A Method for Conditional Regulation of Protein Stability in Native or Near-Native Form.
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ABSTRACT:
SUBMITTER: Miyamae Y
PROVIDER: S-EPMC7749034 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Fast Fourier transform (FFT) correlation methods of protein-protein docking, combined with the clustering of low energy conformations, can find a number of local minima on the energy surface. For most complexes, the locations of the near-native structures can be constrained to the 30 largest clusters, each surrounding a local minimum. However, no reliable further discrimination can be obtained by energy measures because the differences in the energy levels between the minima are comparable with the errors in the energy evaluation. In fact, no current scoring function accounts for the entropic contributions that relate to the width rather than the depth of the minima. Since structures at narrow minima loose more entropy, some of the nonnative states can be detected by determining whether or not a local minimum is surrounded by a broad region of attraction on the energy surface. The analysis is based on starting Monte Carlo Minimization (MCM) runs from random points around each minimum, and observing whether a certain fraction of trajectories converge to a small region within the cluster. The cluster is considered stable if such a strong attractor exists, has at least 10 convergent trajectories, is relatively close to the original cluster center, and contains a low energy structure. We studied the stability of clusters for enzyme-inhibitor and antibody-antigen complexes in the Protein Docking Benchmark. The analysis yields three main results. First, all clusters that are close to the native structure are stable. Second, restricting considerations to stable clusters eliminates around half of the false positives, that is, solutions that are low in energy but far from the native structure of the complex. Third, dividing the conformational space into clusters and determining the stability of each cluster, the combined approach is less dependent on a priori information than exploring the potential conformational space by Monte Carlo minimizations.
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Project description:Banana lectin (BL) is a homodimeric protein categorized among jacalin-related family of lectins. The effect of acidic pH was examined on conformational stability of BL by using circular dichroism, intrinsic fluorescence, 1-anilino-8-napthalene sulfonate (ANS) binding, size exclusion chromatography (SEC) and dynamic light scattering (DLS). During acid denaturation of BL, the monomerization of native dimeric protein was found at pH 2.0. The elution profile from SEC showed two different peaks (59.65 ml & 87.98 ml) at pH 2.0 while single peak (61.45 ml) at pH 7.4. The hydrodynamic radii (R h) of native BL was 2.9 nm while at pH 2.0 two species were found with R h of 1.7 and 3.7 nm. Furthermore at, pH 2.0 the secondary structures of BL remained unaltered while tertiary structure was significantly disrupted with the exposure of hydrophobic clusters confirming the existence of molten globule like state. The unfolding of BL with different subunit status was further evaluated by urea and temperature mediated denaturation to check their stability. As inferred from high Cm and ΔG values, the monomeric form of BL offers more resistance towards chemical denaturation than the native dimeric form. Besides, dimeric BL exhibited a Tm of 77°C while no loss in secondary structures was observed in monomers even up to 95°C. To the best of our knowledge, this is the first report on monomeric subunit of lectins showing more stability against denaturants than its native dimeric state.
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Project description:The thermodynamic stability of proteins is typically measured at high denaturant concentrations and then extrapolated back to zero denaturant conditions to obtain unfolding free energies under native conditions. For membrane proteins, the extrapolations are fraught with considerable uncertainty as the denaturants may have complex effects on the membrane or micellar structure. We therefore sought to measure stability under native conditions, using a method that does not perturb the properties of the membrane or membrane mimetics. We use a technique called steric trapping to measure the thermodynamic stability of bacteriorhodopsin in bicelles and micelles. We find that bacteriorhodopsin has a high thermodynamic stability, with an unfolding free energy of ?11 kcal/mol in dimyristoyl phosphatidylcholine bicelles. Nevertheless, the stability is much lower than predicted by extrapolation of measurements made at high denaturant concentrations. We investigated the discrepancy and found that unfolding free energy is not linear with denaturant concentration. Apparently, long extrapolations of helical membrane protein unfolding free energies must be treated with caution. Steric trapping, however, provides a method for making these measurements.
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Project description:The identification of near native protein-protein complexes among a set of decoys remains highly challenging. A strategy for improving the success rate of near native detection is to enrich near native docking decoys in a small number of top ranked decoys. Recently, we found that a combination of three scoring functions (energy, conservation, and interface propensity) can predict the location of binding interface regions with reasonable accuracy. Here, these three scoring functions are modified and combined into a consensus scoring function called ENDES for enriching near native docking decoys. We found that all individual scores result in enrichment for the majority of 28 targets in ZDOCK2.3 decoy set and the 22 targets in Benchmark 2.0. Among the three scores, the interface propensity score yields the highest enrichment in both sets of protein complexes. When these scores are combined into the ENDES consensus score, a significant increase in enrichment of near-native structures is found. For example, when 2000 dock decoys are reduced to 200 decoys by ENDES, the fraction of near-native structures in docking decoys increases by a factor of about six in average. ENDES was implemented into a computer program that is available for download at http://sparks.informatics.iupui.edu.
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Project description:BACKGROUND:In ab initio protein-structure predictions, a large set of structural decoys are often generated, with the requirement to select best five or three candidates from the decoys. The clustered central structures with the most number of neighbors are frequently regarded as the near-native protein structures with the lowest free energy; however, limitations in clustering methods and three-dimensional structural-distance assessments make identifying exact order of the best five or three near-native candidate structures difficult. RESULTS:To address this issue, we propose a method that re-ranks the candidate structures via random forest classification using intra- and inter-cluster features from the results of the clustering. Comparative analysis indicated that our method was better able to identify the order of the candidate structures as comparing with current methods SPICKR, Calibur, and Durandal. The results confirmed that the identification of the first model were closer to the native structure in 12 of 43 cases versus four for SPICKER, and the same as the native structure in up to 27 of 43 cases versus 14 for Calibur and up to eight of 43 cases versus two for Durandal. CONCLUSIONS:In this study, we presented an improved method based on random forest classification to transform the problem of re-ranking the candidate structures by an binary classification. Our results indicate that this method is a powerful method for the problem and the effect of this method is better than other methods.
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Project description:Clustering is one of the most powerful tools in computational biology. The conventional wisdom is that events that occur in clusters are probably not random. In protein docking, the underlying principle is that clustering occurs because long-range electrostatic and/or desolvation forces steer the proteins to a low free-energy attractor at the binding region. Something similar occurs in the docking of small molecules, although in this case shorter-range van der Waals forces play a more critical role. Based on the above, we have developed two different clustering strategies to predict docked conformations based on the clustering properties of a uniform sampling of low free-energy protein-protein and protein-small molecule complexes. We report on significant improvements in the automated prediction and discrimination of docked conformations by using the cluster size and consensus as a ranking criterion. We show that the success of clustering depends on identifying the appropriate clustering radius of the system. The clustering radius for protein-protein complexes is consistent with the range of the electrostatics and desolvation free energies (i.e., between 4 and 9 Angstroms); for protein-small molecule docking, the radius is set by van der Waals interactions (i.e., at approximately 2 Angstroms). Without any a priori information, a simple analysis of the histogram of distance separations between the set of docked conformations can evaluate the clustering properties of the data set. Clustering is observed when the histogram is bimodal. Data clustering is optimal if one chooses the clustering radius to be the minimum after the first peak of the bimodal distribution. We show that using this optimal radius further improves the discrimination of near-native complex structures.
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Project description:Spinal muscular atrophy (SMA) is caused by mutations of the survival of motor neuron (SMN1) gene and deficiency of full-length SMN protein (FL-SMN). All SMA patients retain one or more copies of the SMN2 gene, but the principal protein product of SMN2 lacks exon 7 (SMNDelta7) and is unable to compensate for a deficiency of FL-SMN. SMN is known to oligomerize and form a multimeric protein complex; however, the mechanisms regulating stability and degradation of FL-SMN and SMNDelta7 proteins have been largely unexplored. Using pulse-chase analysis, we characterized SMN protein turnover and confirmed that SMN was ubiquitinated and degraded by the ubiquitin proteasome system (UPS). The SMNDelta7 protein had a twofold shorter half-life than FL-SMN in cells despite similar intrinsic rates of turnover by the UPS in a cell-free assay. Mutations that inhibited SMN oligomerization and complex formation reduced the FL-SMN half-life. Furthermore, recruitment of SMN into large macromolecular complexes as well as increased association with several Gemin proteins was regulated in part by protein kinase A. Together, our data indicate that SMN protein stability is modulated by complex formation. Promotion of the SMN complex formation may be an important novel therapeutic strategy for SMA.
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Project description:How to refine a near-native structure to make it closer to its native conformation is an unsolved problem in protein-structure and protein-protein complex-structure prediction. In this article, we first test several scoring functions for selecting locally resampled near-native protein-protein docking conformations and then propose a computationally efficient protocol for structure refinement via local resampling and energy minimization. The proposed method employs a statistical energy function based on a Distance-scaled Ideal-gas REference state (DFIRE) as an initial filter and an empirical energy function EMPIRE (EMpirical Protein-InteRaction Energy) for optimization and re-ranking. Significant improvement of final top-1 ranked structures over initial near-native structures is observed in the ZDOCK 2.3 decoy set for Benchmark 1.0 (74% whose global rmsd reduced by 0.5 A or more and only 7% increased by 0.5 A or more). Less significant improvement is observed for Benchmark 2.0 (38% versus 33%). Possible reasons are discussed.
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Project description:BackgroundComputational analysis of protein-protein interaction provided the crucial information to increase the binding affinity without a change in basic conformation. Several docking programs were used to predict the near-native poses of the protein-protein complex in 10 top-rankings. The universal criteria for discriminating the near-native pose are not available since there are several classes of recognition protein. Currently, the explicit criteria for identifying the near-native pose of ankyrin-protein complexes (APKs) have not been reported yet.ResultsIn this study, we established an ensemble computational model for discriminating the near-native docking pose of APKs named "AnkPlex". A dataset of APKs was generated from seven X-ray APKs, which consisted of 3 internal domains, using the reliable docking tool ZDOCK. The dataset was composed of 669 and 44,334 near-native and non-near-native poses, respectively, and it was used to generate eleven informative features. Subsequently, a re-scoring rank was generated by AnkPlex using a combination of a decision tree algorithm and logistic regression. AnkPlex achieved superior efficiency with ≥1 near-native complexes in the 10 top-rankings for nine X-ray complexes compared to ZDOCK, which only obtained six X-ray complexes. In addition, feature analysis demonstrated that the van der Waals feature was the dominant near-native pose out of the potential ankyrin-protein docking poses.ConclusionThe AnkPlex model achieved a success at predicting near-native docking poses and led to the discovery of informative characteristics that could further improve our understanding of the ankyrin-protein complex. Our computational study could be useful for predicting the near-native poses of binding proteins and desired targets, especially for ankyrin-protein complexes. The AnkPlex web server is freely accessible at http://ankplex.ams.cmu.ac.th .
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Project description:Unlike the core structural elements of a protein like regular secondary structure, template based modeling (TBM) has difficulty with loop regions due to their variability in sequence and structure as well as the sparse sampling from a limited number of homologous templates. We present a novel, knowledge-based method for loop sampling that leverages homologous torsion angle information to estimate a continuous joint backbone dihedral angle density at each loop position. The φ,ψ distributions are estimated via a Dirichlet process mixture of hidden Markov models (DPM-HMM). Models are quickly generated based on samples from these distributions and were enriched using an end-to-end distance filter. The performance of the DPM-HMM method was evaluated against a diverse test set in a leave-one-out approach. Candidates as low as 0.45 Å RMSD and with a worst case of 3.66 Å were produced. For the canonical loops like the immunoglobulin complementarity-determining regions (mean RMSD <2.0 Å), the DPM-HMM method performs as well or better than the best templates, demonstrating that our automated method recaptures these canonical loops without inclusion of any IgG specific terms or manual intervention. In cases with poor or few good templates (mean RMSD >7.0 Å), this sampling method produces a population of loop structures to around 3.66 Å for loops up to 17 residues. In a direct test of sampling to the Loopy algorithm, our method demonstrates the ability to sample nearer native structures for both the canonical CDRH1 and non-canonical CDRH3 loops. Lastly, in the realistic test conditions of the CASP9 experiment, successful application of DPM-HMM for 90 loops from 45 TBM targets shows the general applicability of our sampling method in loop modeling problem. These results demonstrate that our DPM-HMM produces an advantage by consistently sampling near native loop structure. The software used in this analysis is available for download at http://www.stat.tamu.edu/~dahl/software/cortorgles/.
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