Comparison of cellooligosaccharide conformations in complexes with proteins with energy maps for cellobiose.
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ABSTRACT:
SUBMITTER: French AD
PROVIDER: S-EPMC8607818 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:During protein folding and as part of some conformational changes that regulate protein function, the polypeptide chain must traverse high-energy barriers that separate the commonly adopted low-energy conformations. How distortions in peptide geometry allow these barrier-crossing transitions is a fundamental open question. One such important transition involves the movement of a non-glycine residue between the left side of the Ramachandran plot (that is, ? < 0°) and the right side (that is, ? > 0°). We report that high-energy conformations with ? ~ 0°, normally expected to occur only as fleeting transition states, are stably trapped in certain highly resolved native protein structures and that an analysis of these residues provides a detailed, experimentally derived map of the bond angle distortions taking place along the transition path. This unanticipated information lays to rest any uncertainty about whether such transitions are possible and how they occur, and in doing so lays a firm foundation for theoretical studies to better understand the transitions between basins that have been little studied but are integrally involved in protein folding and function. Also, the context of one such residue shows that even a designed highly stable protein can harbor substantial unfavorable interactions.
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Project description:Limitations in protein homology modeling often arise from the inability to adequately model loops. In this paper we focus on the selection of loop conformations. We present a complete computational treatment that allows the screening of loop conformations to identify those that best fit a molecular model. The stability of a loop in a protein is evaluated via computations of conformational free energies in solution, i.e., the free energy difference between the reference structure and the modeled one. A thermodynamic cycle is used for calculation of the conformational free energy, in which the total free energy of the reference state (i.e., gas phase) is the CHARMm potential energy. The electrostatic contribution of the solvation free energy is obtained from solving the finite-difference Poisson-Boltzmann equation. The nonpolar contribution is based on a surface area-based expression. We applied this computational scheme to a simple but well-characterized system, the antibody hypervariable loop (complementarity-determining region, CDR). Instead of creating loop conformations, we generated a database of loops extracted from high-resolution crystal structures of proteins, which display geometrical similarities with antibody CDRs. We inserted loops from our database into a framework of an antibody; then we calculated the conformational free energies of each loop. Results show that we successfully identified loops with a "reference-like" CDR geometry, with the lowest conformational free energy in gas phase only. Surprisingly, the solvation energy term plays a confusing role, sometimes discriminating "reference-like" CDR geometry and many times allowing "non-reference-like" conformations to have the lowest conformational free energies (for short loops). Most "reference-like" loop conformations are separated from others by a gap in the gas phase conformational free energy scale. Naturally, loops from antibody molecules are found to be the best models for long CDRs (> or = 6 residues), mainly because of a better packing of backbone atoms into the framework of the antibody model.
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Project description:BackgroundStructural excursions of a protein at equilibrium are key to biomolecular recognition and function modulation. Protein modeling research is driven by the need to aid wet laboratories in characterizing equilibrium protein dynamics. In principle, structural excursions of a protein can be directly observed via simulation of its dynamics, but the disparate temporal scales involved in such excursions make this approach computationally impractical. On the other hand, an informative representation of the structure space available to a protein at equilibrium can be obtained efficiently via stochastic optimization, but this approach does not directly yield information on equilibrium dynamics.MethodsWe present here a novel methodology that first builds a multi-dimensional map of the energy landscape that underlies the structure space of a given protein and then queries the computed map for energetically-feasible excursions between structures of interest. An evolutionary algorithm builds such maps with a practical computational budget. Graphical techniques analyze a computed multi-dimensional map and expose interesting features of an energy landscape, such as basins and barriers. A path searching algorithm then queries a nearest-neighbor graph representation of a computed map for energetically-feasible basin-to-basin excursions.ResultsEvaluation is conducted on intrinsically-dynamic proteins of importance in human biology and disease. Visual statistical analysis of the maps of energy landscapes computed by the proposed methodology reveals features already captured in the wet laboratory, as well as new features indicative of interesting, unknown thermodynamically-stable and semi-stable regions of the equilibrium structure space. Comparison of maps and structural excursions computed by the proposed methodology on sequence variants of a protein sheds light on the role of equilibrium structure and dynamics in the sequence-function relationship.ConclusionsApplications show that the proposed methodology is effective at locating basins in complex energy landscapes and computing basin-basin excursions of a protein with a practical computational budget. While the actual temporal scales spanned by a structural excursion cannot be directly obtained due to the foregoing of simulation of dynamics, hypotheses can be formulated regarding the impact of sequence mutations on protein function. These hypotheses are valuable in instigating further research in wet laboratories.
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Project description:Recent experimental studies of the denatured state and theoretical analyses of the folding landscape suggest that there are a large multiplicity of low-energy, partially folded conformations near the native state. In this report, we describe a strategy for predicting protein structure based on the working hypothesis that there are a greater number of low-energy conformations surrounding the correct fold than there are surrounding low-energy incorrect folds. To test this idea, 12 ensembles of 500 to 1,000 low-energy structures for 10 small proteins were analyzed by calculating the rms deviation of the Calpha coordinates between each conformation and every other conformation in the ensemble. In all 12 cases, the conformation with the greatest number of conformations within 4-A rms deviation was closer to the native structure than were the majority of conformations in the ensemble, and in most cases it was among the closest 1 to 5%. These results suggest that, to fold efficiently and retain robustness to changes in amino acid sequence, proteins may have evolved a native structure situated within a broad basin of low-energy conformations, a feature which could facilitate the prediction of protein structure at low resolution.
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Project description:The relationship between the genotype (sequence) and the phenotype (structure) of macromolecules affects their ability to evolve new structures and functions. We here compare the genotype space organization of proteins and RNA molecules to identify differences that may affect this ability. To this end, we computationally study the genotype-phenotype relationship for short RNA and lattice proteins of a reduced monomer alphabet size, to make exhaustive analysis and direct comparison of their genotype spaces feasible. We find that many fewer protein molecules than RNA molecules fold, but they fold into many more structures than RNA. In consequence, protein phenotypes have smaller genotype networks whose member genotypes tend to be more similar than for RNA phenotypes. Neighborhoods in sequence space of a given radius around an RNA molecule contain more novel structures than for protein molecules. We compare this property to evidence from natural RNA and protein molecules, and conclude that RNA genotype space may be more conducive to the evolution of new structure phenotypes.
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Project description:In the last decade it has become evident that disordered states of proteins play important physiological and pathological roles and that the transient tertiary interactions often present in these systems can play a role in their biological activity. The structural characterization of such states has so far largely relied on ensemble representations, which in principle account for both their local and global structural features. However, these approaches are inherently of low resolution due to the large number of degrees of freedom of conformational ensembles and to the sparse nature of the experimental data used to determine them. Here, we overcome these limitations by showing that tertiary interactions in disordered states can be mapped at high resolution by fitting paramagnetic relaxation enhancement data to a small number of conformations, which can be as low as one. This result opens up the possibility of determining the topology of cooperatively collapsed and hidden folded states when these are present in the vast conformational landscape accessible to disordered states of proteins. As a first application, we study the long-range tertiary interactions of acid-unfolded apomyoglobin from experimentally measured paramagnetic relaxation enhancement data.
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Project description:Bulk RNA-seq comparison of kidney organoids bioprinted in 3 different conformations with varying starting cell densities. Density is dictated by the ratio of bioprinter tip movement to the amount of extrusion, where higher ratios spread cells over a larger surface area. We compare organoids printed with no movement ('blob', ratio 0) to those with moderate ('line 3', ratio 20) or high movement ('line 1', ratio 40).
2020-09-01 | GSE138733 | GEO
Project description:The potentially tridentate ligand bis[(1-methyl-2-benzimidazolyl)ethyl]amine (2BB) was employed to prepare copper complexes [(2BB)CuI]OTf and [(2BB)CuII(H2O)2](OTf)2 as bioinspired models of lytic polysaccharide copper-dependent monooxygenase (LPMO) enzymes. Solid-state characterization of [(2BB)CuI]OTf revealed a Cu(I) center with a T-shaped coordination environment and metric parameters in the range of those observed in reduced LPMOs. Solution characterization of [(2BB)CuII(H2O)2](OTf)2 indicates that [(2BB)CuII(H2O)2]2+ is the main species from pH 4 to 7.5; above pH 7.5, the hydroxo-bridged species [{(2BB)CuII(H2O) x }2(μ-OH)2]2+ is also present, on the basis of cyclic voltammetry and mass spectrometry. These observations imply that deprotonation of the central amine of Cu(II)-coordinated 2BB is precluded, and by extension, amine deprotonation in the histidine brace of LPMOs appears unlikely at neutral pH. The complexes [(2BB)CuI]OTf and [(2BB)CuII(H2O)2](OTf)2 act as precursors for the oxidative degradation of cellobiose as a cellulose model substrate. Spectroscopic and reactivity studies indicate that a dicopper(II) side-on peroxide complex generated from [(2BB)CuI]OTf/O2 or [(2BB)CuII(H2O)2](OTf)2/H2O2/NEt3 oxidizes cellobiose both in acetonitrile and aqueous phosphate buffer solutions, as evidenced from product analysis by high-performance liquid chromatography-mass spectrometry. The mixture of [(2BB)CuII(H2O)2](OTf)2/H2O2/NEt3 results in more extensive cellobiose degradation. Likewise, the use of both [(2BB)CuI]OTf and [(2BB)CuII(H2O)2](OTf)2 with KO2 afforded cellobiose oxidation products. In all cases, a common Cu(II) complex formulated as [(2BB)CuII(OH)(H2O)]+ was detected by mass spectrometry as the final form of the complex.
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Project description:We describe a new generation of algorithms capable of mapping the structure and conformations of macromolecules and their complexes from large ensembles of heterogeneous snapshots, and demonstrate the feasibility of determining both discrete and continuous macromolecular conformational spectra. These algorithms naturally incorporate conformational heterogeneity without resort to sorting and classification, or prior knowledge of the type of heterogeneity present. They are applicable to single-particle diffraction and image datasets produced by X-ray lasers and cryo-electron microscopy, respectively, and particularly suitable for systems not easily amenable to purification or crystallization.
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