Abstract:Self‐assembling and molecular folding are ubiquitous in Nature: they drive the organization of systems ranging from living creatures to DNA molecules. Elucidating the complex dynamics underlying these phenomena is of crucial importance. However, a tool for the analysis of the various phenomena involved in protein/peptide aggregation is still missing. Here, an innovative software is developed and validated for the identification and visualization of b‐structuring and b‐sheet formation in both simulated systems … Show more
“…Indeed, the grain components of the triplets arise from the alternating alignment of opposite charge groups of Lysine and Aspartic Acid residues, resulting in -sheet rich aggregates. As a matter of fact, as shown by Saracino et al 13 , SAPs organization trend can be predicted by analyzing the first 500 ns of CG-MD simulation trajectories. Figure 3 Analysis of (LDLK) CG-MD trajectory simulation.…”
Section: Resultsmentioning
confidence: 98%
“…The tested structures were: the laminin-g-like module (PDB ID 1d2s), a high-molecular weight protein belonging to the extra-cellular matrix and constituting the biologically active part of the basal lamina ( ) 19 ; the fibrils (PDB ID 2mxu), the initial and predominant constituents of the amyloid plaques that characterize Alzheimer’s disease ( ) 20 ; an engineered Boriella OspA structure (PDB ID 2fkg) consisting of -hairpin repeats connected by turn motifs ( ) 21 ; the Escherichia coli -clamp (PDB ID 3bep), a sub-unit of the DNA polymerase III holoenzime, characterized by antiparallel -sheet structures ( ) 22 . Figure 2 shows the result of this preliminary validation phase, presenting a comparison of the output produced by HyperBeta and rendered by HyperBeta ’s visualization tool by setting the angular threshold and the distance threshold nm (see Supplementary File #1 for further information and for comparison with Morphoscanner 13 ), against the structures identified by STRIDE 23 , a well-established tool for secondary structure assignment, and rendered with VMD 24 . The putative -sheets identified through HyperBeta in CG models correspond to the -sheets identified in united-atom (UA) models through STRIDE and rendered with VMD.…”
Section: Resultsmentioning
confidence: 99%
“…Successively, five different SAPs MD trajectories were analyzed using HyperBeta with the same settings for and . These trajectories were previously analyzed by Saracino et al 13 . These systems comprised a total of identical 100 peptides for BMHP1-derived SAP sequences (B26: Btn-GGGPFASTKT , GL = 10; B24: Btn-GGGAFASTKT, GL = 10; 30: WGGGAFASTKT, GL = 10) and (LDLK) SAP (GL = 12), and 50 plus 50 opposite charged peptides for the complementary assembling peptides (CAPs), whose sequences are (LDLD) and (LKLK) (GL = 12).…”
Section: Resultsmentioning
confidence: 99%
“…Figure 3 shows that, after 500 ns (from top to bottom, left to right), the (LDLK) SAPs organize themselves into multiple independent cross- fibril seeds, which subsequently assemble into a “patchwork”-like aggregate 13 . Despite the huge variations of the number of triplets and components over time, the ratio between the different backbone grain types involved in the formation of the triplets remains stable.…”
Section: Resultsmentioning
confidence: 99%
“…HyperBeta was specifically developed for the analysis of CG-MD simulations of SAPs systems, and allows the automatic identification and real-time rendering of -sheets in MARTINI CG-MD dynamics. Differently from Morphoscanner 13 , a tool for CG-MD simulations of SAPs systems, HyperBeta relies on an approach based on hyper-graphs and additional geometric constraints. Moreover, HyperBeta calculates several statistics about the composition of -sheets, and embeds a high-quality 3D engine that exploits sophisticated visual cues to simplify the interpretation of distance and depth relationships among the grains and the peptides.…”
Self-assembling processes are ubiquitous phenomena that drive the organization and the hierarchical formation of complex molecular systems. The investigation of assembling dynamics, emerging from the interactions among biomolecules like amino-acids and polypeptides, is fundamental to determine how a mixture of simple objects can yield a complex structure at the nano-scale level. In this paper we present HyperBeta, a novel open-source software that exploits an innovative algorithm based on hyper-graphs to efficiently identify and graphically represent the dynamics of $$\beta$$
β
-sheets formation. Differently from the existing tools, HyperBeta directly manipulates data generated by means of coarse-grained molecular dynamics simulation tools (GROMACS), performed using the MARTINI force field. Coarse-grained molecular structures are visualized using HyperBeta ’s proprietary real-time high-quality 3D engine, which provides a plethora of analysis tools and statistical information, controlled by means of an intuitive event-based graphical user interface. The high-quality renderer relies on a variety of visual cues to improve the readability and interpretability of distance and depth relationships between peptides. We show that HyperBeta is able to track the $$\beta$$
β
-sheets formation in coarse-grained molecular dynamics simulations, and provides a completely new and efficient mean for the investigation of the kinetics of these nano-structures. HyperBeta will therefore facilitate biotechnological and medical research where these structural elements play a crucial role, such as the development of novel high-performance biomaterials in tissue engineering, or a better comprehension of the molecular mechanisms at the basis of complex pathologies like Alzheimer’s disease.
“…Indeed, the grain components of the triplets arise from the alternating alignment of opposite charge groups of Lysine and Aspartic Acid residues, resulting in -sheet rich aggregates. As a matter of fact, as shown by Saracino et al 13 , SAPs organization trend can be predicted by analyzing the first 500 ns of CG-MD simulation trajectories. Figure 3 Analysis of (LDLK) CG-MD trajectory simulation.…”
Section: Resultsmentioning
confidence: 98%
“…The tested structures were: the laminin-g-like module (PDB ID 1d2s), a high-molecular weight protein belonging to the extra-cellular matrix and constituting the biologically active part of the basal lamina ( ) 19 ; the fibrils (PDB ID 2mxu), the initial and predominant constituents of the amyloid plaques that characterize Alzheimer’s disease ( ) 20 ; an engineered Boriella OspA structure (PDB ID 2fkg) consisting of -hairpin repeats connected by turn motifs ( ) 21 ; the Escherichia coli -clamp (PDB ID 3bep), a sub-unit of the DNA polymerase III holoenzime, characterized by antiparallel -sheet structures ( ) 22 . Figure 2 shows the result of this preliminary validation phase, presenting a comparison of the output produced by HyperBeta and rendered by HyperBeta ’s visualization tool by setting the angular threshold and the distance threshold nm (see Supplementary File #1 for further information and for comparison with Morphoscanner 13 ), against the structures identified by STRIDE 23 , a well-established tool for secondary structure assignment, and rendered with VMD 24 . The putative -sheets identified through HyperBeta in CG models correspond to the -sheets identified in united-atom (UA) models through STRIDE and rendered with VMD.…”
Section: Resultsmentioning
confidence: 99%
“…Successively, five different SAPs MD trajectories were analyzed using HyperBeta with the same settings for and . These trajectories were previously analyzed by Saracino et al 13 . These systems comprised a total of identical 100 peptides for BMHP1-derived SAP sequences (B26: Btn-GGGPFASTKT , GL = 10; B24: Btn-GGGAFASTKT, GL = 10; 30: WGGGAFASTKT, GL = 10) and (LDLK) SAP (GL = 12), and 50 plus 50 opposite charged peptides for the complementary assembling peptides (CAPs), whose sequences are (LDLD) and (LKLK) (GL = 12).…”
Section: Resultsmentioning
confidence: 99%
“…Figure 3 shows that, after 500 ns (from top to bottom, left to right), the (LDLK) SAPs organize themselves into multiple independent cross- fibril seeds, which subsequently assemble into a “patchwork”-like aggregate 13 . Despite the huge variations of the number of triplets and components over time, the ratio between the different backbone grain types involved in the formation of the triplets remains stable.…”
Section: Resultsmentioning
confidence: 99%
“…HyperBeta was specifically developed for the analysis of CG-MD simulations of SAPs systems, and allows the automatic identification and real-time rendering of -sheets in MARTINI CG-MD dynamics. Differently from Morphoscanner 13 , a tool for CG-MD simulations of SAPs systems, HyperBeta relies on an approach based on hyper-graphs and additional geometric constraints. Moreover, HyperBeta calculates several statistics about the composition of -sheets, and embeds a high-quality 3D engine that exploits sophisticated visual cues to simplify the interpretation of distance and depth relationships among the grains and the peptides.…”
Self-assembling processes are ubiquitous phenomena that drive the organization and the hierarchical formation of complex molecular systems. The investigation of assembling dynamics, emerging from the interactions among biomolecules like amino-acids and polypeptides, is fundamental to determine how a mixture of simple objects can yield a complex structure at the nano-scale level. In this paper we present HyperBeta, a novel open-source software that exploits an innovative algorithm based on hyper-graphs to efficiently identify and graphically represent the dynamics of $$\beta$$
β
-sheets formation. Differently from the existing tools, HyperBeta directly manipulates data generated by means of coarse-grained molecular dynamics simulation tools (GROMACS), performed using the MARTINI force field. Coarse-grained molecular structures are visualized using HyperBeta ’s proprietary real-time high-quality 3D engine, which provides a plethora of analysis tools and statistical information, controlled by means of an intuitive event-based graphical user interface. The high-quality renderer relies on a variety of visual cues to improve the readability and interpretability of distance and depth relationships between peptides. We show that HyperBeta is able to track the $$\beta$$
β
-sheets formation in coarse-grained molecular dynamics simulations, and provides a completely new and efficient mean for the investigation of the kinetics of these nano-structures. HyperBeta will therefore facilitate biotechnological and medical research where these structural elements play a crucial role, such as the development of novel high-performance biomaterials in tissue engineering, or a better comprehension of the molecular mechanisms at the basis of complex pathologies like Alzheimer’s disease.
The Martini model, a coarse-grained force field for molecular dynamics simulations, has been around for nearly two decades. Originally developed for lipidbased systems by the groups of Marrink and Tieleman, the Martini model has over the years been extended as a community effort to the current level of a general-purpose force field. Apart from the obvious benefit of a reduction in computational cost, the popularity of the model is largely due to the systematic yet intuitive building-block approach that underlies the model, as well as the open nature of the development and its continuous validation. The easy implementation in the widely used Gromacs software suite has also been instrumental. Since its conception in 2002, the Martini model underwent a gradual refinement of the bead interactions and a widening scope of applications. In this review, we look back at this development, culminating with the release of the Martini 3 version in 2021. The power of the model is illustrated with key examples of recent important findings in biological and material sciences enabled with Martini, as well as examples from areas where coarse-grained resolution is essential, namely highthroughput applications, systems with large complexity, and simulations approaching the scale of whole cells.
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