QM/MM Study of the Catalytic Mechanism and Substrate Specificity of the Aromatic Substrate C-Methyltransferase Fur6.

In the field of medical chemistry and other organic chemistry, introducing a methyl group into a designed position has been difficult to achieve. However, owing to the vigorous developments in the field of enzymology, methyltransferases are considered potential tools for addressing this problem. Within the methyltransferase family, Fur6 catalyzes the methylation of C3 of 1,2,4,5,7-pentahydroxynaphthalene (PHN) using S-adenosyl-l-methionine (SAM) as the methyl donor. Here, we report the catalytic mechanism and substrate specificity of Fur6 based on computational studies. Our molecular dynamics (MD) simulation studies reveal the reactive form of PHN and its interactions with the enzyme. Our hybrid quantum mechanics/molecular mechanics (QM/MM) calculations suggest the reaction pathway of the methyl transfer step in which the energy barrier is 8.6 kcal mol-1. Our free-energy calculations with a polarizable continuum model (PCM) indicate that the final deprotonation step of the methylated intermediate occurs after it is ejected into the water solvent from the active center pocket of Fur6. Additionally, our studies on the protonation states, the highest occupied molecular orbital (HOMOs), and the energy barriers of the methylation reaction for the analogs of PHN demonstrate the mechanism of the specificity to PHN. Our study provides valuable insights into Fur6 chemistry, contributing to a deeper understanding of molecular mechanisms and offering an opportunity to engineer the enzyme to achieve high yields of the desired product(s).

Comprehensive computational analysis of the SRK-SP11 molecular interaction underlying self-incompatibility in Brassicaceae using improved structure prediction for cysteine-rich proteins.

Plants employ self-incompatibility (SI) to promote cross-fertilization. In Brassicaceae, this process is regulated by the formation of a complex between the pistil determinant S receptor kinase (SRK) and the pollen determinant S-locus protein 11 (SP11, also known as S-locus cysteine-rich protein, SCR). In our previous study, we used the crystal structures of two eSRK-SP11 complexes in Brassica rapa S8 and S9 haplotypes and nine computationally predicted complex models to demonstrate that only the SRK ectodomain (eSRK) and SP11 pairs derived from the same S haplotype exhibit high binding free energy. However, predicting the eSRK-SP11 complex structures for the other 100 + S haplotypes and genera remains difficult because of SP11 polymorphism in sequence and structure. Although protein structure prediction using AlphaFold2 exhibits considerably high accuracy for most protein monomers and complexes, 46% of the predicted SP11 structures that we tested showed < 75 mean per-residue confidence score (pLDDT). Here, we demonstrate that the use of curated multiple sequence alignment (MSA) for cysteine-rich proteins significantly improved model accuracy for SP11 and eSRK-SP11 complexes. Additionally, we calculated the binding free energies of the predicted eSRK-SP11 complexes using molecular dynamics (MD) simulations and observed that some Arabidopsis haplotypes formed a binding mode that was critically different from that of B. rapa S8 and S9. Thus, our computational results provide insights into the haplotype-specific eSRK-SP11 binding modes in Brassicaceae at the residue level. The predicted models are freely available at Zenodo, https://doi.org/10.5281/zenodo.8047768.

Feedback-AVPGAN: Feedback-guided generative adversarial network for generating antiviral peptides.

In this study, we propose Feedback-AVPGAN, a system that aims to computationally generate novel antiviral peptides (AVPs). This system relies on the key premise of the Generative Adversarial Network (GAN) model and the Feedback method. GAN, a generative modeling approach that uses deep learning methods, comprises a generator and a discriminator. The generator is used to generate peptides; the generated proteins are fed to the discriminator to distinguish between the AVPs and non-AVPs. The original GAN design uses actual data to train the discriminator. However, not many AVPs have been experimentally obtained. To solve this problem, we used the Feedback method to allow the discriminator to learn from the existing as well as generated synthetic data. We implemented this method using a classifier module that classifies each peptide sequence generated by the GAN generator as AVP or non-AVP. The classifier uses the transformer network and achieves high classification accuracy. This mechanism enables the efficient generation of peptides with a high probability of exhibiting antiviral activity. Using the Feedback method, we evaluated various algorithms and their performance. Moreover, we modeled the structure of the generated peptides using AlphaFold2 and determined the peptides having similar physicochemical properties and structures to those of known AVPs, although with different sequences.

Prediction of protein mononucleotide binding sites using AlphaFold2 and machine learning.

In this study, we developed a system that predicts the binding sites of proteins for five mononucleotides (AMP, ADP, ATP, GDP, and GTP). The system comprises two machine learning (ML)-based predictors using a convolutional neural network and a gradient boosting machine, two template-based predictors based on sequence and structure alignment, and a predictor that performs ensemble learning of these four predictors. In this study, data augmentation of ligand binding sites with similar ligand structures was performed. For example, in the prediction of ADP-binding sites using ML methods, the binding sites of AMP and ATP, which have similar structures, are considered. In addition, we constructed the structure models using AlphaFold2, a highly accurate protein prediction method. The secondary structure and dihedral angle information obtained using the model structures were used as ML predictor features. Additionally, in the template-based predictor, the structures of the binding sites were used as templates to be explored based on structure alignment to identify the binding site of the target. Consequently, the template-based predictor based on structure alignment showed the best performance among the four individual predictors, and the ensemble predictor achieved the best performance, with an area under the curve of 0.958 for all mononucleotides.

Extracellular pH sensing by plant cell-surface peptide-receptor complexes.

The extracellular pH is a vital regulator of various biological processes in plants. However, how plants perceive extracellular pH remains obscure. Here, we report that plant cell-surface peptide-receptor complexes can function as extracellular pH sensors. We found that pattern-triggered immunity (PTI) dramatically alkalinizes the acidic extracellular pH in root apical meristem (RAM) region, which is essential for root meristem growth factor 1 (RGF1)-mediated RAM growth. The extracellular alkalinization progressively inhibits the acidic-dependent interaction between RGF1 and its receptors (RGFRs) through the pH sensor sulfotyrosine. Conversely, extracellular alkalinization promotes the alkaline-dependent binding of plant elicitor peptides (Peps) to its receptors (PEPRs) through the pH sensor Glu/Asp, thereby promoting immunity. A domain swap between RGFR and PEPR switches the pH dependency of RAM growth. Thus, our results reveal a mechanism of extracellular pH sensing by plant peptide-receptor complexes and provide insights into the extracellular pH-mediated regulation of growth and immunity in the RAM.

ColabFold: making protein folding accessible to all.

ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold's 40-60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at https://github.com/sokrypton/ColabFold and its novel environmental databases are available at https://colabfold.mmseqs.com .

Mutagenesis analysis of GMN motif in Arabidopsis thaliana Mg2+ transporter MRS2-1.

Magnesium is an important nutrient for plants, but much is still unknown about plant Mg2+ transporters. Combining with the structural prediction of AlphaFold2, we used mutagenesis and 28Mg uptake assay to study the highly conserved "GMN" motif of Arabidopsis thaliana MRS2-1 (AtMRS2-1) transporter. We demonstrated that the glycine and methionine in GMN motif are essential for AtMRS2-1 to transport Mg2+.

Structural and Molecular Basis of the Catalytic Mechanism of Geranyl Pyrophosphate C6-Methyltransferase: Creation of an Unprecedented Farnesyl Pyrophosphate C6-Methyltransferase.

Prenyl pyrophosphate methyltransferases enhance the structural diversity of terpenoids. However, the molecular basis of their catalytic mechanisms is poorly understood. In this study, using multiple strategies, we characterized a geranyl pyrophosphate (GPP) C6-methyltransferase, BezA. Biochemical analysis revealed that BezA requires Mg2+ and solely methylates GPP. The crystal structures of BezA and its complex with S-adenosyl homocysteine were solved at 2.10 and 2.56 Å, respectively. Further analyses using site-directed mutagenesis, molecular docking, molecular dynamics simulations, and quantum mechanics/molecular mechanics calculations revealed the molecular basis of the methylation reaction. Importantly, the function of E170 as a catalytic base to complete the methylation reaction was established. We also succeeded in switching the substrate specificity by introducing a W210A substitution, resulting in an unprecedented farnesyl pyrophosphate C6-methyltransferase.

Prediction and analysis of antifreeze proteins.

Antifreeze proteins (AFPs) are proteins that protect cellular fluids and body fluids from freezing by inhibiting the nucleation and growth of ice crystals and preventing ice recrystallization, thereby contributing to the maintenance of life in living organisms. They exist in fish, insects, microorganisms, and fungi. However, the number of known AFPs is currently limited, and it is essential to construct a reliable dataset of AFPs and develop a bioinformatics tool to predict AFPs. In this work, we first collected AFPs sequences from UniProtKB considering the reliability of annotations and, based on these datasets, developed a prediction system using random forest. We achieved accuracies of 0.961 and 0.947 for non-redundant sequences with less than 90% and 30% identities and achieved the accuracy of 0.953 for representative sequences for each species. Using the ability of random forest, we identified the sequence features that contributed to the prediction. Some sequence features were common to AFPs from different species. These features include the Cys content, Ala-Ala content, Trp-Gly content, and the amino acids' distribution related to the disorder propensity. The computer program and the dataset developed in this work are available from the GitHub site: https://github.com/ryomiya/Prediction-and-analysis-of-antifreeze-proteins.

EXPRORER: Rational Cosolvent Set Construction Method for Cosolvent Molecular Dynamics Using Large-Scale Computation.

Cosolvent molecular dynamics (CMD) simulations involve an MD simulation of a protein in the presence of explicit water molecules mixed with cosolvent molecules to perform hotspot detection, binding site identification, and binding energy estimation, while other existing methods (e.g., MixMD, SILCS, and MDmix) utilize small molecules that represent functional groups of compounds. However, the cosolvent selections employed in these methods differ and there are only a few cosolvents that are commonly used in these methods. In this study, we proposed a systematic method for constructing a set of cosolvents for drug discovery, termed the EXtended PRObes set construction by REpresentative Retrieval (EXPRORER). First, we extracted typical substructures from FDA-approved drugs, generated 138 cosolvent structures, and for each cosolvent molecule, we conducted CMD simulations to generate a spatial probability distribution map of cosolvent atoms (PMAP). Analyses of PMAP similarity revealed that a cosolvent pair with a PMAP similarity greater than 0.70-0.75 shared similar structural features. We present a method for the construction of a cosolvent subset that satisfies a similarity threshold for all cosolvents, and we tested the constructed sets for four proteins. To our knowledge, this is the first study to include a systematic proposal for cosolvent set construction, and thus, the EXPRORER cosolvents will provide deeper insights into ligand binding sites of various proteins.

Mechanism of self/nonself-discrimination in Brassica self-incompatibility.

Self-incompatibility (SI) is a breeding system that promotes cross-fertilization. In Brassica, pollen rejection is induced by a haplotype-specific interaction between pistil determinant SRK (S receptor kinase) and pollen determinant SP11 (S-locus Protein 11, also named SCR) from the S-locus. Although the structure of the B. rapa S9-SRK ectodomain (eSRK) and S9-SP11 complex has been determined, it remains unclear how SRK discriminates self- and nonself-SP11. Here, we uncover the detailed mechanism of self/nonself-discrimination in Brassica SI by determining the S8-eSRK-S8-SP11 crystal structure and performing molecular dynamics (MD) simulations. Comprehensive binding analysis of eSRK and SP11 structures reveals that the binding free energies are most stable for cognate eSRK-SP11 combinations. Residue-based contribution analysis suggests that the modes of eSRK-SP11 interactions differ between intra- and inter-subgroup (a group of phylogenetically neighboring haplotypes) combinations. Our data establish a model of self/nonself-discrimination in Brassica SI.

Functional effect of nobiletin as a food-derived allosteric modulator of mouse CRFR2ß in skeletal muscle.

Activation of corticotropin-releasing factor receptor 2ß (CRFR2ß) results in increased skeletal muscle mass and the prevention of muscle atrophy. Using a luciferase reporter assay, we screened 357 functional food factors that activate CRFR2ß and, subsequently, confirmed that nobiletin (NBT) increases CRFR2ß activity. Additionally, we found that NBT augments the activity of the endogenous peptide ligand urocortin 2 (Ucn2) in a concentration-dependent manner. Computational simulation of CRFR2ß confirmed that transmembrane domains (TMs) 1 and 2 are important for the synergistic activity of NBT and also identified important amino acids in these domains. Finally, we demonstrated that a co-administration of Ucn2 and NBT increases the hypertrophic signal in mouse skeletal muscle. These observations demonstrate that NBT can activate CRFR2ß and amplify the agonistic activity of Ucn2 and that such food-derived molecules have the potential to enhance endogenous G protein-coupled receptor ligand activities and contribute to the maintenance of skeletal muscle mass and function.

A prospective compound screening contest identified broader inhibitors for Sirtuin 1.

Potential inhibitors of a target biomolecule, NAD-dependent deacetylase Sirtuin 1, were identified by a contest-based approach, in which participants were asked to propose a prioritized list of 400 compounds from a designated compound library containing 2.5 million compounds using in silico methods and scoring. Our aim was to identify target enzyme inhibitors and to benchmark computer-aided drug discovery methods under the same experimental conditions. Collecting compound lists derived from various methods is advantageous for aggregating compounds with structurally diversified properties compared with the use of a single method. The inhibitory action on Sirtuin 1 of approximately half of the proposed compounds was experimentally accessed. Ultimately, seven structurally diverse compounds were identified.

Functional characterisation of two ferric-ion coordination modes of TtFbpA, the periplasmic subunit of an ABC-type iron transporter from Thermus thermophilus HB8.

The ferric ion binding protein A of Thermus thermophilus HB8 (TtFbpA) is the periplasmic subunit of an ABC-type iron transporter. Two Fe3+-bound crystal structures at pH 5.5 and pH 7.5 and one apo structure have been reported for TtFbpA. In addition to three Tyr residues, TtFbpA coordinates with Fe3+ using two monodentate HCO3- and one H2O to form a six-coordinated mode at pH 5.5 or one bidentate CO32- to form a five-coordinated mode at pH 7.5. We investigated the biological significance of these Fe3+-bound forms of TtFbpA and the synergistic anions (HCO3- and CO32-). Quantum mechanical calculations in silico indicated that only these coordination modes were plausible out of six possibilities. Comparison of the crystal structures revealed a key motif, RZX1X2L(I/V), that would couple the Fe3+ coordination mode and the TtFbpA protein conformation. Both gel filtration chromatography and isothermal titration calorimetry showed that TtFbpA could bind Fe3+ at pH 7.5 but not at pH 5.5. Isothermal titration calorimetry also revealed that the binding at pH 7.5 was a three-step endothermic reaction that required NaHCO3. These results indicate that the holo structure at pH 5.5 is unstable in solution and may correspond to a transition state of TtFbpA-Fe3+ binding at pH 7.5 because HCO3- is much more abundant than CO32- at both pH values. Reorganisation of the synergistic ions and coupled protein conformational change will occur to form the stable TtFbpA-Fe3+ complex at pH 7.5, but not at pH 5.5. Identification of such a transition state will contribute to molecular design of novel FbpA inhibitors.

Understanding the Molecular Mechanism Underlying the High Catalytic Activity of p-Hydroxybenzoate Hydroxylase Mutants for Producing Gallic Acid.

p-Hydroxybenzoate hydroxylase (PHBH) is a flavoprotein monooxygenase that catalyzes the hydroxylation of p-hydroxybenzoate (p-OHB) to 3,4-dihydroxybenzoate (3,4-DOHB). PHBH can bind to other benzoate derivatives in addition to p-OHB; however, hydroxylation does not occur on 3,4-DOHB. Replacement of Tyr385 with Phe forms a mutant, which enables the production of 3,4,5-trihydroxybenzonate (gallic acid) from 3,4-DOHB, although the catalytic activity of the mutant is quite low. In this study, we report how the L199V/Y385F double mutant exhibits activity for producing gallic acid 4.3-fold higher than that of the Y385F single mutant. This improvement in catalytic activity is primarily due to the suppression of a shunt reaction that wastes reduced nicotinamide adenine dinucleotide phosphate by producing H2O2. To further elucidate the molecular mechanism underlying this higher catalytic activity, we performed molecular dynamics simulations and quantum mechanics/molecular mechanics calculations, in addition to determining the crystal structure of the Y385F·3,4-DOHB complex. The simulations showed that the Y385F mutation facilitates the deprotonation of the 4-hydroxy group of 3,4-DOHB, which is necessary for initiating hydroxylation. Moreover, the L199V mutation in addition to the Y385F mutation allows the OH moiety in the peroxide group of C-(4a)-flavin hydroperoxide to come into the proximity of the C5 atom of 3,4-DOHB. Overall, this study provides a consistent explanation for the change in the catalytic activity of PHBH caused by mutations, which will enable us to better design an enzyme with different activities.

Identifying short disorder-to-order binding regions in disordered proteins with a deep convolutional neural network method.

Molecular recognition features (MoRFs) are key functional regions of intrinsically disordered proteins (IDPs), which play important roles in the molecular interaction network of cells and are implicated in many serious human diseases. Identifying MoRFs is essential for both functional studies of IDPs and drug design. This study adopts the cutting-edge machine learning method of artificial intelligence to develop a powerful model for improving MoRFs prediction. We proposed a method, named as en_DCNNMoRF (ensemble deep convolutional neural network-based MoRF predictor). It combines the outcomes of two independent deep convolutional neural network (DCNN) classifiers that take advantage of different features. The first, DCNNMoRF1, employs position-specific scoring matrix (PSSM) and 22 types of amino acid-related factors to describe protein sequences. The second, DCNNMoRF2, employs PSSM and 13 types of amino acid indexes to describe protein sequences. For both single classifiers, DCNN with a novel two-dimensional attention mechanism was adopted, and an average strategy was added to further process the output probabilities of each DCNN model. Finally, en_DCNNMoRF combined the two models by averaging their final scores. When compared with other well-known tools applied to the same datasets, the accuracy of the novel proposed method was comparable with that of state-of-the-art methods. The related web server can be accessed freely via http://vivace.bi.a.u-tokyo.ac.jp:8008/fang/en_MoRFs.php .

MoRFPred_en: Sequence-based prediction of MoRFs using an ensemble learning strategy.

Molecular recognition features (MoRFs) usually act as "hub" sites in the interaction networks of intrinsically disordered proteins (IDPs). Because an increasing number of serious diseases have been found to be associated with disordered proteins, identifying MoRFs has become increasingly important. In this study, we propose an ensemble learning strategy, named MoRFPred_en, to predict MoRFs from protein sequences. This approach combines four submodels that utilize different sequence-derived features for the prediction, including a multichannel one-dimensional convolutional neural network (CNN_1D multichannel) based model, two deep two-dimensional convolutional neural network (DCNN_2D) based models, and a support vector machine (SVM) based model. When compared with other methods on the same datasets, the MoRFPred_en approach produced better results than existing state-of-the-art MoRF prediction methods, achieving an AUC of 0.762 on the VALIDATION419 dataset, 0.795 on the TEST45 dataset, and 0.776 on the TEST49 dataset. Availability: http://vivace.bi.a.u-tokyo.ac.jp:8008/fang/MoRFPred_en.php.

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.