Development of tools and database for analysis of metal binding sites in protein

A Comprehensive Review of Computation-Based Metal-Binding Prediction Approaches at the Residue Level

Clear evidence has shown that metal ions strongly connect and delicately tune the dynamic homeostasis in living bodies. They have been proved to be associated with protein structure, stability, regulation, and function. Even small changes in the concentration of metal ions can shift their effects from natural beneficial functions to harmful. This leads to degenerative diseases, malignant tumors, and cancers. Accurate characterizations and predictions of metalloproteins at the residue level promise informative clues to the investigation of intrinsic mechanisms of protein-metal ion interactions. Compared to biophysical or biochemical wet-lab technologies, computational methods provide open web interfaces of high-resolution databases and high-throughput predictors for efficient investigation of metal-binding residues. This review surveys and details 18 public databases of metal-protein binding. We collect a comprehensive set of 44 computation-based methods and classify them into four categories, namely, learning-, docking-, template-, and meta-based methods. We analyze the benchmark datasets, assessment criteria, feature construction, and algorithms. We also compare several methods on two benchmark testing datasets and include a discussion about currently publicly available predictive tools. Finally, we summarize the challenges and underlying limitations of the current studies and propose several prospective directions concerning the future development of the related databases and methods.

Ab initio coordination chemistry for nickel chelation motifs

Chelation therapy is one of the most appreciated methods in the treatment of metal induced disease predisposition. Coordination chemistry provides a way to understand metal association in biological structures. In this work we have implemented coordination chemistry to study nickel coordination due to its high impact in industrial usage and thereby health consequences. This paper reports the analysis of nickel coordination from a large dataset of nickel bound structures and sequences. Coordination patterns predicted from the structures are reported in terms of donors, chelate length, coordination number, chelate geometry, structural fold and architecture. The analysis revealed histidine as the most favored residue in nickel coordination. The most common chelates identified were histidine based namely HHH, HDH, HEH and HH spaced at specific intervals. Though a maximum coordination number of 8 was observed, the presence of a single protein donor was noted to be mandatory in nickel coordination. The coordination pattern did not reveal any specific fold, nevertheless we report preferable residue spacing for specific structural architecture. In contrast, the analysis of nickel binding proteins from bacterial and archeal species revealed no common coordination patterns. Nickel binding sequence motifs were noted to be organism specific and protein class specific. As a result we identified about 13 signatures derived from 13 classes of nickel binding proteins. The specifications on nickel coordination presented in this paper will prove beneficial for developing better chelation strategies.

An integrative computational model for large-scale identification of metalloproteins in microbial genomes: a focus on iron-sulfur cluster proteins

Metalloproteins represent a ubiquitous group of molecules which are crucial to the survival of all living organisms. While several metal-binding motifs have been defined, it remains challenging to confidently identify metalloproteins from primary protein sequences using computational approaches alone. Here, we describe a comprehensive strategy based on a machine learning approach to design and assess a penalized generalized linear model. We used this strategy to detect members of the iron-sulfur cluster protein family. A new category of descriptors, whose profile is based on profile hidden Markov models, encoding structural information was combined with public descriptors into a linear model. The model was trained and tested on distinct datasets composed of well-characterized iron-sulfur protein sequences, and the resulting model provided higher sensitivity compared to a motif-based approach, while maintaining a good level of specificity. Analysis of this linear model allows us to detect and quantify the contribution of each descriptor, providing us with a better understanding of this complex protein family along with valuable indications for further experimental characterization. Two newly-identified proteins, YhcC and YdiJ, were functionally validated as genuine iron-sulfur proteins, confirming the prediction. The computational model was then applied to over 550 prokaryotic genomes to screen for iron-sulfur proteomes; the results are publicly available at: . This study represents a proof-of-concept for the application of a penalized linear model to identify metalloprotein superfamilies on a large-scale. The application employed here, screening for iron-sulfur proteomes, provides new candidates for further biochemical and structural analysis as well as new resources for an extensive exploration of iron-sulfuromes in the microbial world.

A novel metal transporter mediating manganese export (MntX) regulates the Mn to Fe intracellular ratio and Neisseria meningitidis virulence

Neisseria meningitidis (Nm) and N. gonorrhoeae (Ng) are adapted to different environments within their human host. If the basis of this difference has not yet been fully understood, previous studies (including our own data) have reported that, unlike Ng, Nm tolerates high manganese concentrations. As transition metals are essential regulators of cell growth and host pathogen interactions, we aimed to address mechanisms of Nm Mn²⁺ tolerance and its pathogenic consequences. Using bioinformatics, gene deletion and heterologous expression we identified a conserved bacterial manganese resistance factor MntX (formerly YebN). The predicted structure suggests that MntX represents a new family of transporters exporting Mn. In the Neisseria genus, this exporter is present and functional in all Nm isolates but it is mutated in a majority of Ng strains and commonly absent in nonpathogenic species. In Nm, Mn²⁺ export via MntX regulates the intracellular Mn/Fe ratio and protects against manganese toxicity that is exacerbated in low iron conditions. MntX is also important for N. meningitidis to resist killing by human serum and for survival in mice blood during septicemia. The present work thus points to new clues about Mn homeostasis, its interplay with Fe metabolism and the influence on N. meningitidis physiology and pathogenicity.

CoMFA and CoMSIA studies on 5-hydroxyindole-3-carboxylate derivatives as 5-lipoxygenase inhibitors: generation of homology model and docking studies

In this study, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of 2-substituted 5-hydroxyindole-3-carboxylate derivatives as potent 5-LOX inhibitors with IC(50) values ranging from 0.031 to 13.4 μM. Two datasets of same molecules were prepared with two different partial atomic charges; one with Gasteiger-Huckel and another with the ESPFIT charges obtained from the gaussian package. CoMFA and CoMSIA models were generated for both the datasets and the results were analysed. With regard to the non-cross validated r(2) values (r(ncv)(2)) and cross-validated q(2) values (q(cv)(2)) of the resulting QSAR models, the dataset with ESPFIT charges yielded higher values; hence it was further used in the study. The CoMFA and CoMSIA models have been further validated for their stability and robustness using group validation and bootstrapping techniques and for their predictive abilities using an external test set of ten compounds. The predictive power of the CoMSIA model was higher than the CoMFA model, the high predictive r(2) values of the test set reveals that the models prove to be useful tools for activity prediction of newly designed 5-LOX inhibitors. The ESPFIT-derived charges yielded better models than those based on charges calculated from Gasteiger-Huckel charges. We generated a homology model for human 5-LOX and identified the key residues at the binding site. The 3D-QSAR models were compared with the interactions at the active site to further elucidate the accuracy of the models. The data generated from 3D-QSAR study was used to design potential 5-LOX inhibitors.