Identifying metal binding amino acids based on backbone geometries as a tool for metalloprotein engineering

DisDock: A Deep Learning Method for Metal Ion-Protein Redocking

The structures of metalloproteins are essential for comprehending their functions and interactions. The breakthrough of AlphaFold has made it possible to predict protein structures with experimental accuracy. However, the type of metal ion that a metalloprotein binds and the binding structure are still not readily available, even with the predicted protein structure. In this study, we present DisDock, a deep learning method for predicting protein-metal docking. DisDock takes distogram of randomly initialized protein-ligand configuration as input and outputs the distogram of the predicted binding complex. It combines the U-net architecture with self-attention modules to enhance model performance. Taking inspiration from the physical principle that atoms in closer proximity display a stronger mutual attraction, this predictor capitalizes on geometric information to uncover latent characteristics indicative of atom interactions. To train our model, we employ a high-quality metalloprotein dataset sourced from the Mother of All Databases (MOAD). Experimental results demonstrate that our approach outperforms other existing methods in prediction accuracy for various types of metal ions.

Metalloproteins and metalloproteomics in health and disease

Metalloproteins represents more than one third of human proteome, with huge variation in physiological functions and pathological implications, depending on the metal/metals involved and tissue context. Their functions range from catalysis, bioenergetics, redox, to DNA repair, cell proliferation, signaling, transport of vital elements, and immunity. The human metalloproteomic studies revealed that many families of metalloproteins along with individual metalloproteins are dysregulated under several clinical conditions. Also, several sorts of interaction between redox- active or redox- inert metalloproteins are observed in health and disease. Metalloproteins profiling shows distinct alterations in neurodegenerative diseases, cancer, inflammation, infection, diabetes mellitus, among other diseases. This makes metalloproteins -either individually or as families- a promising target for several therapeutic approaches. Inhibitors and activators of metalloenzymes, metal chelators, along with artificial metalloproteins could be versatile in diagnosis and treatment of several diseases, in addition to other biomedical and industrial applications.

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.

Quantification of carboxylate-bridged di-zinc site stability in protein due ferri by single-molecule force spectroscopy

Carboxylate-bridged diiron proteins belong to a protein family involved in different physiological processes. These proteins share the conservative EXXH motif, which provides the carboxylate bridge and is critical for metal binding. Here, we choose de novo-designed single-chain due ferri protein (DFsc), a four-helical protein with two EXXH motifs as a model protein, to study the stability of the carboxylate-bridged di-metal binding site. The mechanical and kinetic properties of the di-Zn site in DFsc were obtained by atomic force microscopy-based single-molecule force spectroscopy. Zn-DFsc showed a considerable rupture force of ~200 pN, while the apo-protein is mechanically labile. In addition, multiple rupture pathways were observed with different probabilities, indicating the importance of the EXXH-based carboxylate-bridged metal site. These results demonstrate carboxylate-bridged di-metal site is mechanically stable and improve our understanding of this important type of metalloprotein.

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.

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.

Machine Learning Approaches for Metalloproteins

Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts to predict the structural and functional roles of metalloproteins. Ultimately, having a comprehensive understanding of metalloproteins will lead to tangible applications, such as designing potent inhibitors in drug discovery. Recently, there has been an acceleration in the number of studies applying machine learning to predict metalloprotein properties, primarily driven by the advent of more sophisticated machine learning algorithms. This review covers how machine learning tools have consolidated and expanded our comprehension of various aspects of metalloproteins (structure, function, stability, ligand-binding interactions, and inhibitors). Future avenues of exploration are also discussed.

Identifying metal binding amino acids based on backbone geometries as a tool for metalloprotein engineering

Metal cofactors within proteins perform a versatile set of essential cellular functions. In order to take advantage of the diverse functionality of metalloproteins, researchers have been working to design or modify metal binding sites in proteins to rationally tune the function or activity of the metal cofactor. This study has performed an analysis on the backbone atom geometries of metal-binding amino acids among 10 different metal binding sites within the entire protein data bank. A set of 13 geometric parameters (features) was identified that is capable of predicting the presence of a metal cofactor in the protein structure with overall accuracies of up to 97% given only the relative positions of their backbone atoms. The decision tree machine-learning algorithm used can quickly analyze an entire protein structure for the presence of sets of primary metal coordination spheres upon mutagenesis, independent of their original amino acid identities. The methodology was designed for application in the field of metalloprotein engineering. A cluster analysis using the data set was also performed and demonstrated that the features chosen are useful for identifying clusters of structurally similar metal-binding sites.