What Makes Telomeres Unique?

Telomeres are repetitive nucleotide sequences, which are essential for protecting the termini of chromosomes. Thousands of such repetitions are necessary to maintain the stability of the whole chromosome. Several similar repeated telomeric sequences have been found in different species, but why has nature chosen them? What features do telomeres have in common? In this article, we study the physical properties of human-like (TTAGGG), plant (TTTAGG), insect (TTAGG), and Candida guilermondi (GGTGTAC) telomeres in comparison with seven control, nontelomeric sequences. We used steered molecular dynamics with the nucleic acid united residue (NARES) coarse-grained force field, which we compared with the all-atom AMBER14 force field and experimental data. Our results reveal important features in all of the telomeric sequences, including their exceptionally high mechanical resistance and stability to untangling and stretching, compared to those of nontelomeric sequences. We find that the additional stability of the telomeres comes from their ability to form triplex structures and wrap around loose chains of linear DNA by regrabbing the chain. We find that, with slower pulling speed, regrabbing and triplex formation is more frequent. We also found that some of the sequences can form triplexes experimentally, such as TTTTTCCCC, and can mimic telomeric properties

What Makes Telomeres Unique?

Telomeres are repetitive nucleotide sequences, which are essential for protecting the termini of chromosomes. Thousands of such repetitions are necessary to maintain the stability of the whole chromosome. Several similar repeated telomeric sequences have been found in different species, but why has nature chosen them? What features do telomeres have in common? In this article, we study the physical properties of human-like (TTAGGG), plant (TTTAGG), insect (TTAGG), and Candida guilermondi (GGTGTAC) telomeres in comparison with seven control, nontelomeric sequences. We used steered molecular dynamics with the nucleic acid united residue (NARES) coarse-grained force field, which we compared with the all-atom AMBER14 force field and experimental data. Our results reveal important features in all of the telomeric sequences, including their exceptionally high mechanical resistance and stability to untangling and stretching, compared to those of nontelomeric sequences. We find that the additional stability of the telomeres comes from their ability to form triplex structures and wrap around loose chains of linear DNA by regrabbing the chain. We find that, with slower pulling speed, regrabbing and triplex formation is more frequent. We also found that some of the sequences can form triplexes experimentally, such as TTTTTCCCC, and can mimic telomeric properties

Physics-Based Potentials for the Coupling between Backbone- and Side-Chain-Local Conformational States in the United Residue (UNRES) Force Field for Protein Simulations

The UNited RESidue (UNRES) model of polypeptide chains is a coarse-grained model in which each amino-acid residue is reduced to two interaction sites, namely, a united peptide group (p) located halfway between the two neighboring α-carbon atoms (C^αs), which serve only as geometrical points, and a united side chain (SC) attached to the respective C^α. Owing to this simplification, millisecond molecular dynamics simulations of large systems can be performed. While UNRES predicts overall folds well, it reproduces the details of local chain conformation with lower accuracy. Recently, we implemented new knowledge-based torsional potentials (Krupa et al. <i>J. Chem. Theory Comput.</i> <b>2013</b>, <i>9</i>, 4620–4632) that depend on the virtual-bond dihedral angles involving side chains: C^α···C^α···C^α···SC (τ⁽¹⁾), SC···C^α···C^α···C^α (τ⁽²⁾), and SC···C^α···C^α···SC (τ⁽³⁾) in the UNRES force field. These potentials resulted in significant improvement of the simulated structures, especially in the loop regions. In this work, we introduce the physics-based counterparts of these potentials, which we derived from the all-atom energy surfaces of terminally blocked amino-acid residues by Boltzmann integration over the angles λ⁽¹⁾ and λ⁽²⁾ for rotation about the C^α···C^α virtual-bond angles and over the side-chain angles χ. The energy surfaces were, in turn, calculated by using the semiempirical AM1 method of molecular quantum mechanics. Entropy contribution was evaluated with use of the harmonic approximation from Hessian matrices. One-dimensional Fourier series in the respective virtual-bond-dihedral angles were fitted to the calculated potentials, and these expressions have been implemented in the UNRES force field. Basic calibration of the UNRES force field with the new potentials was carried out with eight training proteins, by selecting the optimal weight of the new energy terms and reducing the weight of the regular torsional terms. The force field was subsequently benchmarked with a set of 22 proteins not used in the calibration. The new potentials result in a decrease of the root-mean-square deviation of the average conformation from the respective experimental structure by 0.86 Å on average; however, improvement of up to 5 Å was observed for some proteins

Exploring Structural Insights of Aβ42 and α‑Synuclein Monomers and Heterodimer: A Comparative Study Using Implicit and Explicit Solvent Simulations

Protein misfolding, aggregation, and fibril formation play a central role in the development of severe neurological disorders, including Alzheimer’s and Parkinson’s diseases. The structural stability of mature fibrils in these diseases is of great importance, as organisms struggle to effectively eliminate amyloid plaques. To address this issue, it is crucial to investigate the early stages of fibril formation when monomers aggregate into small, toxic, and soluble oligomers. However, these structures are inherently disordered, making them challenging to study through experimental approaches. Recently, it has been shown experimentally that amyloid-β 42 (Aβ42) and α-synuclein (α-Syn) can coassemble. This has motivated us to investigate the interaction between their monomers as a first step toward exploring the possibility of forming heterodimeric complexes. In particular, our study involves the utilization of various Amber and CHARMM force-fields, employing both implicit and explicit solvent models in replica exchange and conventional simulation modes. This comprehensive approach allowed us to assess the strengths and weaknesses of these solvent models and force fields in comparison to experimental and theoretical findings, ensuring the highest level of robustness. Our investigations revealed that Aβ42 and α-Syn monomers can indeed form stable heterodimers, and the resulting heterodimeric model exhibits stronger interactions compared to the Aβ42 dimer. The binding of α-Syn to Aβ42 reduces the propensity of Aβ42 to adopt fibril-prone conformations and induces significant changes in its conformational properties. Notably, in AMBER-FB15 and CHARMM36m force fields with the use of explicit solvent, the presence of Aβ42 significantly increases the β-content of α-Syn, consistent with the experiments showing that Aβ42 triggers α-Syn aggregation. Our analysis clearly shows that although the use of implicit solvent resulted in too large compactness of monomeric α-Syn, structural properties of monomeric Aβ42 and the heterodimer were preserved in explicit-solvent simulations. We anticipate that our study sheds light on the interaction between α-Syn and Aβ42 proteins, thus providing the atom-level model required to assess the initial stage of aggregation mechanisms related to Alzheimer’s and Parkinson’s diseases

Exploring Structural Insights of Aβ42 and α‑Synuclein Monomers and Heterodimer: A Comparative Study Using Implicit and Explicit Solvent Simulations

Protein misfolding, aggregation, and fibril formation play a central role in the development of severe neurological disorders, including Alzheimer’s and Parkinson’s diseases. The structural stability of mature fibrils in these diseases is of great importance, as organisms struggle to effectively eliminate amyloid plaques. To address this issue, it is crucial to investigate the early stages of fibril formation when monomers aggregate into small, toxic, and soluble oligomers. However, these structures are inherently disordered, making them challenging to study through experimental approaches. Recently, it has been shown experimentally that amyloid-β 42 (Aβ42) and α-synuclein (α-Syn) can coassemble. This has motivated us to investigate the interaction between their monomers as a first step toward exploring the possibility of forming heterodimeric complexes. In particular, our study involves the utilization of various Amber and CHARMM force-fields, employing both implicit and explicit solvent models in replica exchange and conventional simulation modes. This comprehensive approach allowed us to assess the strengths and weaknesses of these solvent models and force fields in comparison to experimental and theoretical findings, ensuring the highest level of robustness. Our investigations revealed that Aβ42 and α-Syn monomers can indeed form stable heterodimers, and the resulting heterodimeric model exhibits stronger interactions compared to the Aβ42 dimer. The binding of α-Syn to Aβ42 reduces the propensity of Aβ42 to adopt fibril-prone conformations and induces significant changes in its conformational properties. Notably, in AMBER-FB15 and CHARMM36m force fields with the use of explicit solvent, the presence of Aβ42 significantly increases the β-content of α-Syn, consistent with the experiments showing that Aβ42 triggers α-Syn aggregation. Our analysis clearly shows that although the use of implicit solvent resulted in too large compactness of monomeric α-Syn, structural properties of monomeric Aβ42 and the heterodimer were preserved in explicit-solvent simulations. We anticipate that our study sheds light on the interaction between α-Syn and Aβ42 proteins, thus providing the atom-level model required to assess the initial stage of aggregation mechanisms related to Alzheimer’s and Parkinson’s diseases

Exploring Structural Insights of Aβ42 and α‑Synuclein Monomers and Heterodimer: A Comparative Study Using Implicit and Explicit Solvent Simulations

Protein misfolding, aggregation, and fibril formation play a central role in the development of severe neurological disorders, including Alzheimer’s and Parkinson’s diseases. The structural stability of mature fibrils in these diseases is of great importance, as organisms struggle to effectively eliminate amyloid plaques. To address this issue, it is crucial to investigate the early stages of fibril formation when monomers aggregate into small, toxic, and soluble oligomers. However, these structures are inherently disordered, making them challenging to study through experimental approaches. Recently, it has been shown experimentally that amyloid-β 42 (Aβ42) and α-synuclein (α-Syn) can coassemble. This has motivated us to investigate the interaction between their monomers as a first step toward exploring the possibility of forming heterodimeric complexes. In particular, our study involves the utilization of various Amber and CHARMM force-fields, employing both implicit and explicit solvent models in replica exchange and conventional simulation modes. This comprehensive approach allowed us to assess the strengths and weaknesses of these solvent models and force fields in comparison to experimental and theoretical findings, ensuring the highest level of robustness. Our investigations revealed that Aβ42 and α-Syn monomers can indeed form stable heterodimers, and the resulting heterodimeric model exhibits stronger interactions compared to the Aβ42 dimer. The binding of α-Syn to Aβ42 reduces the propensity of Aβ42 to adopt fibril-prone conformations and induces significant changes in its conformational properties. Notably, in AMBER-FB15 and CHARMM36m force fields with the use of explicit solvent, the presence of Aβ42 significantly increases the β-content of α-Syn, consistent with the experiments showing that Aβ42 triggers α-Syn aggregation. Our analysis clearly shows that although the use of implicit solvent resulted in too large compactness of monomeric α-Syn, structural properties of monomeric Aβ42 and the heterodimer were preserved in explicit-solvent simulations. We anticipate that our study sheds light on the interaction between α-Syn and Aβ42 proteins, thus providing the atom-level model required to assess the initial stage of aggregation mechanisms related to Alzheimer’s and Parkinson’s diseases

Role of the sulfur to α-carbon thioether bridges in thurincin H

<p>Thurincin H is a small protein produced by <i>Bacillus thuringiensis</i> SF361 with gram-positive antimicrobial properties. The toxins produced by <i>B. thuringiensis</i> are widely used in the agriculture as, e.g. natural preservatives in dairy products. The structure of thurincin H possesses four covalent sulfur to -carbon bonds that involve the cysteine side-chains; these bonds are probably responsible for the shape and stability of the protein and, thereby, for its antimicrobial properties. To examine the influence of the formation of the sulfur-carbon bonds on the folding pathways and stability of the protein, a series of canonical and multiplexed replica-exchange simulations with the coarse-grained UNRES force field was carried out without and with distance restraints imposed on selected S-C atom pairs. It was found that the order of the formation and breaking of the S-C thioether bonds significantly impacts on the foldability and stability of the thurincin H. It was also observed that thioether bridges play a major role in stabilizing the global fold of the protein, although it significantly diminishes the entropy of the system. The maximum foldability of thurincin H was observed in the presence of the optimal set of three out of four thioether bridges. Thus, the results suggest that the presence of ThnB enzyme and other agents that catalyze the formation of thioether bridges can be essential for correct folding of thurincin H and that the formation of the fourth bridge does not seem to facilitate folding; instead, it seems to rigidify the loop and prevent proteolysis.</p

Maximum Likelihood Calibration of the UNRES Force Field for Simulation of Protein Structure and Dynamics

By using the maximum likelihood method for force-field calibration recently developed in our laboratory, which is aimed at achieving the agreement between the simulated conformational ensembles of selected training proteins and the corresponding ensembles determined experimentally at various temperatures, the physics-based coarse-grained UNRES force field for simulations of protein structure and dynamics was optimized with seven small training proteins exhibiting a variety of secondary and tertiary structures. Four runs of optimization, in which the number of optimized force-field parameters was gradually increased, were carried out, and the resulting force fields were subsequently tested with a set of 22 α-, 12 β-, and 12 α + β-proteins not used in optimization. The variant in which energy-term weights, local, and correlation potentials, side-chain radii, and anisotropies were optimized turned out to be the most transferable and outperformed all previous versions of UNRES on the test set

Prediction of Protein Structure by Template-Based Modeling Combined with the UNRES Force Field

A new approach to the prediction of protein structures that uses distance and backbone virtual-bond dihedral angle restraints derived from template-based models and simulations with the united residue (UNRES) force field is proposed. The approach combines the accuracy and reliability of template-based methods for the segments of the target sequence with high similarity to those having known structures with the ability of UNRES to pack the domains correctly. Multiplexed replica-exchange molecular dynamics with restraints derived from template-based models of a given target, in which each restraint is weighted according to the accuracy of the prediction of the corresponding section of the molecule, is used to search the conformational space, and the weighted histogram analysis method and cluster analysis are applied to determine the families of the most probable conformations, from which candidate predictions are selected. To test the capability of the method to recover template-based models from restraints, five single-domain proteins with structures that have been well-predicted by template-based methods were used; it was found that the resulting structures were of the same quality as the best of the original models. To assess whether the new approach can improve template-based predictions with incorrectly predicted domain packing, four such targets were selected from the CASP10 targets; for three of them the new approach resulted in significantly better predictions compared with the original template-based models. The new approach can be used to predict the structures of proteins for which good templates can be found for sections of the sequence or an overall good template can be found for the entire sequence but the prediction quality is remarkably weaker in putative domain-linker regions

Use of Restraints from Consensus Fragments of Multiple Server Models To Enhance Protein-Structure Prediction Capability of the UNRES Force Field

Recently, we developed a new approach to protein-structure prediction, which combines template-based modeling with the physics-based coarse-grained UNited RESidue (UNRES) force field. In this approach, restrained multiplexed replica exchange molecular dynamics simulations with UNRES, with the C^α-distance and virtual-bond-dihedral-angle restraints derived from knowledge-based models are carried out. In this work, we report a test of this approach in the 11th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP11), in which we used the template-based models from early-stage predictions by the LEE group CASP11 server (group 038, called “nns”), and further improvement of the method. The quality of the models obtained in CASP11 was better than that resulting from unrestrained UNRES simulations; however, the obtained models were generally worse than the final nns models. Calculations with the final nns models, performed after CASP11, resulted in substantial improvement, especially for multi-domain proteins. Based on these results, we modified the procedure by deriving restraints from models from multiple servers, in this study the four top-performing servers in CASP11 (nns, BAKER-ROSETTA­SERVER, Zhang-server, and QUARK), and implementing either all restraints or only the restraints on the fragments that appear similar in the majority of models (the <i>consensus fragments</i>), outlier models discarded. Tests with 29 CASP11 human-prediction targets with length less than 400 amino-acid residues demonstrated that the consensus-fragment approach gave better results, i.e., lower α-carbon root-mean-square deviation from the experimental structures, higher template modeling score, and global distance test total score values than the best of the parent server models. Apart from global improvement (repacking and improving the orientation of domains and other substructures), improvement was also reached for template-based modeling targets, indicating that the approach has refinement capacity. Therefore, the consensus-fragment analysis is able to remove lower-quality models and poor-quality parts of the models without knowing the experimental structure