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Register a new userProtein-protein interactions enhance the thermal resilience of SpyRing enzymes: a molecular dynamic simulation study
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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.
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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.
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.
Significant progress in computer hardware and software have enabled molecular dynamics (MD) simulations to model complex biological phenomena such as protein folding. However, enabling MD simulations to access biologically relevant timescales (e.g., beyond milliseconds) still remains challenging. These limitations include (1) quantifying which set of states have already been (sufficiently) sampled in an ensemble of MD runs, and (2) identifying novel states from which simulations can be initiated to sample rare events (e.g., sampling folding events). With the recent success of deep learning and artificial intelligence techniques in analyzing large datasets, we posit that these techniques can also be used to adaptively guide MD simulations to model such complex biological phenomena. Leveraging our recently developed unsupervised deep learning technique to cluster protein folding trajectories into partially folded intermediates, we build an iterative workflow that enables our generative model to be coupled with all-atom MD simulations to fold small protein systems on emerging high performance computing platforms. We demonstrate our approach in folding Fs-peptide and the $betabetaalpha$ (BBA) fold, FSD-EY. Our adaptive workflow enables us to achieve an overall root-mean squared deviation (RMSD) to the native state of 1.6$~AA$ and 4.4~$AA$ respectively for Fs-peptide and FSD-EY. We also highlight some emerging challenges in the context of designing scalable workflows when data intensive deep learning techniques are coupled to compute intensive MD simulations.
We describe the results obtained from an improved model for protein folding. We find that a good agreement with the native structure of a 46 residue long, five-letter protein segment is obtained by carefully tuning the parameters of the self-avoiding energy. In particular we find an improved free-energy profile. We also compare the efficiency of the multidimensional replica exchange method with the widely used parallel tempering.
In the present work, we review the fundamental methods which have been developed in the last few years for classifying into families and clans the distribution of amino acids in protein databases. This is done through functions of random variables, the Entropy Measures of probabilities of occurrence of the amino acids. An intensive study of the Pfam databases is presented with restrictions to families which could be represented by rectangular arrays of amino acids with m rows (protein domains) and n columns (amino acids). This work is also an invitation to scientific research groups worldwide to undertake the statistical analysis with different numbers of rows and columns since we believe in the mathematical characterization of the distribution of amino acids as a fundamental insight on the determination of protein structure and evolution.
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