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

Harnessing the potential of bacterial oxidative folding to aid protein production

Protein folding is central to both biological function and recombinant protein production. In bacterial expression systems, which are easy to use and offer high protein yields, production of the protein of interest in its native fold can be hampered by the limitations of endogenous posttranslational modification systems. Disulfide bond formation, entailing the covalent linkage of proximal cysteine amino acids, is a fundamental posttranslational modification reaction that often underpins protein stability, especially in extracytoplasmic environments. When these bonds are not formed correctly, the yield and activity of the resultant protein are dramatically decreased. Although the mechanism of oxidative protein folding is well understood, unwanted or incorrect disulfide bond formation often presents a stumbling block for the expression of cysteine-containing proteins in bacteria. It is therefore important to consider the biochemistry of prokaryotic disulfide bond formation systems in the context of protein production, in order to take advantage of the full potential of such pathways in biotechnology applications. Here, we provide a critical overview of the use of bacterial oxidative folding in protein production so far, and propose a practical decision-making workflow for exploiting disulfide bond formation for the expression of any given protein of interest.

Rational Design of Disulfide Bonds for Enhancing the Thermostability of the 1,4-α-Glucan Branching Enzyme from Geobacillus thermoglucosidans STB02

Disulfide bonds play crucial roles in thermostabilization, recognition, or activation of proteins. They are vital in maintaining the respective conformations of globular structures, thereby enhancing thermostability. Bioinformatic approaches provide practical strategies to build disulfide bonds based on structural information. We constructed nine mutants by rational analysis of the 1,4-α-glucan branching enzyme (EC 2.4.1.18) from Geobacillus thermoglucosidans STB02, which catalyzes the synthesis of α-1,6-glucosidic bonds by acting on α-(1,4) and/or α-(1,6) glucosidic linkages. Four of the mutations enhanced thermostability, and five of them had adverse or negligible effects on stability. Circular dichroism spectra and intrinsic fluorescence analysis showed that introducing disulfide bonds might only affect secondary structures. The results also demonstrated that the distances of Cα carbons and thiol groups, as well as the sequence between the two cysteines, need to be considered when designing disulfide bonds.

SVM-dependent pairwise HMM: an application to protein pairwise alignments

Motivation

Methods able to provide reliable protein alignments are crucial for many bioinformatics applications. In the last years many different algorithms have been developed and various kinds of information, from sequence conservation to secondary structure, have been used to improve the alignment performances. This is especially relevant for proteins with highly divergent sequences. However, recent works suggest that different features may have different importance in diverse protein classes and it would be an advantage to have more customizable approaches, capable to deal with different alignment definitions.

Results

Here we present Rigapollo, a highly flexible pairwise alignment method based on a pairwise HMM-SVM that can use any type of information to build alignments. Rigapollo lets the user decide the optimal features to align their protein class of interest. It outperforms current state of the art methods on two well-known benchmark datasets when aligning highly divergent sequences.

Availability and implementation

A Python implementation of the algorithm is available at http://ibsquare.be/rigapollo.

Contact

wim.vranken@vub.be.

Supplementary information

Supplementary data are available at Bioinformatics online.

Chemistry and Enzymology of Disulfide Cross-Linking in Proteins

Cysteine thiols are among the most reactive functional groups in proteins, and their pairing in disulfide linkages is a common post-translational modification in proteins entering the secretory pathway. This modest amino acid alteration, the mere removal of a pair of hydrogen atoms from juxtaposed cysteine residues, contrasts with the substantial changes that characterize most other post-translational reactions. However, the wide variety of proteins that contain disulfides, the profound impact of cross-linking on the behavior of the protein polymer, the numerous and diverse players in intracellular pathways for disulfide formation, and the distinct biological settings in which disulfide bond formation can take place belie the simplicity of the process. Here we lay the groundwork for appreciating the mechanisms and consequences of disulfide bond formation in vivo by reviewing chemical principles underlying cysteine pairing and oxidation. We then show how enzymes tune redox-active cofactors and recruit oxidants to improve the specificity and efficiency of disulfide formation. Finally, we discuss disulfide bond formation in a cellular context and identify important principles that contribute to productive thiol oxidation in complex, crowded, dynamic environments.

Observation selection bias in contact prediction and its implications for structural bioinformatics

Next Generation Sequencing is dramatically increasing the number of known protein sequences, with related experimentally determined protein structures lagging behind. Structural bioinformatics is attempting to close this gap by developing approaches that predict structure-level characteristics for uncharacterized protein sequences, with most of the developed methods relying heavily on evolutionary information collected from homologous sequences. Here we show that there is a substantial observational selection bias in this approach: the predictions are validated on proteins with known structures from the PDB, but exactly for those proteins significantly more homologs are available compared to less studied sequences randomly extracted from Uniprot. Structural bioinformatics methods that were developed this way are thus likely to have over-estimated performances; we demonstrate this for two contact prediction methods, where performances drop up to 60% when taking into account a more realistic amount of evolutionary information. We provide a bias-free dataset for the validation for contact prediction methods called NOUMENON.

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.

Soft Computing Methods for Disulfide Connectivity Prediction

The problem of protein structure prediction (PSP) is one of the main challenges in structural bioinformatics. To tackle this problem, PSP can be divided into several subproblems. One of these subproblems is the prediction of disulfide bonds. The disulfide connectivity prediction problem consists in identifying which nonadjacent cysteines would be cross-linked from all possible candidates. Determining the disulfide bond connectivity between the cysteines of a protein is desirable as a previous step of the 3D PSP, as the protein conformational search space is highly reduced. The most representative soft computing approaches for the disulfide bonds connectivity prediction problem of the last decade are summarized in this paper. Certain aspects, such as the different methodologies based on soft computing approaches (artificial neural network or support vector machine) or features of the algorithms, are used for the classification of these methods.