Wybutosine hypomodification of tRNAphe activates HERVK and impairs neuronal differentiation.

We previously reported that loss of function of TYW1 led to cerebral palsy with severe intellectual disability through reduced neural proliferation. However, whether TYW1 loss affects neural differentiation is unknown. In this study, we first demonstrated that TYW1 loss blocked the formation of OHyW in tRNAphe and therefore affected the translation efficiency of UUU codon. Using the brain organoid model, we showed impaired neuron differentiation when TYW1 was depleted. Interestingly, retrotransposons were differentially regulated in TYW1-/- hESCs (human embryonic stem cells). In particular, one kind of human-specific endogenous retrovirus-K (HERVK/HML2), whose reactivation impaired human neurodevelopment, was significantly up-regulated in TYW1-/- hESCs. Consistently, a UUU codon-enriched protein, SMARCAD1, which was a key factor in controlling endogenous retroviruses, was reduced. Taken together, TYW1 loss leads to up-regulation of HERVK in hESCs by down-regulated SMARCAD1, thus impairing neuron differentiation.

Neonicotinoid insecticides and metabolites levels in neonatal first urine from southern China: Exploring links to preterm birth.

Neonicotinoids (NEOs) have indeed become the most widely used insecticides worldwide. Concerns have been raised about their potential impact on newborns due to maternal exposure and their unique neurotoxic mode of action. However, it is still poorly understood whether in utero exposure of pregnant women to environmental NEOs and their metabolites can cause carryover effects on vulnerable newborns and subsequent health consequences. In this study, we determined the concentrations of 13 NEOs and their metabolites in the first urine collected from 92 newborns, both preterm and full-term, in southern China during 2020 and 2021. NEOs and their metabolites were identified in 91 urine samples, with over 93% of samples containing a cocktail of these compounds, confirming their maternal-fetal transfer. N-desmethyl-acetamiprid, imidaclothiz, clothianidin and flonicamid were the most commonly detected analytes, with detection frequencies of 59-87% and medians of 0.024-0.291 ng/mL in the urine. The relative abundance of imidaclothiz was significantly higher in preterm newborns, those with head circumferences below 33 cm, birth lengths less than 47 cm, and weights below 2500 g (p < 0.05). When comparing newborns in the 2nd quartile of imidaclothiz concentrations with those in the 1st quartile, we observed a significant increase in the odds of preterm outcomes in the unadjusted model (odds ratio = 3.24, 95% confidence interval = 1.02-10.3). These results suggest that exposure to elevated concentrations of imidaclothiz may be associated with preterm birth.

A sequence-based model for identifying proteins undergoing liquid-liquid phase separation/forming fibril aggregates via machine learning.

Liquid-liquid phase separation (LLPS) and the solid aggregate (also referred to as amyloid aggregates) formation of proteins, have gained significant attention in recent years due to their associations with various physiological and pathological processes in living organisms. The systematic investigation of the differences and connections between proteins undergoing LLPS and those forming amyloid fibrils at the sequence level has not yet been explored. In this research, we aim to address this gap by comparing the two types of proteins across 36 features using collected data available currently. The statistical comparison results indicate that, 24 of the selected 36 features exhibit significant difference between the two protein groups. A LLPS-Fibrils binary classification model built on these 24 features using random forest reveals that the fraction of intrinsically disordered residues (FIDR ) is identified as the most crucial feature. While, in the further three-class LLPS-Fibrils-Background classification model built on the same screened features, the composition of cysteine and that of leucine show more significant contributions than others. Through feature ablation analysis, we finally constructed a model FLFB (Feature-based LLPS-Fibrils-Background protein predictor) using six refined features, with an average area under the receiver operating characteristics of 0.83. This work indicates using sequence features and a machine learning model, proteins undergoing LLPS or forming amyloid fibrils can be identified.

Effects and mechanisms of Zhizi Chuanxiong herb pair against atherosclerosis: an integration of network pharmacology, molecular docking, and experimental validation.

BACKGROUND: The Zhizi Chuanxiong herb pair (ZCHP) can delay pathological progression of atherosclerosis (AS); however, its pharmacological mechanism remains unclear because of its complex components. The purpose of current study is to systematically investigate the anti-AS mechanism of ZCHP. METHODS: The databases of TCMSP, STITCH, SwissTargetPrediction, BATMAN-TCM, and ETCM were searched to predict the potential targets of ZCHP components. Disease targets associated with AS was retrieved from the GEO database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analyses were executed using DAVID 6.8. Molecular docking method was employed to evaluate the core target binding to blood components, and animal experiments were performed to test action mechanism. RESULTS: A ZCHP-components-targets-AS network was constructed by using Cytoscape, included 11 main components and 52 candidate targets. Crucial genes were shown in the protein-protein interaction network, including TNF, IL-1ß, IGF1, MMP9, COL1A1, CCR5, HMOX1, PTGS1, SELE, and SYK. KEGG enrichment illustrated that the NF-κB, Fc epsilon RI, and TNF signaling pathways were important for AS treatment. These results were validated by molecular docking. In ApoE-/- mice, ZCHP significantly reduced intima-media thickness, pulse wave velocity, plaque area, and serum lipid levels while increasing the difference between the end-diastolic and end-systolic diameters. Furthermore, ZCHP significantly decreased the mRNA and protein levels of TNF-α and IL-1ß, suppressed NF-κB activation, and inhibited the M1 macrophage polarization marker CD86 in ApoE-/- mice. CONCLUSION: This study combining network pharmacology, molecular biology, and animal experiments showed that ZCHP can alleviate AS by suppressing the TNF/NF-κB axis and M1 macrophage polarization.

PLANET: A Multi-objective Graph Neural Network Model for Protein-Ligand Binding Affinity Prediction.

Predicting protein-ligand binding affinity is a central issue in drug design. Various deep learning models have been published in recent years, where many of them rely on 3D protein-ligand complex structures as input and tend to focus on the single task of reproducing binding affinity. In this study, we have developed a graph neural network model called PLANET (Protein-Ligand Affinity prediction NETwork). This model takes the graph-represented 3D structure of the binding pocket on the target protein and the 2D chemical structure of the ligand molecule as input. It was trained through a multi-objective process with three related tasks, including deriving the protein-ligand binding affinity, protein-ligand contact map, and ligand distance matrix. Besides the protein-ligand complexes with known binding affinity data retrieved from the PDBbind database, a large number of non-binder decoys were also added to the training data for deriving the final model of PLANET. When tested on the CASF-2016 benchmark, PLANET exhibited a scoring power comparable to the best result yielded by other deep learning models as well as a reasonable ranking power and docking power. In virtual screening trials conducted on the DUD-E benchmark, PLANET's performance was notably better than several deep learning and machine learning models. As on the LIT-PCBA benchmark, PLANET achieved comparable accuracy as the conventional docking program Glide, but it only spent less than 1% of Glide's computation time to finish the same job because PLANET did not need exhaustive conformational sampling. Considering the decent accuracy and efficiency of PLANET in binding affinity prediction, it may become a useful tool for conducting large-scale virtual screening.

Current status and emerging trends of cardiac metabolism from the past 20 years: A bibliometric study.

Background: Abnormal cardiac metabolism is a key factor in the development of cardiovascular diseases. Consequently, there has been considerable emphasis on researching and developing drugs that regulate metabolism. This study employed bibliometric methods to comprehensively and objectively analyze the relevant literature, offering insights into the knowledge dynamics in this field. Methods: The data source for this study was the Web of Science Core Collection (WoSCC), from which the collected data were imported into bibliometric software for analysis. Results: The United States was the leading contributor, accounting for 38.33 % of publications. The University of Washington and Damian J. Tyler were the most active institution and author, respectively. The American Journal of Physiology-Heart and Circulatory Physiology, Journal of Molecular and Cellular Cardiology, Cardiovascular Research, Circulation Research, and American Journal of Physiology-Endocrinology and Metabolism were highly influential journals that published numerous high-quality articles on cardiac metabolism. Common keywords in this research area included heart failure, insulin resistance, skeletal muscle, mitochondria, as well as topic words such as cardiac metabolism, fatty acid oxidation, glucose metabolism, and myocardial metabolism. Co-citation analysis has shown that research on heart failure and in vitro modeling of cardiovascular disease has gained prominence in recent years and making it a research hotspot. Conclusion: Research on cardiac metabolism is steadily growing, with a specific focus on heart failure and the interplay between mitochondrial dysfunction, insulin resistance, and cardiac metabolism. An emerging trend in this field involves the enhancement of maturation in human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) through the manipulation of cardiac metabolism.

A serum metabolomics study of vascular cognitive impairment patients based on Traditional Chinese medicine syndrome differentiation.

Objective: Vascular cognitive impairment (VCI) accounts for approximately 50%-70% of all dementia cases and poses a significant burden on existing medical systems. Identifying an optimal strategy for preventing VCI and developing efficient symptomatic treatments remains a significant challenge. Syndrome differentiation represents a fundamental approach for personalized diagnosis and treatment in Traditional Chinese Medicine (TCM) and aligns with the principles of precision medicine. The objective of this study was to elucidate the metabolic characteristics of VCI based on TCM syndrome differentiation, thus providing novel insights into the diagnosis and treatment of VCI. Methods: A 2-year cross-sectional cognitive survey was conducted in four communities in Beijing between September 2020 and November 2022. The syndrome differentiation of participants was based on the Kidney-Yang Deficiency Syndrome Scale (KYDSS), which was originally developed by Delphi expert consultation. The identification of serum metabolites was performed by Ultra performance liquid chromatography (UPLC) analysis coupled with an electrospray ionization quadruple time-of-flight mass spectrometer (ESI-QTOF MS). Multivariate, univariate, and pathway analyses were used to investigate metabolic changes. Logistic regression models were also used to construct metabolite panels that were capable of discerning distinct groups. Phospholipase A2 (PLA2) levels were measured by a commercial ELISA kit. Results: A total of 2,337 residents completed the survey, and the prevalence of VCI was 9.84%. Of the patients with VCI, those with Kidney-Yang deficiency syndrome (VCIS) accounted for 70.87% of cases and exhibited more severe cognitive impairments. A total of 80 participants were included in metabolomics study, including 30 with VCIS, 20 without Kidney-Yang deficiency syndrome (VCINS), and 30 healthy control participants (C). Ultimately, 45 differential metabolites were identified when comparing the VCIS group with group C, 65 differential metabolites between the VCINS group and group C, and 27 differential metabolites between the VCIS group and the VCINS group. The downregulation of phosphatidylethanolamine (PE), and phosphatidylcholine (PC) along with the upregulation of lysophosphatidylethanolamine (LPE), lysophosphatidylcholine (LPC), phosphatidic acid (PA) and phospholipase A2 (PLA2) can be considered as the general metabolic characteristics associated with VCI. Dysfunction of glycerophospholipids, particularly LPEs and PCs, was identified as a key metabolic characteristic of VCIS. In particular Glycerophospho-N-Arachidonoyl Ethanolamine (GP-NArE) was discovered for the first time in VCI patients and is considered to represent a potential biomarker for VCIS. The upregulation of PLA2 expression was implicated in the induction of alterations in glycerophospholipid metabolism in both VCIS and VCINS. Moreover, robust diagnostic models were established based on these metabolites, achieving high AUC values of 0.9322, 0.9550, and 0.9450, respectively. Conclusion: These findings contribute valuable information relating to the intricate relationship between metabolic disorders in VCI, neurodegeneration and vascular/neuroinflammation. Our findings also provide a TCM perspective for the precise diagnosis and treatment of VCI in the context of precision medicine.

Integrative analysis of single-cell embryo data reveals transcriptome signatures for the human pre-implantation inner cell mass.

As the source of embryonic stem cells (ESCs), inner cell mass (ICM) can form all tissues of the embryo proper, however, its role in early human lineage specification remains controversial. Although a stepwise differentiation model has been proposed suggesting the existence of ICM as a distinct developmental stage, the underlying molecular mechanism remains unclear. In the present study, we perform an integrated analysis on the public human preimplantation embryonic single-cell transcriptomic data and apply a trajectory inference algorithm to measure the cell plasticity. In our results, ICM population can be clearly discriminated on the dimension-reduced graph and confirmed by compelling evidences, thus validating the two-step hypothesis of lineage commitment. According to the branch probabilities and differentiation potential, we determine the precise time points for two lineage segregations. Further analysis on gene expression dynamics and regulatory network indicates that transcription factors including GSC, PRDM1, and SPIC may underlie the decisions of ICM fate. In addition, new human ICM marker genes, such as EPHA4 and CCR8 are discovered and validated by immunofluorescence. Given the potential clinical applications of ESCs, our analysis provides a further understanding of human ICM cells and facilitates the exploration of more unique characteristics in early human development.

Evaluation of sequence-based predictors for phase-separating protein.

Liquid-liquid phase separation (LLPS) of proteins and nucleic acids underlies the formation of biomolecular condensates in cell. Dysregulation of protein LLPS is closely implicated in a range of intractable diseases. A variety of tools for predicting phase-separating proteins (PSPs) have been developed with the increasing experimental data accumulated and several related databases released. Comparing their performance directly can be challenging due to they were built on different algorithms and datasets. In this study, we evaluate eleven available PSPs predictors using negative testing datasets, including folded proteins, the human proteome, and non-PSPs under near physiological conditions, based on our recently updated LLPSDB v2.0 database. Our results show that the new generation predictors FuzDrop, DeePhase and PSPredictor perform better on folded proteins as a negative test set, while LLPhyScore outperforms other tools on the human proteome. However, none of the predictors could accurately identify experimentally verified non-PSPs. Furthermore, the correlation between predicted scores and experimentally measured saturation concentrations of protein A1-LCD and its mutants suggests that, these predictors could not consistently predict the protein LLPS propensity rationally. Further investigation with more diverse sequences for training, as well as considering features such as refined sequence pattern characterization that comprehensively reflects molecular physiochemical interactions, may improve the performance of PSPs prediction.

Mapping current status and emerging trends in NETosis: A bibliometric study.

BACKGROUND: NETosis is a critical innate immune mechanism of neutrophils that contributes to the accelerated progression of autoimmune diseases, thrombosis, cancer, and coronavirus disease 2019 (COVID-19). This study qualitatively and quantitatively analyzed the relevant literature by bibliometric methods in order to provide a more comprehensive and objective view of the knowledge dynamics in the field. METHODS: The literature on NETosis was downloaded from the Web of Science Core Collection, analyzed with VOSviewer, CiteSpace, and Microsoft for co-authorship, co-occurrence, and co-citation analysis. RESULTS: In the field of NETosis, the United States was the most influential countries. Harvard University was the most active institutions. Mariana J. Kaplan and Brinkmann V were, respectively, the most prolific and most co-cited authors. Frontiers in Immunology, Journal of Immunology, Plos One, Blood, Science, Journal of Cell Biology, and Nature Medicine were the most influential journals. The top 15 keywords are associated with immunological and NETosis formation mechanisms. The keywords with the strongest burst detection were mainly related to COVID-19 (coronavirus, ACE2, SARS coronavirus, cytokine storm, pneumonia, neutrophil to lymphocyte ratio), and cancer (circulating tumor cell). CONCLUSION: Research on NETosis is currently booming. The mechanism of NETosis and its role in innate immunity, autoimmune diseases, especially systemic lupus erythematosus and rheumatoid arthritis, and thrombosis are the focus of research in the field of NETosis. A future study will concentrate on the function of NETosis in COVID-19 and recurrent metastasis of cancer.

The PLOD2/succinate axis regulates the epithelial-mesenchymal plasticity and cancer cell stemness.

Aberrant accumulation of succinate has been detected in many cancers. However, the cellular function and regulation of succinate in cancer progression is not completely understood. Using stable isotope-resolved metabolomics analysis, we showed that the epithelial mesenchymal transition (EMT) was associated with profound changes in metabolites, including elevation of cytoplasmic succinate levels. The treatment with cell-permeable succinate induced mesenchymal phenotypes in mammary epithelial cells and enhanced cancer cell stemness. Chromatin immunoprecipitation and sequence analysis showed that elevated cytoplasmic succinate levels were sufficient to reduce global 5-hydroxymethylcytosinene (5hmC) accumulation and induce transcriptional repression of EMT-related genes. We showed that expression of procollagen-lysine,2-oxoglutarate 5-dioxygenase 2 (PLOD2) was associated with elevation of cytoplasmic succinate during the EMT process. Silencing of PLOD2 expression in breast cancer cells reduced succinate levels and inhibited cancer cell mesenchymal phenotypes and stemness, which was accompanied by elevated 5hmC levels in chromatin. Importantly, exogenous succinate rescued cancer cell stemness and 5hmC levels in PLOD2-silenced cells, suggesting that PLOD2 promotes cancer progression at least partially through succinate. These results reveal the previously unidentified function of succinate in enhancing cancer cell plasticity and stemness.

Binding of berberine derivates to G-quadruplex: insight from a computational study.

Human telomerase exhibits significant activity in cancer cells relative to normal cells, which contributes to the immortal proliferation of cancer cells. To counter this, the stabilization of G-quadruplexes formed in the guanine-rich sequence of the cancer cell chromosome has emerged as a promising avenue for anti-cancer therapy. Berberine (BER), an alkaloid that is derived from traditional Chinese medicines, has shown potential for stabilizing G-quadruplexes. To investigate the atomic interactions between G-quadruplexes and BER and its derivatives, molecular dynamics simulations were conducted. Modeling the interactions between G-quadruplexes and ligands accurately is challenging due to the strong negative charge of nucleic acids. Thus, various force fields and charge models for the G-quadruplex and ligands were tested to obtain precise simulation results. The binding energies were calculated by a combination of molecular mechanics/generalized Born surface area and interaction entropy methods, and the calculated results correlated well with experimental results. B-factor and hydrogen bond analyses demonstrated that the G-quadruplex was more stable in the presence of ligands than in the absence of ligands. Calculation of the binding free energy showed that the BER derivatives bind to a G-quadruplex with higher affinity than that of BER. The breakdown of the binding free energy to per-nucleotide energies suggested that the first G-tetrad played a primary role in binding. Additionally, energy and geometric properties analyses indicated that van der Waals interactions were the most favorable interactions between the derivatives and the G-quadruplexes. Overall, these findings provide crucial atomic-level insights into the binding of G-quadruplexes and their inhibitors.

Modular networks and genomic variation during progression from stable angina pectoris through ischemic cardiomyopathy to chronic heart failure.

BACKGROUND: Analyzing disease-disease relationships plays an important role for understanding etiology, disease classification, and drug repositioning. However, as cardiovascular diseases with causative links, the molecular relationship among stable angina pectoris (SAP), ischemic cardiomyopathy (ICM) and chronic heart failure (CHF) is not clear. METHODS: In this study, by integrating the multi-database data, we constructed paired disease progression modules (PDPMs) to identified relationship among SAP, ICM and CHF based on module reconstruction pairs (MRPs) of K-value calculation (a Euclidean distance optimization by integrating module topology parameters and their weights) methods. Finally, enrichment analysis, literature validation and structural variation (SV) were performed to verify the relationship between the three diseases in PDPMs. RESULTS: Total 16 PDPMs were found with K > 0.3777 among SAP, ICM and CHF, in which 6 pairs in SAP-ICM, 5 pairs for both ICM-CHF and SAP-CHF. SAP-ICM was the most closely related by having the smallest average K-value (K = 0.3899) while the maximum is SAP-CHF (K = 0.4006). According to the function of the validation gene, inflammatory response were through each stage of SAP-ICM-CHF, while SAP-ICM was uniquely involved in fibrosis, and genes were related in affecting the upstream of PI3K-Akt signaling pathway. 4 of the 11 genes (FLT1, KDR, ANGPT2 and PGF) in SAP-ICM-CHF related to angiogenesis in HIF-1 signaling pathway. Furthermore, we identified 62.96% SVs were protein deletion in SAP-ICM-CHF, and 53.85% SVs were defined as protein replication in SAP-ICM, while ICM-CHF genes were mainly affected by protein deletion. CONCLUSION: The PDPMs analysis approach combined with genomic structural variation provides a new avenue for determining target associations contributing to disease progression and reveals that inflammation and angiogenesis may be important links among SAP, ICM and CHF progression.

Impact of feedback bandwidth on Raman random fiber laser remote-sensing.

In the ultra-long distance sensing domain, recently Raman random fiber laser (RRFL) demonstrated advantages of ultrawide sensing-bandwidth in dynamic sensing, compared with pulse-probing cases. However, such a scheme is still in the preliminary stage, and the key parameters such as sensitivity have not been characterized. In this work, a time-dependent spectrum-balanced model is proposed, which can accurately and quickly describe the spectral shape of RRFL and the evolution of the power and the spectrum. Based on this model, the relationship between the sensitivity and the feedback bandwidth is studied. The calculated results show that the sensitivity is inversely proportional to the feedback bandwidth. Then in the proof-of-concept experiment, by changing the bandwidth of sensing FBG, the results of sensitivity are well coincident with the simulation. This work provides an effective platform for studying the evolution of RRFL spectrum, as well as a novel way for further enhancing the performance of the dynamic sensing system based on ultra-long RRFL.

A bibliometric analysis of DNA methylation in cardiovascular diseases from 2001 to 2021.

BACKGROUND: DNA methylation is a dynamically reversible form of epigenetics. Dynamic regulation plays an important role in cardiovascular diseases (CVDs). However, there have been few bibliometric studies in this field. We aimed to visualize the research results and hotspots of DNA methylation in CVDs using a bibliometric analysis to provide a scientific direction for future research. METHODS: Publications related to DNA methylation in CVDs from January 1, 2001, to September 15, 2021, were searched and confirmed from the Web of Science Core Collection. CiteSpace 5.7 and VOSviewer 1.6.15 were used for bibliometric and knowledge-map analyses. RESULTS: A total of 2617 publications were included in 912 academic journals by 15,584 authors from 963 institutions from 85 countries/regions. Among them, the United States of America, China, and England were the top 3 countries contributing to the field of DNA methylation. Harvard University, Columbia University, and University of Cambridge were the top 3 contributing institutions in terms of publications and were closely linked. PLoS One was the most published and co-cited journal. Baccarelli Andrea A published the most content, while Barker DJP had the highest frequency of co-citations. The keyword cluster focused on the mechanism, methyl-containing substance, exposure/risk factor, and biomarker. In terms of research hotspots, references with strong bursts, which are still ongoing, recently included "epigenetic clock" (2017-2021), "obesity, smoking, aging, and DNA methylation" (2017-2021), and "biomarker and epigenome-wide association study" (2019-2021). CONCLUSIONS: We used bibliometric and visual methods to identify research hotspots and trends in DNA methylation in CVDs. Epigenetic clocks, biomarkers, environmental exposure, and lifestyle may become the focus and frontier of future research.

A fast-slow method to treat solute dynamics in explicit solvent.

Aiming to reduce the computational cost in the current explicit solvent molecular dynamics (MD) simulation, this paper proposes a fast-slow method for the fast MD simulation of biomolecules in explicit solvent. This fast-slow method divides the entire system into two parts: a core layer (typically solute or biomolecule) and a peripheral layer (typically solvent molecules). The core layer is treated using standard MD method but the peripheral layer is treated by a slower dynamics method to reduce the computational cost. We compared four different simulation models in testing calculations for several small proteins. These include gas-phase, implicit solvent, fast-slow explicit solvent and standard explicit solvent MD simulations. Our study shows that gas-phase and implicit solvent models do not provide a realistic solvent environment and fail to correctly produce reliable dynamic structures of proteins. On the other hand, the fast-slow method can essentially reproduce the same solvent effect as the standard explicit solvent model while gaining an order of magnitude in efficiency. This fast-slow method thus provides an efficient approach for accelerating the MD simulation of biomolecules in explicit solvent.

HergSPred: Accurate Classification of hERG Blockers/Nonblockers with Machine-Learning Models.

The human ether-à-go-go-related gene (hERG) K+ channel plays an important role in cardiac action potentials. The inhibition of the hERG channel may lead to long QT syndrome (LQTS) and even sudden cardiac death. Due to severe hERG-related cardiotoxicity, many drugs have been withdrawn from the market. Therefore, it is necessary to estimate the chemical blockade of hERG in the early stage of drug discovery. In this study, we collected 12,850 compounds with hERG inhibition data from the literature and trained a series of hERG blocking classification models based on the MACCS and Morgan fingerprints. A consensus model named HergSPred was generated based on the individual models using voting principles. The accuracy of HergSPred is higher than previous models using identical training and test sets. Moreover, we analyzed the contribution of each input fingerprint to the prediction output to obtain intuitive chemical insights into the hERG inhibition, which allows visualization of warning substructures that may cause cardiotoxicity in the input compound. The model is available at http://www.icdrug.com/ICDrug/T.

Generation of non-iterative phase-only hologram based on a hybrid phase mask.

The random phase method and quadratic phase method are most widely used in the generation of non-iterative phase holograms. However, the former leads to the reconstruction being severely disturbed by speckle noise, with serious loss of detailed information, and the latter leads to the reconstruction being contaminated with ringing artifacts. To solve these problems, we present a novel, to the best of our knowledge, method capable of generating non-iterative phase holograms, hereafter referred to as hybrid-phase-only holograms (HPOHs). Our proposal is to use a weight factor to combine the random phase and quadratic phase to generate a hybrid phase mask. The hybrid phase mask is then superimposed on the target image to obtain a complex hologram by simple Fourier transform. Followed by retaining the phase of the complex hologram, we can generate the corresponding HPOH. The effects of different weight factors on the holographic reconstructions are discussed. Numerical simulations of reconstruction quality associated with the proposed method, random phase method, and quadratic phase method are presented for comparison purposes. Optical experiments based on liquid crystal on silicon also demonstrate the validity of the method.

Discovery of novel inhibitors of CDK2 using docking and physics-based binding free energy calculation.

Cyclin-dependent kinase (CDK) is a serine/threonine protein kinase family that cooperates with cyclin and plays an important role in the regulation of cell cycle. Cyclin-dependent kinase 2 is an important member of the CDK family and holds great promise as an anti-cancer drug target. In this study, we used molecular docking and physics-based binding free energy calculation method AS-IE that explicitly calculated protein-ligand binding entropy to discover novel inhibitors of CDK2. A total of 17 inhibitors were discovered with the best IC50 reaching ~2 µM. Decomposition of the binding free energy using AS-IE reveals key protein-ligand interactions that determines the activity. These results provided a good example of drug design using physics-based free energy calculation method such as AS-IE and the novel compounds offered a good start point for further development of CDK2 inhibitors.

HobPre: accurate prediction of human oral bioavailability for small molecules.

Human oral bioavailability (HOB) is a key factor in determining the fate of new drugs in clinical trials. HOB is conventionally measured using expensive and time-consuming experimental tests. The use of computational models to evaluate HOB before the synthesis of new drugs will be beneficial to the drug development process. In this study, a total of 1588 drug molecules with HOB data were collected from the literature for the development of a classifying model that uses the consensus predictions of five random forest models. The consensus model shows excellent prediction accuracies on two independent test sets with two cutoffs of 20% and 50% for classification of molecules. The analysis of the importance of the input variables allowed the identification of the main molecular descriptors that affect the HOB class value. The model is available as a web server at www.icdrug.com/ICDrug/ADMET for quick assessment of oral bioavailability for small molecules. The results from this study provide an accurate and easy-to-use tool for screening of drug candidates based on HOB, which may be used to reduce the risk of failure in late stage of drug development.