PoSSuM v.3: A Major Expansion of the PoSSuM Database for Finding Similar Binding Sites of Proteins.

Information on structures of protein-ligand complexes, including comparisons of known and putative protein-ligand-binding pockets, is valuable for protein annotation and drug discovery and development. To facilitate biomedical and pharmaceutical research, we developed PoSSuM (https://possum.cbrc.pj.aist.go.jp/PoSSuM/), a database for identifying similar binding pockets in proteins. The current PoSSuM database includes 191 million similar pairs among almost 10 million identified pockets. PoSSuM drug search (PoSSuMds) is a resource for investigating ligand and receptor diversity among a set of pockets that can bind to an approved drug compound. The enhanced PoSSuMds covers pockets associated with both approved drugs and drug candidates in clinical trials from the latest release of ChEMBL. Additionally, we developed two new databases: PoSSuMAg for investigating antibody-antigen interactions and PoSSuMAF to simplify exploring putative pockets in AlphaFold human protein models.

Rebuilding Ring-Type Assembly of Peroxiredoxin by Chemical Modification.

Direct control of the protein quaternary structure (QS) is challenging owing to the complexity of the protein structure. As a protein with a characteristic QS, peroxiredoxin from Aeropyrum pernix K1 (ApPrx) forms a decamer, wherein five dimers associate to form a ring. Here, we disrupted and reconstituted ApPrx QS via amino acid mutations and chemical modifications targeting hot spots for protein assembly. The decameric QS of an ApPrx* mutant, wherein all cysteine residues in wild-type ApPrx were mutated to serine, was destructed to dimers via an F80C mutation. The dimeric ApPrx*F80C mutant was then modified with a small molecule and successfully assembled as a decamer. Structural analysis confirmed that an artificially installed chemical moiety potentially facilitates suitable protein-protein interactions to rebuild a native structure. Rebuilding of dodecamer was also achieved through an additional amino acid mutation. This study describes a facile method to regulate the protein assembly state.

Autoencoder-Based Detection of Dynamic Allostery Triggered by Ligand Binding Based on Molecular Dynamics.

Dynamic allostery on proteins, triggered by regulator binding or chemical modifications, transmits information from the binding site to distant regions, dramatically altering protein function. It is accompanied by subtle changes in side-chain conformations of the protein, indicating that the changes in dynamics, and not rigid or large conformational changes, are essential to understand regulation of protein function. Although a lot of experimental and theoretical studies have been dedicated to investigate this issue, the regulation mechanism of protein function is still being debated. Here, we propose an autoencoder-based method that can detect dynamic allostery. The method is based on the comparison of time fluctuations of protein structures, in the form of distance matrices, obtained from molecular dynamics simulations in ligand-bound and -unbound forms. Our method detected that the changes in dynamics by ligand binding in the PDZ2 domain led to the reorganization of correlative fluctuation motions among residue pairs, which revealed a different view of the correlated motions from the PCA and DCCM. In addition, other correlative motions were also found as a result of the dynamic perturbation from the ligand binding, which may lead to dynamic allostery. This autoencoder-based method would be usefully applied to the signal transduction and mutagenesis systems involved in protein functions and severe diseases.

Protein Data Bank Japan (PDBj): updated user interfaces, resource description framework, analysis tools for large structures.

The Protein Data Bank Japan (PDBj, http://pdbj.org), a member of the worldwide Protein Data Bank (wwPDB), accepts and processes the deposited data of experimentally determined macromolecular structures. While maintaining the archive in collaboration with other wwPDB partners, PDBj also provides a wide range of services and tools for analyzing structures and functions of proteins. We herein outline the updated web user interfaces together with RESTful web services and the backend relational database that support the former. To enhance the interoperability of the PDB data, we have previously developed PDB/RDF, PDB data in the Resource Description Framework (RDF) format, which is now a wwPDB standard called wwPDB/RDF. We have enhanced the connectivity of the wwPDB/RDF data by incorporating various external data resources. Services for searching, comparing and analyzing the ever-increasing large structures determined by hybrid methods are also described.

A study of CDR3 loop dynamics reveals distinct mechanisms of peptide recognition by T-cell receptors exhibiting different levels of cross-reactivity.

T-cell receptors (TCRs) can productively interact with many different peptides bound within the MHC binding groove. This property varies with the level of cross-reactivity of TCRs; some TCRs are particularly hyper cross-reactive while others exhibit greater specificity. To elucidate the mechanism behind these differences, we studied five TCRs in complex with the same class II MHC (1Ab )-peptide (3K), that are known to exhibit different levels of cross-reactivity. Although these complexes have similar binding affinities, the interface areas between the TCR and the peptide-MHC (pMHC) differ significantly. We investigated static and dynamic structural features of the TCR-pMHC complexes and of TCRs in a free state, as well as the relationship between binding affinity and interface area. It was found that the TCRs known to exhibit lower levels of cross-reactivity bound to pMHC using an induced-fitting mechanism, forming large and tight interfaces rich in specific hydrogen bonds. In contrast, TCRs known to exhibit high levels of cross-reactivity used a more rigid binding mechanism where non-specific π-interactions involving the bulky Trp residue in CDR3ß dominated. As entropy loss upon binding in these highly degenerate and rigid TCRs is smaller than that in less degenerate TCRs, they can better tolerate changes in residues distal from the major contacts with MHC-bound peptide. Hence, our dynamics study revealed that differences in the peptide recognition mechanisms by TCRs appear to correlate with the levels of T-cell cross-reactivity.

Substrate recognition of N,N'-diacetylchitobiose deacetylase from Pyrococcus horikoshii.

Enzymes of carbohydrate esterase (CE) family 14 catalyze hydrolysis of N-acetyl groups at the non-reducing end of the N-acetylglucosamine (GlcNAc) residue of chitooligosaccharides or related compounds. N,N'-diacetylchitobiose deacetylase (Dac) belongs to the CE-14 family and plays a role in the chitinolytic pathway in archaea by deacetylating N,N'-diacetylchitobiose (GlcNAc2), which is the end product of chitinase. In this study, we revealed the structural basis of reaction specificity in CE-14 deacetylases by solving a crystal structure of Dac from Pyrococcus horikoshii (Ph-Dac) in complex with a novel reaction intermediate analog. We developed 2-deoxy-2-methylphosphoramido-d-glucose (MPG) as the analog of the tetrahedral oxyanion intermediate of the monosaccharide substrate GlcNAc. The crystal structure of Ph-Dac in complex with MPG demonstrated that Arg92, Asp115, and His152 side chains interact with hydroxyl groups of the glucose moiety of the non-reducing-end GlcNAc residue. The amino acid residues responsible for recognition of the MPG glucose moiety are spatially conserved in other CE-14 deacetylases. Molecular dynamics simulation of the structure of the Ph-Dac-GlcNAc2 complex indicated that the reducing GlcNAc residue is placed in a large intermolecular cleft and is not involved with specific interactions with the enzyme. This observation was consistent with results indicating that Ph-Dac displayed similar kinetic parameters for both GlcNAc and GlcNAc2. This study provides the structural basis of reaction-site specificity of Dac and related CE-14 enzymes.

Retinoblastoma-binding Protein 4-regulated Classical Nuclear Transport Is Involved in Cellular Senescence.

Nucleocytoplasmic trafficking is a fundamental cellular process in eukaryotic cells. Here, we demonstrated that retinoblastoma-binding protein 4 (RBBP4) functions as a novel regulatory factor to increase the efficiency of importin α/ß-mediated nuclear import. RBBP4 accelerates the release of importin ß1 from importin α via competitive binding to the importin ß-binding domain of importin α in the presence of RanGTP. Therefore, it facilitates importin α/ß-mediated nuclear import. We showed that the importin α/ß pathway is down-regulated in replicative senescent cells, concomitant with a decrease in RBBP4 level. Knockdown of RBBP4 caused both suppression of nuclear transport and induction of cellular senescence. This is the first report to identify a factor that competes with importin ß1 to bind to importin α, and it demonstrates that the loss of this factor can trigger cellular senescence.

Kotai Antibody Builder: automated high-resolution structural modeling of antibodies.

MOTIVATION: Kotai Antibody Builder is a Web service for tertiary structural modeling of antibody variable regions. It consists of three main steps: hybrid template selection by sequence alignment and canonical rules, 3D rendering of alignments and CDR-H3 loop modeling. For the last step, in addition to rule-based heuristics used to build the initial model, a refinement option is available that uses fragment assembly followed by knowledge-based scoring. Using targets from the Second Antibody Modeling Assessment, we demonstrate that Kotai Antibody Builder generates models with an overall accuracy equal to that of the best-performing semi-automated predictors using expert knowledge. AVAILABILITY AND IMPLEMENTATION: Kotai Antibody Builder is available at http://kotaiab.org CONTACT: standley@ifrec.osaka-u.ac.jp.

High-resolution modeling of antibody structures by a combination of bioinformatics, expert knowledge, and molecular simulations.

In the second antibody modeling assessment, we used a semiautomated template-based structure modeling approach for 11 blinded antibody variable region (Fv) targets. The structural modeling method involved several steps, including template selection for framework and canonical structures of complementary determining regions (CDRs), homology modeling, energy minimization, and expert inspection. The submitted models for Fv modeling in Stage 1 had the lowest average backbone root mean square deviation (RMSD) (1.06 Å). Comparison to crystal structures showed the most accurate Fv models were generated for 4 out of 11 targets. We found that the successful modeling in Stage 1 mainly was due to expert-guided template selection for CDRs, especially for CDR-H3, based on our previously proposed empirical method (H3-rules) and the use of position specific scoring matrix-based scoring. Loop refinement using fragment assembly and multicanonical molecular dynamics (McMD) was applied to CDR-H3 loop modeling in Stage 2. Fragment assembly and McMD produced putative structural ensembles with low free energy values that were scored based on the OSCAR all-atom force field and conformation density in principal component analysis space, respectively, as well as the degree of consensus between the two sampling methods. The quality of 8 out of 10 targets improved as compared with Stage 1. For 4 out of 10 Stage-2 targets, our method generated top-scoring models with RMSD values of less than 1 Å. In this article, we discuss the strengths and weaknesses of our approach as well as possible directions for improvement to generate better predictions in the future.

Blind prediction of interfacial water positions in CAPRI.

We report the first assessment of blind predictions of water positions at protein-protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community-wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions-20 groups submitted a total of 195 models-were assessed by measuring the recall fraction of water-mediated protein contacts. Of the 176 high- or medium-quality docking models-a very good docking performance per se-only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 Å, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high-quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein-water interactions and their role in stabilizing protein complexes.

[The first scene in the editing of the Hongzhi Pharmacopoeia: deciphering the 9th of August 1503, in the Ming dynasty].

On the 9th of August, 1503 the 10th Ming Emperor, Hongzhi, conveyed his desire to the Grand Secretariat that the Pharmacopoeia be edited. In response, the Grand Secretariat conveyed to the Emperor its intended procedure; namely, to choose two directors-general from among the members of the Hanlin Academy and to have the Imperial Academy of Medicine actually carry out the editing of the Pharmacopoeia. The Imperial Academy of Medicine, however, proposed another idea to the Emperor; that Imperial Academy members only be responsible for the editing. In consequence, the Grand Secretariat insisted on testing the ability of the Imperial Academy members. Though the Hongzhi Emperor agreed with the Grand Secretariat's policy and ordered it, the Imperial Academy of Medicine withdrew on the grounds that its ability was insufficient. The Hongzhi Emperor consented to this and sent a new order that the Hanlin Academy alone should carry out the editing. Nonetheless, as a face-saving measure, the Imperial Academy of Medicine was chosen to select a member as an editor, and the editing of the Pharmacopoeia commenced. The editing was completed on the 3rd of March, 1505, and it was then presented to the Hongzhi Emperor. This Pharmacopoeia, was entitled "Yuzhi bencao pinhui jinyao," and included a preface by The Hongzhi Emperor.

Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions.

Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies.

Structure-based analyses of gut microbiome-related proteins by neural networks and molecular dynamics simulations.

An imbalance in the gut microbiome is linked to immune disorders, such as autoimmune, allergic, and chronic inflammatory disorders. Elucidation of disease mechanisms is a matter of urgency. It requires precise elucidation of the structure-based mechanisms of protein interactions involved in disease onset. In addition, an understanding of the protein dynamics is vital because these fluctuations affect the function and interaction of disease-associated proteins. Experimental evaluation of not only protein interactions, functions, and structures but also the dynamics are time-consuming; therefore, computational predictions are necessary to elucidate disease mechanisms. Here, we introduce recent studies on structure-based analyses of proteins using computational approaches, particularly artificial intelligence (AI) and molecular dynamics (MD) simulations.

Protein-protein interaction prediction methods: from docking-based to AI-based approaches.

Protein-protein interactions (PPIs), such as protein-protein inhibitor, antibody-antigen complex, and supercomplexes play diverse and important roles in cells. Recent advances in structural analysis methods, including cryo-EM, for the determination of protein complex structures are remarkable. Nevertheless, much room remains for improvement and utilization of computational methods to predict PPIs because of the large number and great diversity of unresolved complex structures. This review introduces a wide array of computational methods, including our own, for estimating PPIs including antibody-antigen interactions, offering both historical and forward-looking perspectives.

Autoencoder-based detection of the residues involved in G protein-coupled receptor signaling.

Regulator binding and mutations alter protein dynamics. The transmission of the signal of these alterations to distant sites through protein motion results in changes in protein expression and cell function. The detection of residues involved in signal transmission contributes to an elucidation of the mechanisms underlying processes as vast as cellular function and disease pathogenesis. We developed an autoencoder (AE) based method that detects residues essential for signaling by comparing the fluctuation data, particularly the time fluctuation of the side-chain distances between residues, during molecular dynamics simulations between the ligand-bound and -unbound forms or wild-type and mutant forms of proteins. Here, the AE-based method was applied to the G protein-coupled receptor (GPCR) system, particularly a class A-type GPCR, CXCR4, to detect the essential residues involved in signaling. Among the residues involved in the signaling of the homolog CXCR2, which were extracted from the literature based on the complex structures of the ligand and G protein, our method could detect more than half of the essential residues involved in G protein signaling, including those spanning the fifth and sixth transmembrane helices in the intracellular region, despite the lack of information regarding the interaction with G protein in our CXCR4 models.

Neural networks for protein structure and function prediction and dynamic analysis.

Hardware and software advancements along with the accumulation of large amounts of data in recent years have together spurred a remarkable growth in the application of neural networks to various scientific fields. Machine learning based on neural networks with multiple (hidden) layers is becoming an extremely powerful approach for analyzing data. With the accumulation of large amounts of protein data such as structural and functional assay data, the effects of such approaches within the field of protein informatics are increasing. Here, we introduce our recent studies based on applications of neural networks for protein structure and function prediction and dynamic analysis involving: (i) inter-residue contact prediction based on a multiple sequence alignment (MSA) of amino acid sequences, (ii) prediction of protein-compound interaction using assay data, and (iii) detection of protein allostery from trajectories of molecular dynamic (MD) simulation.

Structural Modeling and Ligand-Binding Prediction for Analysis of Structure-Unknown and Function-Unknown Proteins Using FORTE Alignment and PoSSuM Pocket Search.

Structural data of biomolecules, such as those of proteins and nucleic acids, provide much information for estimation of their functions. For structure-unknown proteins, structure information is obtainable by modeling their structures based on sequence similarity of proteins. Moreover, information related to ligands or ligand-binding sites is necessary to elucidate protein functions because the binding of ligands can engender not only the activation and inactivation of the proteins but also the modification of protein functions. This chapter presents methods using our profile-profile alignment server FORTE and the PoSSuM ligand-binding site database for prediction of the structure and potential ligand-binding sites of structure-unknown and function-unknown proteins, aimed at protein function prediction.

Comprehensive analysis of PPARγ agonist activities of stereo-, regio-, and enantio-isomers of hydroxyoctadecadienoic acids.

Hydroxyoctadecadienoic acids (HODEs) are produced by oxidation and reduction of linoleates. There are several regio- and stereo-isomers of HODE, and their concentrations in vivo are higher than those of other lipids. Although conformational isomers may have different biological activities, comparative analysis of intracellular function of HODE isomers has not yet been performed. We evaluated the transcriptional activity of peroxisome proliferator-activated receptor γ (PPARγ), a therapeutic target for diabetes, and analyzed PPARγ agonist activity of HODE isomers. The lowest scores for docking poses of 12 types of HODE isomers (9-, 10-, 12-, and 13-HODEs) were almost similar in docking simulation of HODEs into PPARγ ligand-binding domain (LBD). Direct binding of HODE isomers to PPARγ LBD was determined by water-ligand observed via gradient spectroscopy (WaterLOGSY) NMR experiments. In contrast, there were differences in PPARγ agonist activities among 9- and 13-HODE stereo-isomers and 12- and 13-HODE enantio-isomers in a dual-luciferase reporter assay. Interestingly, the activity of 9-HODEs was less than that of other regio-isomers, and 9-(E,E)-HODE tended to decrease PPARγ-target gene expression during the maturation of 3T3-L1 cells. In addition, 10- and 12-(Z,E)-HODEs, which we previously proposed as biomarkers for early-stage diabetes, exerted PPARγ agonist activity. These results indicate that all HODE isomers have PPARγ-binding affinity; however, they have different PPARγ agonist activity. Our findings may help to understand the biological function of lipid peroxidation products.

Actin Cytoskeletal Reorganization Function of JRAB/MICAL-L2 Is Fine-tuned by Intramolecular Interaction between First LIM Zinc Finger and C-terminal Coiled-coil Domains.

JRAB/MICAL-L2 is an effector protein of Rab13, a member of the Rab family of small GTPase. JRAB/MICAL-L2 consists of a calponin homology domain, a LIM domain, and a coiled-coil domain. JRAB/MICAL-L2 engages in intramolecular interaction between the N-terminal LIM domain and the C-terminal coiled-coil domain, and changes its conformation from closed to open under the effect of Rab13. Open-form JRAB/MICAL-L2 induces the formation of peripheral ruffles via an interaction between its calponin homology domain and filamin. Here, we report that the LIM domain, independent of the C-terminus, is also necessary for the function of open-form JRAB/MICAL-L2. In mechanistic terms, two zinc finger domains within the LIM domain bind the first and second molecules of actin at the minus end, potentially inhibiting the depolymerization of actin filaments (F-actin). The first zinc finger domain also contributes to the intramolecular interaction of JRAB/MICAL-L2. Moreover, the residues of the first zinc finger domain that are responsible for the intramolecular interaction are also involved in the association with F-actin. Together, our findings show that the function of open-form JRAB/MICAL-L2 mediated by the LIM domain is fine-tuned by the intramolecular interaction between the first zinc finger domain and the C-terminal domain.

PreBI: prediction of biological interfaces of proteins in crystals.

PreBI is a server that predicts biological interfaces in protein crystal structures, according to the complementarity and the area of the interface. The server accepts a coordinate file in the PDB format, and all of the possible interfaces are generated automatically, according to the symmetry operations given in the coordinate file. For all of the interfaces generated, the complementarities of the electrostatic potential, hydrophobicity and shape of the interfaces are analyzed, and the most probable biological interface is identified according to the combination of the degree of complementarity derived from the database analyses and the area of the interface. The results can be checked through an interactive viewer, and the most probable complex can be downloaded as atomic coordinates in the PDB format. PreBI is available at http://pre-s.protein.osaka-u.ac.jp/~prebi/.