The fragment transformation method to detect the protein structural motifs

From sequence to function: Exploring biophysical properties of bacteriophage BFK20 lytic transglycosylase domain from the minor tail protein gp15

Bacteriophages have evolved different mechanisms of infection and penetration of bacterial cell walls. In Siphoviridae-like viruses, the inner tail proteins have a pivotal role in these processes and often encode lytic protein domains which increase infection efficiency. A soluble lytic transglycosylase (SLT) domain was identified in the minor tail protein gp15 from the BFK20 bacteriophage. Six fragments containing this SLT domain with adjacent regions of different lengths were cloned, expressed and purified. The biophysical properties of the two best expressing fragments were characterized by nanoDSF and CD spectroscopy, which showed that both fragments had a high refolding ability of 90 %. 3D modeling indicated that the bacteriophage BFK20 SLT domain is structurally similar to lysozyme. The degradation activity of these SLT proteins was evaluated using a lysozyme activity assay. BFK20 might use its transglycosylase activity to allow efficient phage DNA entry into the host cell by degrading bacterial peptidoglycan.

The chromatin remodeler SMARCA5 binds to d-block metal supports: Characterization of affinities by IMAC chromatography and QM analysis

The ISWI family protein SMARCA5 contains the ATP-binding pocket that coordinates the catalytic Mg2+ ion and water molecules for ATP hydrolysis. In this study, we demonstrate that SMARCA5 can also possess an alternative metal-binding ability. First, we isolated SMARCA5 on the cobalt column (IMAC) to near homogeneity. Examination of the interactions of SMARCA5 with metal-chelating supports showed that, apart from Co2+, it binds to Cu2+, Zn2+ and Ni2+. The efficiency of the binding to the last-listed metal was influenced by the chelating ligand, resulting in a strong preference for Ni-NTA over the Ni-CM-Asp equivalent. To gain insight in the preferential affinity for the Ni-NTA ligand, QM calculations were performed on model systems and metal-ligand complexes with a limited protein fragment of SMARCA5 containing the double-histidine (dHis) motif. The calculations correlated the observed affinity with the relative stability of the d-block metals to tetradentate ligand coordination over tridentate, as well as their overall octahedral coordination capacity. Likewise, binding free energies derived from model imidazole complexes mirrored the observed Ni-NTA/Ni-CM-Asp preferential affinity. Finally, similar calculations on complexes with a SMARCA5 peptide fragment derived from the AlphaFold structural prediction, captured almost accurately the expected relative stability of the TM complexes, and produced a large energetic separation (~10 kcal∙mol-1) between Ni-NTA and Ni-CM-Asp in favour of the former.

Non-covalent Lasso Entanglements in Folded Proteins: Prevalence, Functional Implications, and Evolutionary Significance

One-third of protein domains in the CATH database contain a recently discovered tertiary topological motif: non-covalent lasso entanglements, in which a segment of the protein backbone forms a loop closed by non-covalent interactions between residues and is threaded one or more times by the N- or C-terminal backbone segment. Unknown is how frequently this structural motif appears across the proteomes of organisms. And the correlation of these motifs with various classes of protein function and biological processes have not been quantified. Here, using a combination of protein crystal structures, AlphaFold2 predictions, and Gene Ontology terms we show that in E. coli, S. cerevisiae and H. sapiens that 71%, 52% and 49% of globular proteins contain one-or-more non-covalent lasso entanglements in their native fold, and that some of these are highly complex with multiple threading events. Further, proteins containing these tertiary motifs are consistently enriched in certain functions and biological processes across these organisms and depleted in others, strongly indicating an influence of evolutionary selection pressures acting positively and negatively on the distribution of these motifs. Together, these results demonstrate that non-covalent lasso entanglements are widespread and indicate they may be extensively utilized for protein function and subcellular processes, thus impacting phenotype.

In silico approaches to investigate enzyme immobilization: a comprehensive systematic review

Enzymes are popular catalysts with many applications, especially in industry. Biocatalyst usage on a large scale is facing some limitations, such as low operational stability, low recyclability, and high enzyme cost. Enzyme immobilization is a beneficial strategy to solve these problems. Bioinformatics tools can often correctly predict immobilization outcomes, resulting in a cost-effective experimental phase with the least time consumed. This study provides an overview of in silico methods predicting immobilization processes via a comprehensive systematic review of published articles till 11 December 2022. It also mentions the strengths and weaknesses of the processes and explains the computational analyses in each method that are required for immobilization assessment. In this regard, Web of Science and Scopus databases were screened to gain relevant publications. After screening the gathered documents (n = 3873), 60 articles were selected for the review. The selected papers have applied in silico procedures including only molecular dynamics (MD) simulations (n = 20), parallel tempering Monte Carlo (PTMC) and MD simulations (n = 3), MD and docking (n = 1), density functional theory (DFT) and MD (n = 1), only docking (n = 11), metal ion binding site prediction (MIB) server and docking (n = 2), docking and DFT (n = 1), docking and analysis of enzyme surfaces (n = 1), only DFT (n = 1), only MIB server (n = 2), analysis of an enzyme structure and surface (n = 12), rational design of immobilized derivatives (RDID) software (n = 3), and dissipative particle dynamics (DPD; n = 2). In most included studies (n = 51), enzyme immobilization was investigated experimentally in addition to in silico evaluation.

Insulin-cobalt core-shell nanoparticles for receptor-targeted bioimaging and diabetic wound healing

Diabetic wounds represent a major issue in medical care and need advanced therapeutic and tissue imaging systems for better management. The utilization of nano-formulations involving proteins like insulin and metal ions plays significant roles in controlling wound outcomes by decreasing inflammation or reducing microbial load. This work reports the easy one-pot synthesis of extremely stable, biocompatible, and highly fluorescent insulin-cobalt core-shell nanoparticles (ICoNPs) with enhanced quantum yield for their highly specific receptor-targeted bioimaging and normal and diabetic wound healing in vitro (HEKa cell line). The particles were characterized using physicochemical properties, biocompatibility, and wound healing applications. FTIR bands at 670.35 cm^-1, 849.79, and 973.73 indicating the Co-O bending, CoO-OH bond, and Co-OH bending, respectively, confirm the protein-metal interactions, which is further supported by the Raman spectra. In silico studies indicate the presence of cobalt binding sites on the insulin chain B at 8 GLY, 9 SER, and 10 HIS positions. The particles exhibit a magnificent loading efficiency of 89.48 ± 0.049% and excellent release properties (86.54 ± 2.15% within 24 h). Further, based on fluorescent properties, the recovery process can be monitored under an appropriate setup, and the binding of ICoNPs to insulin receptors was confirmed by bioimaging. This work helps synthesize effective therapeutics with numerous wound-healing promoting and monitoring applications.

MIB2: metal ion-binding site prediction and modeling server

MOTIVATION

MIB2 (metal ion-binding) attempts to overcome the limitation of structure-based prediction approaches, with many proteins lacking a solved structure. MIB2 also offers more accurate prediction performance and more metal ion types.

RESULTS

MIB2 utilizes both the (PS)2 method and the AlphaFold Protein Structure Database to acquire predicted structures to perform metal ion docking and predict binding residues. MIB2 offers marked improvements over MIB by collecting more MIB residue templates and using the metal ion type-specific scoring function. It offers a total of 18 types of metal ions for binding site predictions.

AVAILABILITY AND IMPLEMENTATION

Freely available on the web at http://bioinfo.cmu.edu.tw/MIB2/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A comprehensive in silico exploration of the impacts of missense variants on two different conformations of human pirin protein

BACKGROUND

Pirin, a member of the cupin superfamily, is an iron-binding non-heme protein. It acts as a coregulator of several transcription factors, especially the members of NFκB transcription factor family. Based on the redox state of its iron cofactor, it can assume two different conformations and thereby act as a redox sensor inside the nucleus. Previous studies suggested that pirin may be associated with cancer, inflammatory diseases as well as COVID-19 severities. Hence, it is important to explore the pathogenicity of its missense variants. In this study, we used a number of in silico tools to investigate the effects of missense variants of pirin on its structure, stability, metal cofactor binding affinity and interactions with partner proteins. In addition, we used protein dynamics simulation to elucidate the effects of selected variants on its dynamics. Furthermore, we calculated the frequencies of haplotypes containing pirin missense variants across five major super-populations (African, Admixed American, East Asian, European and South Asian).

RESULTS

Among a total of 153 missense variants of pirin, 45 were uniformly predicted to be pathogenic. Of these, seven variants can be considered for further experimental studies. Variants R59P and L116P were predicted to significantly destabilize and damage pirin structure, substantially reduce its affinity to its binding partners and alter pirin residue fluctuation profile via changing the flexibility of several key residues. Additionally, variants R59Q, F78V, G98D, V151D and L220P were found to impact pirin structure and function in multiple ways. As no haplotype was identified to be harboring more than one missense variant, further interrogation of the individual effects of these seven missense variants is highly recommended.

CONCLUSIONS

Pirin is involved in the transcriptional regulation of several genes and can play an important role in inflammatory responses. The variants predicted to be pathogenic in this study may thus contribute to a better understanding of the underlying molecular mechanisms of various inflammatory diseases. Future studies should be focused on clarifying if any of these variants can be used as disease biomarkers.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s42269-022-00917-7.

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.

Iron Deprivation Modulates the Exoproteome in Paracoccidioides brasiliensis

Fungi of the Paracoccidioides genus are the etiological agents of the systemic mycosis paracoccidioidomycosis and, when in the host, they find a challenging environment that is scarce in nutrients and micronutrients, such as Fe, which is indispensable for the survival of the pathogen. Previous studies have shown that fungi of this genus, in response to Fe deprivation, are able to synthesize and capture siderophores (Fe³⁺ chelators), use Fe-containing host proteins as a source of the metal, and use a non-canonical reductive pathway for Fe³⁺ assimilation. Despite all of these findings, there are still gaps that need to be filled in the pathogen response to metal deprivation. To contribute to the knowledge related to this subject, we obtained the exoproteome of Paracoccidioides brasiliensis (Pb18) undergoing Fe deprivation and by nanoUPLC-MS^E. One hundred forty-one proteins were identified, and out of these, 64 proteins were predicted to be secreted. We also identified the regulation of several virulence factors. Among the results, we highlight Cyb5 as a secreted molecule of Paracoccidioides in the exoproteome obtained during Fe deprivation. Cyb5 is described as necessary for the Fe deprivation response of Saccharomyces cerevisiae and Aspergillus fumigatus. Experimental data and molecular modeling indicated that Cyb5 can bind to Fe ions in vitro, suggesting that it can be relevant in the arsenal of molecules related to iron homeostasis in P. brasiliensis.

mebipred: identifying metal binding potential in protein sequence

Motivation

metal-binding proteins have a central role in maintaining life processes. Nearly one-third of known protein structures contain metal ions that are used for a variety of needs, such as catalysis, DNA/RNA binding, protein structure stability, etc. Identifying metal-binding proteins is thus crucial for understanding the mechanisms of cellular activity. However, experimental annotation of protein metal-binding potential is severely lacking, while computational techniques are often imprecise and of limited applicability.

Results

we developed a novel machine learning-based method, mebipred, for identifying metal-binding proteins from sequence-derived features. This method is over 80% accurate in recognizing proteins that bind metal ion-containing ligands; the specific identity of 11 ubiquitously present metal ions can also be annotated. mebipred is reference-free, i.e. no sequence alignments are involved, and is thus faster than alignment-based methods; it is also more accurate than other sequence-based prediction methods. Additionally, mebipred can identify protein metal-binding capabilities from short sequence stretches, e.g. translated sequencing reads, and, thus, may be useful for the annotation of metal requirements of metagenomic samples. We performed an analysis of available microbiome data and found that ocean, hot spring sediments and soil microbiomes use a more diverse set of metals than human host-related ones. For human microbiomes, physiological conditions explain the observed metal preferences. Similarly, subtle changes in ocean sample ion concentration affect the abundance of relevant metal-binding proteins. These results highlight mebipred’s utility in analyzing microbiome metal requirements.

Availability and implementation

mebipred is available as a web server at services.bromberglab.org/mebipred and as a standalone package at https://pypi.org/project/mymetal/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Protection of lead-induced cytotoxicity using paramagnetic nickel–insulin quantum clusters

Pb-toxicity is associated with inflammation which leads to delay in wound healing. Pb²⁺ utilizes calcium ion channels to enter the cell. Therefore, to achieve effective healing in a Pb-poisoned system, capturing Pb²⁺ from the circulatory system would be an effective approach without hampering the activity of the calcium ion channel. In this work insulin–nickel fluorescent quantum clusters (INiQCs) have been synthesized and used for the specific detection of Pb²⁺ ions in vitro and in cell-free systems. INiQCs (0.09 μM) can detect Pb²⁺ concentrations as low as 10 pM effectively in a cell-free system using the fluorescence turn-off method. In vitro INiQCs (0.45 μM) can detect Pb²⁺ concentrations as low as 1 μM. INiQCs also promote wound healing which can easily be monitored using the bright fluorescence of INiQCs. INiQCs also help to overcome the wound recovery inhibitory effect of Pb²⁺in vitro using lead nitrate. This work helps to generate effective biocompatible therapeutics for wound recovery in Pb²⁺ poisoned individuals.

Receptor targeted ferromagnetic Insulin–Nickel Quantum fluorescence Clusters (INiQCs) can specifically detect Pb²⁺ and prevents Pb²⁺ poisoning.

Bioengineering of non-pathogenic Escherichia coli to enrich for accumulation of environmental copper

Heavy metal sequestration from industrial wastes and agricultural soils is a long-standing challenge. This is more critical for copper since copper pollution is hazardous both for the environment and for human health. In this study, we applied an integrated approach of Darwin’s theory of natural selection with bacterial genetic engineering to generate a biological system with an application for the accumulation of Cu²⁺ ions. A library of recombinant non-pathogenic Escherichia coli strains was engineered to express seven potential Cu²⁺ binding peptides encoded by a ‘synthetic degenerate’ DNA motif and fused to Maltose Binding Protein (MBP). Most of these peptide-MBP chimeras conferred tolerance to high concentrations of copper sulphate, and in certain cases in the order of 160-fold higher than the recognised EC₅₀ toxic levels of copper in soils. UV–Vis spectroscopic analysis indicated a molar ratio of peptide-copper complexes, while a combination of bioinformatics-based structure modelling, Cu²⁺ ion docking, and MD simulations of peptide-MBP chimeras corroborated the extent of Cu²⁺ binding among the peptides. Further, in silico analysis predicted the peptides possessed binding affinity toward a broad range of divalent metal ions. Thus, we report on an efficient, cost-effective, and environment-friendly prototype biological system that is potentially capable of copper bioaccumulation, and which could easily be adapted for the removal of other hazardous heavy metals or the bio-mining of rare metals.

Insulin-copper quantum clusters preparation and receptor targeted bioimaging

Protein embedded fluorescence quantum clusters (QCs) have received a great amount of interest among the researchers because of their high aqueous solubility, stability, cost efficiency, and target specificity. Considerable advancement has happened in making functional quantum clusters with target specificity. This work reports the simple synthesis of insulin protected copper quantum clusters (ICuQCs) and its receptor-targeted bioimaging applications. The preparation of copper quantum clusters (CuQCs) was done simply by one-pot synthesis method by changing the pH of the insulin protein firstly to 10.5 basic pH than physiological pH. At physiological pH, the mixture incubated in oven 37 ⁰C at 240 rpm has been developed to process initially polydisperse, non-fluorescent, and unstable CuDs into monodispersed (∼2-3 nm), highly fluorescent, and extremely stable ICuQCs in the same phase (aqueous) using insulin as protein. HRTEM image show uniform distribution of CuDs within the protein matrix. Metal ion binding site prediction and docking server (MIB) results show that chain B of insulin contains 3 templates contains 5 amino acid residues which bind with Cu²⁺ metal ion. Groove 1 contains GLY8 and HIS10 bind has the highest binding potential towards Cu metal ions. The methodology adopted in this study should largely contribute to the practical applications of this new class of QCs. In view of the protein protection, coupled with direct synthesis and easy functionalization, this hybrid QC-protein system is expected to have numerous optical and bioimaging applications in the future.

MIB: Metal Ion-Binding Site Prediction and Docking Server

The structure of a protein determines its biological function(s) and its interactions with other factors; the binding regions tend to be conserved in sequence and structure, and the interacting residues involved are usually in close 3D space. The Protein Data Bank currently contains more than 110 000 protein structures, approximately one-third of which contain metal ions. Identifying and characterizing metal ion-binding sites is thus essential for investigating a protein's function(s) and interactions. However, experimental approaches are time-consuming and costly. The web server reported here was built to predict metal ion-binding residues and to generate the predicted metal ion-bound 3D structure. Binding templates have been constructed for regions that bind 12 types of metal ion-binding residues have been used to construct binding templates. The templates include residues within 3.5 Å of the metal ion, and the fragment transformation method was used for structural comparison between query proteins and templates without any data training. Through the adjustment of scoring functions, which are based on the similarity of structure and binding residues. Twelve kinds of metal ions (Ca²⁺, Cu²⁺, Fe³⁺, Mg²⁺, Mn²⁺, Zn²⁺, Cd²⁺, Fe²⁺, Ni²⁺, Hg²⁺, Co²⁺, and Cu⁺) binding residues prediction are supported. MIB also provides the metal ions docking after prediction. The MIB server is available at http://bioinfo.cmu.edu.tw/MIB/ .

Predicting flavin and nicotinamide adenine dinucleotide-binding sites in proteins using the fragment transformation method

We developed a computational method to identify NAD- and FAD-binding sites in proteins. First, we extracted from the Protein Data Bank structures of proteins that bind to at least one of these ligands. NAD-/FAD-binding residue templates were then constructed by identifying binding residues through the ligand-binding database BioLiP. The fragment transformation method was used to identify structures within query proteins that resembled the ligand-binding templates. By comparing residue types and their relative spatial positions, potential binding sites were identified and a ligand-binding potential for each residue was calculated. Setting the false positive rate at 5%, our method predicted NAD- and FAD-binding sites at true positive rates of 67.1% and 68.4%, respectively. Our method provides excellent results for identifying FAD- and NAD-binding sites in proteins, and the most important is that the requirement of conservation of residue types and local structures in the FAD- and NAD-binding sites can be verified.

Prediction of metal ion-binding sites in proteins using the fragment transformation method

The structure of a protein determines its function and its interactions with other factors. Regions of proteins that interact with ligands, substrates, and/or other proteins, tend to be conserved both in sequence and structure, and the residues involved are usually in close spatial proximity. More than 70,000 protein structures are currently found in the Protein Data Bank, and approximately one-third contain metal ions essential for function. Identifying and characterizing metal ion-binding sites experimentally is time-consuming and costly. Many computational methods have been developed to identify metal ion-binding sites, and most use only sequence information. For the work reported herein, we developed a method that uses sequence and structural information to predict the residues in metal ion-binding sites. Six types of metal ion-binding templates- those involving Ca(2+), Cu(2+), Fe(3+), Mg(2+), Mn(2+), and Zn(2+)-were constructed using the residues within 3.5 Å of the center of the metal ion. Using the fragment transformation method, we then compared known metal ion-binding sites with the templates to assess the accuracy of our method. Our method achieved an overall 94.6 % accuracy with a true positive rate of 60.5 % at a 5 % false positive rate and therefore constitutes a significant improvement in metal-binding site prediction.

Identification of protein functional surfaces by the concept of a split pocket

The function of a protein is often fulfilled via molecular interactions on its surfaces, so identifying the functional surface(s) of a protein is helpful for understanding its function. Here, we introduce the concept of a split pocket, which is a pocket that is split by a cognate ligand. We use a geometric approach that is site-specific. Specifically, we first compute a set of all pockets in the protein with its ligand(s) and a set of all pockets with the ligand(s) removed and then compare the two sets of pockets to identify the split pocket(s) of the protein. To reduce the search space and expedite the process of surface partitioning, we design probe radii according to the physicochemical textures of molecules. Our method achieves a success rate of 96% on a benchmark test set. We conduct a large-scale computation to identify approximately 19,000 split pockets from 11,328 structures (1.16 million potential pockets); for each pocket, we obtain residue composition, solvent-accessible area, and molecular volume. With this database of split pockets, our method can be used to predict the functional surfaces of unbound structures. Indeed, the functional surface of an unbound protein may often be found from its similarity to remotely related bound forms that belong to distinct folds. Finally, we apply our method to identify glucose-binding proteins, including unbound structures. Our study demonstrates the power of geometric and evolutionary matching for studying protein functional evolution and provides a framework for classifying protein functions by local spatial patterns of functional surfaces.