Predicting disulfide bond connectivity in proteins by correlated mutations analysis

Killer Knots: Molecular Evolution of Inhibitor Cystine Knot Toxins in Wandering Spiders (Araneae: Ctenidae)

Venom expressed by the nearly 50,000 species of spiders on Earth largely remains an untapped reservoir of a diverse array of biomolecules with potential for pharmacological and agricultural applications. A large fraction of the noxious components of spider venoms are a functionally diverse family of structurally related polypeptides with an inhibitor cystine knot (ICK) motif. The cysteine-rich nature of these toxins makes structural elucidation difficult, and most studies have focused on venom components from the small handful of medically relevant spider species such as the highly aggressive Brazilian wandering spider Phoneutria nigriventer. To alleviate difficulties associated with the study of ICK toxins in spiders, we devised a comprehensive approach to explore the evolutionary patterns that have shaped ICK functional diversification using venom gland transcriptomes and proteomes from phylogenetically distinct lineages of wandering spiders and their close relatives. We identified 626 unique ICK toxins belonging to seven topological elaborations. Phylogenetic tests of episodic diversification revealed distinct regions between cysteine residues that demonstrated differential evidence of positive or negative selection, which may have structural implications towards the specificity and efficacy of these toxins. Increased taxon sampling and whole genome sequencing will provide invaluable insights to further understand the evolutionary processes that have given rise to this diverse class of toxins.

From nanoparticles to crystals: one-pot programmable biosynthesis of photothermal gold structures and their use for biomedical applications

Inspired by nature, green chemistry uses various biomolecules, such as proteins, as reducing agents to synthesize metallic nanostructures. This methodology provides an alternative route to conventional harsh synthetic processes, which include polluting chemicals. Tuning the resulting nanostructure properties, such as their size and shape, is challenging as the exact mechanism involved in their formation is still not well understood. This work reports a well-controlled method to program gold nanostructures' shape, size, and aggregation state using only one protein type, mucin, as a reduction and capping material in a one-pot bio-assisted reaction. Using mucin as a gold reduction template while varying its tertiary structure via the pH of the synthesis, we demonstrate that spherical, coral-shaped, and hexagonal gold crystals can be obtained and that the size can be tuned over three orders of magnitude. This is achieved by leveraging the protein's intrinsic reducing properties and pH-induced conformational changes. The systematic study of the reaction kinetics and growth steps developed here provides an understanding of the mechanism behind this phenomenon. We further show that the prepared gold nanostructures exhibit tunable photothermal properties that can be optimized for various hyperthermia-induced antibacterial applications.

Biochemical Characterization of a Novel Redox-Regulated Metacaspase in a Marine Diatom

Programmed cell death (PCD) in marine microalgae was suggested to be one of the mechanisms that facilitates bloom demise, yet its molecular components in phytoplankton are unknown. Phytoplankton are completely lacking any of the canonical components of PCD, such as caspases, but possess metacaspases. Metacaspases were shown to regulate PCD in plants and some protists, but their roles in algae and other organisms are still elusive. Here, we identified and biochemically characterized a type III metacaspase from the model diatom Phaeodactylum tricornutum, termed PtMCA-IIIc. Through expression of recombinant PtMCA-IIIc in E. coli, we revealed that PtMCA-IIIc exhibits a calcium-dependent protease activity, including auto-processing and cleavage after arginine. Similar metacaspase activity was detected in P. tricornutum cell extracts. PtMCA-IIIc overexpressing cells exhibited higher metacaspase activity, while CRISPR/Cas9-mediated knockout cells had decreased metacaspase activity compared to WT cells. Site-directed mutagenesis of cysteines that were predicted to form a disulfide bond decreased recombinant PtMCA-IIIc activity, suggesting its enhancement under oxidizing conditions. One of those cysteines was oxidized, detected in redox proteomics, specifically in response to lethal concentrations of hydrogen peroxide and a diatom derived aldehyde. Phylogenetic analysis revealed that this cysteine-pair is unique and widespread among diatom type III metacaspases. The characterization of a cell death associated protein in diatoms provides insights into the evolutionary origins of PCD and its ecological significance in algal bloom dynamics.

Molecular Evolutionary Analysis of Nematode Zona Pellucida (ZP) Modules Reveals Disulfide-Bond Reshuffling and Standalone ZP-C Domains

Zona pellucida (ZP) modules mediate extracellular protein-protein interactions and contribute to important biological processes including syngamy and cellular morphogenesis. Although some biomedically relevant ZP modules are well studied, little is known about the protein family's broad-scale diversity and evolution. The increasing availability of sequenced genomes from "nonmodel" systems provides a valuable opportunity to address this issue and to use comparative approaches to gain new insights into ZP module biology. Here, through phylogenetic and structural exploration of ZP module diversity across the nematode phylum, I report evidence that speaks to two important aspects of ZP module biology. First, I show that ZP-C domains-which in some modules act as regulators of ZP-N domain-mediated polymerization activity, and which have never before been found in isolation-can indeed be found as standalone domains. These standalone ZP-C domain proteins originated in independent (paralogous) lineages prior to the diversification of extant nematodes, after which they evolved under strong stabilizing selection, suggesting the presence of ZP-N domain-independent functionality. Second, I provide a much-needed phylogenetic perspective on disulfide bond variability, uncovering evidence for both convergent evolution and disulfide-bond reshuffling. This result has implications for our evolutionary understanding and classification of ZP module structural diversity and highlights the usefulness of phylogenetics and diverse sampling for protein structural biology. All told, these findings set the stage for broad-scale (cross-phyla) evolutionary analysis of ZP modules and position Caenorhabditis elegans and other nematodes as important experimental systems for exploring the evolution of ZP modules and their constituent domains.

diSBPred: A machine learning based approach for disulfide bond prediction

The protein disulfide bond is a covalent bond that forms during post-translational modification by the oxidation of a pair of cysteines. In protein, the disulfide bond is the most frequent covalent link between amino acids after the peptide bond. It plays a significant role in three-dimensional (3D) ab initio protein structure prediction (aiPSP), stabilizing protein conformation, post-translational modification, and protein folding. In aiPSP, the location of disulfide bonds can strongly reduce the conformational space searching by imposing geometrical constraints. Existing experimental techniques for the determination of disulfide bonds are time-consuming and expensive. Thus, developing sequence-based computational methods for disulfide bond prediction becomes indispensable. This study proposed a stacking-based machine learning approach for disulfide bond prediction (diSBPred). Various useful sequence and structure-based features are extracted for effective training, including conservation profile, residue solvent accessibility, torsion angle flexibility, disorder probability, a sequential distance between cysteines, and more. The prediction of disulfide bonds is carried out in two stages: first, individual cysteines are predicted as either bonding or non-bonding; second, the cysteine-pairs are predicted as either bonding or non-bonding by including the results from cysteine bonding prediction as a feature. The examination of the relevance of the features employed in this study and the features utilized in the existing nearest neighbor algorithm (NNA) method shows that the features used in this study improve about 7.39 % in jackknife validation balanced accuracy. Moreover, for individual cysteine bonding prediction and cysteine-pair bonding prediction, diSBPred provides a 10-fold cross-validation balanced accuracy of 82.29 % and 94.20 %, respectively. Altogether, our predictor achieves an improvement of 43.25 % based on balanced accuracy compared to the existing NNA based approach. Thus, diSBPred can be utilized to annotate the cysteine bonding residues of protein sequences whose structures are unknown as well as improve the accuracy of the aiPSP method, which can further aid in experimental studies of the disulfide bond and structure determination.

Yosshi: a web-server for disulfide engineering by bioinformatic analysis of diverse protein families

Disulfide bonds play a significant role in protein stability, function or regulation but are poorly conserved among evolutionarily related proteins. The Yosshi can help to understand the role of S–S bonds by comparing sequences and structures of homologs with diverse properties and different disulfide connectivity patterns within a common structural fold of a superfamily, and assist to select the most promising hot-spots to improve stability of proteins/enzymes or modulate their functions by introducing naturally occurring crosslinks. The bioinformatic analysis is supported by the integrated Mustguseal web-server to construct large structure-guided sequence alignments of functionally diverse protein families that can include thousands of proteins based on all available information in public databases. The Yosshi+Mustguseal is a new integrated web-tool for a systematic homology-driven analysis and engineering of S–S bonds that facilitates a broader interpretation of disulfides not just as a factor of structural stability, but rather as a mechanism to implement functional diversity within a superfamily. The results can be downloaded as a content-rich PyMol session file or further studied online using the HTML5-based interactive analysis tools. Both web-servers are free and open to all users at https://biokinet.belozersky.msu.ru/yosshi and there is no login requirement.

Assessing the accuracy of contact predictions in CASP13

The accuracy of sequence-based tertiary contact predictions was assessed in a blind prediction experiment at the CASP13 meeting. After 4 years of significant improvements in prediction accuracy, another dramatic advance has taken place since CASP12 was held 2 years ago. The precision of predicting the top L/5 contacts in the free modeling category, where L is the corresponding length of the protein in residues, has exceeded 70%. As a comparison, the best-performing group at CASP12 with a 47% precision would have finished below the top 1/3 of the CASP13 groups. Extensively trained deep neural network approaches dominate the top performing algorithms, which appear to efficiently integrate information on coevolving residues and interacting fragments or possibly utilize memories of sequence similarities and sometimes can deliver accurate results even in the absence of virtually any target specific evolutionary information. If the current performance is evaluated by F-score on L contacts, it stands around 24% right now, which, despite the tremendous impact and advance in improving its utility for structure modeling, also suggests that there is much room left for further improvement.

Did evolution create a flexible ligand-binding cavity in the urokinase receptor through deletion of a plesiotypic disulfide bond?

The urokinase receptor (uPAR) is a founding member of a small protein family with multiple Ly6/uPAR (LU) domains. The motif defining these LU domains contains five plesiotypic disulfide bonds stabilizing its prototypical three-fingered fold having three protruding loops. Notwithstanding the detailed knowledge on structure-function relationships in uPAR, one puzzling enigma remains unexplored. Why does the first LU domain in uPAR (DI) lack one of its consensus disulfide bonds, when the absence of this particular disulfide bond impairs the correct folding of other single LU domain-containing proteins? Here, using a variety of contemporary biophysical methods, we found that reintroducing the two missing half-cystines in uPAR DI caused the spontaneous formation of the corresponding consensus 7-8 LU domain disulfide bond. Importantly, constraints due to this cross-link impaired (i) the binding of uPAR to its primary ligand urokinase and (ii) the flexible interdomain assembly of the three LU domains in uPAR. We conclude that the evolutionary deletion of this particular disulfide bond in uPAR DI may have enabled the assembly of a high-affinity urokinase-binding cavity involving all three LU domains in uPAR. Of note, an analogous neofunctionalization occurred in snake venom α-neurotoxins upon loss of another pair of the plesiotypic LU domain half-cystines. In summary, elimination of the 7-8 consensus disulfide bond in the first LU domain of uPAR did have significant functional and structural consequences.

Identifying functionally informative evolutionary sequence profiles

Motivation

Multiple sequence alignments (MSAs) can provide essential input to many bioinformatics applications, including protein structure prediction and functional annotation. However, the optimal selection of sequences to obtain biologically informative MSAs for such purposes is poorly explored, and has traditionally been performed manually.

Results

We present Selection of Alignment by Maximal Mutual Information (SAMMI), an automated, sequence-based approach to objectively select an optimal MSA from a large set of alternatives sampled from a general sequence database search. The hypothesis of this approach is that the mutual information among MSA columns will be maximal for those MSAs that contain the most diverse set possible of the most structurally and functionally homogeneous protein sequences. SAMMI was tested to select MSAs for functional site residue prediction by analysis of conservation patterns on a set of 435 proteins obtained from protein-ligand (peptides, nucleic acids and small substrates) and protein-protein interaction databases. Availability and implementation: A freely accessible program, including source code, implementing SAMMI is available at https://github.com/nelsongil92/SAMMI.git.

Contact

andras.fiser@einstein.yu.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Molecular cloning, in-silico characterization and functional validation of monodehydroascorbate reductase gene in Eleusine coracana

Ascorbic acid is a ubiquitous water soluble antioxidant that plays a critical role in plant growth and environmental stress tolerance. It acts as a free radical scavenger as well as a source of reducing power for several cellular processes. Because of its pivotal role in regulating plant growth under optimal as well as sub-optimal conditions, it becomes obligatory for plants to maintain a pool of reduced ascorbic acid. Several cellular processes help in maintaining the reduced ascorbic acid pool, by regulating its synthesis and regeneration processes. Current study demonstrates that monodehydroascorbate reductase is an important enzyme responsible for maintaining the reduced ascorbate pool, by optimizing the recycling of oxidized ascorbate. Cloning and functional characterization of this important stress inducible gene is of great significance for its imperative use in plant stress management. Therefore, we have cloned and functionally validated the role of monodehydroascorbate reductase gene (mdar) from a drought tolerant variety of Eleusine coracana. The cloned Ecmdar gene comprises of 1437bp CDS, encoding a 478 amino acid long polypeptide. The active site analysis showed presence of conserved Tyr₃₄₈ residue, facilitating the catalytic activity in electron transfer mechanism. qPCR expression profiling of Ecmdar under stress indicated that it is an early responsive gene. The analysis of Ecmdar overexpressing Arabidopsis transgenic lines suggests that monodehydroascorbate reductase acts as a key stress regulator by modulating the activity of antioxidant enzymes to strengthen the ROS scavenging ability and maintains ROS homeostasis. Thus, it is evident that Ecmdar is an important gene for cellular homeostasis and its over-expression could be successfully used to strengthen stress tolerance in crop plants.

From structure to redox: The diverse functional roles of disulfides and implications in disease

This review provides a comprehensive overview of the functional roles of disulfide bonds and their relevance to human disease. The critical roles of disulfide bonds in protein structure stabilization and redox regulation of protein activity are addressed. Disulfide bonds are essential to the structural stability of many proteins within the secretory pathway and can exist as intramolecular or inter-domain disulfides. The proper formation of these bonds often relies on folding chaperones and oxidases such as members of the protein disulfide isomerase (PDI) family. Many of the PDI family members catalyze disulfide-bond formation, reduction, and isomerization through redox-active disulfides and perturbed PDI activity is characteristic of carcinomas and neurodegenerative diseases.  In addition to catalytic function in oxidoreductases, redox-active disulfides are also found on a diverse array of cellular proteins and act to regulate protein activity and localization in response to oxidative changes in the local environment. These redox-active disulfides are either dynamic intramolecular protein disulfides or mixed disulfides with small-molecule thiols generating glutathionylation and cysteinylation adducts. The oxidation and reduction of redox-active disulfides are mediated by cellular reactive oxygen species and activity of reductases, such as glutaredoxin and thioredoxin.  Dysregulation of cellular redox conditions and resulting changes in mixed disulfide formation are directly linked to diseases such as cardiovascular disease and Parkinson's disease.

Prediction of reversible disulfide based on features from local structural signatures

Background

Disulfide bonds are traditionally considered to play only structural roles. In recent years, increasing evidence suggests that the disulfide proteome is made up of structural disulfides and reversible disulfides. Unlike structural disulfides, reversible disulfides are usually of important functional roles and may serve as redox switches. Interestingly, only specific disulfide bonds are reversible while others are not. However, whether reversible disulfides can be predicted based on structural information remains largely unknown.

Methods

In this study, two datasets with both types of disulfides were compiled using independent approaches. By comparison of various features extracted from the local structural signatures, we identified several features that differ significantly between reversible and structural disulfides, including disulfide bond length, along with the number, amino acid composition, secondary structure and physical-chemical properties of surrounding amino acids. A SVM-based classifier was developed for predicting reversible disulfides.

Results

By 10-fold cross-validation, the model achieved accuracy of 0.750, sensitivity of 0.352, specificity of 0.953, MCC of 0.405 and AUC of 0.751 using the RevSS_PDB dataset. The robustness was further validated by using RevSS_RedoxDB as independent testing dataset. This model was applied to proteins with known structures in the PDB database. The results show that one third of the predicted reversible disulfide containing proteins are well-known redox enzymes, while the remaining are non-enzyme proteins. Given that reversible disulfides are frequently reported from functionally important non-enzyme proteins such as transcription factors, the predictions may provide valuable candidates of novel reversible disulfides for further experimental investigation.

Conclusions

This study provides the first comparative analysis between the reversible and the structural disulfides. Distinct features remarkably different between these two groups of disulfides were identified, and a SVM-based classifier for predicting reversible disulfides was developed accordingly. A web server named RevssPred can be accessed freely from: http://biocomputer.bio.cuhk.edu.hk/RevssPred.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3668-8) contains supplementary material, which is available to authorized users.

Heme binding and peroxidase activity of a secreted minicatalase

Microbial pathogens often require efficient and robust H₂ O₂ scavenger activities to survive in the presence of reactive oxygen species generated by inflammatory responses. In addition to catalases and peroxidases, enzymes known to scavenge H₂ O₂ , a novel class of secreted minicatalases is found in diderm bacteria. Here, we characterize the Helicobacter pylori (Hp) minicatalase: a monomeric hemoprotein with catalase core homology. Overexpression of Hp minicatalase rescued a catalase/peroxidase-deficient Escherichia coli phenotype under aerobic conditions and limited H₂ O₂ stress. The purified enzyme lacks catalase activity, but has strong (k_cat > 100 s^-1 ) H₂ O₂ -dependent peroxidase activity toward a variety of organic substrates. Our investigations into heme binding revealed that the heme cofactor is assembled in the periplasm to form the functional holoprotein. Furthermore, we observed the presence of a disulfide bond near the heme cavity of Hp minicatalase, which is conserved in secreted minicatalases and, therefore, may play a role in heme binding.

Investigating the Molecular Mechanisms Behind Uncharacterized Cysteine Losses from Prediction of Their Oxidation State

Cysteines are among the rarest amino acids in nature, and are both functionally and structurally very important for proteins. The ability of cysteines to form disulfide bonds is especially relevant, both for constraining the folded state of the protein and for performing enzymatic duties. But how does the variation record of human proteins reflect their functional importance and structural role, especially with regard to deleterious mutations? We created HUMCYS, a manually curated dataset of single amino acid variants that (1) have a known disease/neutral phenotypic outcome and (2) cause the loss of a cysteine, in order to investigate how mutated cysteines relate to structural aspects such as surface accessibility and cysteine oxidation state. We also have developed a sequence-based in silico cysteine oxidation predictor to overcome the scarcity of experimentally derived oxidation annotations, and applied it to extend our analysis to classes of proteins for which the experimental determination of their structure is technically challenging, such as transmembrane proteins. Our investigation shows that we can gain insights into the reason behind the outcome of cysteine losses in otherwise uncharacterized proteins, and we discuss the possible molecular mechanisms leading to deleterious phenotypes, such as the involvement of the mutated cysteine in a structurally or enzymatically relevant disulfide bond.

Prediction of redox-sensitive cysteines using sequential distance and other sequence-based features

Background

Reactive oxygen species can modify the structure and function of proteins and may also act as important signaling molecules in various cellular processes. Cysteine thiol groups of proteins are particularly susceptible to oxidation. Meanwhile, their reversible oxidation is of critical roles for redox regulation and signaling. Recently, several computational tools have been developed for predicting redox-sensitive cysteines; however, those methods either only focus on catalytic redox-sensitive cysteines in thiol oxidoreductases, or heavily depend on protein structural data, thus cannot be widely used.

Results

In this study, we analyzed various sequence-based features potentially related to cysteine redox-sensitivity, and identified three types of features for efficient computational prediction of redox-sensitive cysteines. These features are: sequential distance to the nearby cysteines, PSSM profile and predicted secondary structure of flanking residues. After further feature selection using SVM-RFE, we developed Redox-Sensitive Cysteine Predictor (RSCP), a SVM based classifier for redox-sensitive cysteine prediction using primary sequence only. Using 10-fold cross-validation on RSC758 dataset, the accuracy, sensitivity, specificity, MCC and AUC were estimated as 0.679, 0.602, 0.756, 0.362 and 0.727, respectively. When evaluated using 10-fold cross-validation with BALOSCTdb dataset which has structure information, the model achieved performance comparable to current structure-based method. Further validation using an independent dataset indicates it is robust and of relatively better accuracy for predicting redox-sensitive cysteines from non-enzyme proteins.

Conclusions

In this study, we developed a sequence-based classifier for predicting redox-sensitive cysteines. The major advantage of this method is that it does not rely on protein structure data, which ensures more extensive application compared to other current implementations. Accurate prediction of redox-sensitive cysteines not only enhances our understanding about the redox sensitivity of cysteine, it may also complement the proteomics approach and facilitate further experimental investigation of important redox-sensitive cysteines.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1185-4) contains supplementary material, which is available to authorized users.

Computational Design of Hypothetical New Peptides Based on a Cyclotide Scaffold as HIV gp120 Inhibitor

Cyclotides are a family of triple disulfide cyclic peptides with exceptional resistance to thermal/chemical denaturation and enzymatic degradation. Several cyclotides have been shown to possess anti-HIV activity, including kalata B1 (KB1). However, the use of cyclotides as anti-HIV therapies remains limited due to the high toxicity in normal cells. Therefore, grafting anti-HIV epitopes onto a cyclotide might be a promising approach for reducing toxicity and simultaneously improving anti-HIV activity. Viral envelope glycoprotein gp120 is required for entry of HIV into CD4+ T cells. However, due to a high degree of variability and physical shielding, the design of drugs targeting gp120 remains challenging. We created a computational protocol in which molecular modeling techniques were combined with a genetic algorithm (GA) to automate the design of new cyclotides with improved binding to HIV gp120. We found that the group of modified cyclotides has better binding scores (23.1%) compared to the KB1. By using molecular dynamic (MD) simulation as a post filter for the final candidates, we identified two novel cyclotides, GA763 and GA190, which exhibited better interaction energies (36.6% and 22.8%, respectively) when binding to gp120 compared to KB1. This computational design represents an alternative tool for modifying peptides, including cyclotides and other stable peptides, as therapeutic agents before the synthesis process.

Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins

MOTIVATION

Cysteine-rich proteins cover many important families in nature but there are currently no methods specifically designed for modeling the structure of these proteins. The accuracy of disulfide connectivity pattern prediction, particularly for the proteins of higher-order connections, e.g., >3 bonds, is too low to effectively assist structure assembly simulations.

RESULTS

We propose a new hierarchical order reduction protocol called Cyscon for disulfide-bonding prediction. The most confident disulfide bonds are first identified and bonding prediction is then focused on the remaining cysteine residues based on SVR training. Compared with purely machine learning-based approaches, Cyscon improved the average accuracy of connectivity pattern prediction by 21.9%. For proteins with more than 5 disulfide bonds, Cyscon improved the accuracy by 585% on the benchmark set of PDBCYS. When applied to 158 non-redundant cysteine-rich proteins, Cyscon predictions helped increase (or decrease) the TM-score (or RMSD) of the ab initio QUARK modeling by 12.1% (or 14.4%). This result demonstrates a new avenue to improve the ab initio structure modeling for cysteine-rich proteins.

AVAILABILITY AND IMPLEMENTATION

http://www.csbio.sjtu.edu.cn/bioinf/Cyscon/

CONTACT

zhng@umich.edu or hbshen@sjtu.edu.cn.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An Evolutionary View on Disulfide Bond Connectivities Prediction Using Phylogenetic Trees and a Simple Cysteine Mutation Model

Disulfide bonds are crucial for many structural and functional aspects of proteins. They have a stabilizing role during folding, can regulate enzymatic activity and can trigger allosteric changes in the protein structure. Moreover, knowledge of the topology of the disulfide connectivity can be relevant in genomic annotation tasks and can provide long range constraints for ab-initio protein structure predictors. In this paper we describe PhyloCys, a novel unsupervised predictor of disulfide bond connectivity from known cysteine oxidation states. For each query protein, PhyloCys retrieves and aligns homologs with HHblits and builds a phylogenetic tree using ClustalW. A simplified model of cysteine co-evolution is then applied to the tree in order to hypothesize the presence of oxidized cysteines in the inner nodes of the tree, which represent ancestral protein sequences. The tree is then traversed from the leaves to the root and the putative disulfide connectivity is inferred by observing repeated patterns of tandem mutations between a sequence and its ancestors. A final correction is applied using the Edmonds-Gabow maximum weight perfect matching algorithm. The evolutionary approach applied in PhyloCys results in disulfide bond predictions equivalent to Sephiroth, another approach that takes whole sequence information into account, and is 26-29% better than state of the art methods based on cysteine covariance patterns in multiple sequence alignments, while requiring one order of magnitude fewer homologous sequences (10(3) instead of 10(4)), thus extending its range of applicability. The software described in this article and the datasets used are available at http://ibsquare.be/phylocys.

Disulfide Connectivity Prediction Based on Modelled Protein 3D Structural Information and Random Forest Regression

Disulfide connectivity is an important protein structural characteristic. Accurately predicting disulfide connectivity solely from protein sequence helps to improve the intrinsic understanding of protein structure and function, especially in the post-genome era where large volume of sequenced proteins without being functional annotated is quickly accumulated. In this study, a new feature extracted from the predicted protein 3D structural information is proposed and integrated with traditional features to form discriminative features. Based on the extracted features, a random forest regression model is performed to predict protein disulfide connectivity. We compare the proposed method with popular existing predictors by performing both cross-validation and independent validation tests on benchmark datasets. The experimental results demonstrate the superiority of the proposed method over existing predictors. We believe the superiority of the proposed method benefits from both the good discriminative capability of the newly developed features and the powerful modelling capability of the random forest. The web server implementation, called TargetDisulfide, and the benchmark datasets are freely available at: http://csbio.njust.edu.cn/bioinf/TargetDisulfide for academic use.

A word of caution about biological inference - Revisiting cysteine covalent state predictions

•

High prediction accuracy is often believed to validate implicit biological assumptions.

•

Cys redox state predictions assume a local sequence environmental effect.

•

Removing local sequence signals did not reduce prediction accuracy.

•

Cys redox state predictions apparently correlate with subcellular localization.

•

Subcellular localization depends on global sequence composition.

The success of methods for predicting the redox state of cysteine residues from the sequence environment seemed to validate the basic assumption that this state is mainly determined locally. However, the accuracy of predictions on randomized sequences or of non-cysteine residues remained high, suggesting that these predictions rather capture global features of proteins such as subcellular localization, which depends on composition. This illustrates that even high prediction accuracy is insufficient to validate implicit assumptions about a biological phenomenon. Correctly identifying the relevant underlying biochemical reasons for the success of a method is essential to gain proper biological insights and develop more accurate and novel bioinformatics tools.

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

Background

Recently, 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.

Results

In 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.

Conclusions

In 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.

CD-HIT: accelerated for clustering the next-generation sequencing data

Summary: CD-HIT is a widely used program for clustering biological sequences to reduce sequence redundancy and improve the performance of other sequence analyses. In response to the rapid increase in the amount of sequencing data produced by the next-generation sequencing technologies, we have developed a new CD-HIT program accelerated with a novel parallelization strategy and some other techniques to allow efficient clustering of such datasets. Our tests demonstrated very good speedup derived from the parallelization for up to ∼24 cores and a quasi-linear speedup for up to ∼8 cores. The enhanced CD-HIT is capable of handling very large datasets in much shorter time than previous versions.

Availability:http://cd-hit.org.

Contact:liwz@sdsc.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Identification, cDNA cloning and heterologous expression of a hyaluronidase from the tarantula Brachypelma vagans venom

Hyaluronidases (Hyal) present in the venom of poisonous animals have been considered as "spreading factors" that facilitate a fast penetration of the venom in the prey. We have found that hyaluronidase from the tarantula Brachypelma vagans venom (BvHyal) displays a substrate-specific Hyal activity against hyaluronan. By using a combined strategy based on peptide sequencing and RT-PCR, we have cloned a BvHyal cDNA. Active recombinant BvHyal was efficiently expressed in a baculovirus system in insect cell.

Unexpected diversity in Shisa-like proteins suggests the importance of their roles as transmembrane adaptors

The Shisa family of single-transmembrane proteins is characterized by an N-terminal cysteine-rich domain and a proline-rich C-terminal region. Its founding member, Xenopus Shisa, promotes head development by antagonizing Wnt and FGF signaling. Recently, a mouse brain-specific Shisa protein CKAMP44 (Shisa9) was shown to play an important role in AMPA receptor desensitization. We used sequence similarity searches against protein, genome and EST databases to study the evolutionary origin and phylogenetic distribution of Shisa homologs. In addition to nine Shisa subfamilies in vertebrates, we detected distantly related Shisa homologs that possess an N-terminal domain with six conserved cysteines. These Shisa-like proteins include FAM159 and KIAA1644 mainly from vertebrates, and members from various bilaterian invertebrates and Porifera, suggesting their presence in the last common ancestor of Metazoa. Shisa-like genes have undergone large expansions in Branchiostoma floridae and Saccoglossus kowalevskii, and appear to have been lost in certain insects. Pattern-based searches against eukaryotic proteomes also uncovered several other families of predicted single-transmembrane proteins with a similar cysteine-rich domain. We refer to these proteins (Shisa/Shisa-like, WBP1/VOPP1, CX, DUF2650, TMEM92, and CYYR1) as STMC6 proteins (single-transmembrane proteins with conserved 6 cysteines). STMC6 genes are widespread in Metazoa, with the human genome containing 17 members. Frequent occurrences of PY motifs in STMC6 proteins suggest that most of them could interact with WW-domain-containing proteins, such as the NEDD4 family E3 ubiquitin ligases, and could play critical roles in protein degradation and sorting. STMC6 proteins are likely transmembrane adaptors that regulate membrane proteins such as cell surface receptors.

Improving the accuracy of predicting disulfide connectivity by feature selection

Disulfide bonds are primary covalent cross-links formed between two cysteine residues in the same or different protein polypeptide chains, which play important roles in the folding and stability of proteins. However, computational prediction of disulfide connectivity directly from protein primary sequences is challenging due to the nonlocal nature of disulfide bonds in the context of sequences, and the number of possible disulfide patterns grows exponentially when the number of cysteine residues increases. In the previous studies, disulfide connectivity prediction was usually performed in high-dimensional feature space, which can cause a variety of problems in statistical learning, such as the dimension disaster, overfitting, and feature redundancy. In this study, we propose an efficient feature selection technique for analyzing the importance of each feature component. On the basis of this approach, we selected the most important features for predicting the connectivity pattern of intra-chain disulfide bonds. Our results have shown that the high-dimensional features contain redundant information, and the prediction performance can be further improved when these high-dimensional features are reduced to a lower but more compact dimensional space. Our results also indicate that the global protein features contribute little to the formation and prediction of disulfide bonds, while the local sequential and structural information play important roles. All these findings provide important insights for structural studies of disulfide-rich proteins.

Disulfide bond acquisition through eukaryotic protein evolution

Disulfide bonds play critical roles in protein stability and function. They are generally considered to be strongly conserved among species. Although there is compelling evidence in the literature for this conservation on a case-by-case basis, comparative genomic analyses of disulfide conservation have in the past been limited. By analyzing the conservation of all structurally validated disulfide bonds from the Protein Data Bank across 29 completely sequenced eukaryotic genomes, we observe elevated conservation of disulfide-bonded cysteines (half-cystines) compared with unpaired cysteines and other amino acids. Remarkably, half-cystines are even more conserved than tryptophan--the most conserved amino acid. Overall, once disulfide bonds are acquired in proteins, they are rarely lost. Moreover, the acquisition of disulfide bonds shows a strong positive correlation (R(2) = 0.74) with organismal complexity. Although the correlation weakens (R(2) = 0.59) when yeast is excluded from the analysis, this trend is still apparent when compared with the slightly negative correlation of unpaired cysteine acquisition with organismal complexity. The accrual of disulfide bonds is likely to reflect the demand for greater sophistication in protein function in complex species. Our findings provide further support for the increasing usage of cysteines in modern proteomes and suggest that there has been positive selection for disulfide bonds through eukaryotic evolution. Finally, we show that the acquisition of the functionally relevant disulfide bond in domain 2 of the CD4 protein occurred independently in primates and rodents.

DBCP: a web server for disulfide bonding connectivity pattern prediction without the prior knowledge of the bonding state of cysteines

The proper prediction of the location of disulfide bridges is efficient in helping to solve the protein folding problem. Most of the previous works on the prediction of disulfide connectivity pattern use the prior knowledge of the bonding state of cysteines. The DBCP web server provides prediction of disulfide bonding connectivity pattern without the prior knowledge of the bonding state of cysteines. The method used in this server improves the accuracy of disulfide connectivity pattern prediction (Q_p) over the previous studies reported in the literature. This DBCP server can be accessed at http://120.107.8.16/dbcp or http://140.120.14.136/dbcp.

Regional covariation and its application for predicting protein contact patches

Correlated mutation analysis (CMA) is an effective approach for predicting functional and structural residue interactions from multiple sequence alignments (MSAs) of proteins. As nearby residues may also play a role in a given functional interaction, we were interested in seeing whether covarying sites were clustered, and whether this could be used to enhance the predictive power of CMA. A large-scale search for coevolving regions within protein domains revealed that if two sites in a MSA covary, then neighboring sites in the alignment also typically covary, resulting in clusters of covarying residues. The program PatchD(http://www.uhnres.utoronto.ca/labs/tillier/) was developed to measure the covariation between disconnected sequence clusters to reveal patch covariation. Patches that exhibit strong covariation identify multiple residues that are generally nearby in the protein structure, suggesting that the detection of covarying patches can be used in conjunction with traditional CMA approaches to reveal functional interaction partners.

Towards accurate residue-residue hydrophobic contact prediction for alpha helical proteins via integer linear optimization

A new optimization-based method is presented to predict the hydrophobic residue contacts in alpha-helical proteins. The proposed approach uses a high resolution distance dependent force field to calculate the interaction energy between different residues of a protein. The formulation predicts the hydrophobic contacts by minimizing the sum of these contact energies. These residue contacts are highly useful in narrowing down the conformational space searched by protein structure prediction algorithms. The proposed algorithm also offers the algorithmic advantage of producing a rank ordered list of the best contact sets. This model was tested on four independent alpha-helical protein test sets and was found to perform very well. The average accuracy of the predictions (separated by at least six residues) obtained using the presented method was approximately 66% for single domain proteins. The average true positive and false positive distances were also calculated for each protein test set and they are 8.87 and 14.67 A, respectively.