Structure and IgE cross-reactivity among walnut and peanut vicilin leader sequences

Background: Vicilin seed storage proteins are translated with N-terminal leader sequences (LSs) that are cleaved to yield the mature protein. These LSs were thought to be unstructured and rapidly degraded. However, Ara h 1 and Jug r 2 LS (A1LS, J2LS) have been identified in seeds, and immunodominant IgE epitopes detected. Here, common sequences containing structured CxxxC-repeat motifs were identified as potential mediators of IgE cross-reactivity despite very low (17%) sequence identity. Method: Linear IgE epitopes were identified by peptide microarrays, in which overlapping 15-mer peptides on glass slides, were incubated with sera from peanut, walnut or dual allergic individuals. Similar epitopes were computationally predicted. Peanut A1LS and walnut J2LS fragments (J2.1, J2.2, J2.3) each with a CxxxC vicilin LS motif were identified, cloned, expressed, purified and their structures solved using solution-NMR to locate and assess epitopes on the structure. Results: A1LS and J2LSs reveal similar helix-turn-helix motifs connected by disulfide bonds between adjacent CxxxC repeats forming α-hairpin structures. Peanut-allergic IgE bound more frequently to the J2LSs, regardless of walnut allergic status or A1LS binding. IgE binding pattern to peptides from both J2LS and A1LS, along with structure and computational predictions, suggest that the structure and conserved amino acid properties of peptides determine cross-reactivity. The properties of LS IgE epitopes were closely related to epitopes in 2S albumins. Conclusion: The shared α-hairpin structure is a stable scaffold that contributes to cross-reactivity despite low sequence identity. Biophysical properties are a better predictor of distant cross-reactivity than traditional measures of evolutionary conservation.

Role of Chiral Configuration in the Photoinduced Interaction of D- and L-Tryptophan with Optical Isomers of Ketoprofen in Linked Systems

The study of the L- and D-amino acid properties in proteins and peptides has attracted considerable attention in recent years, as the replacement of even one L-amino acid by its D-analogue due to aging of the body is resulted in a number of pathological conditions, including Alzheimer’s and Parkinson’s diseases. A recent trend is using short model systems to study the peculiarities of proteins with D-amino acids. In this report, the comparison of the excited states quenching of L- and D-tryptophan (Trp) in a model donor–acceptor dyad with (R)- and (S)-ketoprofen (KP-Trp) was carried out by photochemically induced dynamic nuclear polarization (CIDNP) and fluorescence spectroscopy. Quenching of the Trp excited states, which occurs via two mechanisms: prevailing resonance energy transfer (RET) and electron transfer (ET), indeed demonstrates some peculiarities for all three studied configurations of the dyad: (R,S)-, (S,R)-, and (S,S)-. Thus, the ET efficiency is identical for (S,R)- and (R,S)-enantiomers, while RET differs by 1.6 times. For (S,S)-, the CIDNP coefficient is almost an order of magnitude greater than for (R,S)- and (S,R)-. To understand the source of this difference, hyperpolarization of (S,S)-and (R,S)- has been calculated using theory involving the electron dipole–dipole interaction in the secular equation.

An online GPCR structure analysis platform

We present an online, interactive platform for comparative analysis of all available G protein-coupled receptor structures while correlating to functional data. The comprehensive platform encompasses structure similarity, secondary structure, protein backbone packing and movement, residue-residue contact networks, amino acid properties and prospective design of experimental mutagenesis studies. This lets any researcher tap the potential of sophisticated structural analyses enabling a plethora of basic and applied research studies.

Graph Signal Processing on protein residue networks helps in studying its biophysical properties

Understanding the physical and chemical properties of proteins is vital, and many efforts have been made to study the emergent properties of the macro-molecules as a combination of long chains of amino acids. Here, we present a graph signal processing based approach to model the biophysical property of proteins. For each protein inter-residue proximity-based network is used as basis graph and the respective amino acid properties are used as node-signals. Signals on node are decomposed on network’s Laplacian eigenbasis using graph Fourier transformations. We found that the intensity in low-frequency components of graph signals of residue features could be used to model few biophysical properties of proteins. Specifically, using our approach, we could model protein folding-rate, globularity and fraction of alpha-helices and beta-sheets. Our approach also allows amalgamation of different types of chemical and graph theoretic properties of residue to be used together in a multi-variable regression model to predict biophysical properties.

InMeRF: prediction of pathogenicity of missense variants by individual modeling for each amino acid substitution

In predicting the pathogenicity of a nonsynonymous single-nucleotide variant (nsSNV), a radical change in amino acid properties is prone to be classified as being pathogenic. However, not all such nsSNVs are associated with human diseases. We generated random forest (RF) models individually for each amino acid substitution to differentiate pathogenic nsSNVs in the Human Gene Mutation Database and common nsSNVs in dbSNP. We named a set of our models ‘Individual Meta RF’ (InMeRF). Ten-fold cross-validation of InMeRF showed that the areas under the curves (AUCs) of receiver operating characteristic (ROC) and precision–recall curves were on average 0.941 and 0.957, respectively. To compare InMeRF with seven other tools, the eight tools were generated using the same training dataset, and were compared using the same three testing datasets. ROC-AUCs of InMeRF were ranked first in the eight tools. We applied InMeRF to 155 pathogenic and 125 common nsSNVs in seven major genes causing congenital myasthenic syndromes, as well as in VANGL1 causing spina bifida, and found that the sensitivity and specificity of InMeRF were 0.942 and 0.848, respectively. We made the InMeRF web service, and also made genome-wide InMeRF scores available online (https://www.med.nagoya-u.ac.jp/neurogenetics/InMeRF/).

A Novel Amino Acid Properties Selection Method for Protein Fold Classification

Background: Amino acid physicochemical properties encoded in protein primary structure play a crucial role in protein folding. However, it is not yet clear which of the properties are the most suitable for protein fold classification. Objective: To avoid exhaustively searching the total properties space, an amino acid properties selection method was proposed in this study to rapidly obtain a suitable properties combination for protein fold classification. Method: The proposed amino acid properties selection method was based on sequential floating forward selection strategy. Beginning with an empty set, variable number of features were added iteratively until achieving the iteration termination condition. Results: The experimental results indicate that the proposed method improved prediction accuracies by 0.26-5% on a widely used benchmark dataset with appropriately selected amino acid properties. Conclusion: The proposed properties selection method can be extended to other biomolecule property related classification problems in bioinformatics.