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Register a new userRole of electrostatic interactions in amyloid beta-protein (Abeta) oligomer formation: A discrete molecular dynamics study
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Pathological folding and oligomer formation of the amyloid beta-protein (Abeta) are widely perceived as central to Alzheimers disease (AD). Experimental approaches to study Abeta self-assembly are problematic, because most relevant aggregates are quasi-stable and inhomogeneous. We apply a discrete molecular dynamics (DMD) approach combined with a four-bead protein model to study oligomer formation of the amyloid beta-protein (Abeta). We address the differences between the two most common Abeta alloforms, Abeta40 and Abeta42, which oligomerize differently in vitro. We study how the presence of electrostatic interactions (EIs) between pairs of charged amino acids affects Abeta40 and Abeta42 oligomer formation. Our results indicate that EIs promote formation of larger oligomers in both Abeta40 and Abeta42. The Abeta40 size distribution remains unimodal, whereas the Abeta42 distribution is trimodal, as observed experimentally. Abeta42 folded structure is characterized by a turn in the C-terminus that is not present in Abeta40. We show that the same C-terminal region is also responsible for the strongest intermolecular contacts in Abeta42 pentamers and larger oligomers. Our results suggest that this C-terminal region plays a key role in the formation of Abeta42 oligomers and the relative importance of this region increases in the presence of EIs. These results suggest that inhibitors targeting the C-terminal region of Abeta42 oligomers may be able to prevent oligomer formation or structurally modify the assemblies to reduce their toxicity.
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Recently a technique based on the interaction between adhesion proteins extracted from Streptococcus pyogenes, known as SpyRing, has been widely used to improve the thermal resilience of enzymes, the assembly of biostructures, cancer cell recognition and other fields. In SpyRing, the two termini of the target enzyme are respectively linked to the peptide SpyTag and its protein partner SpyCatcher. SpyTag spontaneously reacts with SpyCatcher to form an isopeptide bond, with which the target enzyme forms a close ring structure. It was believed that the covalent cyclization of protein skeleton caused by SpyRing reduces the conformational entropy of biological structure and improves its rigidity, thus improving the thermal resilience of the target enzyme. However, the effects of SpyTag/ SpyCatcher interaction with this enzyme are poorly understood, and their regulation of enzyme properties remains unclear. Here, for simplicity, we took the single domain enzyme lichenase from Bacillus subtilis 168 as an example, studied the interface interactions in the SpyRing system by molecular dynamics simulations, and examined the effects of the changes of electrostatic interaction and van der Waals interaction on the thermal resilience of target enzyme. The simulations showed that the interface between SpyTag/SpyCatcher and lichenase is different from that found by geometric matching method and highlighted key mutations that affect the intensity of interactions at the interface and might have effect on the thermal resilience of the enzyme. Our calculations provided new insights into the rational designs in the SpyRing.
Recent experiments with amyloid-beta (Abeta) peptide suggest that formation of toxic oligomers may be an important contribution to the onset of Alzheimers disease. The toxicity of Abeta oligomers depends on their structure, which is governed by assembly dynamics. Due to limitations of current experimental techniques, a detailed knowledge of oligomer structure at the atomic level is missing. We introduce a molecular dynamics approach to study Abeta dimer formation: (1) we use discrete molecular dynamics simulations of a coarse-grained model to identify a variety of dimer conformations, and (2) we employ all-atom molecular mechanics simulations to estimate the thermodynamic stability of all dimer conformations. Our simulations of a coarse-grained Abeta peptide model predicts ten different planar beta-strand dimer conformations. We then estimate the free energies of all dimer conformations in all-atom molecular mechanics simulations with explicit water. We compare the free energies of Abeta(1-42) and Abeta(1-40) dimers. We find that (a) all dimer conformations have higher free energies compared to their corresponding monomeric states, and (b) the free energy difference between the Abeta(1-42) and the analogous Abeta(1-40) dimer conformation is not significant. Our results suggest that Abeta oligomerization is not accompanied by the formation of stable planar beta-strand Abeta dimers.
Exploring the protein-folding problem has been a long-standing challenge in molecular biology. Protein folding is highly dependent on folding of secondary structures as the way to pave a native folding pathway. Here, we demonstrate that a feature of a large hydrophobic surface area covering most side-chains on one side or the other side of adjacent $beta$-strands of a $beta$-sheet is prevail in almost all experimentally determined $beta$-sheets, indicating that folding of $beta$-sheets is most likely triggered by multistage hydrophobic interactions among neighbored side-chains of unfolded polypeptides, enable $beta$-sheets fold reproducibly following explicit physical folding codes in aqueous environments. $beta$-turns often contain five types of residues characterized with relatively small exposed hydrophobic proportions of their side-chains, that is explained as these residues can block hydrophobic effect among neighbored side-chains in sequence. Temperature dependence of the folding of $beta$-sheet is thus attributed to temperature dependence of the strength of the hydrophobicity. The hydrophobic-effect-based mechanism responsible for $beta$-sheets folding is verified by bioinformatics analyses of thousands of results available from experiments. The folding codes in amino acid sequence that dictate formation of a $beta$-hairpin can be deciphered through evaluating hydrophobic interaction among side-chains of an unfolded polypeptide from a $beta$-strand-like thermodynamic metastable state.
Background: Typically, proteins perform key biological functions by interacting with each other. As a consequence, predicting which protein pairs interact is a fundamental problem. Experimental methods are slow, expensive, and may be error prone. Many computational methods have been proposed to identify candidate interacting pairs. When accurate, they can serve as an inexpensive, preliminary filtering stage, to be followed by downstream experimental validation. Among such methods, sequence-based ones are very promising. Results: We present MPS(T&B) (Maximum Protein Similarity Topological and Biological), a new algorithm that leverages both topological and biological information to predict protein-protein interactions. We comprehensively compare MPS(T) and MPS(T&B) with state-of-the-art approaches on reliable PPIs datasets, showing that they have competitive or higher accuracy on biologically validated test sets. Conclusion: MPS(T) and MPS(T&B) are topological only and topological plus sequence-based computational methods that can effectively predict the entire human interactome.
We propose a criterion for optimal parameter selection in coarse-grained models of proteins, and develop a refined elastic network model (ENM) of bovine trypsinogen. The unimodal density-of-states distribution of the trypsinogen ENM disagrees with the bimodal distribution obtained from an all-atom model; however, the bimodal distribution is recovered by strengthening interactions between atoms that are backbone neighbors. We use the backbone-enhanced model to analyze allosteric mechanisms of trypsinogen, and find relatively strong communication between the regulatory and active sites.
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