Residues In Protein Structures Research Articles

π‐π stacking interactions mediate molecular recognition between arginine and tryptophan containing peptides derived from human islet polypeptide

AbstractCation‐π interactions, often found in protein assemblies, are characterized by favorable electrostatic interactions between an aromatic π‐electron surface and a positively charged species. There are evidences that reveal the importance of cation‐π interactions between arginine and aromatic residues in protein structure and function. In this paper, the effect of cation‐π interactions on the aggregation propensity of peptides derived from human islet polypeptide (hIAPP) was explored using UV resonance Raman and fluorescence spectroscopy. By employing an analog of hIAPP22–29 in which Phe‐23 is replaced with tryptophan (NWGAILSS), we were able to demonstrate an increase in the amyloidogenic propensity of this mutant in the presence of Zn2+ that is attributable to cation‐π interactions. In contrast, no cation‐π interactions were observed when the cationic F23R analog of hIAPP22–29 (NRGAILSS) was allowed to interact with NWGAILSS. From these observations, it was surmised that in these peptides, the dominant interaction between arginine and tryptophan involves the π‐cloud of the guanidino group and the indole ring, not cation‐π interactions. The spectroscopic data, supported by density functional theory‐based simulation results, suggest that arginine‐tryptophan interaction involves π‐π stacking where the guanidino group is oriented parallel to the indole ring. These hydrophobic interactions, coupled with the hydrotropic effect of the guanidine functionality of arginine, led to a delay in the aggregation kinetics of NWGAILSS. These unique interactions were further exploited to design a peptide inhibitor of full‐length amylin self‐assembly.

Comparison of Protective Effects of Phenolic Acids on Protein Glycation of BSA Supported by In Vitro and Docking Studies.

Several diabetic complications are associated with forming advanced glycation end products (AGEs). Different chemical and natural compounds are able to prevent the development of these products. In this study, glycosylation was induced as a model by incubating bovine serum albumin (BSA) with glucose. Consequently, BSA was treated with glucose and different concentrations (1.25, 2.5, and 5 μM) of syringic acid, gallic acid, ellagic acid, ferulic acid, paracoumaric acid, and caffeic acid for 4 and 6 weeks. Biochemical experiments comprise measurements of fluorescent AGEs, protein carbonyl contents, total thiol, hemolysis tests, and also malondialdehyde (MDA) levels in RBC. These demonstrated the antiglycating mechanism of these phenolic acids. Most of the phenolic acids used in this study reduced MDA levels and protected thiol residues in protein structures. They also inhibited the formation of fluorescent AGEs and RBC lysis, except gallic acid. Moreover, ferulic acid, paracoumaric acid, and caffeic acid proteins significantly prevent carbonylation. Molecular docking and simulation studies showed that ellagic, caffeic, gallic, and syringic acids could interact with lysine and arginine residues in the active site of BSA and stabilize its structure to inhibit the formation of AGEs. Our results suggest that phenolic acid could be used as a potential phytochemical against protein glycation and related diabetic complications.

Exploring the quality of protein structural models from a Bayesian perspective.

We explore how ideas and practices common in Bayesian modeling can be applied to help assess the quality of 3D protein structural models. The basic premise of our approach is that the evaluation of a Bayesian statistical model's fit may reveal aspects of the quality of a structure when the fitted data is related to protein structural properties. Therefore, we fit a Bayesian hierarchical linear regression model to experimental and theoretical 13 Cα chemical shifts. Then, we propose two complementary approaches for the evaluation of such fitting: (a) in terms of the expected differences between experimental and posterior predicted values; (b) in terms of the leave-one-out cross-validation point-wise predictive accuracy. Finally, we present visualizations that can help interpret these evaluations. The analyses presented in this article are aimed to aid in detecting problematic residues in protein structures. The code developed for this work is available on: https://github.com/BIOS-IMASL/Hierarchical-Bayes-NMR-Validation.

Disordered Residues and Patterns in the Protein Data Bank.

We created a new library of disordered patterns and disordered residues in the Protein Data Bank (PDB). To obtain such datasets, we clustered the PDB and obtained the groups of chains with different identities and marked disordered residues. We elaborated a new procedure for finding disordered patterns and created a new version of the library. This library includes three sets of patterns: unique patterns, patterns consisting of two kinds of amino acids, and homo-repeats. Using this database, the user can: (1) find homologues in the entire Protein Data Bank; (2) perform a statistical analysis of disordered residues in protein structures; (3) search for disordered patterns and homo-repeats; (4) search for disordered regions in different chains of the same protein; (5) download clusters of protein chains with different identity from our database and library of disordered patterns; and (6) observe 3D structure interactively using MView. A new library of disordered patterns will help improve the accuracy of predictions for residues that will be structured or unstructured in a given region.

IMRPS: Inserted and Modified Residues in Protein Structures. A database

Modified residues present in proteins are the result of post-translational modifications (PTMs). These PTMs increase the functional diversity of the proteome and influence various biological processes and diseased conditions. Therefore, identification and understanding of PTMs in various protein structures is of great significance. In view of this, an online database, Inserted and Modified Residues in Protein Structures (IMRPS), has been developed. IMRPS is a derived database that furnishes information on the residues modified and inserted in the protein structures available in the Protein Data Bank (PDB). The database is equipped with a graphical user interface and has an option to view the data for non-redundant protein structures (25 and 90%) as well. A quality criteria cutoff has been incorporated to assist in displaying the specific set of PDB codes. The entire protein structure along with the inserted or modified residues can be visualized in JSmol. This database will be updated regularly (presently, every three months) and can be accessed through the URL http://cluster.physics.iisc.ac.in/imrps/.

Mass spectrometry assisted arginine side chains assignment of NMR resonances in natural abundance proteins.

Arginine side chains play critical roles in many protein-ligand interactions and enzyme catalysis. Unambiguous resonance assignment is a prerequisite for the nuclear magnetic resonance (NMR) spectroscopy studies of arginine side chains dynamics and hydrogen exchange properties from which one can expect to elucidate in more detail the roles of arginine residues in protein structure and function. Here we present a new mass spectrometry (MS)-based method for assigning the side-chain resonances of arginine residues in 2D 1H-15N NMR spectra. The method requires no additional isotopic labeling, and relies on knowledge of the amino acid sequence, the modification of the guanidino groups and liquid chromatography-mass spectrometry rather than the protein's structure or properties. Correlating the modification rates can connect cross-peak positions from NMR data with MS data to support resonances assignments. In the present work, we have extended our original application to natural abundance human ubiquitin to provide ε-NH assessments of three arginine for this well-studied protein.

Network analysis of dynamically important residues in protein structures mediating ligand-binding conformational changes.

According to the generalized conformational selection model, ligand binding involves the co-existence of at least two conformers with different ligand-affinities in a dynamical equilibrium. Conformational transitions between them should be guaranteed by intramolecular vibrational dynamics associated to each conformation. These motions are, therefore, related to the biological function of a protein. Positions whose mutations are found to alter these vibrations the most can be defined as key positions, that is, dynamically important residues that mediate the ligand-binding conformational change. In a previous study, we have shown that these positions are evolutionarily conserved. They correspond to buried aliphatic residues mostly localized in regular structured regions of the protein like β-sheets and α-helices. In the present paper, we perform a network analysis of these key positions for a large dataset of paired protein structures in the ligand-free and ligand-bound form. We observe that networks of interactions between these key positions present larger and more integrated networks with faster transmission of the information. Besides, networks of residues result that are robust to conformational changes. Our results reveal that the conformational diversity of proteins seems to be guaranteed by a network of strongly interconnected key positions rather than individual residues.

Abstract 1655: A structural approach for prioritizing variants in personalized Oncology

Abstract In personalized oncology, treatment strategies take individualized genomic aberrations into consideration and relies heavily on how the cancer genomes are interpreted. With immense efforts put into cohort studies, knowledge of pathogenicity at the gene level had been accumulated. On the other hand, the increasing amount of personalized clinical trials and the accompanying need of interpreting pathogenecity at the sub-gene level emphasize the importance of resolving heterogeneous significance within genes. There had been some initiatives where people identified mutation hotspots within genes, looking at either highly recurrent single residues or mutation clusters. The hotspot clusters can be inferred from protein sequences as well as from protein structures, where residues distant in sequence may be brought together through protein folding. Nonetheless, these information are not well utilized in routine clinical settings yet. In this study, we aim to build a consensus list of prevalent mutational events for molecular stratification of patients. This is achieved by collecting sequence and structural mutation hotspots from different algorithms, and further combine structural information to identify their neighboring residues in protein structures. Specifically, around 1400 residue hotspots are collected from CancerHotspots and DominoEffect. An additional of around 250 structural hotspots are identified by three structural algorithms, namely 3DHotSpots, HotMAPS and HotSpot3D. These results are mostly derived from TCGA PanCancer cohorts, with CancerHotspots included more other studies in the analysis. With this consensus information in place, we further set up the mechanism to map from genomic variants to protein sequence and protein structures. This enables us to annotate the variants found in patient genome and see whether they co-localize with hotspot residues, in their structural neighborhood, or in structural informative domains like interaction interfaces. In summary, a higher prevalence of variants might imply higher significance and more well-studied roles than other low prevalent mutant alleles. Transferring this knowledge from the public cohorts helps researchers quickly prioritize variants found in patient genomes. Annotating structural significance of variants further provides us with more confidence in identifying driving events. With these procedures automated, the goal is to help facilitate clinical evaluations to meet the growing number of patients recruited to personalized oncology trials. Citation Format: Siao-Han Wong, Brors Benedikt. A structural approach for prioritizing variants in personalized Oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1655.

Application of the DBSCAN Algorithm to Detect Hydrophobic Clusters in Protein Structures

Hydrophobic cores and hydrophobic clusters play a key role in globular protein folding, providing a framework for functionally important amino acid residues of proteins and enzymes. A program was developed for clustering amino acid residues in protein structures, which takes into account their hydrophobicity and utilizes the DBSCAN algorithm. The program is described and its scope and applicability are outlined.

Thinking Outside the Informatics Box: Computed Chemical Properties for Protein Function Annotation

There are now over 14,000 Structural Genomics (SG) protein structures deposited in the Protein Data Bank (PDB) and most of these are of unknown or uncertain biochemical function. Reliable computational methods for the prediction of the function of protein structures is an important current need. Typically, functions are assigned using informatics‐based approaches. The annotation of protein function by automated means has led to high rates of misannotations in some databases. Here we present a complementary and powerful approach based on computed chemical properties of the individual residues in a protein structure. Partial Order Optimum Likelihood (POOL) is used to predict the residues in the query protein structure that are important for catalysis. Typically these include the residues in the first layer that make direct contact with the substrate molecule(s) and also some residues in the second and third layers that play supporting roles in the catalytic process. Then, for proteins of known biochemical function, Graph Representation of Active Sites for Prediction of Function (GRASP‐Func) establishes local arrays of POOL‐predicted residues that are common to proteins of the same function. Then local arrays of POOL‐predicted residues of the query (SG) protein are aligned with those of the proteins of known function for the different functional types. These alignments, each SG protein against each functional family, are scored in order to predict the most likely function of the SG proteins. Results are reported for the SG members of the Ribulose Phosphate Binding Barrel (RPBB), Clp‐Crotonase, and Haloacid Dehalogenase superfamilies. While we find the SG proteins in the RPBB superfamily to be well annotated, we predict very high annotation error rates (about 70%) in the Clp‐Crotonase superfamily. Of particular interest are cases of predicted misannotation, where our prediction differs from that of the assigned function. Experimental testing of our predictions is performed by direct biochemical assays to verify our predictions.Support or Funding InformationNational Science Foundation under grant number CHE‐1305655, MathWorks, Inc. and a PhRMA Foundation Fellowship awarded to CLM.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Characterizing the relation of functional and Early Folding Residues in protein structures using the example of aminoacyl-tRNA synthetases.

Proteins are chains of amino acids which adopt a three-dimensional structure and are then able to catalyze chemical reactions or propagate signals in organisms. Without external influence, many proteins fold into their native structure, and a small number of Early Folding Residues (EFR) have previously been shown to initiate the formation of secondary structure elements and guide their respective assembly. Using the two diverse superfamilies of aminoacyl-tRNA synthetases (aaRS), it is shown that the position of EFR is preserved over the course of evolution even when the corresponding sequence conservation is small. Folding initiation sites are positioned in the center of secondary structure elements, independent of aaRS class. In class I, the predicted position of EFR resembles an ancient structural packing motif present in many seemingly unrelated proteins. Furthermore, it is shown that EFR and functionally relevant residues in aaRS are almost entirely disjoint sets of residues. The Start2Fold database is used to investigate whether this separation of EFR and functional residues can be observed for other proteins. EFR are found to constitute crucial connectors of protein regions which are distant at sequence level. Especially, these residues exhibit a high number of non-covalent residue-residue contacts such as hydrogen bonds and hydrophobic interactions. This tendency also manifests as energetically stable local regions, as substantiated by a knowledge-based potential. Despite profound differences regarding how EFR and functional residues are embedded in protein structures, a strict separation of structurally and functionally relevant residues cannot be observed for a more general collection of proteins.

3DCONS-DB: A Database of Position-Specific Scoring Matrices in Protein Structures.

Many studies have used position-specific scoring matrices (PSSM) profiles to characterize residues in protein structures and to predict a broad range of protein features. Moreover, PSSM profiles of Protein Data Bank (PDB) entries have been recalculated in many works for different purposes. Although the computational cost of calculating a single PSSM profile is affordable, many statistical studies or machine learning-based methods used thousands of profiles to achieve their goals, thereby leading to a substantial increase of the computational cost. In this work we present a new database compiling PSSM profiles for the proteins of the PDB. Currently, the database contains 333,532 protein chain profiles involving 123,135 different PDB entries.

3D clusters of somatic mutations in cancer reveal numerous rare mutations as functional targets

Many mutations in cancer are of unknown functional significance. Standard methods use statistically significant recurrence of mutations in tumor samples as an indicator of functional impact. We extend such analyses into the long tail of rare mutations by considering recurrence of mutations in clusters of spatially close residues in protein structures. Analyzing 10,000 tumor exomes, we identify more than 3000 rarely mutated residues in proteins as potentially functional and experimentally validate several in RAC1 and MAP2K1. These potential driver mutations (web resources: 3dhotspots.org and cBioPortal.org) can extend the scope of genomically informed clinical trials and of personalized choice of therapy.

Application of information theory to a three-body coarse-grained representation of proteins in the PDB: insights into the structural and evolutionary roles of residues in protein structure.

Knowledge-based methods for analyzing protein structures, such as statistical potentials, primarily consider the distances between pairs of bodies (atoms or groups of atoms). Considerations of several bodies simultaneously are generally used to characterize bonded structural elements or those in close contact with each other, but historically do not consider atoms that are not in direct contact with each other. In this report, we introduce an information-theoretic method for detecting and quantifying distance-dependent through-space multibody relationships between the sidechains of three residues. The technique introduced is capable of producing convergent and consistent results when applied to a sufficiently large database of randomly chosen, experimentally solved protein structures. The results of our study can be shown to reproduce established physico-chemical properties of residues as well as more recently discovered properties and interactions. These results offer insight into the numerous roles that residues play in protein structure, as well as relationships between residue function, protein structure, and evolution. The techniques and insights presented in this work should be useful in the future development of novel knowledge-based tools for the evaluation of protein structure.

Graph Kernels and Applications in Protein Classification

Protein classification is a well established research field concerned with the discovery of molecule’s properties through informa­tional techniques. Graph-based kernels provide a nice framework combining machine learning techniques with graph theory. In this paper we introduce a novel graph kernel method for an­notating functional residues in protein structures. A structure­ is first modeled as a protein contact graph, where nodes ­corres­pond to residues and edges connect spatially neighboring resi­dues. In experiments on classification of graph models of proteins, the method based on Weisfeiler-Lehman shortest path kernel with complement graphs outperformed other state-of-art methods.

Identification of Allosteric Disulfides from Prestress Analysis

Disulfide bonds serve to form physical cross-links between residues in protein structures, thereby stabilizing the protein fold. Apart from this purely structural role, they can also be chemically active, participating in redox reactions, and they may even potentially act as allosteric switches controlling protein functions. Specific types of disulfide bonds have been identified in static protein structures from their distinctive pattern of dihedral bond angles, and the allosteric function of such bonds is purported to be related to the torsional strain they store. Using all-atom molecular-dynamics simulations for ∼700 disulfide bonded proteins, we analyzed the intramolecular mechanical forces in 20 classes of disulfide bonds. We found that two particular classes, the −RHStaple and the −/+RHHook disulfides, are indeed more stressed than other disulfide bonds, but the stress is carried primarily by stretching of the S-S bond and bending of the neighboring bond angles, rather than by dihedral torsion. This stress corresponds to a tension force of magnitude ∼200 pN, which is balanced by repulsive van der Waals interactions between the cysteine Cα atoms. We confirm stretching of the S-S bond to be a general feature of the −RHStaples and the −/+RHHooks by analyzing ∼20,000 static protein structures. Given that forced stretching of S-S bonds is known to accelerate their cleavage, we propose that prestress of allosteric disulfide bonds has the potential to alter the reactivity of a disulfide, thereby allowing us to readily switch between functional states.

2P002 タンパク質における埋もれた極性残基の構造と置換パターンの網羅的解析(01A. 蛋白質:構造,ポスター,第52回日本生物物理学会年会(2014年度))

2P002 タンパク質における埋もれた極性残基の構造と置換パターンの網羅的解析(01A. 蛋白質:構造,ポスター,第52回日本生物物理学会年会(2014年度))

Prediction of disulfide connectivity in proteins with machine-learning methods and correlated mutations

BackgroundRecently, information derived by correlated mutations in proteins has regained relevance for predicting protein contacts. This is due to new forms of mutual information analysis that have been proven to be more suitable to highlight direct coupling between pairs of residues in protein structures and to the large number of protein chains that are currently available for statistical validation. It was previously discussed that disulfide bond topology in proteins is also constrained by correlated mutations.ResultsIn this paper we exploit information derived from a corrected mutual information analysis and from the inverse of the covariance matrix to address the problem of the prediction of the topology of disulfide bonds in Eukaryotes. Recently, we have shown that Support Vector Regression (SVR) can improve the prediction for the disulfide connectivity patterns. Here we show that the inclusion of the correlated mutation information increases of 5 percentage points the SVR performance (from 54% to 59%). When this approach is used in combination with a method previously developed by us and scoring at the state of art in predicting both location and topology of disulfide bonds in Eukaryotes (DisLocate), the per-protein accuracy is 38%, 2 percentage points higher than that previously obtained.ConclusionsIn this paper we show that the inclusion of information derived from correlated mutations can improve the performance of the state of the art methods for predicting disulfide connectivity patterns in Eukaryotic proteins. Our analysis also provides support to the notion that improving methods to extract evolutionary information from multiple sequence alignments greatly contributes to the scoring performance of predictors suited to detect relevant features from protein chains.

Mechanism of Neurotoxicity of Prion and Alzheimer's Disease-Related Proteins: Molecular Insights from Bioinformatically Identified ω-Conotoxin-Like Pharmacophores

Prion diseases are fatal neurodegenerative disorders caused by altered forms of the prion protein (PrPC). It was reported that dysregulation of cellular Ca2+ homeostasis is recurrent in these diseases and that scrapie-infected cells exhibit Ca2+ perturbation via specific impairment of N-type calcium channels. However, it is not known whether such dysfunction is secondary to the broad neuronal damage accompanying prion diseases or whether it underlies pathological interactions of prions with calcium channels. In this research, we examined this latter possibility by searching for channel binding signatures in PrPC through structural comparison with known N-type channel blockers. To this aim, a computational method devised by us to recognize similar distributions of basic residues in protein structures enabled us to find that the bioactive groups representing the pharmacophores of ω-conotoxins GVIA and MVIIA can be overlaid onto similar residues within the PrPC globular domain. This finding, together with the knowledge that Ca2+ homeostasis disruption is common to other neurodegenerative disorders, led us to search for and identify an ω-conotoxin-like pharmacophore also in the Alzheimer's Aβ(1-42) peptide. These results point to the potential ability of prions and Aβ(1-42) to bind calcium channels as the elusive neurotoxic mechanism common to seemingly unrelated fatal neuropathies.

Protein structure determination of insulin of zebra fish (Danio rerio) by homology modeling and structure analysis

The protein sequence of insulin of zebra fish is obtained from UniProt. Due to lack of their structure, structure prediction is necessary, because the structure of protein plays an important role in their function. Our work is based on the production of two protein structure, from the same sequence, by computational approach and finally validates these generated structures. In this work two different widely acceptable online web tool are used for generating structure from the protein sequences of insulin of zebra fish. These are Swiss Model web server and ESyPred3D web server. After getting structure from this two web tool, the structures are passed by a series of quality tests. ProQ web software is used for checking quality of these generated structures. 3d-ss web tool is used for superimposition between two generated structures. It can compare between two structures. The Ramachandran plot is calculated by using VegaZZ software. CASTp (Computer Atlas of Surface Topology of protein) is a web tool, used to predict active sides with their respective volume and area. Finally ProFunc tool is used for analysis of two structures. Key-Words: CASTp, Homology Modeling, ProQ, VegaZZ, Zebra fish insulin I. Introduction Insulin is a peptide hormone, produced by beta cells of the pancreas, and is central to regulating carbohydrate and fat metabolism in the body. Insulin causes cells in the liver, skeletal muscles, and fat tissue to absorb glucose from the blood. In the liver and skeletal muscles, glucose is stored as glycogen. Protein structure plays an important role in their function. There are many sequence of this protein are present in UniProt database. But due to their lack of structure, homology modeling is necessary. But this work is based on the insulin of the zebra fish. Homology modeling, also known as comparative modeling of protein, refers to constructing an atomic- resolution model of the target protein from its amino acid sequence and an experimental three-dimensional structure of a related homologous protein (the template). Homology modeling relies on the identification of one or more known protein structures likely to resemble the structure of the query sequence. Evolutionarily related proteins have similar sequences and naturally occurring homologous proteins have similar protein structure. It has been shown that three-dimensional protein structure is evolutionarily more conserved than would be expected on the basis of sequence conservation alone. The sequence alignment and template structure are then used to produce a structural model of the target. Because protein structures are more conserved than DNA sequences, detectable levels of sequence similarity usually imply significant structural similarity. The quality of the homology model is dependent on the quality of the sequence alignment and template structure. In this work two widely acceptable structure prediction tools are used, these are Swiss Model and ESyPred3D. Both are runs in windows environment. The structures are then validating using ProQ. Superimposition is done by 3d-ss. Ramachandran plot (also known as a Ramachandran diagram or a (φ,ψ) plot), originally developed in 1963 by G. N. Ramachandran, C. Ramakrishnan, and V. Sasisekharan, is a way to visualize backbone dihedral angles ψ against φ of amino acid residues in protein structure. Ramachandran plot is calculated by VegaZZ software. CASTp is used to predict active sides with their respective volume and area. The ProFunc server ( http://www.ebi.ac.uk/thornton-srv/databases/profunc/index.html ) had been developed to help identifying the likely biochemical function of a protein from its three-dimensional structure.