Biochemistry & Physiology: Open Access
Open Access

Cysteine synthase; Protein–peptide interactions; Protein structures

Introduction

Multifunctional proteins, also known as moonlighting proteins (MP), exhibit functional diversity in a condition-dependent manner or bind to another protein and alter its activity. This new paradigm is supported by increasing experimental evidence that many proteins are multifunctional in nature and frequently perform these functions, which are not related to a native function, by the same domain of the protein. In the principal type, changes in cell restriction, articulation in various cell types, changes in oligomerization, and substance changes, for example, post-translational adjustments modify its functions. In the subsequent sort, a multifunctional MP like CS balances the utilitarian properties of different proteins by restricting to them progressively in a setting subordinate manner [1-3]. By framing transient edifices with proteins engaged with various cell processes, MPs control the elements of those cycles.

To investigate the functional space of proteins, mapping only MPs is insufficient. To expand the functional space of proteins, it is necessary to map the moonlighting space of each MP. If we can map the extended functional space of each MP, we will be able to use proteins to carry out tasks that were previously unavailable. When a protein moonlights through binding to other proteins, altering its oligomeric state or cellular localization, or any other means, we refer to its extended functional space as “moonlighting space [4]. By mapping the entire protein–protein interaction space, or “interactome,” of CS, we hypothesized that one could expand both the EMS and the moonlighting space of CS. As a result, by mapping the “interactome” of CS, we were able to map the extreme multifunctional biochemistry of CS. To extract CS-binding proteins from the NCBI database, we have developed an “in-house” computational protocol.

Methods

All of the known CS binders have an ILE at the C-terminus, but the remaining 10–15 residues of CS binding proteins share very little or no sequence information. As a result, we developed a three-step method for locating CS binders. First, we created a search dataset called I-Proteins from all of the proteins with terminal ILE from PubMed. Next, we extracted “CS binding motifs” consisting of the terminal ILE and having a length of 5–7 residues using the multiple sequence alignment (MSA) technique. We used the motifs to match the C-terminals of proteins in the I-protein database in the final step. We designed these three search protocols to reduce the number of false positives as much as possible because our goal was to validate all of his using a minimal proteinpeptide interaction system. Conventional algorithm-based methods may not be able to predict with greater accuracy due to the lack of additional information on “CS-Binding motifs” in non-SAT proteins or non-SAT CS binders. Additionally, one is unable to use artificial intelligence-based methods due to the lack of information on the binding motif and the binding partners [5-8]. As a result, in order to increase the precision of our prediction, we devised the approach outlined here.

However, none of the I-Proteins that were compared to any of the four templates shared the same last five C-terminal residues. The consistent state dynamic tests for PlCS, LsCS, and SaCS were completed utilizing the corrosive ninhydrin examine for cysteine [9]. The arrangement of cysteine was observed at 560 nm (the annihilation coefficient of cysteine is 28,000 M−1 cm−1). All reactions were carried out at 30 °C in 0.1 M HEPES, pH 7.0, in a volume of 150 L, and the progression of the reaction was observed at 560 nm. OAS concentrations ranged from 0.1 mM to 10 mM, with the Na2S concentration remaining constant at 3 mM. The standard curve, which was estimated using known cysteine concentrations, was used to calculate the amount of produced cysteine.

Discussion

We tried to come up with a “protein-specific” strategy for looking into the CS multi-functionality space. We were able to investigate CS’s EMS and subsequent moonlighting space by mapping its interacting space, which is tightly connected to its protein–protein interaction space. This is, to the best of our knowledge, the first study to concentrate on developing prediction and validation methods for mapping a single protein’s EMS and moonlighting space. It is common knowledge that when CS binds to its target proteins, it alters the stability and catalytic properties of the acetyltransferase activity of SAT, activates the toxin activity of nuclease, and modifies CymR’s DNA binding properties. We employed an integrated strategy that began with the development of computational methods for examining the EMS of CS. We used analytical and structural techniques to validate and comprehend the biochemistry of the extreme multifunctional domain of CS because the mapping of EMS is the first step toward mapping the moonlighting space subset. On the other hand, the hetero-protein complex that is formed between CS and CS-binder is sensitive to the levels of upstream metabolites such as sulfate, sulfide, and OAS, and the complex dissociates as the levels of these metabolites rise [10]. As a result, the abundance of CS in bacteria may guarantee that the process of producing cysteine is unaffected. Still, any changes in the concentration of upstream metabolites like OAS, sulfide, and sulfate will dynamically control how CS interacts with its binding partners and, consequently, how CS’s biochemistry in bacteria moonlights. As a result, comprehensive mapping of CS’s moonlighting space will improve our comprehension of its global regulatory function in plant and bacterial physiology.

Declaration of Competing Interest

The authors declare that they have no competing interest

Acknowledgment

None

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