r-BRICS - A Revised BRICS Module That Breaks Ring Structures and Carbon Chains.

Molecular fragmentation has been frequently used for machine learning, molecular modeling, and drug discovery studies. However, the current molecular fragmentation tools often lead to large fragments that are useful to limited tasks. Specifically, long aliphatic chains, certain connected ring structures, fused rings, as well as various nitrogen-containing molecular entities often remain intact when using BRICS. With no known methods to solve this issue, we find that the fragments taken from BRICS are inflexible for tasks such as fragment-based machine learning, coarse-graining, and ligand-protein interaction assessment. In this work, a revised BRICS (r-BRICS) module is developed to allow more flexible fragmentation on a wider variety of molecules. It is shown that r-BRICS generates smaller fragments than BRICS, allowing localized fragment assessments. Furthermore, r-BRICS generates a fragment database with significantly more unique small fragments than BRICS, which is potentially useful for fragment-based drug discovery.

Unsupervised and supervised AI on molecular dynamics simulations reveals complex characteristics of HLA-A2-peptide immunogenicity.

Immunologic recognition of peptide antigens bound to class I major histocompatibility complex (MHC) molecules is essential to both novel immunotherapeutic development and human health at large. Current methods for predicting antigen peptide immunogenicity rely primarily on simple sequence representations, which allow for some understanding of immunogenic features but provide inadequate consideration of the full scale of molecular mechanisms tied to peptide recognition. We here characterize contributions that unsupervised and supervised artificial intelligence (AI) methods can make toward understanding and predicting MHC(HLA-A2)-peptide complex immunogenicity when applied to large ensembles of molecular dynamics simulations. We first show that an unsupervised AI method allows us to identify subtle features that drive immunogenicity differences between a cancer neoantigen and its wild-type peptide counterpart. Next, we demonstrate that a supervised AI method for class I MHC(HLA-A2)-peptide complex classification significantly outperforms a sequence model on small datasets corrected for trivial sequence correlations. Furthermore, we show that both unsupervised and supervised approaches reveal determinants of immunogenicity based on time-dependent molecular fluctuations and anchor position dynamics outside the MHC binding groove. We discuss implications of these structural and dynamic immunogenicity correlates for the induction of T cell responses and therapeutic T cell receptor design.

Possible Co-Evolution of Polyglutamine and Polyproline in Huntingtin Protein: Proline-Rich Domain as Transient Folding Chaperone.

Huntington's disease is an inherited neurodegenerative disorder caused by the overduplication of CAG repeats in the Huntingtin gene. Recent findings revealed that among the orthologs, the expansion of CAG repeats (polyQ) in the Huntingtin gene occurs in tandem with the duplication of CCG repeats (polyP). However, the molecular mechanism of this possible co-evolution remains unknown. We examined the structures of Huntingtin exon 1 (HttEx1) from six species along with five designed mutants. We found that the polyP segments "chaperone" the rest of the HttEx1 by forming ad hoc polyP binding grooves. Such a process elongates the otherwise poorly solvated polyQ domain, while modulating its secondary structure propensity from ß-strands to α-helices. This chaperoning effect is achieved mostly through transient hydrogen bond interactions between polyP and the rest of HttEx1, resulting in a striking golden ratio of ∼2:1 between the chain lengths of polyQ and polyP.

Simplified, interpretable graph convolutional neural networks for small molecule activity prediction.

We here present a streamlined, explainable graph convolutional neural network (gCNN) architecture for small molecule activity prediction. We first conduct a hyperparameter optimization across nearly 800 protein targets that produces a simplified gCNN QSAR architecture, and we observe that such a model can yield performance improvements over both standard gCNN and RF methods on difficult-to-classify test sets. Additionally, we discuss how reductions in convolutional layer dimensions potentially speak to the "anatomical" needs of gCNNs with respect to radial coarse graining of molecular substructure. We augment this simplified architecture with saliency map technology that highlights molecular substructures relevant to activity, and we perform saliency analysis on nearly 100 data-rich protein targets. We show that resultant substructural clusters are useful visualization tools for understanding substructure-activity relationships. We go on to highlight connections between our models' saliency predictions and observations made in the medicinal chemistry literature, focusing on four case studies of past lead finding and lead optimization campaigns.

Dynamics-Based Peptide-MHC Binding Optimization by a Convolutional Variational Autoencoder: A Use-Case Model for CASTELO.

An unsolved challenge in the development of antigen-specific immunotherapies is determining the optimal antigens to target. Comprehension of antigen-major histocompatibility complex (MHC) binding is paramount toward achieving this goal. Here, we apply CASTELO, a combined machine learning-molecular dynamics (ML-MD) approach, to identify per-residue antigen binding contributions and then design novel antigens of increased MHC-II binding affinity for a type 1 diabetes-implicated system. We build upon a small-molecule lead optimization algorithm by training a convolutional variational autoencoder (CVAE) on MD trajectories of 48 different systems across four antigens and four HLA serotypes. We develop several new machine learning metrics including a structure-based anchor residue classification model as well as cluster comparison scores. ML-MD predictions agree well with experimental binding results and free energy perturbation-predicted binding affinities. Moreover, ML-MD metrics are independent of traditional MD stability metrics such as contact area and root-mean-square fluctuations (RMSF), which do not reflect binding affinity data. Our work supports the role of structure-based deep learning techniques in antigen-specific immunotherapy design.

CASTELO: clustered atom subtypes aided lead optimization-a combined machine learning and molecular modeling method.

BACKGROUND: Drug discovery is a multi-stage process that comprises two costly major steps: pre-clinical research and clinical trials. Among its stages, lead optimization easily consumes more than half of the pre-clinical budget. We propose a combined machine learning and molecular modeling approach that partially automates lead optimization workflow in silico, providing suggestions for modification hot spots. RESULTS: The initial data collection is achieved with physics-based molecular dynamics simulation. Contact matrices are calculated as the preliminary features extracted from the simulations. To take advantage of the temporal information from the simulations, we enhanced contact matrices data with temporal dynamism representation, which are then modeled with unsupervised convolutional variational autoencoder (CVAE). Finally, conventional and CVAE-based clustering methods are compared with metrics to rank the submolecular structures and propose potential candidates for lead optimization. CONCLUSION: With no need for extensive structure-activity data, our method provides new hints for drug modification hotspots which can be used to improve drug potency and reduce the lead optimization time. It can potentially become a valuable tool for medicinal chemists.

Dioxybenzone triggers enhanced estrogenic effect via metabolic activation: in silico, in vitro and in vivo investigation.

Dioxybenzone is widely used in cosmetics and personal care products and frequently detected in multiple environmental media and human samples. However, the current understanding of the metabolic susceptibility of dioxybenzone and the potential endocrine disruption through its metabolites in mimicking human estrogens remains largely unclear. Here we investigated the in vitro metabolism of dioxybenzone, detected the residue of metabolites in rats, and determined the estrogenic disrupting effects of these metabolites toward estrogen receptor α (ERα). In vitro metabolism revealed two major metabolites from dioxybenzone, i.e., M1 through the demethylation of methoxy moiety and M2 through hydroxylation of aromatic carbon. M1 and M2 were both rapidly detected in rat plasma upon exposure to dioxybenzone, which were then distributed into organs of rats in the order of livers > kidneys > uteri > ovaries. The 100 ns molecular dynamics simulation revealed that M1 and M2 formed hydrogen bond to residue Leu387 and Glu353, respectively, on ERα ligand binding domain, leading to a reduced binding free energy. M1 and M2 also significantly induced estrogenic effect in comparison to dioxybenzone as validated by the recombinant ERα yeast two-hybrid assay and uterotrophic assay. Overall, our study revealed the potential of metabolic activation of dioxybenzone to induce estrogenic disrupting effects, suggesting the need for incorporating metabolic evaluation into the health risk assessment of benzophenones and their structurally similar analogs.

Structural Basis of the Potential Binding Mechanism of Remdesivir to SARS-CoV-2 RNA-Dependent RNA Polymerase.

Starting from late 2019, the coronavirus disease 2019 (COVID-19) has emerged as a once-in-a-century pandemic with deadly consequences, which urgently calls for new treatments, cures, and supporting apparatuses. Recently, because of its positive results in clinical trials, remdesivir was approved by the Food and Drug Administration to treat COVID-19 through Emergency Use Authorization. Here, we used molecular dynamics simulations and free energy perturbation methods to study the inhibition mechanism of remdesivir to its target SARS-CoV-2 virus RNA-dependent RNA polymerase (RdRp). We first constructed the homology model of this polymerase based on a previously available structure of SARS-CoV NSP12 RdRp (with a sequence identity of 95.8%). We then built a putative preinsertion binding structure by aligning the remdesivir + RdRp complex to the ATP bound poliovirus RdRp without the RNA template. The putative binding structure was further optimized with molecular dynamics simulations. The resulting stable preinsertion state of remdesivir appeared to form hydrogen bonds with the RNA template when aligned with the newly solved cryo-EM structure of SARS-CoV-2 RdRp. The relative binding free energy between remdesivir and ATP was calculated to be -2.80 ± 0.84 kcal/mol, where remdesivir bound much stronger to SARS-CoV-2 RdRp than the natural substrate ATP. The ∼100-fold improvement in the Kd from remdesivir over ATP indicates an effective replacement of ATP in blocking of the RdRp preinsertion site. Key residues D618, S549, and R555 are found to be the contributors to the binding affinity of remdesivir. These findings suggest that remdesivir can potentially act as a SARS-CoV-2 RNA-chain terminator, effectively stopping its RNA replication, with key residues also identified for future lead optimization and/or drug resistance studies.

Modeling and Structural Characterization of the Sweet Taste Receptor Heterodimer.

Sweet taste receptor, a heterodimer belonging to the class C G-protein coupled receptor (GPCR) family and composed of the T1R2 and T1R3 subunits, is responsible for the perception of natural sugars, sweet proteins, various d-amino acids, as well as artificial sweeteners. Despite the critical importance of the sweet receptor not only in mediating gustation but also in its role in the food industry, the architecture of the T1R2-T1R3 complex and the mechanism by which extracellular stimuli induce conformational changes that are propagated to the intracellular milieu, i.e., the signal transduction pathway, remain largely unknown. Here, we constructed and characterized a full-length structural model of the T1R2-T1R3 receptor, including both the transmembrane (TM) and extracellular (EC) domains of the heterodimer, using comparative modeling and extensive all-atom molecular dynamics simulations. Several heterodimer interfaces were first examined for the TM domain, and conformational changes occurring at the intracellular side and associated with the receptor's activation were characterized. From the analysis on the simulated data, putative allosteric binding sites for ligands, ions, and cholesterol were proposed. Also, insights into the protein interface of the TM domain upon activation are provided. The EC domain of the heterodimer, including both the Venus flytrap and cysteine-rich domains, was also investigated. Several important intersubunit interactions located at regions responsible for the receptor's proper function were observed, which resemble those recently identified in other class C GPCR members. Integration of the results from the TM and EC domains facilitates the generation of a full-length T1R2-T1R3 receptor. These findings along with the full-length structural model of the T1R2-T1R3 receptor provide a structural framework that may assist in understanding the mechanistic details associated with the receptor activation process for the sweet T1R2-T1R3 receptor as well as other members of the same family.

Parameterization of Molybdenum Disulfide Interacting with Water Using the Free Energy Perturbation Method.

Water contact angles (WCA) are often used to parametrize force field parameters of novel 2D nanomaterials, such as molybdenum disulfide (MoS2), which has emerged as a promising nanomaterial in many biomedical applications due to its unique and impressive properties. However, there is a wide range of water-MoS2 contact angles in the literature depending on the aging process on the surface of a MoS2 nanosheet and/or substrate material. In this study, we revisit and optimize existing parameters for the basal plane of MoS2 with two popular water models, TIP3P and SPC/E, using the wide range of WCAs from various experiments. We develop and deploy the free energy perturbation method for parametrizing MoS2 with experimentally determined WCAs for both fresh and aged surfaces. Energy decomposition analysis on the simulation trajectories reveals that MoS2-water interaction is dominated by van der Waals interaction, which mainly comes from the top layer of MoS2. We conclude that to describe both fresh and aged MoS2 surfaces it is convenient to only adjust the Lennard-Jones parameter εS (the depth of the potential well of a sulfur atom), which displays a surprisingly linear correlation with WCAs.

Stability of Ligands on Nanoparticles Regulating the Integrity of Biological Membranes at the Nano-Lipid Interface.

When nanoparticles interact with cellular or organelle membranes, the coating ligands are known to affect the integrity of the membranes, which regulate cell death and inflammation. However, the molecular mechanisms of this modulation remain unresolved. Here, we use synchrotron X-ray liquid surface scattering and molecular dynamics simulations to study interface structures between phospholipids and gold nanorods (AuNRs) coated by surfactant and polyelectrolyte. These ligands are two types of widely used surface modification with different self-assembled structures and stabilities on the surface of nanoparticles. We reveal distinct mechanisms of the ligand stability in disrupting membrane integrity. We find that the cationic surfactant ligand cetyltrimethylammonium bromide detaches from the AuNRs and inserts into phospholipids, resulting in reduced membrane thickness by compressing the phospholipids to align with the shorter ligand. Conversely, the cationic polyelectrolyte ligand poly(diallyldimethylammonium chloride) is more stable on AuNRs; although it adsorbs onto the membrane, it does not cause much impairment. The distinct coating ligand interactions with phospholipids are further verified by cellular responses including impaired lysosomal membranes and triggered inflammatory effects in macrophages. Together, the quantitative analysis of interface structures elucidates key bio-nano interactions and highlights the importance of surface ligand stability for safety and rational design of nanoparticles.

Robust Antibacterial Activity of Tungsten Oxide (WO3-x) Nanodots.

Antibacterial agents are an important tool in the prevention of bacterial infections. Inorganic materials are attractive due to their high stability under a variety of conditions compared to organic antibacterial agents. Herein tungsten oxide nanodots (WO3-x), synthesized by a simple one-pot synthetic approach, were found to exhibit strong antibacterial capabilities. The analyses with colony-forming units (CFU) showed an excellent antibacterial activity of WO3-x against both Gram-negative Escherichia coli (E. coli) and Gram-positive Staphylococcus aureus (S. aureus) strains. The scanning electron microscopy (SEM) and transmission electron microscopy (TEM) images revealed clear damages to the bacterial cell membranes, which was further confirmed by molecular dynamics simulations. Additionally, exposure to simulated sunlight was found to further increase the germicidal activity of WO3-x nanodots, a 30 min exposure to sunlight combined with 50 µg/mL WO3-x nanodots showed a 70% decrease in E. coli viability compared to without exposure. Electron spin resonance spectroscopy (ESR) was used to elucidate the underlying mechanism of this photocatalytic activity through the generation of hydroxyl radical species. The cell counting kit-8 (CCK-8) and the live/dead assay were further employed to evaluate the cytotoxicity of WO3-x nanodots on eukaryotic cells, which demonstrated their general biocompatibility. In summary, our results suggest WO3-x nanodots have considerable potential in antibacterial applications, while also being biocompatible at large.

Cannabinoids-induced peripheral analgesia depends on activation of BK channels.

The endogenous cannabinoid system is involved in the physiological inhibitory control of pain and is of particular interest for the development of therapeutic approaches for pain management. Selective activation of the peripheral CB1 cannabinoid receptor has been shown to suppress the heightened firing of primary afferents, which is the peripheral mechanism underlying neuropathic pain after nerve injury. However, the mechanism underlying this effect of CB1 receptor remains unclear. The large-conductance calcium-activated potassium (BK) channels have been reported to participate in anticonvulsant and vasorelaxant effects of cannabinoids. We asked whether BK channels participate in cannabinoids-induced analgesia and firing-suppressing effects in primary afferents after nerve injury. Here, using mice with chronic constriction injury (CCI)-induced neuropathic pain, antinociception action and firing-suppressing effect of HU210 were measured before and after BK channel blocker application. We found that local peripheral application of HU210 alleviated CCI-induced pain behavior and suppressed the heightened firing of injured fibers. Co-administration of IBTX with HU210 significantly reversed the analgesia and the firing-suppressing effect of HU210. This result indicated that the peripheral analgesic effects of cannabinoids depends on activation of BK channels.

Exploring the binding mechanism between human profilin (PFN1) and polyproline-10 through binding mode screening.

The large magnitude of protein-protein interaction (PPI) pairs within the human interactome necessitates the development of predictive models and screening tools to better understand this fundamental molecular communication. However, despite enormous efforts from various groups to develop predictive techniques in the last decade, PPI complex structures are in general still very challenging to predict due to the large number of degrees of freedom. In this study, we use the binding complex of human profilin (PFN1) and polyproline-10 (P10) as a model system to examine various approaches, with the aim of going beyond normal protein docking for PPI prediction and evaluation. The potential of mean force (PMF) was first obtained from the time-consuming umbrella sampling, which confirmed that the most stable binding structure identified by the maximal PMF difference is indeed the crystallographic binding structure. Moreover, crucial residues previously identified in experimental studies, W3, H133, and S137 of PFN1, were found to form favorable hydrogen bonds with P10, suggesting a zipping process during the binding between PFN1 and P10. We then explored both regular molecular dynamics (MD) and steered molecular dynamics (SMD) simulations, seeking for better criteria of ranking the PPI prediction. Despite valuable information obtained from conventional MD simulations, neither the commonly used interaction energy between the two binding parties nor the long-term root mean square displacement correlates well with the PMF results. On the other hand, with a sizable collection of trajectories, we demonstrated that the average and minimal rupture works calculated from SMD simulations correlate fairly well with the PMFs (R 2 = 0.67), making this a promising PPI screening method.

Binding Specificity Determines the Cytochrome P450 3A4 Mediated Enantioselective Metabolism of Metconazole.

Cytochrome P450 3A4 (CYP3A4) is a promiscuous enzyme, mediating the biotransformations of ∼50% of clinically used drugs, many of which are chiral molecules. Probing the interactions between CYP3A4 and chiral chemicals is thus essential for the elucidation of molecular mechanisms of enantioselective metabolism. We developed a stepwise-restrained-molecular-dynamics (MD) method to model human CYP3A4 in a complex with cis-metconazole (MEZ) isomers and performed conventional MD simulations with a total simulation time of 2.2 µs to probe the molecular interactions. Our current study, which employs a combined experimental and theoretical approach, reports for the first time on the distinct conformational changes of CYP3A4 that are induced by the enantioselective binding of cis-MEZ enantiomers. CYP3A4 preferably metabolizes cis-RS MEZ over the cis-SR isomer, with the resultant enantiomer fraction for cis-MEZ increasing rapidly from 0.5 to 0.82. cis-RS MEZ adopts a more extended structure in the active pocket with its Cl atom exposed to the solvent, whereas cis-SR MEZ sits within the hydrophobic core of the active pocket. Free-energy-perturbation calculations indicate that unfavorable van der Waals interactions between the cis-MEZ isomers and the CYP3A4 binding pocket predominantly contribute to their binding-affinity differences. These results demonstrate that binding specificity determines the cytochrome P450 3A4 mediated enantioselective metabolism of cis-MEZ.

Emerging ß-Sheet Rich Conformations in Supercompact Huntingtin Exon-1 Mutant Structures.

There exists strong correlation between the extended polyglutamines (polyQ) within exon-1 of Huntingtin protein (Htt) and age onset of Huntington's disease (HD); however, the underlying molecular mechanism is still poorly understood. Here we apply extensive molecular dynamics simulations to study the folding of Htt-exon-1 across five different polyQ-lengths. We find an increase in secondary structure motifs at longer Q-lengths, including ß-sheet content that seems to contribute to the formation of increasingly compact structures. More strikingly, these longer Q-lengths adopt supercompact structures as evidenced by a surprisingly small power-law scaling exponent (0.22) between the radius-of-gyration and Q-length that is substantially below expected values for compact globule structures (∼0.33) and unstructured proteins (∼0.50). Hydrogen bond analyses further revealed that the supercompact behavior of polyQ is mainly due to the "glue-like" behavior of glutamine's side chains with significantly more side chain-side chain H-bonds than regular proteins in the Protein Data Bank (PDB). The orientation of the glutamine side chains also tend to be "buried" inside, explaining why polyQ domains are insoluble on their own.

Structural perturbations on huntingtin N17 domain during its folding on 2D-nanomaterials.

A globular protein's folded structure in its physiological environment is largely determined by its amino acid sequence. Recently, newly discovered transformer proteins as well as intrinsically disordered proteins may adopt the folding-upon-binding mechanism where their secondary structures are highly dependent on their binding partners. Due to the various applications of nanomaterials in biological sensors and potential wearable devices, it is important to discover possible conformational changes of proteins on nanomaterials. Here, through molecular dynamics simulations, we show that the first 17 residues of the huntingtin protein (HTT-N17) exhibit appreciable differences during its folding on 2D-nanomaterials, such as graphene and MoS2 nanosheets. Namely, the protein is disordered on the graphene surface but is helical on the MoS2 surface. Despite that the amphiphilic environment at the nanosheet-water interface promotes the folding of the amphipathic proteins (such as HTT-N17), competitions between protein-nanosheet and intra-protein interactions yield very different protein conformations. Therefore, as engineered binding partners, nanomaterials might significantly affect the structures of adsorbed proteins.

Molecular Mechanism of Stabilizing the Helical Structure of Huntingtin N17 in a Micellar Environment.

Huntington's disease is a deadly neurodegenerative disease caused by the fibrilization of huntingtin (HTT) exon-1 protein mutants. Despite extensive efforts over the past decade, much remains unknown about the structures of (mutant) HTT exon-1 and their enigmatic roles in aggregation. Particularly, whether the first 17 residues in the N-terminal (HTT-N17) adopt a helical or a coiled structure remains unclear. Here, with the rigorous study of molecular dynamics simulations, we explored the most possible structures of HTT-N17 in both dodecylphosphocholine (DPC) micelles and aqueous solution, using three commonly applied force fields (OPLS-AA/L, CHARMM36, and AMBER99sb*-ILDNP) to examine the underlying molecular mechanisms and rule out potential artifacts. We show that local environments are essential for determining the secondary structure of HTT-N17. This is evidenced by the insertion of five hydrophobic residues of HTT-N17 into the DPC micelle, which promotes the formation of an amphipathic helix, whereas such amphipathic helices unfold quickly in aqueous solution. A relatively low free-energy barrier (∼3 kcal/mol) for the secondary structure transformation was also observed for all three force fields from their respective folding-free-energy landscapes, which accounts for possible HTT-N17 conformational changes upon environmental shifts such as membrane binding and protein complex aggregation.

Graphene Oxide Nanosheets Retard Cellular Migration via Disruption of Actin Cytoskeleton.

Graphene and graphene-based nanomaterials are broadly used for various biomedical applications due to their unique physiochemical properties. However, how graphene-based nanomaterials interact with biological systems has not been thoroughly studied. This study shows that graphene oxide (GO) nanosheets retard A549 lung carcinoma cell migration through nanosheet-mediated disruption of intracellular actin filaments. After GO nanosheets treatment, A549 cells display slower migration and the structure of the intracellular actin filaments is dramatically changed. It is found that GO nanosheets are capable of absorbing large amount of actin and changing the secondary structures of actin monomers. Large-scale all-atom molecular dynamics simulations further reveal the interactions between GO nanosheets and actin filaments at molecular details. GO nanosheets can insert into the interstrand gap of actin tetramer (helical repeating unit of actin filament) and cause the separation of the tetramer which eventually leads to the disruption of actin filaments. These findings offer a novel mechanism of GO nanosheet induced biophysical responses and provide more insights into their potential for biomedical applications.