Rigidity and flexibility of biological networks

The network approach became a widely used tool to understand the behaviour of complex systems in the last decade. We start from a short description of structural rigidity theory. A detailed account on the combinatorial rigidity analysis of protein structures, as well as local flexibility measures of proteins and their applications in explaining allostery and thermostability is given. We also briefly discuss the network aspects of cytoskeletal tensegrity. Finally, we show the importance of the balance between functional flexibility and rigidity in protein-protein interaction, metabolic, gene regulatory and neuronal networks. Our summary raises the possibility that the concepts of flexibility and rigidity can be generalized to all networks.Comment: 21 pages, 4 figures, 1 tabl

RedMDStream: Parameterization and Simulation Toolbox for Coarse-Grained Molecular Dynamics Models

AbstractCoarse-grained (CG) models in molecular dynamics (MD) are powerful tools to simulate the dynamics of large biomolecular systems on micro- to millisecond timescales. However, the CG model, potential energy terms, and parameters are typically not transferable between different molecules and problems. So parameterizing CG force fields, which is both tedious and time-consuming, is often necessary. We present RedMDStream, a software for developing, testing, and simulating biomolecules with CG MD models. Development includes an automatic procedure for the optimization of potential energy parameters based on metaheuristic methods. As an example we describe the parameterization of a simple CG MD model of an RNA hairpin

Augmenting the anisotropic network model with torsional potentials improves PATH performance, enabling detailed comparison with experimental rate data

PATH algorithms for identifying conformational transition states provide computational parameters-time to the transition state, conformational free energy differences, and transition state activation energies-for comparison to experimental data and can be carried out sufficiently rapidly to use in the high throughput mode. These advantages are especially useful for interpreting results from combinatorial mutagenesis experiments. This report updates the previously published algorithm with enhancements that improve correlations between PATH convergence parameters derived from virtual variant structures generated by RosettaBackrub and previously published kinetic data for a complete, four-way combinatorial mutagenesis of a conformational switch in Tryptophanyl-tRNA synthetase

Better force fields start with better data: A data set of cation dipeptide interactions

We present a data set from a first-principles study of amino-methylated and acetylated (capped) dipeptides of the 20 proteinogenic amino acids – including alternative possible side chain protonation states and their interactions with selected divalent cations (Ca2+, Mg2+ and Ba2+). The data covers 21,909 stationary points on the respective potential-energy surfaces in a wide relative energy range of up to 4 eV (390 kJ/mol). Relevant properties of interest, like partial charges, were derived for the conformers. The motivation was to provide a solid data basis for force field parameterization and further applications like machine learning or benchmarking. In particular the process of creating all this data on the same first-principles footing, i.e. density-functional theory calculations employing the generalized gradient approximation with a van der Waals correction, makes this data suitable for first principles data-driven force field development. To make the data accessible across domain borders and to machines, we formalized the metadata in an ontology

Experimental and computational validation of models of fluorescent and luminescent reporter genes in bacteria

<p>Abstract</p> <p>Background</p> <p>Fluorescent and luminescent reporter genes have become popular tools for the real-time monitoring of gene expression in living cells. However, mathematical models are necessary for extracting biologically meaningful quantities from the primary data.</p> <p>Results</p> <p>We present a rigorous method for deriving relative protein synthesis rates (mRNA concentrations) and protein concentrations by means of kinetic models of gene expression. We experimentally and computationally validate this approach in the case of the protein Fis, a global regulator of transcription in <it>Escherichia coli</it>. We show that the mRNA and protein concentration profiles predicted from the models agree quite well with direct measurements obtained by Northern and Western blots, respectively. Moreover, we present computational procedures for taking into account systematic biases like the folding time of the fluorescent reporter protein and differences in the half-lives of reporter and host gene products. The results show that large differences in protein half-lives, more than mRNA half-lives, may be critical for the interpretation of reporter gene data in the analysis of the dynamics of regulatory systems.</p> <p>Conclusions</p> <p>The paper contributes to the development of sound methods for the interpretation of reporter gene data, notably in the context of the reconstruction and validation of models of regulatory networks. The results have wide applicability for the analysis of gene expression in bacteria and may be extended to higher organisms.</p

Effects of genetic variants in the TSPO gene on protein structure and stability

The 18 kDa translocator protein (TSPO) is an evolutionary conserved cholesterol binding protein localized in the outer mitochondrial membrane. Expression of TSPO is upregulated in activated microglia in various neuroinflammatory, neurodegenerative, and neoplastic disorders. Therefore, TSPO radioligands are used as biomarkers in positron emission tomography (PET) studies. In particular, a common A147T polymorphism in the TSPO gene affects binding of several high affinity TSPO radioligands. Given the relevance of TSPO as a diagnostic biomarker in disease processes, we systematically searched for mutations in the human TSPO gene by a wide array of evolution and structure based bioinformatics tools and identified potentially deleterious missense mutations. The two most frequently observed missense mutations A147T and R162H were further analysed in structural models of human wildtype and mutant TSPO proteins. The effects of missense mutations were studied on the atomic level using molecular dynamics simulations. To analyse putative effects of A147T and R162H variants on protein stability we established primary dermal fibroblast cultures from wt and homozygous A147T and R162H donors. Stability of endogenous TSPO protein, which is abundantly expressed in fibroblasts, was studied using cycloheximide protein degradation assay. Our data show that the A147T mutation significantly alters the flexibility and stability of the mutant protein. Furthermore both A147T and R162H mutations decreased the half-life of the mutant proteins by about 25 percent, which could in part explain its effect on reduced pregnenolone production and susceptibility to neuropsychiatric disorders. The present study is the first comprehensive bioinformatic analysis of genetic variants in the TSPO gene, thereby extending the knowledge about the clinical relevance of TSPO nsSNPs

Recent advances in describing and driving crystal nucleation using machine learning and artificial intelligence

With the advent of faster computer processors and especially graphics processing units (GPUs) over the last few decades, the use of data-intensive machine learning (ML) and artificial intelligence (AI) has increased greatly, and the study of crystal nucleation has been one of the beneficiaries. In this review, we outline how ML and AI have been applied to address four outstanding difficulties of crystal nucleation: how to discover better reaction coordinates (RCs) for describing accurately non-classical nucleation situations; the development of more accurate force fields for describing the nucleation of multiple polymorphs or phases for a single system; more robust identification methods for determining crystal phases and structures; and as a method to yield improved course-grained models for studying nucleation.Comment: 15 pages; 1 figur

Emerging Era of Biomolecular Membrane Simulations: Automated Physically-Justified Force Field Development and Quality-Evaluated Databanks

Peer reviewe

Emerging Era of Biomolecular Membrane Simulations: Automated Physically-Justified Force Field Development and Quality-Evaluated Databanks

Peer reviewe