Identifying Metal Binding Sites in Proteins Using Homologous Structures, the MADE Approach

Abstract

In order to identify the locations of metal ions in the binding sites of proteins, we have developed a method named the MADE (MAcromolecular DEnsity and Structure Analysis) approach. The MADE approach represents an evolution of our previous toolset, the ProBiS H2O (MD) methodology, for the identification of conserved water molecules. Our method uses experimental structures of proteins homologous to a query, which are subsequently superimposed upon it. Areas with a particular species present in a similar location among many homologous protein structures are identified using a clustering algorithm. Dense clusters likely represent positions containing species important to the query protein structure or function. We analyze well-characterized apo protein structures and show that the MADE approach can identify clusters corresponding to the expected positions of metal ions in their binding sites. The greatest advantage of our method lies in its generality. It can in principle be applied to any species found in protein records; it is not only limited to metal ions. We additionally demonstrate that the MADE approach can be successfully applied to predict the location of cofactors in computer-modeled structures, e.g., via AlphaFold. We also conduct a careful protein superposition method comparison and find our methodology robust and the results largely independent of the selected protein superposition algorithm. We postulate that with increasing structural data availability, additional applications of the MADE approach will be possible such as non-protein systems, water network identification, protein binding site elaboration, and analysis of binding events, all in a dynamic manner. We have implemented the MADE approach as a plugin for the PyMOL molecular visualization tool. The MADE plugin is available free of charge at .

A Reversible Spherical Geometric Conversion of Protein Backbone Structure Coordinate Matrix to Three Independent Vectors of ρ, θ and φ

“…For instance, physics-based simulations utilize principles of molecular mechanics and dynamics to simulate folding pathways, while homology modeling (e.g., algorithms such as PSI-BLAST, HHblits, and HMMER) leverages evolutionary relationships between proteins to infer structures [13][14][15][16][17][18][19][20]. Of recent further interest, machine learning techniques, particularly deep learning, have emerged as powerful tools for predicting protein structures by learning patterns from large datasets [4,[21][22][23][24][25][26][27][28][29][30]. Recent advancements in deep learning, exemplified by AlphaFold, have revolutionized protein structure prediction.…”