Protein 3-dimensional Structures Research Articles

Computational construction of hepatitis C virus core protein tertiary structure with various modeling methods.

Hepatitis C virus (HCV) infection is one of the major causes of chronic liver inflammation, in many cases, leading to liver fibrosis, cirrhosis, and cancer. HCV core protein is the structural protein responsible for HCV nucleocapsid formation. It was indicated that in addition to its primary virus capsid forming function, interactions between the HCV core protein and host cell surface receptors also found to contribute to systematic and local inflammation of the host. Therefore, a detailed understanding of interactions between HCV core protein and host cell receptors should be of great help in the development of strategies for the treatment of hepatitis induced by HCV. However, there is currently no experimentally resolved complete HCV core protein structure available for structure-based analysis on HCV core protein-host receptor interactions. In this study, we applied different computational tools for the construction of HCV core protein 3-dimensional structures. Multiple-template homology modeling and template-free modeling methods were applied, and several structural models of HCV core proteins with different lengths (amino acids 1-116, and 1-191) were yielded. Molecular dynamics (MD) simulations were used to refine the modeled structures. The modeled structures from different modeling methods were compared, and the root-mean-square-deviation (RMSD) and free energy for each modeled structure was calculated. We further applied molecular docking to study the interactions between HCV core protein and host Toll-like receptors (TLRs), the inflammation related receptors suggested to be major targeted of HCV core protein. Structures of TLR1/2 and TLR2/6 heterodimers were applied in our study. Based on the in silico investigations, inhibitory peptides for reducing host inflammation can then be designed for validations in cellular experiments.

PSCAN: Spatial scan tests guided by protein structures improve complex disease gene discovery and signal variant detection

Germline disease-causing variants are generally more spatially clustered in protein 3-dimensional structures than benign variants. Motivated by this tendency, we develop a fast and powerful protein-structure-based scan (PSCAN) approach for evaluating gene-level associations with complex disease and detecting signal variants. We validate PSCAN’s performance on synthetic data and two real data sets for lipid traits and Alzheimer’s disease. Our results demonstrate that PSCAN performs competitively with existing gene-level tests while increasing power and identifying more specific signal variant sets. Furthermore, PSCAN enables generation of hypotheses about the molecular basis for the associations in the context of protein structures and functional domains.

Whole exome sequencing revealed a novel homozygous variant in the DGKE catalytic domain: a case report of familial hemolytic uremic syndrome

BackgroundAtypical hemolytic uremic syndrome (aHUS) is a rare disease characterized by microangiopathic hemolytic anemia caused by small vessel thrombosis, thrombocytopenia, and renal failure. The common cause of aHUS is a dysregulation in the alternative complement pathway. Mutations in none complement genes such as diacylglycerol kinase epsilon (DGKE) can also result in this syndrome.Case presentationHere, we report on a 19-year-old female with the clinical diagnosis of aHUS, who has unaffected consanguineous parents and an older sibling who was deceased from aHUS when she was seven months old.We performed whole exome sequencing (WES) followed by evaluation of detected variants for functional significance, using several online prediction tools. Next, in order to confirm the detected pathogenic variant in proband and segregation analysis in her family, Sanger sequencing was done. The novel variant was analyzed in terms of its impact on the protein 3-dimensional structure by computational structural modeling.The results revealed that the proband carried a novel homozygous missense variant in DGKE located in exon 6 of the gene (NM_003647.3, c.942C > G [p.Asn314Lys]), and in silico analysis anticipated it as damaging. Protein computational study confirmed the influence of potential pathogenic variant on structural stability and protein function.ConclusionWe suggest that some variations in the catalytic domain of DGKE like p.Asn314Lys which can cause alterations in secondary and 3-D structure of protein, might lead to aHUS.

Network analysis of synonymous codon usage.

Most amino acids are encoded by multiple synonymous codons, some of which are used more rarely than others. Analyses of positions of such rare codons in protein sequences revealed that rare codons can impact co-translational protein folding and that positions of some rare codons are evolutionarily conserved. Analyses of their positions in protein 3-dimensional structures, which are richer in biochemical information than sequences alone, might further explain the role of rare codons in protein folding. We model protein structures as networks and use network centrality to measure the structural position of an amino acid. We first validate that amino acids buried within the structural core are network-central, and those on the surface are not. Then, we study potential differences between network centralities and thus structural positions of amino acids encoded by conserved rare, non-conserved rare and commonly used codons. We find that in 84% of proteins, the three codon categories occupy significantly different structural positions. We examine protein groups showing different codon centrality trends, i.e. different relationships between structural positions of the three codon categories. We see several cases of all proteins from our data with some structural or functional property being in the same group. Also, we see a case of all proteins in some group having the same property. Our work shows that codon usage is linked to the final protein structure and thus possibly to co-translational protein folding. https://nd.edu/∼cone/CodonUsage/. Supplementary data are available at Bioinformatics online.

Assessment of Accuracies of Protein 3-Dimensional Prediction Software

Protein 3-dimensional structure prediction is determination of the 3-dimensional structure of a protein from its amino acid sequence by using protein structure prediction software. By understanding protein’s 3-dimensional structure, we should be able to figure out the function of the said protein. We already have several protein prediction software, but the purpose of this study is to determine how accurate they are, and if the results presented are true and to what extent. To determine how accurate protein 3-dimensional structure prediction software are, we compared x-ray crystallography determined protein structures to software predicted 3-dimensonal protein structures. All of the software used showed good accuracy, and according to our results, “i-Tasser” software was the most accurate, closely followed by RaptorX.

Identification and Characterization of Variant Intolerant Sites across Human Protein 3-Dimensional Structures

Identification and Characterization of Variant Intolerant Sites across Human Protein 3-Dimensional Structures

Prediction of delayed retention of antibodies in hydrophobic interaction chromatography from sequence using machine learning.

The hydrophobicity of a monoclonal antibody is an important biophysical property relevant for its developability into a therapeutic. In addition to characterizing heterogeneity, Hydrophobic Interaction Chromatography (HIC) is an assay that is often used to quantify the hydrophobicity of an antibody to assess downstream risks. Earlier studies have shown that retention times in this assay can be correlated to amino-acid or atomic propensities weighted by the surface areas obtained from protein 3-dimensional structures. The goal of this study is to develop models to enable prediction of delayed HIC retention times directly from sequence. We utilize the randomforest machine learning approach to estimate the surface exposure of amino-acid side-chains in the variable region directly from the antibody sequence. We obtain mean-absolute errors of 4.6% for the prediction of surface exposure. Using experimental HIC data along with the estimated surface areas, we derive an amino-acid propensity scale that enables prediction of antibodies likely to have delayed retention times in the assay. We achieve a cross-validation Area Under Curve of 0.85 for the Receiver Operating Characteristic curve of our model. The low computational expense and high accuracy of this approach enables real-time assessment of hydrophobic character to enable prioritization of antibodies during the discovery process and rational engineering to reduce hydrophobic liabilities. Structure data, aligned sequences, experimental data and prediction scores for test-cases, and R scripts used in this work are provided as part of the Supplementary Material. tushar.jain@adimab.com. Supplementary data are available at Bioinformatics online.

Prediction of protein solvent accessibility using PSO-SVR with multiple sequence-derived features and weighted sliding window scheme.

BackgroundThe prediction of solvent accessibility could provide valuable clues for analyzing protein structure and functions, such as protein 3-Dimensional structure and B-cell epitope prediction. To fully decipher the protein-protein interaction process, an initial but crucial step is to calculate the protein solvent accessibility, especially when the tertiary structure of the protein is unknown. Although some efforts have been put into the protein solvent accessibility prediction, the performance of existing methods is far from satisfaction.MethodsIn order to develop the high-accuracy model, we focus on some possible aspects concerning the prediction performance, including several sequence-derived features, a weighted sliding window scheme and the parameters optimization of machine learning approach. To address above issues, we take following strategies. Firstly, we explore various features which have been observed to be associated with the residue solvent accessibility. These discriminative features include protein evolutionary information, predicted protein secondary structure, native disorder, physicochemical propensities and several sequence-based structural descriptors of residues. Secondly, the different contributions of adjacent residues in sliding window are observed, thus a weighted sliding window scheme is proposed to differentiate the contributions of adjacent residues on the central residue. Thirdly, particle swarm optimization (PSO) is employed to search the global best parameters for the proposed predictor.ResultsEvaluated by 3-fold cross-validation, our method achieves the mean absolute error (MAE) of 14.1% and the person correlation coefficient (PCC) of 0.75 for our new-compiled dataset. When compared with the state-of-the-art prediction models in the two benchmark datasets, our method demonstrates better performance. Experimental results demonstrate that our PSAP achieves high performances and outperforms many existing predictors. A web server called PSAP is built and freely available at http://59.73.198.144:8088/SolventAccessibility/.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-014-0031-3) contains supplementary material, which is available to authorized users.

An Evolution-Based Approach to De Novo Protein Design and Case Study on Mycobacterium tuberculosis

Computational protein design is a reverse procedure of protein folding and structure prediction, where constructing structures from evolutionarily related proteins has been demonstrated to be the most reliable method for protein 3-dimensional structure prediction. Following this spirit, we developed a novel method to design new protein sequences based on evolutionarily related protein families. For a given target structure, a set of proteins having similar fold are identified from the PDB library by structural alignments. A structural profile is then constructed from the protein templates and used to guide the conformational search of amino acid sequence space, where physicochemical packing is accommodated by single-sequence based solvation, torsion angle, and secondary structure predictions. The method was tested on a computational folding experiment based on a large set of 87 protein structures covering different fold classes, which showed that the evolution-based design significantly enhances the foldability and biological functionality of the designed sequences compared to the traditional physics-based force field methods. Without using homologous proteins, the designed sequences can be folded with an average root-mean-square-deviation of 2.1 Å to the target. As a case study, the method is extended to redesign all 243 structurally resolved proteins in the pathogenic bacteria Mycobacterium tuberculosis, which is the second leading cause of death from infectious disease. On a smaller scale, five sequences were randomly selected from the design pool and subjected to experimental validation. The results showed that all the designed proteins are soluble with distinct secondary structure and three have well ordered tertiary structure, as demonstrated by circular dichroism and NMR spectroscopy. Together, these results demonstrate a new avenue in computational protein design that uses knowledge of evolutionary conservation from protein structural families to engineer new protein molecules of improved fold stability and biological functionality.

SVMCRYS: An SVM Approach for the Prediction of Protein Crystallization Propensity from Protein Sequence

X-ray crystallography is the most widely used method for protein 3-dimensional structure determination. Selection of target protein that can yield high quality crystal for X-ray crystallography is a challenging task. Prediction of protein crystallization propensity from sequence information is useful for the selection of target protein for crystallization. Recently, support vector machines have been widely used to solve various biological problems. In this work, we present a SVMCRYS method which use support vector machine to classify protein sequence into 'amenable to crystallization' and 'resistant to crystallization'. SVMCRYS was trained on a dataset containing 728 sequences that gave diffraction quality crystal and 728 sequences where work had been stopped before obtaining crystal. The performance of SVMCRYS method was compared with other sequence-based crystallization prediction methods such as SECRET, CRYSTALP, OB-Score, ParCrys and XtalPred using three different datasets. SVMCRYS achieved better prediction rate with higher sensitivity and specificity. Our analysis suggests that SVMCRYS can be used to predict proteins which are amenable to crystallization and proteins which are difficult for crystallization. The SVMCRYS software, dataset and feature set can be obtained from http://www3.ntu.edu.sg/home/EPNSugan/index_files/svmcrys.htm.

Selecton 2007: advanced models for detecting positive and purifying selection using a Bayesian inference approach

Biologically significant sites in a protein may be identified by contrasting the rates of synonymous (Ks) and non-synonymous (Ka) substitutions. This enables the inference of site-specific positive Darwinian selection and purifying selection. We present here Selecton version 2.2 (http://selecton.bioinfo.tau.ac.il), a web server which automatically calculates the ratio between Ka and Ks (ω) at each site of the protein. This ratio is graphically displayed on each site using a color-coding scheme, indicating either positive selection, purifying selection or lack of selection. Selecton implements an assembly of different evolutionary models, which allow for statistical testing of the hypothesis that a protein has undergone positive selection. Specifically, the recently developed mechanistic-empirical model is introduced, which takes into account the physicochemical properties of amino acids. Advanced options were introduced to allow maximal fine tuning of the server to the user's specific needs, including calculation of statistical support of the ω values, an advanced graphic display of the protein's 3-dimensional structure, use of different genetic codes and inputting of a pre-built phylogenetic tree. Selecton version 2.2 is an effective, user-friendly and freely available web server which implements up-to-date methods for computing site-specific selection forces, and the visualization of these forces on the protein's sequence and structure.

New structural insights from Raman spectroscopy of proteins and their assemblies

Protein structure and stability are sensitive to and dependent on the local interactions of amino acid side chains. A diverse and important type of side-chain interaction is the hydrogen bond. Although numerous hydrogen bonds are resolved in protein 3-dimensional structures, those of the cysteine sulfhydryl group (S-H) are elusive to high-resolution X-ray and NMR methods. However, the nature and strength of sulfhydryl hydrogen bonds (S-H* * *X) are amenable to investigation by Raman spectroscopy. The power of the Raman method for characterizing S-H* * *X interactions is illustrated by resolving the Raman S-H stretching band for each of the eight cysteines per 666-residue subunit in the trimeric tailspike of icosahedral bacteriophage P22. The Raman sulfhydryl signatures of the wild-type tailspike and eight single-site cysteine to serine mutants reveal a heretofore unrecognized diversity of S-H hydrogen bonds in a native protein. The use of Raman spectroscopy to identify the non-hydrogen-bonded state of the tyrosine phenoxyl group is also described. This unusual and unexpected state occurs for all tyrosines in the assembled capsids of filamentous viruses Ff and Pf1. The Raman spectral signature of the non-hydrogen-bonded tyrosine phenoxyl, which is characterized by an extraordinary Raman Fermi doublet intensity ratio (I850/I830 = 6.7), extends and refines the existing correlation for hydrogen-bonded tyrosines. Finally, a novel Raman signature for tryptophan in the Pf3 filamentous virus is identified, which is proposed as diagnostic of "cation-pi interaction" involving the guanidinium group of Arg 37 as a cation donor and the indolyl ring of Trp 38 as a pi-electron acceptor. These studies demonstrate the power of Raman spectroscopy for investigating the interactions of key side chains in native protein assemblies.

Design of folded peptides.

The construction of complex protein folds relies on the precise conversion of a linear polypeptide chain into a compact 3-dimensional structure. The interplay of forces that link sequence and folding is intricate and yet to be firmly elucidated. Examination of protein 3-dimensional structures suggests that complex tertiary folds and quaternary associations can be deconstructed into a limited number of secondary structural elements, such as strands, helices, and turns, which are assembled using loosely structured loops (Figure 1). The stability of a specific fold is determined by tertiary interactions between residues which are distant in sequence. De novo design of existing or novel protein folds demands a thorough understanding of the rules that underlie protein structure and stability.

RGD sequences in several receptor proteins: novel cell adhesion function of receptors?

In the process of homology modelling of the 3-dimensional structure of alleles of the human histocompatibility protein HLA-DQ, we discovered that its RGD tripeptide ( β167–169) forms part of a loop. A search through protein sequence data bases, revealed this cell adhesion motif in 67 integral plasma membrane proteins (in 48 extracellularly, and in the remaining 19 intracellularly), which are bona fide receptors, and none of them has thus far been considered as a cell adhesion protein. The 3-dimensional structure of one of these, the rat neonatal F c receptor, is known and its extracellular RGD sequence is in an adhesion-like loop, a fact that went unnoticed in the original papers. In a few other cases, e.g. rat and mouse growth hormone receptor, and mouse CD40 ligand, homology modelling by ourselves and others reveals that the said sequences are part of a loop, in similarity to all RGD sequences found in proteins with established adhesion function and known 3-dimensional structure. Likewise, inspection of all known protein 3-dimensional structures containing an RGD sequence, and not having a documented cell adhesion function (total of 65 separate entries) shows that such sequence is mostly (52/65 or 80% of cases) part of a loop. We therefore call attention to these surprising findings, discuss the possible cell adhesion role of these receptor proteins, and draw an analogy from the two well characterised examples, that of soluble IGF binding protein 1 and the transcriptional activator protein Tat of HIV, where their RGD sequences have been shown by site-directed mutagenesis to participate in cell-adhesion interactions, without prior knowledge of the location of the tripeptide, or the 3-dimensional structure of the respective protein.