Recognition of Metal Ion Ligand-Binding Residues by Adding Correlation Features and Propensity Factors

The realization of many protein functions is inseparable from the interaction with ligands; in particular, the combination of protein and metal ion ligands performs an important biological function. Currently, it is a challenging work to identify the metal ion ligand-binding residues accurately by computational approaches. In this study, we proposed an improved method to predict the binding residues of 10 metal ion ligands (Zn2+, Cu2+, Fe2+, Fe3+, Co2+, Mn2+, Ca2+, Mg2+, Na+, and K+). Based on the basic feature parameters of amino acids, and physicochemical and predicted structural information, we added another two features of amino acid correlation information and binding residue propensity factors. With the optimized parameters, we used the GBM algorithm to predict metal ion ligand-binding residues. In the obtained results, the Sn and MCC values were over 10.17% and 0.297, respectively. Besides, the Sn and MCC values of transition metals were higher than 34.46% and 0.564, respectively. In order to test the validity of our model, another method (Random Forest) was also used in comparison. The better results of this work indicated that the proposed method would be a valuable tool to predict metal ion ligand-binding residues.

Introducing a New Bond-Forming Activity in an Archaeal DNA Polymerase by Structure-Guided Enzyme Redesign

DNA polymerases have evolved to feature a highly conserved activity across the tree of life: formation of, without exception, phosphodiester linkages that create the repeating sugar-phosphate backbone of DNA. Can this linkage selectivity observed in nature be overcome by design to produce non-natural nucleic acids? Here, we report that structure-guided redesign of an archaeal DNA polymerase (9°N) enables a new polymerase activity that is undetectable in the wild type enzyme: catalyzing the formation of N3′→P5′ phosphoramidate linkages in the presence of 3′-amino-2′,3′-dideoxynucleoside 5′-triphosphate (3′-NH2-ddNTP) building blocks. Replacing a highly conserved metal-binding aspartate in the 9°N active site (Asp-404) with asparagine was key to the emergence of this unnatural enzyme activity. Molecular dynamics simulations provided insights into how a single substitution could enhance the productive positioning of the 3′-amino nucleophile in the active site. Further remodeling of the protein-nucleic acid interface with substitutions in the finger subdomain led to a quadruple-mutant variant (9°N-NRQS) that incorporated 3′-NH2-ddNTPs into a 3′-amino-primer on various DNA templates. This work presents the first example of an active-site substitution of a metal-binding residue that leads to a novel activity in a DNA polymerase, and sheds light on the molecular basis of substrate fidelity and latent promiscuity in enzymes.

A Max-Margin Model for Predicting Residue—Base Contacts in Protein–RNA Interactions

Protein–RNA interactions (PRIs) are essential for many biological processes, so understanding aspects of the sequences and structures involved in PRIs is important for unraveling such processes. Because of the expensive and time-consuming techniques required for experimental determination of complex protein–RNA structures, various computational methods have been developed to predict PRIs. However, most of these methods focus on predicting only RNA-binding regions in proteins or only protein-binding motifs in RNA. Methods for predicting entire residue–base contacts in PRIs have not yet achieved sufficient accuracy. Furthermore, some of these methods require the identification of 3D structures or homologous sequences, which are not available for all protein and RNA sequences. Here, we propose a prediction method for predicting residue–base contacts between proteins and RNAs using only sequence information and structural information predicted from sequences. The method can be applied to any protein–RNA pair, even when rich information such as its 3D structure, is not available. In this method, residue–base contact prediction is formalized as an integer programming problem. We predict a residue–base contact map that maximizes a scoring function based on sequence-based features such as k-mers of sequences and the predicted secondary structure. The scoring function is trained using a max-margin framework from known PRIs with 3D structures. To verify our method, we conducted several computational experiments. The results suggest that our method, which is based on only sequence information, is comparable with RNA-binding residue prediction methods based on known binding data.

Two New M23 Peptidoglycan Hydrolases With Distinct Net Charge

Bacterial peptidoglycan hydrolases play an essential role in cell wall metabolism during bacterial growth, division, and elongation (autolysins) or in the elimination of closely related species from the same ecological niche (bacteriocins). Most studies concerning the peptidoglycan hydrolases present in Gram-positive bacteria have focused on clinically relevant Staphylococcus aureus or the model organism Bacillus subtilis, while knowledge relating to other species remains limited. Here, we report two new peptidoglycan hydrolases from the M23 family of metallopeptidases derived from the same staphylococcal species, Staphylococcus pettenkoferi. They share modular architecture, significant sequence identity (60%), catalytic and binding residue conservation, and similar modes of activation, but differ in gene distribution, putative biological role, and, strikingly, in their isoelectric points (pIs). One of the peptides has a high pI, similar to that reported for all M23 peptidases evaluated to date, whereas the other displays a low pI, a unique feature among M23 peptidases. Consequently, we named them SpM23_B (Staphylococcus pettenkoferi M23 “Basic”) and SpM23_A (Staphylococcus pettenkoferi M23 “Acidic”). Using genetic and biochemical approaches, we have characterized these two novel lytic enzymes, both in vitro and in their physiological context. Our study presents a detailed characterization of two novel and clearly distinct peptidoglycan hydrolases to understand their role in bacterial physiology.

A deep-learning framework for multi-level peptide–protein interaction prediction

Peptide-protein interactions are involved in various fundamental cellular functions and their identification is crucial for designing efficacious peptide therapeutics. Recently, a number of computational methods have been developed to predict peptide-protein interactions. However, most of the existing prediction approaches heavily depend on high-resolution structure data. Here, we present a deep learning framework for multi-level peptide-protein interaction prediction, called CAMP, including binary peptide-protein interaction prediction and corresponding peptide binding residue identification. Comprehensive evaluation demonstrated that CAMP can successfully capture the binary interactions between peptides and proteins and identify the binding residues along the peptides involved in the interactions. In addition, CAMP outperformed other state-of-the-art methods on binary peptide-protein interaction prediction. CAMP can serve as a useful tool in peptide-protein interaction prediction and identification of important binding residues in the peptides, which can thus facilitate the peptide drug discovery process.

An infection-induced RhoB-Beclin 1-Hsp90 complex enhances clearance of uropathogenic Escherichia coli

Host cells use several anti-bacterial pathways to defend against pathogens. Here, using a uropathogenic Escherichia coli (UPEC) infection model, we demonstrate that bacterial infection upregulates RhoB, which subsequently promotes intracellular bacteria clearance by inducing LC3 lipidation and autophagosome formation. RhoB binds with Beclin 1 through its residues at 118 to 140 and the Beclin 1 CCD domain, with RhoB Arg133 being the key binding residue. Binding of RhoB to Beclin 1 enhances the Hsp90-Beclin 1 interaction, preventing Beclin 1 degradation. RhoB also directly interacts with Hsp90, maintaining RhoB levels. UPEC infections increase RhoB, Beclin 1 and LC3 levels in bladder epithelium in vivo, whereas Beclin 1 and LC3 levels as well as UPEC clearance are substantially reduced in RhoB+/− and RhoB−/− mice upon infection. We conclude that when stimulated by UPEC infections, host cells promote UPEC clearance through the RhoB-Beclin 1-HSP90 complex, indicating RhoB may be a useful target when developing UPEC treatment strategies.

GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues

Knowledge of the interactions between proteins and nucleic acids is the basis of understanding various biological activities and designing new drugs. How to accurately identify the nucleic-acid-binding residues remains a challenging task. In this paper, we propose an accurate predictor, GraphBind, for identifying nucleic-acid-binding residues on proteins based on an end-to-end graph neural network. Considering that binding sites often behave in highly conservative patterns on local tertiary structures, we first construct graphs based on the structural contexts of target residues and their spatial neighborhood. Then, hierarchical graph neural networks (HGNNs) are used to embed the latent local patterns of structural and bio-physicochemical characteristics for binding residue recognition. We comprehensively evaluate GraphBind on DNA/RNA benchmark datasets. The results demonstrate the superior performance of GraphBind than state-of-the-art methods. Moreover, GraphBind is extended to other ligand-binding residue prediction to verify its generalization capability. Web server of GraphBind is freely available at http://www.csbio.sjtu.edu.cn/bioinf/GraphBind/.

Structure and dynamics of the drug-bound bacterial transporter EmrE in lipid bilayers

The dimeric transporter, EmrE, effluxes polyaromatic cationic drugs in a proton-coupled manner to confer multidrug resistance in bacteria. Although the protein is known to adopt an antiparallel asymmetric topology, its high-resolution drug-bound structure is so far unknown, limiting our understanding of the molecular basis of promiscuous transport. Here we report an experimental structure of drug-bound EmrE in phospholipid bilayers, determined using 19F and 1H solid-state NMR and a fluorinated substrate, tetra(4-fluorophenyl) phosphonium (F4-TPP+). The drug-binding site, constrained by 214 protein-substrate distances, is dominated by aromatic residues such as W63 and Y60, but is sufficiently spacious for the tetrahedral drug to reorient at physiological temperature. F4-TPP+ lies closer to the proton-binding residue E14 in subunit A than in subunit B, explaining the asymmetric protonation of the protein. The structure gives insight into the molecular mechanism of multidrug recognition by EmrE and establishes the basis for future design of substrate inhibitors to combat antibiotic resistance.