Hb/PEG-g-MWCNT modified electrode showed a pair of well-defined and quasi reversible redox peaks centered at approximate −0.34 V (vs. saturated calomel electrode), which was the characteristic peaks of Hb Fe(III)/Fe(II), in pH 7.0 phosphate buffer solution. Hb immobilized onto the surface of PEG-g-MWCNTs demonstrated good bioelectrocatalytic activities for the reduction of nitrite.