Machine Learning Approaches for Metalloproteins

Twenty years in metalloprotein bioinformatics: A short history of a long journey

The study of the structure and function of metalloproteins is a central subject of inorganic biochemistry. Starting from the 2000s, computational methods have flanked experimental research by exploiting the ever-increasing computing power and the huge amount of data produced by omics technologies. In this article, we retrace the major advancements that brought bioinformatics from being of minor relevance to being an essential tool for today's inorganic biochemists, focusing on the contributions coming from the Magnetic Resonance Center (CERM) of Florence, where we have been developing for twenty years methods and resources to investigate metalloproteins with computational approaches.

Recent advances and future challenges in predictive modeling of metalloproteins by artificial intelligence

Metal coordination is essential for structural/catalytic functions of metalloproteins that mediate a wide range of biological processes in living organisms. Advances in bioinformatics have significantly enhanced our understanding of metal-binding sites and their functional roles in metalloproteins. State-of-the-art computational models developed for metal-binding sites seamlessly integrate protein sequence and structural data to unravel the complexities of metal coordination environments. Our goal in this mini-review is to give an overview of these tools and highlight the current challenges (predicting dynamic metal-binding sites, determining functional metalation states, and designing intricate coordination networks) remaining in the predictive models of metal-binding sites. Addressing these challenges will not only deepen our knowledge of natural metalloproteins but also accelerate the development of artificial metalloproteins with novel and precisely engineered functionalities.

Prediction of Ca2+ Binding Site in Proteins With a Fast and Accurate Method Based on Statistical Mechanics and Analysis of Crystal Structures

Predicting the precise locations of metal binding sites within metalloproteins is a crucial challenge in biophysics. A fast, accurate, and interpretable computational prediction method can complement the experimental studies. In the current work, we have developed a method to predict the location of Ca2+ ions in calcium-binding proteins using a physics-based method with an all-atom description of the proteins, which is substantially faster than the molecular dynamics simulation-based methods with accuracy as good as data-driven approaches. Our methodology uses the three-dimensional reference interaction site model (3D-RISM), a statistical mechanical theory, to calculate Ca2+ ion density around protein structures, and the locations of the Ca2+ ions are obtained from the density. We have taken previously used datasets to assess the efficacy of our method as compared to previous works. Our accuracy is 88%, comparable with the FEATURE program, one of the well-known data-driven methods. Moreover, our method is physical, and the reasons for failures can be ascertained in most cases. We have thoroughly examined the failed cases using different structural and crystallographic measures, such as B-factor, R-factor, electron density map, and geometry at the binding site. It has been found that x-ray structures have issues in many of the failed cases, such as geometric irregularities and dubious assignment of ion positions. Our algorithm, along with the checks for structural accuracy, is a major step in predicting calcium ion positions in metalloproteins.

Computational approaches for the identification of novel metal-binding pharmacophores: advances and challenges

Metalloenzymes are important therapeutic targets for a variety of human diseases. Computational approaches have recently emerged as effective tools to understand metal-ligand interactions and expand the structural diversity of both metalloenzyme inhibitors (MIs) and metal-binding pharmacophores (MBPs). In this review, we highlight key advances in currently available fine-tuning modeling methods and data-driven cheminformatic approaches. We also discuss major challenges to the recognition of novel MBPs and MIs. The evidence provided herein could expedite future computational efforts to guide metalloenzyme-based drug discovery.

MetalHawk: Enhanced Classification of Metal Coordination Geometries by Artificial Neural Networks

The chemical properties of metal complexes are strongly dependent on the number and geometrical arrangement of ligands coordinated to the metal center. Existing methods for determining either coordination number or geometry rely on a trade-off between accuracy and computational costs, which hinders their application to the study of large structure data sets. Here, we propose MetalHawk (https://github.com/vrettasm/MetalHawk), a machine learning-based approach to perform simultaneous classification of metal site coordination number and geometry through artificial neural networks (ANNs), which were trained using the Cambridge Structural Database (CSD) and Metal Protein Data Bank (MetalPDB). We demonstrate that the CSD-trained model can be used to classify sites belonging to the most common coordination numbers and geometry classes with balanced accuracy equal to 96.51% for CSD-deposited metal sites. The CSD-trained model was also found to be capable of classifying bioinorganic metal sites from the MetalPDB database, with balanced accuracy equal to 84.29% on the whole PDB data set and to 91.66% on manually reviewed sites in the PDB validation set. Moreover, we report evidence that the output vectors of the CSD-trained model can be considered as a proxy indicator of metal-site distortions, showing that these can be interpreted as a low-dimensional representation of subtle geometrical features present in metal site structures.

Metal ion availability and homeostasis as drivers of metabolic evolution and enzyme function

Metal ions are potent catalysts and have been available for cellular biochemistry at all stages of evolution. Growing evidence suggests that metal catalysis was critical for the origin of the very first metabolic reactions. With approximately 80% of modern metabolic pathways being dependent on metal ions, metallocatalysis and homeostasis continue to be essential for intracellular metabolic networks and physiology. However, the genetic network that controls metal ion homeostasis and the impact of metal availability on metabolism is poorly understood. Here, we review recent work on gene and protein evolution relevant for better understanding metal ion biology and its role in metabolism. We highlight the importance of analysing the origin and evolution of enzyme catalysis in the context of catalytically relevant metal ions, summarise unanswered questions essential for developing a comprehensive understanding of metal ion homeostasis and advocate for the consideration of metal ion properties and availability in the design and directed evolution of novel enzymes and pathways.

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.