SecStAnT: secondary structure analysis tool for data selection, statistics and models building

Maccari, Giuseppe; Spampinato, Giulia Lia Beatrice; Tozzini, Valentina

doi:10.1093/bioinformatics/btt586

Cited by 8 publications

(11 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We performed a statistical analysis, using SecStAnT [ 49 ], of the proteins participating in the 10 slowest and the 10 fastest pairwise PSC tasks (using TMalign and CE) using the CK34 and RS119 datasets. The parameters on which SecStAnT compares proteins are their secondary structure characteristics: Bond Angle, Dihedral Angle, Theta, and Psi.…”

Section: Resultsmentioning

confidence: 99%

Efficient Multicriteria Protein Structure Comparison on Modern Processor Architectures

Sharma

Μανωλάκος

2015

BioMed Research International

View full text Add to dashboard Cite

Fast increasing computational demand for all-to-all protein structures comparison (PSC) is a result of three confounding factors: rapidly expanding structural proteomics databases, high computational complexity of pairwise protein comparison algorithms, and the trend in the domain towards using multiple criteria for protein structures comparison (MCPSC) and combining results. We have developed a software framework that exploits many-core and multicore CPUs to implement efficient parallel MCPSC in modern processors based on three popular PSC methods, namely, TMalign, CE, and USM. We evaluate and compare the performance and efficiency of the two parallel MCPSC implementations using Intel's experimental many-core Single-Chip Cloud Computer (SCC) as well as Intel's Core i7 multicore processor. We show that the 48-core SCC is more efficient than the latest generation Core i7, achieving a speedup factor of 42 (efficiency of 0.9), making many-core processors an exciting emerging technology for large-scale structural proteomics. We compare and contrast the performance of the two processors on several datasets and also show that MCPSC outperforms its component methods in grouping related domains, achieving a high F-measure of 0.91 on the benchmark CK34 dataset. The software implementation for protein structure comparison using the three methods and combined MCPSC, along with the developed underlying rckskel algorithmic skeletons library, is available via GitHub.

show abstract

Section: Resultsmentioning

confidence: 99%

Efficient Multicriteria Protein Structure Comparison on Modern Processor Architectures

Sharma

Μανωλάκος

2015

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…An important point is how to choose the relative location of the decorating beads. Clearly, the thermal fluctuations of the group that they represent will determine the space distribution of the bead locations, which can be evaluated by means of atomistic simulations of small NPs (Maccari et al, 2014) (Figure 1C). The volume map build using this space distribution will form lobes, whose centroid and dispersion can be determined by clustering procedures (Arkhipov et al, 2006) (Figure 1D).…”

Section: Rational Building Of a Minimalist Np Modelmentioning

confidence: 99%

Building Minimalist Models for Functionalized Metal Nanoparticles

Brancolini

Tozzini

2019

Front. Mol. Biosci.

Self Cite

View full text Add to dashboard Cite

“…This representation displays a great flexibility, being capable of easily passing from structurally biased [Go or network models (Tirion, 1996)] to unbiased (Yap et al, 2008) or partially biased parameterizations (Di Fenza et al, 2009). The latter (Trovato et al, 2013;Maccari et al, 2014) typically retain a bias in the local part of the nonbonded contacts maps to accurately account for the hydrogen bond network (Tozzini and McCammon, 2005). The level of bias can be tuned in the different terms of the FF, as far as the functional forms, including multistability through the bias toward two or more reference structures (Tavanti and Tozzini, 2014).…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Switches Structural Transitions via Coarse-Grained Models

Delfino

Porozov

Stepanov

et al. 2020

Journal of Computational Biology

Self Cite

View full text Add to dashboard Cite

Transitions between different conformational states are ubiquitous in proteins. A vast class of conformation-changing proteins includes evolutionary switches, which vary their conformation as an effect of few mutations or weak environmental variations. However, modeling those processes is extremely difficult due to the need of efficiently exploring a vast conformational space to look for the actual transition path. In this study, we report a strategy that simplifies this task attacking the complexity on several sides. We first apply a minimalist coarse-grained model to the protein, based on an empirical force field with a partial structural bias toward one or both the reference structures. We then explore the transition paths by means of stochastic molecular dynamics and select representative structures by means of a principal path-based clustering algorithm. We finally compare this trajectory with that produced by independent methods adopting a morphing-oriented approach. Our analysis indicates that the minimalist model returns trajectories capable of exploring intermediate states with physical meaning, retaining a very low computational cost, which can allow systematic and extensive exploration of the multistable proteins transition pathways.

show abstract

SecStAnT: secondary structure analysis tool for data selection, statistics and models building

Abstract: SecStAnT is available free of charge at secstant.sourceforge.net/. Source code is freely available on request, implemented in Java and supported on Linux, MS Windows and OSX.

Cited by 8 publications

References 32 publications

Efficient Multicriteria Protein Structure Comparison on Modern Processor Architectures

Efficient Multicriteria Protein Structure Comparison on Modern Processor Architectures

Building Minimalist Models for Functionalized Metal Nanoparticles

Evolutionary Switches Structural Transitions via Coarse-Grained Models

Contact Info

Product

Resources

About