Cryo-EM Structure of the Human Cannabinoid Receptor CB2-Gi Signaling Complex.

Drugs selectively targeting CB2 hold promise for treating neurodegenerative disorders, inflammation, and pain while avoiding psychotropic side effects mediated by CB1. The mechanisms underlying CB2 activation and signaling are poorly understood but critical for drug design. Here we report the cryo-EM structure of the human CB2-Gi signaling complex bound to the agonist WIN 55,212-2. The 3D structure reveals the binding mode of WIN 55,212-2 and structural determinants for distinguishing CB2 agonists from antagonists, which are supported by a pair of rationally designed agonist and antagonist. Further structural analyses with computational docking results uncover the differences between CB2 and CB1 in receptor activation, ligand recognition, and Gi coupling. These findings are expected to facilitate rational structure-based discovery of drugs targeting the cannabinoid system.

RNPS1 inhibits excessive tumor necrosis factor/tumor necrosis factor receptor signaling to support hematopoiesis in mice.

Null mutations of spliceosome components or cofactors are homozygous lethal in eukaryotes, but viable hypomorphic mutations provide an opportunity to understand the physiological impact of individual splicing proteins. We describe a viable missense allele (F181I) of Rnps1 encoding an essential regulator of splicing and nonsense-mediated decay (NMD), identified in a mouse genetic screen for altered immune cell development. Homozygous mice displayed a stem cell­intrinsic defect in hematopoiesis of all lineages due to excessive apoptosis induced by tumor necrosis factor (TNF)­dependent death signaling. Numerous transcript splice variants containing retained introns and skipped exons were detected at elevated frequencies in Rnps1F181I/F181I splenic CD8+ T cells and hematopoietic stem cells (HSCs), but NMD appeared normal. Strikingly, Tnf knockout rescued all hematopoietic cells to normal or near-normal levels in Rnps1F181I/F181I mice and dramatically reduced intron retention in Rnps1F181I/F181I CD8+ T cells and HSCs. Thus, RNPS1 is necessary for accurate splicing, without which disinhibited TNF signaling triggers hematopoietic cell death.

Metabolomics and proteomics insights into subacute ruminal acidosis etiology and inhibition of proliferation of yak rumen epithelial cells in vitro.

BACKGROUND: Untargeted metabolomics and proteomics were employed to investigate the intracellular response of yak rumen epithelial cells (YRECs) to conditions mimicking subacute rumen acidosis (SARA) etiology, including exposure to short-chain fatty acids (SCFA), low pH5.5 (Acid), and lipopolysaccharide (LPS) exposure for 24 h. RESULTS: These treatments significantly altered the cellular morphology of YRECs. Metabolomic analysis identified significant perturbations with SCFA, Acid and LPS treatment affecting 259, 245 and 196 metabolites (VIP > 1, P < 0.05, and fold change (FC) ≥ 1.5 or FC ≤ 0.667). Proteomic analysis revealed that treatment with SCFA, Acid, and LPS resulted in differential expression of 1251, 1396, and 242 proteins, respectively (FC ≥ 1.2 or ≤ 0.83, P < 0.05, FDR < 1%). Treatment with SCFA induced elevated levels of metabolites involved in purine metabolism, glutathione metabolism, and arginine biosynthesis, and dysregulated proteins associated with actin cytoskeleton organization and ribosome pathways. Furthermore, SCFA reduced the number, morphology, and functionality of mitochondria, leading to oxidative damage and inhibition of cell survival. Gene expression analysis revealed a decrease the genes expression of the cytoskeleton and cell cycle, while the genes expression associated with inflammation and autophagy increased (P < 0.05). Acid exposure altered metabolites related to purine metabolism, and affected proteins associated with complement and coagulation cascades and RNA degradation. Acid also leads to mitochondrial dysfunction, alterations in mitochondrial integrity, and reduced ATP generation. It also causes actin filaments to change from filamentous to punctate, affecting cellular cytoskeletal function, and increases inflammation-related molecules, indicating the promotion of inflammatory responses and cellular damage (P < 0.05). LPS treatment induced differential expression of proteins involved in the TNF signaling pathway and cytokine-cytokine receptor interaction, accompanied by alterations in metabolites associated with arachidonic acid metabolism and MAPK signaling (P < 0.05). The inflammatory response and activation of signaling pathways induced by LPS treatment were also confirmed through protein interaction network analysis. The integrated analysis reveals co-enrichment of proteins and metabolites in cellular signaling and metabolic pathways. CONCLUSIONS: In summary, this study contributes to a comprehensive understanding of the detrimental effects of SARA-associated factors on YRECs, elucidating their molecular mechanisms and providing potential therapeutic targets for mitigating SARA.

Co-occurrence of Erdheim-Chester disease and clonally evolving acute myeloid leukemia with FLT3-ITD and PTPN11 mutations.

Erdheim-Chester disease (ECD) is a rare histiocytosis that tends to co-exist with other myeloid malignancies. Here, we use genetic and transcriptomic sequencing to delineate a case of co-occurring BRAFV600E-mutated ECD and acute myeloid leukemia (AML), followed by AML remission and relapse. The AML relapse involved the extinction of clones with KMT2A-AFDN and FLT3-ITD, and the predominance of PTPN11-mutated subclones with distinct transcriptomic features. This case report has highlighted the screening for other myeloid malignancies at the diagnosis of ECD and the clinical significance of PTPN11-mutated AML subclones that require meticulous monitoring.

Good Cycling Stabilities of Sn/SnO2/C/S Composite Electrodes with Introduced Active Tin Atoms in Li-S Batteries.

Polysulfides are easily dissolved in the electrolyte of Li-S batteries after long cycles. Sn atom modification electrodes are beneficial for improving cycling stabilities of Li-S batteries. However, the influence of Sn atoms on the structure and electrochemical performance of SnO2/C composite materials is not explored. Sn/SnO2/C composite materials are developed as sulfur carriers in Li-S batteries in our work. In addition, the cycling stability mechanism of Sn/SnO2/C/S composite electrodes is also elucidated. Results show that introduced Sn/SnO2/C/S composite electrodes display good cycling stability (420.1 mAh·g-1 at 1C after 1000 cycles) in Li-S batteries. The sulfur load of Sn/SnO2/C/S composite electrodes is 80 wt % (2 mg-1·cm-2). The introduction of Sn into Sn/SnO2/C/S composite electrodes plays three roles. The first role is to enhance the structural stability of SnO2. The second role is to help adsorb active sulfur ions. The last role is to promote the electron transportation ability during the initial discharging/charging process. Sn/SnO2/C/S composite electrodes are beneficial for inhibiting the dissolution of polysulfides in electrolytes after long cycles.

Sulfatides are endogenous ligands for the TLR4-MD-2 complex.

Many endogenous molecules, mostly proteins, purportedly activate the Toll-like receptor 4 (TLR4)-myeloid differentiation factor-2 (MD-2) complex, the innate immune receptor for lipopolysaccharide (LPS) derived from gram-negative bacteria. However, there is no structural evidence supporting direct TLR4-MD-2 activation by endogenous ligands. Sulfatides (3-O-sulfogalactosylceramides) are natural, abundant sulfated glycolipids that have variously been shown to initiate or suppress inflammatory responses. We show here that short fatty acid (FA) chain sulfatides directly activate mouse TLR4-MD-2 independent of CD14, trigger MyD88- and TRIF-dependent signaling, and stimulate tumor necrosis factor α (TNFα) and type I interferon (IFN) production in mouse macrophages. In contrast to the agonist activity toward the mouse receptor, the tested sulfatides antagonize TLR4-MD-2 activation by LPS in human macrophage-like cells. The agonistic and antagonistic activities of sulfatides require the presence of the sulfate group and are inversely related to the FA chain length. The crystal structure of mouse TLR4-MD-2 in complex with C16-sulfatide revealed that three C16-sulfatide molecules bound to the MD-2 hydrophobic pocket and induced an active dimer conformation of the receptor complex similar to that induced by LPS or lipid A. The three C16-sulfatide molecules partially mimicked the detailed interactions of lipid A to achieve receptor activation. Our results suggest that sulfatides may mediate sterile inflammation or suppress LPS-stimulated inflammation, and that additional endogenous negatively charged lipids with up to six lipid chains of limited length might also bind to TLR4-MD-2 and activate or inhibit this complex.

Mass spectrometric identification of immunogenic SARS-CoV-2 epitopes and cognate TCRs.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections elicit both humoral and cellular immune responses. For the prevention and treatment of COVID-19, the disease caused by SARS-CoV-2, it has become increasingly apparent that T cell responses are equally if not more important than humoral responses in mediating recovery and immune protection. One major challenge in developing T cell-based therapies for infectious and malignant diseases has been the identification of immunogenic epitopes that can elicit a meaningful T cell response. Traditionally, this has been achieved using sophisticated in silico methods to predict putative epitopes deduced from binding affinities. Our studies find that, in contrast to current convention, "immunodominant" SARS-CoV-2 peptides defined by such in silico methods often fail to elicit T cell responses recognizing naturally presented SARS-CoV-2 epitopes. We postulated that immunogenic epitopes for SARS-CoV-2 are best defined empirically by directly analyzing peptides eluted from the naturally processed peptide-major histocompatibility complex (MHC) and then validating immunogenicity by determining whether such peptides can elicit T cells recognizing SARS-CoV-2 antigen-expressing cells. Using a tandem mass spectrometry approach, we identified epitopes derived from not only structural but also nonstructural genes in regions highly conserved among SARS-CoV-2 strains, including recently recognized variants. Finally, there are no reported T cell receptor-engineered T cell technology that can redirect T cell specificity to recognize and kill SARS-CoV-2 target cells. We report here several SARS-CoV-2 epitopes defined by mass spectrometric analysis of MHC-eluted peptides, provide empiric evidence for their immunogenicity, and demonstrate engineered TCR-redirected killing.

Identification and Functional Characterization of Abiotic Stress Tolerance-Related PLATZ Transcription Factor Family in Barley (Hordeum vulgare L.).

Plant AT-rich sequence and zinc-binding proteins (PLATZs) are a novel category of plant-specific transcription factors involved in growth, development, and abiotic stress responses. However, the PLATZ gene family has not been identified in barley. In this study, a total of 11 HvPLATZs were identified in barley, and they were unevenly distributed on five of the seven chromosomes. The phylogenetic tree, incorporating PLATZs from Arabidopsis, rice, maize, wheat, and barley, could be classified into six clusters, in which HvPLATZs are absent in Cluster VI. HvPLATZs exhibited conserved motif arrangements with a characteristic PLATZ domain. Two segmental duplication events were observed among HvPLATZs. All HvPLATZs were core genes present in 20 genotypes of the barley pan-genome. The HvPLATZ5 coding sequences were conserved among 20 barley genotypes, whereas HvPLATZ4/9/10 exhibited synonymous single nucleotide polymorphisms (SNPs); the remaining ones showed nonsynonymous variations. The expression of HvPLATZ2/3/8 was ubiquitous in various tissues, whereas HvPLATZ7 appeared transcriptionally silent; the remaining genes displayed tissue-specific expression. The expression of HvPLATZs was modulated by salt stress, potassium deficiency, and osmotic stress, with response patterns being time-, tissue-, and stress type-dependent. The heterologous expression of HvPLATZ3/5/6/8/9/10/11 in yeast enhanced tolerance to salt and osmotic stress, whereas the expression of HvPLATZ2 compromised tolerance. These results advance our comprehension and facilitate further functional characterization of HvPLATZs.

Carboxyl-terminal modulator protein (CTMP) deficiency mitigates denervation-induced skeletal muscle atrophy.

Denervated skeletal muscles show decreased Akt activity and phosphorylation, resulting in atrophy. Akt inhibits downstream transcription of atrophy-associated ubiquitin ligases like muscle ring-finger protein 1 (MuRF-1). In addition, reduced Akt signaling contributes to aberrant protein synthesis in muscles. In ALS mice, we recently found that carboxyl-terminator modulator protein (CTMP) expression is increased and correlated with reduced Akt signaling in atrophic skeletal muscle. CTMP has also been implicated in promoting muscle degeneration and catabolism in an in vitro muscle atrophy model. The present study examined whether sciatic nerve injury (SNI) stimulated CTMP expression in denervated skeletal muscle during muscle atrophy. We hypothesized that CTMP deficiency would reduce neurogenic atrophy and reverse Akt signaling downregulation. Compared to the unaffected contralateral muscle, wild-type (WT) gastrocnemius muscle had a significant increase in CTMP (p < 0.05). Furthermore, denervated CTMP knockout (CTMP-KO) gastrocnemius weighed more than WT muscle (p < 0.05). Denervated CTMP-KO gastrocnemius also showed higher Akt and downstream glycogen synthase kinase 3ß (GSK3ß) phosphorylation compared to WT muscle (p < 0.05) as well as ribosomal proteins S6 and 4E-BP1 phosphorylation (p < 0.001 and p < 0.05, respectively). Moreover, CTMP-KO mice showed significantly lower levels of E3 ubiquitin ligase MuRF-1 and myostatin than WT muscle (p < 0.05). Our findings suggest that CTMP is essential to muscle atrophy after denervation and it may act by reducing Akt signaling, protein synthesis, and increasing myocellular catabolism.

Development and test of highly accurate endpoint free energy methods. 1: Evaluation of ABCG2 charge model on solvation free energy prediction and optimization of atom radii suitable for more accurate solvation free energy prediction by the PBSA method.

Accurate estimation of solvation free energy (SFE) lays the foundation for accurate prediction of binding free energy. The Poisson-Boltzmann (PB) or generalized Born (GB) combined with surface area (SA) continuum solvation method (PBSA and GBSA) have been widely used in SFE calculations because they can achieve good balance between accuracy and efficiency. However, the accuracy of these methods can be affected by several factors such as the charge models, polar and nonpolar SFE calculation methods and the atom radii used in the calculation. In this work, the performance of the ABCG2 (AM1-BCC-GAFF2) charge model as well as other two charge models, that is, RESP (Restrained Electrostatic Potential) and AM1-BCC (Austin Model 1-bond charge corrections), on the SFE prediction of 544 small molecules in water by PBSA/GBSA was evaluated. In order to improve the performance of the PBSA prediction based on the ABCG2 charge, we further explored the influence of atom radii on the prediction accuracy and yielded a set of atom radius parameters for more accurate SFE prediction using PBSA based on the ABCG2/GAFF2 by reproducing the thermodynamic integration (TI) calculation results. The PB radius parameters of carbon, oxygen, sulfur, phosphorus, chloride, bromide and iodine, were adjusted. New atom types, on, oi, hn1, hn2, hn3, were introduced to further improve the fitting performance. Then, we tuned the parameters in the nonpolar SFE model using the experimental SFE data and the PB calculation results. By adopting the new radius parameters and new nonpolar SFE model, the root mean square error (RMSE) of the SFE calculation for the 544 molecules decreased from 2.38 to 1.05 kcal/mol. Finally, the new radius parameters were applied in the prediction of protein-ligand binding free energies using the MM-PBSA method. For the eight systems tested, we could observe higher correlation between the experiment data and calculation results and smaller prediction errors for the absolute binding free energies, demonstrating that our new radius parameters can improve the free energy calculation using the MM-PBSA method.

Development and test of highly accurate endpoint free energy methods. 2: Prediction of logarithm of n-octanol-water partition coefficient (logP) for druglike molecules using MM-PBSA method.

The logarithm of n-octanol-water partition coefficient (logP) is frequently used as an indicator of lipophilicity in drug discovery, which has substantial impacts on the absorption, distribution, metabolism, excretion, and toxicity of a drug candidate. Considering that the experimental measurement of the property is costly and time-consuming, it is of great importance to develop reliable prediction models for logP. In this study, we developed a transfer free energy-based logP prediction model-FElogP. FElogP is based on the simple principle that logP is determined by the free energy change of transferring a molecule from water to n-octanol. The underlying physical method to calculate transfer free energy is the molecular mechanics-Poisson Boltzmann surface area (MM-PBSA), thus this method is named as free energy-based logP (FElogP). The superiority of FElogP model was validated by a large set of 707 structurally diverse molecules in the ZINC database for which the measurement was of high quality. Encouragingly, FElogP outperformed several commonly-used QSPR or machine learning-based logP models, as well as some continuum solvation model-based methods. The root-mean-square error (RMSE) and Pearson correlation coefficient (R) between the predicted and measured values are 0.91 log units and 0.71, respectively, while the runner-up, the logP model implemented in OpenBabel had an RMSE of 1.13 log units and R of 0.67. Given the fact that FElogP was not parameterized against experimental logP directly, its excellent performance is likely to be expanded to arbitrary organic molecules covered by the general AMBER force fields.

Landscape of drug-resistance mutations in kinase regulatory hotspots.

More than 48 kinase inhibitors (KIs) have been approved by Food and Drug Administration. However, drug-resistance (DR) eventually occurs, and secondary mutations have been found in the previously targeted primary-mutated cancer cells. Cancer and drug research communities recognize the importance of the kinase domain (KD) mutations for kinasopathies. So far, a systematic investigation of kinase mutations on DR hotspots has not been done yet. In this study, we systematically investigated four types of representative mutation hotspots (gatekeeper, G-loop, αC-helix and A-loop) associated with DR in 538 human protein kinases using large-scale cancer data sets (TCGA, ICGC, COSMIC and GDSC). Our results revealed 358 kinases harboring 3318 mutations that covered 702 drug resistance hotspot residues. Among them, 197 kinases had multiple genetic variants on each residue. We further computationally assessed and validated the epidermal growth factor receptor mutations on protein structure and drug-binding efficacy. This is the first study to provide a landscape view of DR-associated mutation hotspots in kinase's secondary structures, and its knowledge will help the development of effective next-generation KIs for better precision medicine.

In silico binding profile characterization of SARS-CoV-2 spike protein and its mutants bound to human ACE2 receptor.

Severe acute respiratory syndrome coronavirus (SARS-CoV-2), a novel coronavirus, has brought an unprecedented pandemic to the world and affected over 64 million people. The virus infects human using its spike glycoprotein mediated by a crucial area, receptor-binding domain (RBD), to bind to the human ACE2 (hACE2) receptor. Mutations on RBD have been observed in different countries and classified into nine types: A435S, D364Y, G476S, N354D/D364Y, R408I, V341I, V367F, V483A and W436R. Employing molecular dynamics (MD) simulation, we investigated dynamics and structures of the complexes of the prototype and mutant types of SARS-CoV-2 spike RBDs and hACE2. We then probed binding free energies of the prototype and mutant types of RBD with hACE2 protein by using an end-point molecular mechanics Poisson Boltzmann surface area (MM-PBSA) method. According to the result of MM-PBSA binding free energy calculations, we found that V367F and N354D/D364Y mutant types showed enhanced binding affinities with hACE2 compared to the prototype. Our computational protocols were validated by the successful prediction of relative binding free energies between prototype and three mutants: N354D/D364Y, V367F and W436R. Thus, this study provides a reliable computational protocol to fast assess the existing and emerging RBD mutations. More importantly, the binding hotspots identified by using the molecular mechanics generalized Born surface area (MM-GBSA) free energy decomposition approach can guide the rational design of small molecule drugs or vaccines free of drug resistance, to interfere with or eradicate spike-hACE2 binding.

Machine learning on ligand-residue interaction profiles to significantly improve binding affinity prediction.

Structure-based virtual screenings (SBVSs) play an important role in drug discovery projects. However, it is still a challenge to accurately predict the binding affinity of an arbitrary molecule binds to a drug target and prioritize top ligands from an SBVS. In this study, we developed a novel method, using ligand-residue interaction profiles (IPs) to construct machine learning (ML)-based prediction models, to significantly improve the screening performance in SBVSs. Such a kind of the prediction model is called an IP scoring function (IP-SF). We systematically investigated how to improve the performance of IP-SFs from many perspectives, including the sampling methods before interaction energy calculation and different ML algorithms. Using six drug targets with each having hundreds of known ligands, we conducted a critical evaluation on the developed IP-SFs. The IP-SFs employing a gradient boosting decision tree (GBDT) algorithm in conjunction with the MIN + GB simulation protocol achieved the best overall performance. Its scoring power, ranking power and screening power significantly outperformed the Glide SF. First, compared with Glide, the average values of mean absolute error and root mean square error of GBDT/MIN + GB decreased about 38 and 36%, respectively. Second, the mean values of squared correlation coefficient and predictive index increased about 225 and 73%, respectively. Third, more encouragingly, the average value of the areas under the curve of receiver operating characteristic for six targets by GBDT, 0.87, is significantly better than that by Glide, which is only 0.71. Thus, we expected IP-SFs to have broad and promising applications in SBVSs.

Overcoming pancreatic cancer immune resistance by codelivery of CCR2 antagonist using a STING-activating gemcitabine-based nanocarrier.

STING agonist has recently gained much attention for cancer treatment, but the therapeutic potential of STING agonist is hampered by STING-associated tumor immune resistance. In this work, guided by both bioinformatics and computer modeling, we rationally designed a "one stone hits two birds" nanoparticle-based strategy to simultaneously activate STING innate immune response while eliminating STING-associated immune resistance for the treatment of pancreatic ductal adenocarcinoma (PDAC). We discovered that the ultra-small sized micellar system based on gemcitabine-conjugated polymer (PGEM), which showed superior capacity of penetration in pancreatic tumor spheroid model and orthotopic tumor model, could serve as a novel "STING agonist". The activation of STING signaling in dendritic cells (DCs) by PGEM increased both innate nature killer (NK) and adaptive anti-tumor T cell response. However, activation of STING signaling by PGEM in tumor cells also drove the induction of chemokines CCL2 and CCL7, resulting in immune resistance by recruiting tumor associated macrophage (TAM) and myeloid-derived suppressor cells (MDSCs). Through the combination of computer modeling and experimental screening, we developed a dual delivery modality by incorporating a CCR2 (the receptor shared by both CCL2 and CCL7) antagonist PF-6309 (PF) into PGEM micellar system. Our studies demonstrated that PGEM/PF formulation significantly reduced pancreatic tumor burden and induced potent anti-tumor immunity through reversing the CCL2/CCL7-mediated immunosuppression. Moreover, PGEM/PF sensitized PDAC tumors to anti-PD-1 therapy, leading to complete suppression/eradication of the tumors. Our work has shed light to the multi-faceted role of STING activation and provided a novel immunotherapy regimen to maximize the benefit of STING activation for PDAC treatment. In addition, this work paved a new way for bioinformatics and computer modeling-guided rational design of nanomedicine.

Distribution of Bound Conformations in Conformational Ensembles for X-ray Ligands Predicted by the ANI-2X Machine Learning Potential.

In this study, we systematically studied the energy distribution of bioactive conformations of small molecular ligands in their conformational ensembles using ANI-2X, a machine learning potential, in conjunction with one of our recently developed geometry optimization algorithms, known as a conjugate gradient with backtracking line search (CG-BS). We first evaluated the combination of these methods (ANI-2X/CG-BS) using two molecule sets. For the 231-molecule set, ab initio calculations were performed at both the ωB97X/6-31G(d) and B3LYP-D3BJ/DZVP levels for accuracy comparison, while for the 8,992-molecule set, ab initio calculations were carried out at the B3LYP-D3BJ/DZVP level. For each molecule in the two molecular sets, up to 10 conformations were generated, which diminish the influence of individual outliers on the performance evaluation. Encouraged by the performance of ANI-2x/CG-BS in these evaluations, we calculated the energy distributions using ANI-2x/CG-BS for more than 27,000 ligands in the protein data bank (PDB). Each ligand has at least one conformation bound to a biological molecule, and this ligand conformation is labeled as a bound conformation. Besides the bound conformations, up to 200 conformations were generated using OpenEye's Omega2 software (https://docs.eyesopen.com/applications/ omega/) for each conformation. We performed a statistical analysis of how the bound conformation energies are distributed in the ensembles for 17,197 PDB ligands that have their bound conformation energies within the energy ranges of the Omega2-generated conformation ensembles. We found that half of the ligands have their relative conformation energy lower than 2.91 kcal/mol for the bound conformations in comparison with the global conformations, and about 90% of the bound conformations are within 10 kcal/mol above the global conformation energies. This information is useful to guide the construction of libraries for shape-based virtual screening and to improve the docking algorithm to efficiently sample bound conformations.

Phosphorylation at Ser289 Enhances the Oligomerization of Tau Repeat R2.

In tauopathies such as Alzheimer's disease (AD), aberrant phosphorylation causes the dissociation of tau proteins from microtubules. The dissociated tau then aggregates into sequent forms from soluble oligomers to paired helical filaments and insoluble neurofibrillary tangles (NFTs). NFTs is a hallmark of AD, while oligomers are found to be the most toxic form of the tau aggregates. Therefore, understanding tau oligomerization with regard to abnormal phosphorylation is important for the therapeutic development of AD. In this study, we investigated the impact of phosphorylated Ser289, one of the 40 aberrant phosphorylation sites of full-length tau proteins, on monomeric and dimeric structures of tau repeat R2 peptides. We carried out intensive replica exchange molecular dynamics simulation with a total simulation time of up to 0.1 ms. Our result showed that the phosphorylation significantly affected the structures of both the monomer and the dimer. For the monomer, the phosphorylation enhanced ordered-disordered structural transition and intramolecular interaction, leading to more compactness of the phosphorylated R2 compared to the wild-type one. As to the dimer, the phosphorylation increased intermolecular interaction and ß-sheet formation, which can accelerate the oligomerization of R2 peptides. This result suggests that the phosphorylation at Ser289 is likely to promote tau aggregation. We also observed a phosphorylated Ser289-Na+-phosphorylated Ser289 bridge in the phosphorylated R2 dimer, suggesting an important role of cation ions in tau aggregation. Our findings suggest that phosphorylation at Ser289 should be taken into account in the inhibitor screening of tau oligomerization.

Molecular Generation with Reduced Labeling through Constraint Architecture.

In the past few years, a number of machine learning (ML)-based molecular generative models have been proposed for generating molecules with desirable properties, but they all require a large amount of label data of pharmacological and physicochemical properties. However, experimental determination of these labels, especially bioactivity labels, is very expensive. In this study, we analyze the dependence of various multi-property molecule generation models on biological activity label data and propose Frag-G/M, a fragment-based multi-constraint molecular generation framework based on conditional transformer, recurrent neural networks (RNNs), and reinforcement learning (RL). The experimental results illustrate that, using the same number of labels, Frag-G/M can generate more desired molecules than the baselines (several times more than the baselines). Moreover, compared with the known active compounds, the molecules generated by Frag-G/M exhibit higher scaffold diversity than those generated by the baselines, thus making it more promising to be used in real-world drug discovery scenarios.

AmberTools.

AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note.

Development and test of highly accurate endpoint free energy methods. 3: partition coefficient prediction using a Poisson-Boltzmann method combined with a solvent accessible surface area model for SAMPL challenges.

Accurately predicting solvation free energy is the key to predict protein-ligand binding free energy. In addition, the partition coefficient (log P), which is an important physicochemical property that determines the distribution of a drug in vivo, can be derived directly from transfer free energies, i.e., the difference between solvation free energies (SFEs) in different solvents. Within the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) 9 challenge, we applied the Poisson-Boltzmann (PB) surface area (SA) approach to predict the toluene/water transfer free energy and partition coefficient (log Ptoluene/water) from SFEs. For each solute, only a single conformation automatically generated by the free software Open Babel was used. The PB calculation directly adopts our previously optimized boundary definition - a set of general AMBER force field 2 (GAFF2) atom-type based sphere radii for solute atoms. For the non-polar SA model, we newly developed the solvent-related molecular surface tension parameters γ and offset b for toluene and cyclohexane targeting experimental SFEs. This approach yielded the highest predictive accuracy in terms of root mean square error (RMSE) of 1.52 kcal mol-1 in transfer free energy for 16 small drug molecules among all 18 submissions in the SAMPL9 blind prediction challenge. The re-evaluation of the challenge set using multi-conformation strategies based on molecular dynamics (MD) simulations further reduces the prediction RMSE to 1.33 kcal mol-1. At the same time, an additional evaluation of our PBSA method on the SAMPL5 cyclohexane/water distribution coefficient (log Dcyclohexane/water) prediction revealed that our model outperformed COSMO-RS, the best submission model with RMSEPBSA = 1.88 versus RMSECOSMO-RS = 2.11 log units. Two external log Ptoluene/water and log Pcyclohexane/water datasets that contain 110 and 87 data points, respectively, are collected for extra validation and provide an in-depth insight into the error source of the PBSA method.