Zincbindpredict-Prediction of Zinc Binding Sites in Proteins

Databases of ligand-binding pockets and protein-ligand interactions

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

PinMyMetal: A hybrid learning system to accurately model metal binding sites in macromolecules

Metal ions are vital components in many proteins for the inference and engineering of protein function, with coordination complexity linked to structural (4-residue predominate), catalytic (3-residue predominate), or regulatory (2-residue predominate) roles. Computational tools for modeling metal ions in protein structures, especially for transient, reversible, and concentration-dependent regulatory sites, remain immature. We present PinMyMetal (PMM), a sophisticated hybrid machine learning system for predicting zinc ion localization and environment in macromolecular structures. Compared to other predictors, PMM excels in predicting regulatory sites (median deviation of 0.34 Å), demonstrating superior accuracy in locating catalytic sites (median deviation of 0.27 Å) and structural sites (median deviation of 0.14 Å). PMM assigns a certainty score to each predicted site based on local structural and physicochemical features independent of homolog presence. Interactive validation through our server, CheckMyMetal, expands PMM's scope, enabling it to pinpoint and validates diverse functional zinc sites from different structure sources (predicted structures, cryo-EM and crystallography). This facilitates residue-wise assessment and robust metal binding site design. The lightweight PMM system demands minimal computing resources and is available at https://PMM.biocloud.top. While currently trained on zinc, the PMM workflow can easily adapt to other metals through expanded training data.

CLSY docking to Pol IV requires a conserved domain critical for small RNA biogenesis and transposon silencing

Eukaryotes must balance the need for gene transcription by RNA polymerase II (Pol II) against the danger of mutations caused by transposable element (TE) proliferation. In plants, these gene expression and TE silencing activities are divided between different RNA polymerases. Specifically, RNA polymerase IV (Pol IV), which evolved from Pol II, transcribes TEs to generate small interfering RNAs (siRNAs) that guide DNA methylation and block TE transcription by Pol II. While the Pol IV complex is recruited to TEs via SNF2-like CLASSY (CLSY) proteins, how Pol IV partners with the CLSYs remains unknown. Here we identified a conserved CYC-YPMF motif that is specific to Pol IV and is positioned on the complex exterior. Furthermore, we found that this motif is essential for the co-purification of all four CLSYs with Pol IV, but that only one CLSY is present in any given Pol IV complex. These findings support a "one CLSY per Pol IV" model where the CYC-YPMF motif acts as a CLSY-docking site. Indeed, mutations in and around this motif phenocopy pol iv null mutants. Together, these findings provide structural and functional insights into a critical protein feature that distinguishes Pol IV from other RNA polymerases, allowing it to promote genome stability by targeting TEs for silencing.

Hunting down zinc(II)-binding sites in proteins with distance matrices

MOTIVATION

In recent years, high-throughput sequencing technologies have made available the genome sequences of a huge variety of organisms. However, the functional annotation of the encoded proteins often still relies on low-throughput and costly experimental studies. Bioinformatics approaches offer a promising alternative to accelerate this process. In this work, we focus on the binding of zinc(II) ions, which is needed for 5%-10% of any organism's proteins to achieve their physiologically relevant form.

RESULTS

To implement a predictor of zinc(II)-binding sites in the 3D structures of proteins, we used a neural network, followed by a filter of the network output against the local structure of all known sites. The latter was implemented as a function comparing the distance matrices of the Cα and Cβ atoms of the sites. We called the resulting tool Master of Metals (MOM). The structural models for the entire proteome of an organism generated by AlphaFold can be used as input to our tool in order to achieve annotation at the whole organism level within a few hours. To demonstrate this, we applied MOM to the yeast proteome, obtaining a precision of about 76%, based on data for homologous proteins.

AVAILABILITY AND IMPLEMENTATION

Master of Metals has been implemented in Python and is available at https://github.com/cerm-cirmmp/Master-of-metals.

Zinc dynamics regulate early ovarian follicle development

Zinc fluctuations regulate key steps in late oocyte and preimplantation embryo development; however, roles for zinc in preceding stages in early ovarian follicle development, when cooperative interactions exist between the oocyte and somatic cells, are unknown. To understand the roles of zinc during early follicle development, we applied single cell X-ray fluorescence microscopy, a radioactive zinc tracer, and a labile zinc probe to measure zinc in individual mouse oocytes and associated somatic cells within early follicles. Here, we report a significant stage-specific increase and compartmental redistribution in oocyte zinc content upon the initiation of early follicle growth. The increase in zinc correlates with the increased expression of specific zinc transporters, including two that are essential in oocyte maturation. While oocytes in follicles exhibit high tolerance to pronounced changes in zinc availability, somatic survival and proliferation are significantly more sensitive to zinc chelation or supplementation. Finally, transcriptomic, proteomic, and zinc loading analyses reveal enrichment of zinc targets in the ubiquitination pathway. Overall, these results demonstrate that distinct cell type-specific zinc regulations are required for follicle growth and indicate that physiological fluctuation in the localization and availability of this inorganic cofactor has fundamental functions in early gamete development.

Engineered Zn-based nano-pesticides as an opportunity for treatment of phytopathogens in agriculture

People's desire for food has never slowed, despite the deterioration of the global agricultural environment and the threat to food security. People rely on agrochemicals to ensure normal crop growth and to relieve the existing demand pressure. Phytopathogens have acquired resistance to traditional pesticides as a result of pesticdes' abuse. Compared with traditional formulations, nano-pesticides have superior antimicrobial performance and are environmentally friendly. Zn-based nanoparticles (NPs) have shown their potential as strong antipathogen activity. However, their full potential has not been demonstrated yet. Here, we analyzed the prerequisites for the use of Zn-based NPs as nano-pesticides in agriculture including both intrinsic properties of the materials and environmental conditions. We also summarized the mechanisms of Zn-based NPs against phytopathogens including direct and indirect strategies to alleviate plant disease stress. Finally, the current challenges and future directions are highlighted to advance our understanding of this field and guide future studies.

Structural Bioinformatics and Deep Learning of Metalloproteins: Recent Advances and Applications

All living organisms require metal ions for their energy production and metabolic and biosynthetic processes. Within cells, the metal ions involved in the formation of adducts interact with metabolites and macromolecules (proteins and nucleic acids). The proteins that require binding to one or more metal ions in order to be able to carry out their physiological function are called metalloproteins. About one third of all protein structures in the Protein Data Bank involve metalloproteins. Over the past few years there has been tremendous progress in the number of computational tools and techniques making use of 3D structural information to support the investigation of metalloproteins. This trend has been boosted by the successful applications of neural networks and machine/deep learning approaches in molecular and structural biology at large. In this review, we discuss recent advances in the development and availability of resources dealing with metalloproteins from a structure-based perspective. We start by addressing tools for the prediction of metal-binding sites (MBSs) using structural information on apo-proteins. Then, we provide an overview of the methods for and lessons learned from the structural comparison of MBSs in a fold-independent manner. We then move to describing databases of metalloprotein/MBS structures. Finally, we summarizing recent ML/DL applications enhancing the functional interpretation of metalloprotein structures.

The Role of the Metabolism of Zinc and Manganese Ions in Human Cancerogenesis

Metal ion homeostasis is fundamental for life. Specifically, transition metals iron, manganese and zinc play a pivotal role in mitochondrial metabolism and energy generation, anti-oxidation defense, transcriptional regulation and the immune response. The misregulation of expression or mutations in ion carriers and the corresponding changes in Mn²⁺ and Zn²⁺ levels suggest that these ions play a pivotal role in cancer progression. Moreover, coordinated changes in Mn²⁺ and Zn²⁺ ion carriers have been detected, suggesting that particular mechanisms influenced by both ions might be required for the growth of cancer cells, metastasis and immune evasion. Here, we present a review of zinc and manganese pathophysiology suggesting that these ions might cooperatively regulate cancerogenesis. Zn and Mn effects converge on mitochondria-induced apoptosis, transcriptional regulation and the cGAS-STING signaling pathway, mediating the immune response. Both Zn and Mn influence cancer progression and impact treatment efficacy in animal models and clinical trials. We predict that novel strategies targeting the regulation of both Zn and Mn in cancer will complement current therapeutic strategies.

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.

Protein embeddings and deep learning predict binding residues for various ligand classes

One important aspect of protein function is the binding of proteins to ligands, including small molecules, metal ions, and macromolecules such as DNA or RNA. Despite decades of experimental progress many binding sites remain obscure. Here, we proposed bindEmbed21, a method predicting whether a protein residue binds to metal ions, nucleic acids, or small molecules. The Artificial Intelligence (AI)-based method exclusively uses embeddings from the Transformer-based protein Language Model (pLM) ProtT5 as input. Using only single sequences without creating multiple sequence alignments (MSAs), bindEmbed21DL outperformed MSA-based predictions. Combination with homology-based inference increased performance to F1 = 48 ± 3% (95% CI) and MCC = 0.46 ± 0.04 when merging all three ligand classes into one. All results were confirmed by three independent data sets. Focusing on very reliably predicted residues could complement experimental evidence: For the 25% most strongly predicted binding residues, at least 73% were correctly predicted even when ignoring the problem of missing experimental annotations. The new method bindEmbed21 is fast, simple, and broadly applicable-neither using structure nor MSAs. Thereby, it found binding residues in over 42% of all human proteins not otherwise implied in binding and predicted about 6% of all residues as binding to metal ions, nucleic acids, or small molecules.

GraphQL for the delivery of bioinformatics web APIs and application to ZincBind

Motivation

Many bioinformatics resources are provided as 'web services', with large databases and analysis software stored on a central server, and clients interacting with them using the hypertext transport protocol (HTTP). While some provide only a visual HTML interface, requiring a web browser to use them, many provide programmatic access using a web application programming interface (API) which returns XML, JSON or plain text that computer programs can interpret more easily. This allows access to be automated. Initially, many bioinformatics APIs used the 'simple object access protocol' (SOAP) and, more recently, representational state transfer (REST).

Results

GraphQL is a novel, increasingly prevalent alternative to REST and SOAP that represents the available data in the form of a graph to which any conceivable query can be submitted, and which is seeing increasing adoption in industry. Here, we review the principles of GraphQL, outline its particular suitability to the delivery of bioinformatics resources and describe its implementation in our ZincBind resource.

Availability and implementation

https://api.zincbind.net.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.