A unifying probabilistic framework for analyzing residual dipolar couplings

Residual dipolar couplings provide complementary information to the nuclear Overhauser effect measurements that are traditionally used in biomolecular structure determination by NMR. In a de novo structure determination, however, lack of knowledge about the degree and orientation of molecular alignment complicates the analysis of dipolar coupling data. We present a probabilistic framework for analyzing residual dipolar couplings and demonstrate that it is possible to estimate the atomic coordinates, the complete molecular alignment tensor, and the error of the couplings simultaneously. As a by-product, we also obtain estimates of the uncertainty in the coordinates and the alignment tensor. We show that our approach encompasses existing methods for determining the alignment tensor as special cases, including least squares estimation, histogram fitting, and elimination of an explicit alignment tensor in the restraint energy

Computation of protein geometry and its applications: Packing and function prediction

This chapter discusses geometric models of biomolecules and geometric constructs, including the union of ball model, the weigthed Voronoi diagram, the weighted Delaunay triangulation, and the alpha shapes. These geometric constructs enable fast and analytical computaton of shapes of biomoleculres (including features such as voids and pockets) and metric properties (such as area and volume). The algorithms of Delaunay triangulation, computation of voids and pockets, as well volume/area computation are also described. In addition, applications in packing analysis of protein structures and protein function prediction are also discussed.Comment: 32 pages, 9 figure

Structural insight into SUMO chain recognition and manipulation by the ubiquitin ligase RNF4

The small ubiquitin-like modifier (SUMO) can form polymeric chains that are important signals in cellular processes such as meiosis, genome maintenance and stress response. The SUMO-targeted ubiquitin ligase RNF4 engages with SUMO chains on linked substrates and catalyses their ubiquitination, which targets substrates for proteasomal degradation. Here we use a segmental labelling approach combined with solution nuclear magnetic resonance (NMR) spectroscopy and biochemical characterization to reveal how RNF4 manipulates the conformation of the SUMO chain, thereby facilitating optimal delivery of the distal SUMO domain for ubiquitin transfer

PDBe: Protein Data Bank in Europe

The Protein Data Bank in Europe (PDBe; pdbe.org) is a partner in the Worldwide PDB organization (wwPDB; wwpdb.org) and as such actively involved in managing the single global archive of biomacromolecular structure data, the PDB. In addition, PDBe develops tools, services and resources to make structure-related data more accessible to the biomedical community. Here we describe recently developed, extended or improved services, including an animated structure-presentation widget (PDBportfolio), a widget to graphically display the coverage of any UniProt sequence in the PDB (UniPDB), chemistry- and taxonomy-based PDB-archive browsers (PDBeXplore), and a tool for interactive visualization of NMR structures, corresponding experimental data as well as validation and analysis results (Vivaldi)

A practical guide to the simultaneous determination of protein structure and dynamics using metainference

Accurate protein structural ensembles can be determined with metainference, a Bayesian inference method that integrates experimental information with prior knowledge of the system and deals with all sources of uncertainty and errors as well as with system heterogeneity. Furthermore, metainference can be implemented using the metadynamics approach, which enables the computational study of complex biological systems requiring extensive conformational sampling. In this chapter, we provide a step-by-step guide to perform and analyse metadynamic metainference simulations using the ISDB module of the open-source PLUMED library, as well as a series of practical tips to avoid common mistakes. Specifically, we will guide the reader in the process of learning how to model the structural ensemble of a small disordered peptide by combining state-of-the-art molecular mechanics force fields with nuclear magnetic resonance data, including chemical shifts, scalar couplings and residual dipolar couplings.Comment: 49 pages, 9 figure

Farseer-NMR: automatic treatment, analysis and plotting of large, multi-variable NMR data

We present Farseer-NMR (https://git.io/vAueU), a software package to treat, evaluate and combine NMR spectroscopic data from sets of protein-derived peaklists covering a range of experimental conditions. The combined advances in NMR and molecular biology enable the study of complex biomolecular systems such as flexible proteins or large multibody complexes, which display a strong and functionally relevant response to their environmental conditions, e.g. the presence of ligands, site-directed mutations, post translational modifications, molecular crowders or the chemical composition of the solution. These advances have created a growing need to analyse those systems’ responses to multiple variables. The combined analysis of NMR peaklists from large and multivariable datasets has become a new bottleneck in the NMR analysis pipeline, whereby information-rich NMR-derived parameters have to be manually generated, which can be tedious, repetitive and prone to human error, or even unfeasible for very large datasets. There is a persistent gap in the development and distribution of software focused on peaklist treatment, analysis and representation, and specifically able to handle large multivariable datasets, which are becoming more commonplace. In this regard, Farseer-NMR aims to close this longstanding gap in the automated NMR user pipeline and, altogether, reduce the time burden of analysis of large sets of peaklists from days/weeks to seconds/minutes. We have implemented some of the most common, as well as new, routines for calculation of NMR parameters and several publication-quality plotting templates to improve NMR data representation. Farseer-NMR has been written entirely in Python and its modular code base enables facile extension

Combining Experiments and Simulations Using the Maximum Entropy Principle

A key component of computational biology is to compare the results of computer modelling with experimental measurements. Despite substantial progress in the models and algorithms used in many areas of computational biology, such comparisons sometimes reveal that the computations are not in quantitative agreement with experimental data. The principle of maximum entropy is a general procedure for constructing probability distributions in the light of new data, making it a natural tool in cases when an initial model provides results that are at odds with experiments. The number of maximum entropy applications in our field has grown steadily in recent years, in areas as diverse as sequence analysis, structural modelling, and neurobiology. In this Perspectives article, we give a broad introduction to the method, in an attempt to encourage its further adoption. The general procedure is explained in the context of a simple example, after which we proceed with a real-world application in the field of molecular simulations, where the maximum entropy procedure has recently provided new insight. Given the limited accuracy of force fields, macromolecular simulations sometimes produce results that are at not in complete and quantitative accordance with experiments. A common solution to this problem is to explicitly ensure agreement between the two by perturbing the potential energy function towards the experimental data. So far, a general consensus for how such perturbations should be implemented has been lacking. Three very recent papers have explored this problem using the maximum entropy approach, providing both new theoretical and practical insights to the problem. We highlight each of these contributions in turn and conclude with a discussion on remaining challenges

Odorranalectin Is a Small Peptide Lectin with Potential for Drug Delivery and Targeting

BACKGROUND: Lectins are sugar-binding proteins that specifically recognize sugar complexes. Based on the specificity of protein-sugar interactions, different lectins could be used as carrier molecules to target drugs specifically to different cells which express different glycan arrays. In spite of lectin's interesting biological potential for drug targeting and delivery, a potential disadvantage of natural lectins may be large size molecules that results in immunogenicity and toxicity. Smaller peptides which can mimic the function of lectins are promising candidates for drug targeting. PRINCIPAL FINDINGS: Small peptide with lectin-like behavior was screened from amphibian skin secretions and its structure and function were studied by NMR, NMR-titration, SPR and mutant analysis. A lectin-like peptide named odorranalectin was identified from skin secretions of Odorrana grahami. It was composed of 17 aa with a sequence of YASPKCFRYPNGVLACT. L-fucose could specifically inhibit the haemagglutination induced by odorranalectin. (125)I-odorranalectin was stable in mice plasma. In experimental mouse models, odorranalectin was proved to mainly conjugate to liver, spleen and lung after i.v. administration. Odorranalectin showed extremely low toxicity and immunogenicity in mice. The small size and single disulfide bridge of odorranalectin make it easy to manipulate for developing as a drug targeting system. The cyclic peptide of odorranalectin disclosed by solution NMR study adopts a beta-turn conformation stabilized by one intramolecular disulfide bond between Cys6-Cys16 and three hydrogen bonds between Phe7-Ala15, Tyr9-Val13, Tyr9-Gly12. Residues K5, C6, F7, C16 and T17 consist of the binding site of L-fucose on odorranalectin determined by NMR titration and mutant analysis. The structure of odorranalectin in bound form is more stable than in free form. CONCLUSION: These findings identify the smallest lectin so far, and show the application potential of odorranalectin for drug delivery and targeting. It also disclosed a new strategy of amphibian anti-infection

Distinct expression patterns of two Arabidopsis phytocystatin genes, AtCYS1 and AtCYS2, during development and abiotic stresses

The phytocystatins of plants are members of the cystatin superfamily of proteins, which are potent inhibitors of cysteine proteases. The Arabidopsis genome encodes seven phytocystatin isoforms (AtCYSs) in two distantly related AtCYS gene clusters. We selected AtCYS1 and AtCYS2 as representatives for each cluster and then generated transgenic plants expressing the GUS reporter gene under the control of each gene promoter. These plants were used to examine AtCYS expression at various stages of plant development and in response to abiotic stresses. Histochemical analysis of AtCYS1 promoter- and AtCYS2 promoter-GUS transgenic plants revealed that these genes have similar but distinct spatial and temporal expression patterns during normal development. In particular, AtCYS1 was preferentially expressed in the vascular tissue of all organs, whereas AtCYS2 was expressed in trichomes and guard cells in young leaves, caps of roots, and in connecting regions of the immature anthers and filaments and the style and stigma in flowers. In addition, each AtCYS gene has a unique expression profile during abiotic stresses. High temperature and wounding stress enhanced the expression of both AtCYS1 and AtCYS2, but the temporal and spatial patterns of induction differed. From these data, we propose that these two AtCYS genes play important, but distinct, roles in plant development and stress responses