Disulfide connectivity prediction with 70% accuracy using two-level models

Disulfide bridges stabilize protein structures covalently and play an important role in protein folding. Predicting disulfide connectivity precisely helps towards the solution of protein structure prediction. Previous methods for disulfide connectivity prediction either infer the bonding potential of cysteine pairs or rank alternative disulfide bonding patterns. As a result, these methods encode data according to cysteine pairs (pair-wise) or disulfide bonding patterns (pattern-wise). However, using either encoding scheme alone cannot fully utilize the local and global information of proteins, so the accuracies of previous methods are limited. In this work, we propose a novel two-level framework to predict disulfide connectivity. With this framework, both the pair-wise and pattern-wise encoding schemes are considered. Our models were validated on the datasets derived from SWISS-PROT 39 and 43, and the results demonstrate that our models can combine both local and global information. Compared to previous methods, significant improvements were obtained by our models. Our work may also provide insights to further improvements of disulfide connectivity prediction and increase its applicability in protein structure analysis and prediction.     

支持向量机方法预测蛋白质结构中的二硫键

在蛋白质结构预测的研究中,一个重要的问题就是正确预测二硫键的连接,二硫键的准确预测可以减少蛋白质构像的搜索空间,有利于蛋白质3D结构的预测,本文将预测二硫键的连接问题转化成对连接模式的分类问题,并成功地将支持向量机方法引入到预测工作中。通过对半胱氨酸局域序列连接模式的分类预测,可以由蛋白质的一级结构序列预测该蛋白质的二硫键的连接。结果表明蛋白质的二硫键的连接与半胱氨酸局域序列连接模式有重要联系,应用支持向量机方法对蛋白质结构的二硫键预测取得了良好的结果。     

Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure

MOTIVATION: Disulfide bonds are primary covalent crosslinks between two cysteine residues in proteins that play critical roles in stabilizing the protein structures and are commonly found in extracy-toplasmatic or secreted proteins. In protein folding prediction, the localization of disulfide bonds can greatly reduce the search in conformational space. Therefore, there is a great need to develop computational methods capable of accurately predicting disulfide connectivity patterns in proteins that could have potentially important applications. RESULTS: We have developed a novel method to predict disulfide connectivity patterns from protein primary sequence, using a support vector regression (SVR) approach based on multiple sequence feature vectors and predicted secondary structure by the PSIPRED program. The results indicate that our method could achieve a prediction accuracy of 74.4% and 77.9%, respectively, when averaged on proteins with two to five disulfide bridges using 4-fold cross-validation, measured on the protein and cysteine pair on a well-defined non-homologous dataset. We assessed the effects of different sequence encoding schemes on the prediction performance of disulfide connectivity. It has been shown that the sequence encoding scheme based on multiple sequence feature vectors coupled with predicted secondary structure can significantly improve the prediction accuracy, thus enabling our method to outperform most of other currently available predictors. Our work provides a complementary approach to the current algorithms that should be useful in computationally assigning disulfide connectivity patterns and helps in the annotation of protein sequences generated by large-scale whole-genome projects. AVAILABILITY: The prediction web server and Supplementary Material are accessible at http://foo.maths.uq.edu.au/~huber/disulfide     

Predicting disulfide bond connectivity in proteins by correlated mutations analysis

MOTIVATION: Prediction of disulfide bond connectivity facilitates structural and functional annotation of proteins. Previous studies suggest that cysteines of a disulfide bond mutate in a correlated manner. RESULTS: We developed a method that analyzes correlated mutation patterns in multiple sequence alignments in order to predict disulfide bond connectivity. Proteins with known experimental structures and varying numbers of disulfide bonds, and that spanned various evolutionary distances, were aligned. We observed frequent variation of disulfide bond connectivity within members of the same protein families, and it was also observed that in 99% of the cases, cysteine pairs forming non-conserved disulfide bonds mutated in concert. Our data support the notion that substitution of a cysteine in a disulfide bond prompts the substitution of its cysteine partner and that oxidized cysteines appear in pairs. The method we developed predicts disulfide bond connectivity patterns with accuracies of 73, 69 and 61% for proteins with two, three and four disulfide bonds, respectively.     

Prediction of cystine connectivity using SVM

One of the major contributors to protein structures is the formation of disulphide bonds between selected pairs of cysteines at oxidized state. Prediction of such disulphide bridges from sequence is challenging given that the possible combination of cysteine pairs as the number of cysteines increases in a protein. Here, we describe a SVM (support vector machine) model for the prediction of cystine connectivity in a protein sequence with and without a priori knowledge on their bonding state. We make use of a new encoding scheme based on physico-chemical properties and statistical features (probability of occurrence of each amino acid residue in different secondary structure states along with PSI-blast profiles). We evaluate our method in SPX (an extended dataset of SP39 (swiss-prot 39) and SP41 (swiss-prot 41) with known disulphide information from PDB) dataset and compare our results with the recursive neural network model described for the same dataset.     

Paired cysteine mutagenesis to establish the pattern of disulfide bonds in the functional intact secretin receptor

The amino-terminal domain of class B G protein-coupled receptors contains six conserved cysteine residues involved in structurally and functionally critical disulfide bonds. The mapping of these bonds has been unclear, with one pattern based on biochemical and NMR structural characterizations of refolded, nonglycosylated amino-terminal fragments, and another pattern derived from functional characterizations of intact receptors having paired cysteine mutations. In the present study, we determined the disulfide bonding pattern of the prototypic class B secretin receptor by applying the same paired cysteine mutagenesis approach and confirming the predicted bonding pattern with proteolytic cleavage of intact functional receptor. As expected, systematic mutation to serine of the six conserved cysteine residues within this region of the secretin receptor singly and in pairs resulted in loss of function of most constructs. Notable exceptions were single mutations of the 4th and 6th cysteine residues and paired mutations involving the 1st and 3rd, 2nd and 5th, and 4th and 6th conserved cysteines, with secretin eliciting statistically significant cAMP responses above basal levels of activation for each of these constructs. Immunofluorescence microscopy confirmed similar levels of plasma membrane expression for each of the mutated receptors. Furthermore, cyanogen bromide cleaved a series of wild type and mutant secretin receptors, yielding patterns that agreed with our paired cysteine mutagenesis results. In conclusion, these data suggest the same pattern of disulfide bonding as that predicted previously by NMR and thus support a consistent pattern of amino-terminal disulfide bonds in class B G protein-coupled receptors.     

Novel disulfide engineering in human carbonic anhydrase II using the PAIRWISE side-chain geometry database

An analysis of the pairwise side-chain packing geometries of cysteine residues observed in high-resolution protein crystal structures indicates that cysteine pairs have pronounced orientational preferences due to the geometric constraints of disulfide bond formation. A potential function was generated from these observations and used to evaluate models for novel disulfide bonds in human carbonic anhydrase II (HCAII). Three double-cysteine variants of HCAII were purified and the effective concentrations of their thiol groups were determined by titrations with glutathione and dithiothreitol. The effects of the cysteine mutations on the native state structure and stability were characterized by circular dichroism, enzymatic activity, sulfonamide binding, and guanidine hydrochloride titration. These analyses indicate that the PAIRWISE potential is a good predictor of the strength of the disulfide bond itself, but the overall structural and thermodynamic effects on the protein are complicated by additional factors. In particular, the effects of cysteine substitutions on the native state and the stabilization of compact nonnative states by the disulfide can override any stabilizing effect of the cross-link.     

Cysteine separations profiles on protein sequences infer disulfide connectivity

MOTIVATION: Disulfide bonds play an important role in protein folding. A precise prediction of disulfide connectivity can strongly reduce the conformational search space and increase the accuracy in protein structure prediction. Conventional disulfide connectivity predictions use sequence information, and prediction accuracy is limited. Here, by using an alternative scheme with global information for disulfide connectivity prediction, higher performance is obtained with respect to other approaches. RESULT: Cysteine separation profiles have been used to predict the disulfide connectivity of proteins. The separations among oxidized cysteine residues on a protein sequence have been encoded into vectors named cysteine separation profiles (CSPs). Through comparisons of their CSPs, the disulfide connectivity of a test protein is inferred from a non-redundant template set. For non-redundant proteins in SwissProt 39 (SP39) sharing less than 30% sequence identity, the prediction accuracy of a fourfold cross-validation is 49%. The prediction accuracy of disulfide connectivity for proteins in SwissProt 43 (SP43) is even higher (53%). The relationship between the similarity of CSPs and the prediction accuracy is also discussed. The method proposed in this work is relatively simple and can generate higher accuracies compared to conventional methods. It may be also combined with other algorithms for further improvements in protein structure prediction. AVAILABILITY: The program and datasets are available from the authors upon request. CONTACT: cykao@csie.ntu.edu.tw.     

Different dynamics and pathway of disulfide bonds reduction of two human defensins,a molecular dynamics simulation study

Human defensins are a class of antimicrobial peptides that are crucial components of the innate immune system. Both human α defensin type 5 (HD5) and human β defensin type 3 (hBD‐3) have 6 cysteine residues which form 3 pairs of disulfide bonds in oxidizing condition. Disulfide bond linking is important to the protein structure stabilization, and the disulfide bond linking and breaking order have been shown to influence protein function. In this project, microsecond long molecular dynamics simulations were performed to study the structure and dynamics of HD5 and hBD‐3 wildtype and analogs which have all 3 disulfide bonds released in reducing condition. The structure of hBD‐3 was found to be more dynamic and flexible than HD5, based on RMSD, RMSF, and radius of gyration calculations. The disulfide bridge breaking order of HD5 and hBD‐3 in reducing condition was predicted by two kinds of methods, which gave consistent results. It was found that the disulfide bonds breaking pathways for HD5 and hBD‐3 are very different. The breaking of disulfide bonds can influence the dimer interface by making the dimer structure less stable for both kinds of defensin. In order to understand the difference in dynamics and disulfide bond breaking pathway, hydrophilic and hydrophobic accessible surface areas (ASA), buried surface area between cysteine pairs, entropy of cysteine pairs, and internal energy were calculated. Comparing to the wildtype, hBD‐3 analog is more hydrophobic, while HD5 is more hydrophilic. For hBD‐3, the disulfide breaking is mainly entropy driven, while other factors such as the solvation effects may take the major role in controlling HD5 disulfide breaking pathway. Proteins 2017; 85:665–681. © 2016 Wiley Periodicals, Inc.     

Prediction of the disulfide-bonding state of cysteine in proteins

The bonding states of cysteine play important functional and structural roles in proteins. In particular, disulfide bond formation is one of the most important factors influencing the three-dimensional fold of proteins. Proteins of known structure were used to teach computer-simulated neural networks rules for predicting the disulfide-bonding state of a cysteine given only its flanking amino acid sequence. Resulting networks make accurate predictions on sequences different from those used in training, suggesting that local sequence greatly influences cysteines in disulfide bond formation. The average prediction rate after seven independent network experiments is 81.4% for disulfide-bonded and 80.0% for non-disulfide-bonded scenarios. Predictive accuracy is related to the strength of network output activities. Network weights reveal interesting position-dependent amino acid preferences and provide a physical basis for understanding the correlation between the flanking sequence and a cysteine's disulfide-bonding state. Network predictions may be used to increase or decrease the stability of existing disulfide bonds or to aid the search for potential sites to introduce new disulfide bonds.     

Insulin and epidermal growth factor receptors contain the cysteine repeat motif found in the tumor necrosis factor receptor

The insulin receptor (INSR) and epidermal growth factor receptor (EGFR) are representatives of two structurally related subfamilies of tyrosine kinase receptors. Using the Wisconsin GCG sequence analysis programs, we have demonstrated that the cysteinerich regions of INSR and EGFR conform to the structural motif found in the tumor necrosis factorreceptor (TNFR) family. The study also revealed that these regions were not composed of simple repeats of eight cysteine residues as previously proposed and that the second Cysrich region of EGFR contained one fewer TNFR repeat than the first. The sequence alignments identified two cysteineresidues in INSR that could be responsible for the additional disulfide bonds known to be involved in dimer formation. The published data on the alignments for the fibronectin type III repeat region of the INSR together with previous cysteine mutagenesis studies indicated that there were two disulfide bonds linking the α and β chains of the INSR, but only one α-β linkage in the insulin-like growth factor 1 receptor (IG 1R). Database searches and sequence alignments showed that the TNFR motif is also found in the cysteine-rich repeats of laminins and the noncatalytic domains of furin-like proteases. If the starting position of the repeat is altered the characteristic laminin repeat of eight cysteine residues can be shown to consist of a TNFR-like motif fused to the last half of an EGF-like repeat. The overlapping regions of these two motifs are known to have identical disulfide bonding patterns and similar protein folds. © 1995 Wiley-Liss, Inc.     

Isolation and characterization of a resistant core peptide of recombinant human granulocyte-macrophage colony-stimulating factor (GM-CSF); confirmation of the GM-CSF amino acid sequence by mass spectrometry.

A trypsin-resistant core peptide of recombinant human granulocyte-macrophage colony-stimulating factor (rhGM-CSF) was isolated and analyzed by high-energy Cs+ liquid secondary-ion (LSI) mass spectrometric analysis. This analysis provided successful detection of the high-mass disulfide-linked core peptide as well as information confirming the existence of disulfide pairing. Similarly, LSI mass spectrometric analysis of the peptide fragments isolated chromatographically from a Staphylococcus aureus V8 protease digest of rhGM-CSF provided rapid confirmation of the cDNA-derived sequence and determination of the existing disulfide bonds between cysteine residues 54-96 and 88-121. Electrospray ionization mass spectrometry was employed to measure the molecular weight of the intact protein and to determine the number of the disulfide bonds in the protein molecule by comparative analysis of the protein before and after reduction with beta-mercaptoethanol.     

Exploring synonymous codon usage preferences of disulfide-bonded and non-disulfide bonded cysteines in the E. coli genome

High-quality data about protein structures and their gene sequences are essential to the understanding of the relationship between protein folding and protein coding sequences. Firstly we constructed the EcoPDB database, which is a high-quality database of Escherichia coli genes and their corresponding PDB structures. Based on EcoPDB, we presented a novel approach based on information theory to investigate the correlation between cysteine synonymous codon usages and local amino acids flanking cysteines, the correlation between cysteine synonymous codon usages and synonymous codon usages of local amino acids flanking cysteines, as well as the correlation between cysteine synonymous codon usages and the disulfide bonding states of cysteines in the E. coli genome. The results indicate that the nearest neighboring residues and their synonymous codons of the C-terminus have the greatest influence on the usages of the synonymous codons of cysteines and the usage of the synonymous codons has a specific correlation with the disulfide bond formation of cysteines in proteins. The correlations may result from the regulation mechanism of protein structures at gene sequence level and reflect the biological function restriction that cysteines pair to form disulfide bonds. The results may also be helpful in identifying residues that are important for synonymous codon selection of cysteines to introduce disulfide bridges in protein engineering and molecular biology. The approach presented in this paper can also be utilized as a complementary computational method and be applicable to analyse the synonymous codon usages in other model organisms.     

Paired natural cysteine mutation mapping: aid to constraining models of protein tertiary structure.

This paper discusses the benefit of mapping paired cysteine mutation patterns as a guide to identifying the positions of protein disulfide bonds. This information can facilitate the computer modeling of protein tertiary structure. First, a simple, paired natural-cysteine-mutation map is presented that identifies the positions of putative disulfide bonds in protein families. The method is based on the observation that if, during the process of evolution, a disulfide-bonded cysteine residue is not conserved, then it is likely that its counterpart will also be mutated. For each target protein, protein databases were searched for the primary amino acid sequences of all known members of distinct protein families. Primary sequence alignment was carried out using PileUp algorithms in the GCG package. To search for correlated mutations, we listed only the positions where cysteine residues were highly conserved and emphasized the mutated residues. In proteins of known three-dimensional structure, a striking pattern of paired cysteine mutations correlated with the positions of known disulfide bridges. For proteins of unknown architecture, the mutation maps showed several positions where disulfide bridging might occur.     

The susceptibility of disulfide bonds to modification in keratin fibers undergoing tensile stress

Cysteine residues perform a dual role in mammalian hairs. The majority help stabilize the overall assembly of keratins and their associated proteins, but a proportion of inter-molecular disulfide bonds are assumed to be associated with hair mechanical flexibility. Hair cortical microstructure is hierarchical, with a complex macro-molecular organization resulting in arrays of intermediate filaments at a scale of micrometres. Intermolecular disulfide bonds occur within filaments and between them and the surrounding matrix. Wool fibers provide a good model for studying various contributions of differently situated disulfide bonds to fiber mechanics. Within this context, it is not known if all intermolecular disulfide bonds contribute equally, and, if not, then do the disproportionally involved cysteine residues occur at common locations on proteins? In this study, fibers from Romney sheep were subjected to stretching or to their breaking point under wet or dry conditions to detect, through labeling, disulfide bonds that were broken more often than randomly. We found that some cysteines were labeled more often than randomly and that these vary with fiber water content (water disrupts protein-protein hydrogen bonds). Many of the identified cysteine residues were located close to the terminal ends of keratins (head or tail domains) and keratin-associated proteins. Some cysteines in the head and tail domains of type II keratin K85 were labeled in all experimental conditions. When inter-protein hydrogen bonds were disrupted under wet conditions, disulfide labeling occurred in the head domains of type II keratins, likely affecting keratin-keratin-associated protein interactions, and tail domains of the type I keratins, likely affecting keratin-keratin interactions. In contrast, in dry fibers (containing more protein-protein hydrogen bonding), disulfide labeling was also observed in the central domains of affected keratins. This central “rod” region is associated with keratin-keratin interactions between anti-parallel heterodimers in the tetramer of the intermediate filament.     

Disulfide connectivity prediction using recursive neural networks and evolutionary information

MOTIVATION: We focus on the prediction of disulfide bridges in proteins starting from their amino acid sequence and from the knowledge of the disulfide bonding state of each cysteine. The location of disulfide bridges is a structural feature that conveys important information about the protein main chain conformation and can therefore help towards the solution of the folding problem. Existing approaches based on weighted graph matching algorithms do not take advantage of evolutionary information. Recursive neural networks (RNN), on the other hand, can handle in a natural way complex data structures such as graphs whose vertices are labeled by real vectors, allowing us to incorporate multiple alignment profiles in the graphical representation of disulfide connectivity patterns. RESULTS: The core of the method is the use of machine learning tools to rank alternative disulfide connectivity patterns. We develop an ad-hoc RNN architecture for scoring labeled undirected graphs that represent connectivity patterns. In order to compare our algorithm with previous methods, we report experimental results on the SWISS-PROT 39 dataset. We find that using multiple alignment profiles allows us to obtain significant prediction accuracy improvements, clearly demonstrating the important role played by evolutionary information. AVAILABILITY: The Web interface of the predictor is available at http://neural.dsi.unifi.it/cysteines