SecStAnT: Secondary Structure Analysis Tool for data selection, statistics and models building

Abstract Motivation: Atomistic or coarse grained (CG) potentials derived from statistical distributions of internal variables have recently become popular due to the need of simplified interactions for reaching larger scales in simulations or more efficient conformational space sampling. However, the process of parameterization of accurate and predictive statistics-based force fields requires a huge amount of work and is prone to the introduction of bias and errors. Results: This article introduces SecStAnT, a software for the creation and analysis of protein structural datasets with user-defined primary/secondary structure composition, with a particular focus on the CG representation. In addition, the possibility of managing different resolutions and the primary/secondary structure selectivity allow addressing the mapping-backmapping of atomistic to CG representation and study the secondary to primary structure relations. Sample datasets and distributions are reported, including interpretation of structural features. Availability and implementation: 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. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Low-Resolution Models for the Interaction Dynamics of Coated Gold Nanoparticles with β2-microglobulin

A large number of low-resolution models have been proposed in the last decades to reduce the computational cost of molecular dynamics simulations for bio-nano systems, such as those involving the interactions of proteins with functionalized nanoparticles (NPs). For the proteins, “minimalist” models at the one-bead-per residue (Cα-based) level and with implicit solvent are well established. For the gold NPs, widely explored for biotechnological applications, mesoscale (MS) models treating the NP core with a single spheroidal object are commonly proposed. In this representation, the surface details (coating, roughness, etc.) are lost. These, however, and the specificity of the functionalization, have been shown to have fundamental roles for the interaction with proteins. We presented a mixed-resolution coarse-grained (CG) model for gold NPs in which the surface chemistry is reintroduced as superficial smaller beads. We compared molecular dynamics simulationsoftheamyloid β2-microglobulinrepresentedattheminimalistlevelinteractingwithNPs represented with this model or at the MS level. Our finding highlights the importance of describing the surface of the NP at a finer level as the chemical-physical properties of the surface of the NP are crucial to correctly understand the protein-nanoparticle association

The Influence of Graphene Curvature on Hydrogen Adsorption: Towards Hydrogen Storage Devices

The ability of atomic hydrogen to chemisorb on graphene makes the latter a promising material for hydrogen storage. Based on scanning tunneling microscopy techniques, we report on site-selective adsorption of atomic hydrogen on convexly curved regions of monolayer graphene grown on SiC(0001). This system exhibits an intrinsic curvature owing to the interaction with the substrate. We show that at low coverage hydrogen is found on convex areas of the graphene lattice. No hydrogen is detected on concave regions. These findings are in agreement with theoretical models which suggest that both binding energy and adsorption barrier can be tuned by controlling the local curvature of the graphene lattice. This curvature-dependence combined with the known graphene flexibility may be exploited for storage and controlled release of hydrogen at room temperature making it a valuable candidate for the implementation of hydrogen-storage devices

SDPhound, a Mutual Information-Based Method to Investigate Specificity-Determining Positions

Considerable importance in molecular biophysics is attached to influencing by mutagenesis the specific properties of a protein family. The working hypothesis is that mutating residues at few selected positions can affect specificity. Statistical analysis of homologue sequences can identify putative specificity determining positions (SDPs) and help to shed some light on the peculiarities underlying their functional role. In this work, we present an approach to identify such positions inspired by state of the art mutual information-based SDP prediction methods. The algorithm based on this approach provides a systematic procedure to point at the relevant physical characteristics of putative SPDs and can investigate the effects of correlated mutations. The method is tested on two standard benchmarks in the field and further validated in the context of a biologically interesting problem: the multimerization of the Intrinsically Fluorescent Proteins (IFP)

erratum to superlubricity of epitaxial monolayer ws2 on graphene

The article Superlubricity of epitaxial monolayer WS2 on graphene, written by Holger Buch, Antonio Rossi, Stiven Forti, Domenica Convertino, Valentina Tozzini, and Camilla Coletti, was originally published electronically on the publisher's internet portal (currently SpringerLink) on June 18th 2018 without open access. With the author(s)' decision to opt for Open Choice the copyright of the article changed in August 2018 to © The Author(s) 2018 and the article is forthwith distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The original article has been corrected