Dataset Information

Structural bioinformatics analysis of free cysteines in protein environments.

ABSTRACT: Cysteine has been considered as a "hydrophilic" amino acid because of its pK a and its ability to form (weak) hydrogen bonds. However, cysteines are found mostly in hydrophobic environments, either in S-S (disulphide) form or in free cysteine form. When free cysteines are found on the surface of proteins, they are often involved in catalytic residues, as in cysteine proteases, P-loop phosphatases, etc. Additionally, a unique property of cysteines is that their side-chain volume is different from all other amino acids. This study is focused on the discrimination between structural versus active free cysteines based on a local environment analysis which does not appear to have been attempted previously. We have demonstrated the corresponding structural positions associated with free cysteines in their three-dimensional localization environment. We examined protein samples including nine, sequenced, coronavirus proteases and cysteine-rich non-membrane proteins. Our present study shows that the sequential environments of free cysteines of coronavirus proteases are rather hydrophobic and that the free cysteines of non-membrane proteases have a higher amount of contacts to hydrophobic residues and lower amount of contacts to polar or charged residues.

SUBMITTER: Ho SL

PROVIDER: S-EPMC7102755 | biostudies-literature | 2009 Mar

REPOSITORIES: biostudies-literature

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Structural bioinformatics analysis of free cysteines in protein environments.

Ho Sheau Ling SL Wang Andrew H-J AH

Journal of the Taiwan Institute of Chemical Engineers 20090314 2

Cysteine has been considered as a "hydrophilic" amino acid because of its pK a and its ability to form (weak) hydrogen bonds. However, cysteines are found mostly in hydrophobic environments, either in S-S (disulphide) form or in free cysteine form. When free cysteines are found on the surface of proteins, they are often involved in catalytic residues, as in cysteine proteases, P-loop phosphatases, etc. Additionally, a unique property of cysteines is that their side-chain ...[more]

PMID: 32288881

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Project description:Diverse plant pathogens export effector proteins to reprogram host cells. One of the most challenging goals in the molecular plant-microbe field is to functionally characterize the complex repertoires of effectors secreted by these pathogens. For bacterial pathogens, the predominant class of effectors is delivered to host cells by Type III secretion. For oomycetes, the predominant class of effectors is defined by a signal peptide that mediates secretion from the oomycete and a conserved RxLR motif. Downy mildew pathogens and Phytophthora species maintain hundreds of candidate RxLR effector genes in their genomes. Although no primary sequence similarity is evident between bacterial Type III effectors (T3Es) and oomycete RXLR effectors, some bacterial and oomycete effectors have convergently evolved to target the same host proteins. Such effectors might have evolved domains that are functionally similar but sequence-unrelated. We reasoned that alignment-free bioinformatics approaches could be useful to identify structural similarities between bacterial and oomycete effectors. To test this approach, we used partial least squares regression, alignment-free bioinformatics methods to identify effector proteins from the genome of the oomycete Hyaloperonospora arabidopsidis that are similar to the well-studied AvrE1 effector from Pseudomonas syringae. This approach identified five RxLR proteins with putative structural similarity to AvrE1. We focused on one, HaRxL23, because it is an experimentally validated effector and it is conserved between distantly related oomycetes. Several experiments indicate that HaRxL23 is functionally similar to AvrE1, including the ability to partially rescue an AvrE1 loss-of-function mutant. This study provides an example of how an alignment-free bioinformatics approach can identify functionally similar effector proteins in the absence of primary sequence similarity. This approach could be useful to identify effectors that have convergently evolved regardless of whether the shared host target is known.

Project description:BackgroundLarge-scale datasets of protein structures and sequences are becoming ubiquitous in many domains of biological research. Experimental approaches and computational modelling methods are generating biological data at an unprecedented rate. The detailed analysis of structure-sequence relationships is critical to unveil governing principles of protein folding, stability and function. Computational protein design (CPD) has emerged as an important structure-based approach to engineer proteins for novel functions. Generally, CPD workflows rely on the generation of large numbers of structural models to search for the optimal structure-sequence configurations. As such, an important step of the CPD process is the selection of a small subset of sequences to be experimentally characterized. Given the limitations of current CPD scoring functions, multi-step design protocols and elaborated analysis of the decoy populations have become essential for the selection of sequences for experimental characterization and the success of CPD strategies.ResultsHere, we present the rstoolbox, a Python library for the analysis of large-scale structural data tailored for CPD applications. rstoolbox is oriented towards both CPD software users and developers, being easily integrated in analysis workflows. For users, it offers the ability to profile and select decoy sets, which may guide multi-step design protocols or for follow-up experimental characterization. rstoolbox provides intuitive solutions for the visualization of large sequence/structure datasets (e.g. logo plots and heatmaps) and facilitates the analysis of experimental data obtained through traditional biochemical techniques (e.g. circular dichroism and surface plasmon resonance) and high-throughput sequencing. For CPD software developers, it provides a framework to easily benchmark and compare different CPD approaches. Here, we showcase the rstoolbox in both types of applications.Conclusionsrstoolbox is a library for the evaluation of protein structures datasets tailored for CPD data. It provides interactive access through seamless integration with IPython, while still being suitable for high-performance computing. In addition to its functionalities for data analysis and graphical representation, the inclusion of rstoolbox in protein design pipelines will allow to easily standardize the selection of design candidates, as well as, to improve the overall reproducibility and robustness of CPD selection processes.

Dataset Information

Structural bioinformatics analysis of free cysteines in protein environments.

Publications

Structural bioinformatics analysis of free cysteines in protein environments.

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