Yoshitaka Moriwaki

Selective binding of antimicrobial porphyrins to the heme-receptor IsdH-NEAT3 of Staphylococcus aureus

The Isd (iron‐regulated surface determinant) system of the human pathogen Staphylococcus aureus is responsible for the acquisition of heme from the host organism. We recently reported that the extracellular heme receptor IsdH‐NEAT3 captures and transfers noniron antimicrobial porphyrins containing metals in oxidation state (III). However, it is unclear if geometric factors such as the size of the metal (ionic radius) affect binding and transfer of metalloporphyrins. We carried out an ample structural, functional, and thermodynamic analysis of the binding properties of antimicrobial indium(III)‐porphyrin, which bears a much larger metal ion than the iron(III) of the natural ligand heme. The results demonstrate that the NEAT3 receptor recognizes the In(III)‐containing PPIX in a manner very similar to that of heme. Site‐directed mutagenesis identifies Tyr642 as the central element in the recognition mechanism as suggested from the crystal structures. Importantly, the NEAT3 receptor possesses the remarkable ability to capture dimers of metalloporphyrin. Molecular dynamics simulations reveal that IsdH‐NEAT3 does not require conformational changes, or large rearrangements of the residues within its binding site, to accommodate the much larger (heme)2 ligand. We discuss the implications of these findings for the design of potent inhibitors against this family of key receptors of S. aureus.

Heme Binding Mechanism of Structurally Similar Iron-Regulated Surface Determinant Near Transporter Domains of Staphylococcus aureus Exhibiting Different Affinities for Heme

Near transporter (NEAT) domains of the iron-regulated surface determinant (Isd) proteins are essential for the import of nutritional heme from host animals to Gram-positive pathogens such as Staphylococcus aureus. The order of transfer of heme between NEAT domains occurs from IsdH to IsdA to IsdC, without any energy input despite the similarity of their three-dimensional structures. We measured the free energy of binding of heme and various metalloporphyrins to each NEAT domain and found that the affinity of heme and non-iron porphyrins for NEAT domains increased gradually in the same order as that for heme transfer. To gain insight into the atomistic mechanism for the differential affinities, we performed in silico molecular dynamics simulation and in vitro site-directed mutagenesis. The simulations revealed that the negatively charged residues that are abundant in the loop between strand β1b and the 310 helix of IsdH-NEAT3 destabilize the interaction with the propionate group of heme. The higher affinity of IsdC was in part attributed to the formation of a salt bridge between its unique residue, Glu88, and the conserved Arg100 upon binding to heme. In addition, we found that Phe130 of IsdC makes the β7-β8 hairpin less flexible in the ligand-free form, which serves to reduce the magnitude of the entropy loss on binding to heme. We confirmed that substitution of these key residues of IsdC decreased its affinity for heme. Furthermore, IsdC mutants, whose affinities for heme were lower than those of IsdA, transferred heme back to IsdA. Thus, NEAT domains have evolved the characteristic residues on the common structural scaffold such that they exhibit different affinities for heme, thus promoting the efficient transfer of heme.

An iterative compound screening contest method for identifying target protein inhibitors using the tyrosine-protein kinase Yes

We propose a new iterative screening contest method to identify target protein inhibitors. After conducting a compound screening contest in 2014, we report results acquired from a contest held in 2015 in this study. Our aims were to identify target enzyme inhibitors and to benchmark a variety of computer-aided drug discovery methods under identical experimental conditions. In both contests, we employed the tyrosine-protein kinase Yes as an example target protein. Participating groups virtually screened possible inhibitors from a library containing 2.4 million compounds. Compounds were ranked based on functional scores obtained using their respective methods, and the top 181 compounds from each group were selected. Our results from the 2015 contest show an improved hit rate when compared to results from the 2014 contest. In addition, we have successfully identified a statistically-warranted method for identifying target inhibitors. Quantitative analysis of the most successful method gave additional insights into important characteristics of the method used.

Elucidation of potential sites for antibody engineering by fluctuation editing

Target-specific monoclonal antibodies can be routinely acquired, but the sequences of naturally acquired antibodies are not always affinity-matured and methods that increase antigen affinity are desirable. Most biophysical studies have focused on the complementary determining region (CDR), which directly contacts the antigen; however, it remains difficult to increase the affinity as much as desired. While strategies to alter the CDR to increase antibody affinity are abundant, those that target non-CDR regions are scarce. Here we describe a new method, designated fluctuation editing, which identifies potential mutation sites and engineers a high-affinity antibody based on conformational fluctuations observed by NMR relaxation dispersion. Our data show that relaxation dispersion detects important fluctuating residues that are not located in the CDR and that increase antigen–antibody affinity by point mutation. The affinity-increased mutants are shown to fluctuate less in their free form and to form a more packed structure in their antigen-bound form.

Rapid Heme Transfer Reactions between NEAr Transporter Domains of Staphylococcus aureus: A Theoretical Study Using QM/MM and MD Simulations.

In vertebrates, most iron is present as heme or is chelated by proteins. Thus, Gram-positive pathogens such as Staphylococcus aureus have evolved an iron-regulated surface determinant (Isd) system that transports heme across thick cell walls into the cytoplasm. Recent studies have demonstrated that heme is rapidly transferred between the NEAr Transporter (NEAT) domains of the Isd system, despite its high affinity toward each domain, suggesting the presence of an intermediate NEAT•heme•NEAT complex. In the present study, we performed short restrained molecular dynamics (MD) simulations to dock the acceptor NEAT domain to the donor NEAT•heme complex and obtained models where the two NEAT domains were arranged with two-fold pseudo symmetry around the heme molecule. After turning off the restraints, complex structures were stably maintained during subsequent unrestrained MD simulations, except for the hydrogen bond between the propionate group of the heme molecule and the donor NEAT domain, potentially facilitating the transition of heme from the donor to the acceptor. Subsequent structural optimization using the quantum mechanics/molecular mechanics (QM/MM) method showed that two tyrosine residues, one from each NEAT domain, were simultaneously coordinated to the ferric heme iron in the intermediate complex only if they were deprotonated. Based on these results, we propose a reaction scheme for heme transfer between NEAT domains.

Identifying MoRFs in Disordered Proteins Using Enlarged Conserved Features

Identifying the short binding regions, which are called molecular recognition features (MoRFs), within intrinsically disordered proteins (IDPs) is the key step for understanding the function of IDPs, for protein structure determination and for drug design. Due to the complexity of IDPs, highly accurate prediction of MoRFs from its amino acid sequence still remains extremely challenging. Here, inspired by the signal processing technology, we proposed a new method which is based on the enlarged conserved features of sequence for MoRFs prediction. In our approach, only the revised position-specific scoring matrix (PSSM) generated from the sequence was used as input feature, and the support vector machine (SVM) was adopted to build the prediction model. Finally, the output prediction scores were processed by an average strategy to further improve the accuracy. When compared with other single model-based methods on the same datasets, our results were very competitive in terms of accuracy with respect to the state-of-the-art methods.