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

Ye, Nan; Zhou, Feng; Liang, Xingchen; Chai, Haiting; Fan, Jianwei; Li, Bo; Zhang, Jian

doi:10.1155/2022/8965712

Cited by 12 publications

(7 citation statements)

References 164 publications

(240 reference statements)

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“…The identity of the HEK293T cells was confirmed using short tandem repeat analysis. The HEK293T cell line has the following short tandem repeat profile: TH01 (7, 9.3); D21S11 (28, 29, 30.2); D5S818 (7,8,9); D13S317 (11,12,13,14,15); D7S820 (11); D16S539 (9, 13); CSF1PO (11,12,13); Amelogenin (X); vWA (16,18,19,20); TPOX (11). This profile matches 100% to HEK293T cell line profile (CRL-3216; ATCC) if the Alternative Master's algorithm is used, and 83% if the Tanabe algorithm is used.…”

Section: Cell Culture Protein Expression and Purificationmentioning

confidence: 99%

High-Throughput Identification of Calcium Regulated Proteins Across Diverse Proteomes

Locke,

Fields,

Gizinski

et al. 2024

Preprint

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Calcium ions play important roles in nearly every biological process, yet whole-proteome analysis of calcium effectors has been hindered by lack of high-throughput, unbiased, and quantitative methods to identify proteins-calcium engagement. To address this, we adapted protein thermostability assays in the budding yeast, human cells, and mouse mitochondria. Based on calcium-dependent thermostability, we identified 2884 putative calcium-regulated proteins across human, mouse, and yeast proteomes. These data revealed calcium engagement of novel signaling hubs and cellular processes, including metabolic enzymes and the spliceosome. Cross-species comparison of calcium-protein engagement and mutagenesis experiments identified residue-specific cation engagement, even within well-known EF-hand domains. Additionally, we found that the dienoyl-CoA reductase DECR1 binds calcium at physiologically-relevant concentrations with substrate-specific affinity, suggesting direct calcium regulation of mitochondrial fatty acid oxidation. These unbiased, proteomic analyses of calcium effectors establish a key resource to dissect cation engagement and its mechanistic effects across multiple species and diverse biological processes.

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Section: Cell Culture Protein Expression and Purificationmentioning

confidence: 99%

High-Throughput Identification of Calcium Regulated Proteins Across Diverse Proteomes

Locke,

Fields,

Gizinski

et al. 2024

Preprint

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“…Decades of research has been dedicated to discovering computational approaches that can accurately predict the metal ions as well as the positions where they bind to the proteins [3, 17, 8, 5]. A comprehensive review of recent advances in computational approaches for predicting metal binding sites can be found in [30]. Broadly, these approaches can be categorized into following three groups based on the type of attributes they take into account: 1) structure-based methods that use three dimensional secondary structure of proteins as primary data; 2) sequence-based methods that use amino acid sequence as primary data; and 3) combined methods that leverage both structure and sequence attributes.…”

Section: Introductionmentioning

confidence: 99%

MetaLLM: Residue-wise Metal ion Prediction Using Deep Transformer Model

Shishir

Sarker

Rahman

et al. 2023

Preprint

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Proteins bind to metals such as copper, zinc, magnesium, etc., serving various purposes such as importing, exporting, or transporting metal in other parts of the cell as ligands and maintaining stable protein structure to function properly. A metal binding site indicates the single amino acid position where a protein binds a metal ion. Manually identifying metal binding sites is expensive, laborious, and time-consuming. A tiny fraction of the millions of proteins in UniProtKB the most comprehensive protein database are annotated with metal binding sites, leaving many millions of proteins waiting for metal binding site annotation. Developing a computational pipeline is thus essential to keep pace with the growing number of proteins. A significant shortcoming of the existing computational methods is the consideration of the long-term dependency of the residues. Other weaknesses include low accuracy, absence of positional information, hand-engineered features, and a pre-determined set of residues and metal ions. In this paper, we propose MetaLLM, a metal binding site prediction technique, by leveraging the recent progress in self-supervised attention-based (e.g. Transformer) large language models (LLMs) and a considerable amount of protein sequences publicly available. LLMs are capable of modelling long residual dependency in a sequence. The proposed MetaLLM uses a transformer pre-trained on an extensive database of protein sequences and later fine-tuned on metal-binding proteins for multi-label metal ions prediction. A 10-fold cross-validation shows more than 90% precision for the most prevalent metal ions.

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“…Due to the abundance and importance of metal ions in proteins, knowledge about their binding is crucial for our understanding of biological systems. Experimental techniques, like X-ray crystallography, NMR spectroscopy, or cryogenic electron microscopy, provide the most reliable information on this topic, but we can quickly run into problems with their cost, time consumption, or automation of the processes. , When experimental data are difficult to obtain in a timely manner, we can turn to computational methods which allow for relatively quick and easy identification of metal binding sites. Most of them are based either upon analyzing the amino acid sequence of a given protein or upon finding structural motifs that are analogous with the ones in known, well-characterized metal binding proteins. , …”

Section: Introductionmentioning

confidence: 99%

“…8 Some methods combine sequence and structural information, for example, MetSite, 20 IonCom, 11 or 3DLigandSite. 21 Many methods also employ machine learning approaches to predict metal binding, 6 among these are the already listed IonSeq and IonCom 11 tools; a recent example on predicting the Ca Mg 2+ ligand binding with a deep neural network can be found in ref 22. Many of the above listed software tools limit the range of metal ions they detect by focusing only on specific amino acid residues.…”

Section: ■ Introductionmentioning

confidence: 99%

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

Ravnik,

Jukič,

Bren

2023

J. Chem. Inf. Model.

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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 .

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

Cited by 12 publications

References 164 publications

High-Throughput Identification of Calcium Regulated Proteins Across Diverse Proteomes

High-Throughput Identification of Calcium Regulated Proteins Across Diverse Proteomes

MetaLLM: Residue-wise Metal ion Prediction Using Deep Transformer Model

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

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