New insights into structural determinants of prion protein folding and stability

Prions are the etiological agent of fatal neurodegenerative diseases called prion diseases or transmissible spongiform encephalopathies. These maladies can be sporadic, genetic or infectious disorders. Prions are due to post-translational modifications of the cellular prion protein leading to the formation of a β-sheet enriched conformer with altered biochemical properties. The molecular events causing prion formation in sporadic prion diseases are still elusive. Recently, we published a research elucidating the contribution of major structural determinants and environmental factors in prion protein folding and stability. Our study highlighted the crucial role of octarepeats in stabilizing prion protein; the presence of a highly enthalpically stable intermediate state in prion-susceptible species; and the role of disulfide bridge in preserving native fold thus avoiding the misfolding to a β-sheet enriched isoform. Taking advantage from these findings, in this work we present new insights into structural determinants of prion protein folding and stability.     

New insights into structural determinants of prion protein folding and stability

Abstract

Prions are the etiological agent of fatal neurodegenerative diseases called prion diseases or transmissible spongiform encephalopathies. These maladies can be sporadic, genetic or infectious disorders. Prions are due to post-translational modifications of the cellular prion protein leading to the formation of a β-sheet enriched conformer with altered biochemical properties. The molecular events causing prion formation in sporadic prion diseases are still elusive. Recently, we published a research elucidating the contribution of major structural determinants and environmental factors in prion protein folding and stability. Our study highlighted the crucial role of octarepeats in stabilizing prion protein; the presence of a highly enthalpically stable intermediate state in prion-susceptible species; and the role of disulfide bridge in preserving native fold thus avoiding the misfolding to a β-sheet enriched isoform. Taking advantage from these findings, in this work we present new insights into structural determinants of prion protein folding and stability.     

Structural analysis and prediction of protein mutant stability using distance and torsion potentials: role of secondary structure and solvent accessibility

Platelet-activating factor receptor (PAFR) is a member of G-protein coupled receptor (GPCR) superfamily. Understanding the regulation mechanisms of PAFR by its agonists and antagonists at the atomic level is essential for designing PAFR antagonists as drug candidates for treating PAF-mediated diseases. In this study, a 3D model of PAFR was constructed by a hierarchical approach integrating homology modeling, molecular docking and molecular dynamics (MD) simulations. Based on the 3D model, regulation mechanisms of PAFR by agonists and antagonists were investigated via three 8-ns MD simulations on the systems of apo-PAFR, PAFR-PAF and PAFR-GB. The simulations revealed that binding of PAF to PAFR triggers the straightening process of the kinked helix VI, leading to its activated state. In contrast, binding of GB to PAFR locks PAFR in its inactive state.     

Computational studies on mutant protein stability: The correlation between surface thermal expansion and protein stability

Thermal stability of mutant proteins has been investigated using temperature dependent molecular dynamics (MD) simulations in vacuo. The numerical modeling was aimed at mimicking protein expansion upon heating. After the conditions for an expanding protein accessible surface area were established for T4 lysozyme and barnase wild-type proteins, MD simulations were carried out under the same conditions using the crystal structures of several mutant proteins. The computed thermal expansion of the accessible surface area of mutant proteins was found to be strongly correlated with their experimentally measured stabilities. A similar, albeit weaker, correlation was observed for model mutant proteins. This opens the possibility of obtaining stability information directly from protein structure.     

Protein modeling with reduced representation: statistical potentials and protein folding mechanism

A high resolution reduced model of proteins is used in Monte Carlo dynamics studies of the folding mechanism of a small globular protein, the B1 immunoglobulin-binding domain of streptococcal protein G. It is shown that in order to reproduce the physics of the folding transition, the united atom based model requires a set of knowledge-based potentials mimicking the short-range conformational propensities and protein-like chain stiffness, a model of directional and cooperative hydrogen bonds, and properly designed knowledge-based potentials of the long-range interactions between the side groups. The folding of the model protein is cooperative and very fast. In a single trajectory, a number of folding/unfolding cycles were observed. Typically, the folding process is initiated by assembly of a native-like structure of the C-terminal hairpin. In the next stage the rest of the four-ribbon beta-sheet folds. The slowest step of this pathway is the assembly of the central helix on the scaffold of the beta-sheet.     

NMR and molecular modeling reveal key structural features of synthetic nodulation factors

Nod factors are lipochitoligosaccharides originally produced by the soil bacteria Rhizobia that are involved in the symbiotic process with leguminous plants. Some synthetic analogs of the Nod factors present a strong biological activity, and the conformational behavior of these molecules is of interest for structure/function studies. Nod factor analogs containing an insertion of a phenyl group in the acyl chain at the oligosaccharidic non-reducing end were previously synthesized (Grenouillat N, Vauzeilles B, Bono J-J, Samain E, Beau J-M. 2004. Simple synthesis of nodulation-factor analogues exhibiting high affinity towards a specific binding protein. Angew Chem Int Ed Engl. 43:4644). Conformational studies of natural compounds and synthetic analogs have been performed combining molecular dynamics simulations in explicit water and NMR. Data revealed that the glycosidic head group can adopt only restricted conformations, whereas chemical modifications of the lipid chains, highly flexible in a water environment, influence the global shape of the molecules. Collected structural data could be used in the future to rationalize and understand their biological activity and affinity toward a putative receptor.     

Computational and statistical analysis of protein mass spectrometry data

High-throughput proteomics experiments involving tandem mass spectrometry produce large volumes of complex data that require sophisticated computational analyses. As such, the field offers many challenges for computational biologists. In this article, we briefly introduce some of the core computational and statistical problems in the field and then describe a variety of outstanding problems that readers of PLoS Computational Biology might be able to help solve.     

Computational structural analysis of protein interactions and networks

Protein interactions have been at the focus of computational biology in recent years. In particular, interest has come from two different communities--structural and systems biology. Here, we will discuss key systems and structural biology methods that have been used for analysis and prediction of protein-protein interactions and the insight these approaches have provided on the nature and organization of protein-protein interactions inside cells.     

Variations on probabilistic suffix trees: statistical modeling and prediction of protein families

MOTIVATION: We present a method for modeling protein families by means of probabilistic suffix trees (PSTs). The method is based on identifying significant patterns in a set of related protein sequences. The patterns can be of arbitrary length, and the input sequences do not need to be aligned, nor is delineation of domain boundaries required. The method is automatic, and can be applied, without assuming any preliminary biological information, with surprising success. Basic biological considerations such as amino acid background probabilities, and amino acids substitution probabilities can be incorporated to improve performance. RESULTS: The PST can serve as a predictive tool for protein sequence classification, and for detecting conserved patterns (possibly functionally or structurally important) within protein sequences. The method was tested on the Pfam database of protein families with more than satisfactory performance. Exhaustive evaluations show that the PST model detects much more related sequences than pairwise methods such as Gapped-BLAST, and is almost as sensitive as a hidden Markov model that is trained from a multiple alignment of the input sequences, while being much faster.     

Mesophile versus thermophile: insights into the structural mechanisms of kinetic stability

Obtaining detailed knowledge of folding intermediate and transition state (TS) structures is critical for understanding protein folding mechanisms. Comparisons between proteins adapted to survive extreme temperatures with their mesophilic homologs are likely to provide valuable information on the interactions relevant to the unfolding transition. For kinetically stable proteins such as alpha-lytic protease (alphaLP) and its family members, their large free energy barrier to unfolding is central to their biological function. To gain new insights into the mechanisms that underlie kinetic stability, we have determined the structure and high temperature unfolding kinetics of a thermophilic homolog, Thermobifida fusca protease A (TFPA). These studies led to the identification of a specific structural element bridging the N and C-terminal domains of the protease (the "domain bridge") proposed to be associated with the enhanced high temperature kinetic stability in TFPA. Mutagenesis experiments exchanging the TFPA domain bridge into alphaLP validate this hypothesis and illustrate key structural details that contribute to TFPA's increased kinetic thermostability. These results lead to an updated model for the unfolding transition state structure for this important class of proteases in which domain bridge undocking and unfolding occurs at or before the TS. The domain bridge appears to be a structural element that can modulate the degree of kinetic stability of the different members of this class of proteases.     

ZRANB2: structural and functional insights into a novel splicing protein

ZRANB2 was identified originally in a differential display experiment on 2-day and 10-day primary cultures of rat juxtaglomerular cells. During prolonged culture it was found to undergo down-regulation in concert with renin, the archetypical constituent of these cells. ZRANB2 has two zinc fingers that form a novel fold and show striking homology to Ran-binding protein domains. Human ZRANB2 mRNA is alternatively spliced to give two variants with different 3' ends. ZRANB2 has homologues across a range of species, the N-terminal end being particularly conserved. ZRANB2 is present in the nucleus of human cells. It binds to mRNA, as well as the essential splicing factors U170K and U2AF(35) and the novel splicing component SFRS17A (formerly known as XE7). ZRANB2 is one of 20 genes up-regulated in grade III ovarian serous papillary carcinoma. Here, we review current knowledge surrounding ZRANB2.     

High-throughput modeling and analysis of protein structural dynamics

Protein function is a dynamic property closely related to the conformational mechanisms of protein structure in its physiological environment. To understand and control the function of target proteins, it becomes increasingly important to develop methods and tools for predicting collective motions at the molecular level. In this article, we review computational methods for predicting conformational dynamics and discuss software tools for data analysis. In particular, we discuss a high-throughput, web-based system called iGNM for protein structural dynamics. iGNM contains a database of protein motions for more than 20 000 PDB structures and supports online calculations for newly deposited PDB structures or user-modified structures. iGNM allows dynamics analysis of protein structures ranging from enzymes to large complexes and assemblies, and enables the exploration of protein sequence-structure-dynamics-function relations.     

Computational insights into structural and optical properties of P-containing and N-containing ligands capped CdSe clusters

ABSTRACT

The ligand effects on the structures and properties (energetics, binding energies, charge distribution and optical properties) of the (CdSe)_n clusters (n?=?3, 6, and 10) with P-containing (PH₃, PH₂Me, PHMe₂ and PMe₃) and N-containing (NH₃, NH₂Me, NHMe₂ and NMe₃) have been studied using density functional theory. The P atom and N atom in the ligands interact with Cd and form Cd–P and Cd–N bonds. The influence of P-containing ligands can be enhanced with increasing CH₃ of ligands, while the N-containing ligands influence slightly change. A blueshift in absorption band was predicted for the clusters with increasing CH₃ of P-containing ligands. We also found that the calculated binding energies for various ligands are found to decrease in the order PMe₃?>?NH₂Me?>?NHMe₂?>?NH₃?>?NMe₃?>?PHMe₂?>?PH₂Me?>?PH₃. The use of hydrogen atom for modelling of the CdSe cluster passivating ligands is found to yield unphysical results as well.     

Understanding the structural and dynamic consequences of DNA epigenetic modifications: Computational insights into cytosine methylation and hydroxymethylation

We report a series of molecular dynamics (MD) simulations of up to a microsecond combined simulation time designed to probe epigenetically modified DNA sequences. More specifically, by monitoring the effects of methylation and hydroxymethylation of cytosine in different DNA sequences, we show, for the first time, that DNA epigenetic modifications change the molecule's dynamical landscape, increasing the propensity of DNA toward different values of twist and/or roll/tilt angles (in relation to the unmodified DNA) at the modification sites. Moreover, both the extent and position of different modifications have significant effects on the amount of structural variation observed. We propose that these conformational differences, which are dependent on the sequence environment, can provide specificity for protein binding.     

Understanding the structural and dynamic consequences of DNA epigenetic modifications: Computational insights into cytosine methylation and hydroxymethylation

We report a series of molecular dynamics (MD) simulations of up to a microsecond combined simulation time designed to probe epigenetically modified DNA sequences. More specifically, by monitoring the effects of methylation and hydroxymethylation of cytosine in different DNA sequences, we show, for the first time, that DNA epigenetic modifications change the molecule''s dynamical landscape, increasing the propensity of DNA toward different values of twist and/or roll/tilt angles (in relation to the unmodified DNA) at the modification sites. Moreover, both the extent and position of different modifications have significant effects on the amount of structural variation observed. We propose that these conformational differences, which are dependent on the sequence environment, can provide specificity for protein binding.     

Cross-validation of protein structural class prediction using statistical clustering and neural networks.

We present an approach to predicting protein structural class that uses amino acid composition and hydrophobic pattern frequency information as input to two types of neural networks: (1) a three-layer back-propagation network and (2) a learning vector quantization network. The results of these methods are compared to those obtained from a modified Euclidean statistical clustering algorithm. The protein sequence data used to drive these algorithms consist of the normalized frequency of up to 20 amino acid types and six hydrophobic amino acid patterns. From these frequency values the structural class predictions for each protein (all-alpha, all-beta, or alpha-beta classes) are derived. Examples consisting of 64 previously classified proteins were randomly divided into multiple training (56 proteins) and test (8 proteins) sets. The best performing algorithm on the test sets was the learning vector quantization network using 17 inputs, obtaining a prediction accuracy of 80.2%. The Matthews correlation coefficients are statistically significant for all algorithms and all structural classes. The differences between algorithms are in general not statistically significant. These results show that information exists in protein primary sequences that is easily obtainable and useful for the prediction of protein structural class by neural networks as well as by standard statistical clustering algorithms.     

Dichroic statistical model for prediction and analysis of peptide helicity

Traditional statistical models for the prediction of peptide helicity are written in terms of the mean fractional helicity of the peptide residues. Far ultraviolet circular dichroic measurements of peptide solutions are converted to mean fractional helicity by partitioning the observed ellipticity between that of a perfect helix and a random coil. This partition does not adequately represent the ensemble of peptide molecules present in solution that populate imperfect helical conformations of quite variable lengths. A new dichroic statistical model has been written in terms of ellipticity rather than fractional helical content that recognizes (1) the source of ellipticity, peptide bond adsorption; (2) the differential ellipticity of peptide bonds in the terminal and interior helical turns; and (3) the contributions of each participant in a conformational ensemble to the observed ellipticity. Comparative analyses of host/guest peptides indicates that significant differences are obtained between residue w and n weights and ellipticity values using the traditional and dichroic statistical models. Proteins 28:467–480, 1997. © 1997 Wiley-Liss, Inc.     

NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning

The ability to predict local structural features of a protein from the primary sequence is of paramount importance for unraveling its function in absence of experimental structural information. Two main factors affect the utility of potential prediction tools: their accuracy must enable extraction of reliable structural information on the proteins of interest, and their runtime must be low to keep pace with sequencing data being generated at a constantly increasing speed. Here, we present NetSurfP-2.0, a novel tool that can predict the most important local structural features with unprecedented accuracy and runtime. NetSurfP-2.0 is sequence-based and uses an architecture composed of convolutional and long short-term memory neural networks trained on solved protein structures. Using a single integrated model, NetSurfP-2.0 predicts solvent accessibility, secondary structure, structural disorder, and backbone dihedral angles for each residue of the input sequences. We assessed the accuracy of NetSurfP-2.0 on several independent test datasets and found it to consistently produce state-of-the-art predictions for each of its output features. We observe a correlation of 80% between predictions and experimental data for solvent accessibility, and a precision of 85% on secondary structure 3-class predictions. In addition to improved accuracy, the processing time has been optimized to allow predicting more than 1000 proteins in less than 2 hours, and complete proteomes in less than 1 day.     

Reduced representation model of protein structure prediction: statistical potential and genetic algorithms.

A reduced representation model, which has been described in previous reports, was used to predict the folded structures of proteins from their primary sequences and random starting conformations. The molecular structure of each protein has been reduced to its backbone atoms (with ideal fixed bond lengths and valence angles) and each side chain approximated by a single virtual united-atom. The coordinate variables were the backbone dihedral angles phi and psi. A statistical potential function, which included local and nonlocal interactions and was computed from known protein structures, was used in the structure minimization. A novel approach, employing the concepts of genetic algorithms, has been developed to simultaneously optimize a population of conformations. With the information of primary sequence and the radius of gyration of the crystal structure only, and starting from randomly generated initial conformations, I have been able to fold melittin, a protein of 26 residues, with high computational convergence. The computed structures have a root mean square error of 1.66 A (distance matrix error = 0.99 A) on average to the crystal structure. Similar results for avian pancreatic polypeptide inhibitor, a protein of 36 residues, are obtained. Application of the method to apamin, an 18-residue polypeptide with two disulfide bonds, shows that it folds apamin to native-like conformations with the correct disulfide bonds formed.