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BACKGROUND: Protein-ligand binding affinity is of significant importance in structure-based drug design. Recently, the development of machine learning techniques has provided an efficient and accurate way to predict binding affinity. However, the prediction performance largely depends on how molecules are represented. OBJECTIVE: Different molecular descriptors are designed to capture different features. The study aims to identify the optimal circular fingerprints for predicting protein-ligand binding affinity with matched neural network architectures. METHODS: Extended-connectivity fingerprints (ECFP) and protein-ligand extended connectivity fingerprints (PLEC) encode circular atomic and bonding connectivity environments with the preference for intra- and inter-molecular features, respectively. Densely-connected neural networks are employed to map the circular fingerprints of protein-ligand complexes to binding affinities RESULTS: The performance of neural networks is sensitive to the parameters used for ECFP and PLEC fingerprints. The R2_score of the evaluated ECFP and PLEC fingerprints reaches 0.52 and 0.49, higher than that of the improperly set ECFP and PLEC fingerprints with R2_score of 0.45 and 0.38, respectively. Additionally, compared to the predictions from the standalone fingerprints, the ECFP+PLEC conjoint ones slightly improve the prediction accuracy with R2_score of approximately 0.55. CONCLUSION: Both intra- and inter-molecular structural features encoded in the circular fingerprints contribute to the protein-ligand binding affinity. Optimizing the parameters of ECFP and PLEC can enhance performance. The conjoint fingerprint scheme can be generally extended to other molecular descriptors for enhanced feature engineering and improved predictive performance.

Given the difficult of experimental determination, quantitative structure-property relationship (QSPR) and deep learning (DL) provide an important tool to predict physicochemical property of chemical compounds. In this paper, partial least squares (PLS), genetic function approximation (GFA), and deep neural network (DNN) were used to predict the Lee retention index (Lee-RI) of PAHs in SE-52 and DB-5 stationary phases. Four molecular descriptors, molecular weight (MW), quantitative estimate of drug-likeness (QED), atomic charge weighted negative surface area (Jurs_PNSA_3), and relative negative charge (Jurs_RNCG) were selected to construct regression models based on genetic algorithm. For SE-52, PLS model showed best prediction power, followed by DNN and GFA. The relative error (RE), root mean square error (RMSE), and regression coefficient (R

Background: The new coronavirus disease 2019 (COVID-19) is spreading rapidly around the world. Artificial Intelligence (AI) assisted identification and detection of diseases is an effective method of medical diagnosis. Objectives: To present recent advances in AI-assisted diagnosis of COVID-19, we introduce major aspects of AI in the process of diagnosing COVID-19. Methods: In this paper, we firstly cover the latest collection and processing methods of datasets of COVID-19. The processing methods mainly include building public datasets, transfer learning, unsupervised learning and weakly supervised learning, semi-supervised learning methods and so on. Secondly, we introduce the algorithm application and evaluation metrics of AI in medical imaging segmentation and automatic screening. Then, we introduce the quantification and severity assessment of infection in COVID-19 patients based on image segmentation and automatic screening. Finally, we analyze and point out the current AI-assisted diagnosis of COVID-19 problems, which may provide useful clues for future work. Conclusion: AI is critical for COVID-19 diagnosis. Combining chest imaging with AI can not only save time and effort, but also provide more accurate and efficient medical diagnosis results.

The capsid protein (CA), an essential component of human immunodeficiency virus type 1 (HIV-1), represents an appealing target for antivirals. Small molecules targeting the CAI-binding cavity in the C-terminal domain of HIV-1 CA (CA CTD) confer potent antiviral activities. In this study, we report that a small molecule, protoporphyrin IX (PPIX), targets the HIV-1 CA by binding to this pocket. PPIX was identified via

Polycyclic aromatic hydrocarbons (PAHs), a special class of persistent organic pollutants (POPs) with two or more aromatic rings, have received extensive attention owing to their carcinogenic, mutagenic, and teratogenic effects. Quantitative structure-property relationship (QSPR) is powerful chemometric method to correlate structural descriptors of PAHs with their physicochemical properties. In this manuscript, a QSPR study of PAHs was performed to predict their boiling point (bp), octanol-water partition coefficient (LogK

Efficient removal of tetracycline (TC) under visible-light irradiation over TiO2-based photocatalysts remains a challenge based on the fact that the reported photocatalytic systems still suffer from weak visible-light absorption and/or inefficient charge separation. Herein, we constructed {101} and {001} facets co-exposed TiO2 hollow sphere (001-HT) via a gentle NaF treatment, in which the hollow mesoporous feature can trap incident light for a long time to improve photons efficiently. Meanwhile, the as-formed facet heterojunction significantly facilitates the charge separation. As a result, the 001-HT exhibits a high removal rate (~90.1%) of TC under visible-light irradiation, beyond the values of many reported TiO2-based photocatalysts. Most importantly, we further expound the ligand-to-metal charge transfer mechanism towards TiO2-assisted degradation of TC under visible-light irradiation, which effectively clarifies the confusion about the origin of pure TiO2 visible-light activity towards TC degradation because both TiO2 and TC do not exhibit any visible-light catalytic activity. Therefore, this work provides a new insight in revealing the mechanism of visible-light-mediated TC degradation over pure TiO2 photocatalyst.

RNA molecules fold as they are transcribed. Cotranscriptional folding of RNA plays a critical role in RNA functions in vivo. Present computational strategies focus on simulations where large structural changes may not be completely sampled. Here we describe an alternative approach to predicting cotranscriptional RNA folding by zooming in and out of the RNA folding energy landscape. By classifying the RNA structural ensemble into “partitions” based on long, stable helices, we zoom out of the landscape and predict the overall slow folding kinetics from the inter-partition kinetic network, and for each inter-partition transition, we zoom in on the landscape to simulate the kinetics. Applications of the model to the 117-nucleotide E. coli SRP RNA and the 59-nucleotide HIV-1 TAR RNA show agreements with the experimental data and new structural and kinetic insights into biologically significant conformational switches and pathways for these important systems. This approach, by zooming in/out of RNA folding landscape at different resolutions, might allow us to treat large RNAs in vivo with transcriptional pause, transcription speed, and other in vivo effects.

Conventional ion-exchange polymeric membranes have limited selectivity due to their nonuniform and unstable structures. The rigid, regular, high porosity of metal organic framework (MOF) generally provides MOF membrane with exclusion/sieving effect but lack of electrostatic screening. Here we report for the first time a nonbiological highly selective MOF membrane with polyelectrolyte threaded in the nanochannel of metal organic framework (polyelectrolyte∼MOF) and its selective transport of alkali metal cations. Poly(sodium vinyl sulfonated-co-acrylic acid)∼MIL-53(Al) is prepared on anodic aluminum oxide substrate via steps of MOF MIL-53(Al) growth followed by in situ polymerization. The poly(VS-co-AA)∼MIL-53(Al) membrane demonstrates highly specific selectivity in transport of alkali metal cations. Rate of ion transport correlates inversely with the hydrated diameter of the ion reaching a low limiting rate near 0.7 nm hydrated diameter. Charge exclusion is demonstrated with blockage of anion transport under a concentration gradient. The highly uniform porous nanostructure of MOF and ionic function of polyelectrolyte offers the MOF membrane with synergistic selectivity based on exclusion forces of the framework and Coulomb forces from fixed charges of polyelectrolytes in nanochannels.

Elucidation of the role of entropy in enzymatic reactions can be utilized in the de novo design of enzymes or protein engineering. Recently, the change in entropy in the enzyme-catalyzed chorismate mutase reaction brought about uncertainty as a result of an experiment using PchB, in which the measured change in entropy was comparable to the reported value for the uncatalyzed reaction in an aqueous solution, which contrasted with the general proposed entropy-driven mechanism. On the basis of a sufficient sample from quantum mechanics/molecular mechanics molecular dynamics simulations, the entropic effect in the PchB-catalyzed chorismate mutase reaction has been determined for the first time. Calculations suggest that the PchB-catalyzed chorismate mutase reaction is entropy-driven. An additional entropic penalty was revealed from the substrate preorganization process, which led to a remarkable apparent entropic effect in the reaction. Our findings provide an explanation of the uncertainty regarding the large measured entropic effect in experiments and confirm the entropy-driven character of the PchB-catalyzed chorismate mutase reaction. The clarification of the change in entropy in this enzymatic reaction would provide useful clues for future enzyme design projects.

The application of deep learning in the field of drug discovery brings the development and expansion of molecular generative models along with new challenges in this field. One of challenges in de novo molecular generation is how to produce new reasonable molecules with desired pharmacological, physical, and chemical properties. To improve the similarity between the generated molecule and the starting molecule, we propose a new molecule generation model by embedding Long Short-Term Memory (LSTM) and Attention mechanism in CycleGAN architecture, LA-CycleGAN. The network layer of the generator in CycleGAN is fused head and tail to improve the similarity of the generated structure. The embedded LSTM and Attention mechanism can overcome long-term dependency problems in treating the normally used SMILES input. From our quantitative evaluation, we present that LA-CycleGAN expands the chemical space of the molecules and improves the ability of structure conversion. The generated molecules are highly similar to the starting compound structures while obtaining expected molecular properties during cycle generative adversarial network learning, which comprehensively improves the performance of the generative model.

Peptides are widely used for surface modification to develop improved implants, such as cell adhesion RGD peptide and antimicrobial peptide (AMP). However, it is a daunting challenge to identify an optimized condition with the two peptides showing their intended activities and the parameters for reaching such a condition. Herein, we develop a high-throughput strategy, preparing titanium (Ti) surfaces with a gradient in peptide density by click reaction as a platform, to screen the positions with desired functions. Such positions are corresponding to optimized molecular parameters (peptide densities/ratios) and associated preparation parameters (reaction times/reactant concentrations). These parameters are then extracted to prepare nongradient mono- and dual-peptide functionalized Ti surfaces with desired biocompatibility or/and antimicrobial activity in vitro and in vivo. We also demonstrate this strategy could be extended to other materials. Here, we show that the high-throughput versatile strategy holds great promise for rational design and preparation of functional biomaterial surfaces., Optimizing the concentration of different functional peptides on a surface can be a complex process. Here, the authors report on the use of a click immobilization strategy to create gradients of two different functional peptides on a surface to screen different density functions for rapid optimization.

L-type Calcium Channels (LTCCs), also termed as Cav1, belong to voltage-gated calcium channels (VGCCs/Cavs), which play a critical role in a wide spectrum of physiological processes, including neurotransmission, cell cycle, muscular contraction, cardiac action potential and gene expression. Aberrant regulation of calcium channels is involved in neurological, cardiovascular, muscular and psychiatric disorders. Accordingly, LTCCs have been regarded as important drug targets, and a number of LTCC drugs are in clinical use. In this review, the recent development of structures and biological functions of LTCCs are introduced. Moreover, the representative modulators and ligand binding sites of LTCCs are discussed. Finally, molecular modeling and Computer-aided Drug Design (CADD) methods for understanding structure-function relations of LTCCs are summarized.

As an effective strategy to develop novel implants with a biomolecule, the efficiency of a copper-catalysed azide-alkyne cycloaddition (CuAAC) urgently requires improvement. Herein, we constructed a facile CuAAC system with catalytic CuSO4/sodium borohydride (CuAAC-Bor). As Cu nanoclusters (CuNCs) formed in situ, they showed stronger efficiency in immobilizing 41 types of peptides on Ti implants than traditional CuAAC with CuSO4/sodium ascorbate. Then, we mechanically revealed the significance of peptide size on CuAAC efficiency by all-atom molecular dynamics simulation and machine learning, and found that larger peptides preferred to adhere to Ti and CuNCs to increase their reaction opportunity. With CuAAC-Bor, HHC36-engineered implants strongly inhibited 5 types of clinical bacteria in vitro and prevented infection in vivo, and RGD-, DGEA-, QK- or YGFGG-engineered implants had high biocompatibilities with HUVECs/hBMSCs in vitro. Moreover, we demonstrated that QK-engineered implants prepared with CuAAC-Bor promoted the osteogenic differentiation of hBMSCs via the PI3K/Akt signalling pathway and triggered angiogenesis and osteogenesis in vivo. Our study shows that this facile and highly efficient CuAAC-Bor system holds high promise for the preparation of novel biomaterial surfaces.

Dynamic combinatorial library (DCL) has emerged as an efficient tool for ligand discovery and become an important discovery modality in biomedical research. However, the applications of DCLs have been significantly hampered by low library diversity and limited analytical methods capable of processing large libraries. Here, we report a strategy that has addressed this limitation and can select cooperatively binding small-molecule pairs from large-scale dynamic libraries. Our approach is based on DNA-mediated dynamic hybridization, DNA-encoding, and a photo-cross-linking-based decoding scheme. To demonstrate the generality and performance of this approach, a 10 000-member DNA-encoded dynamic library has been prepared and selected against six protein targets. Specific binders have been identified for each target, and we have validated the biological activities of selected ligands for the targets that are implicated in important cellular functions including protein deacetylation and sumoylation. Notably, a series of novel and selective sirtuin-3 inhibitors have been developed. Our study has circumvented a major obstacle in DCL and may provide a broadly applicable method for ligand discovery against biological targets.

This study demonstrates a multi-responsive high-performance bilayered hydrogel actuator (HA) obtained via coating a borax cross-linked N1,N1-diethylethane-1,2-diamine (DEEDA)-modified polyvinyl alcohol (PVA) microgel (PVA–DEEDA–borax) on a polyacrylic acid-coated biaxially oriented polypropylene (BOPP) substrate, and reveals that the amount of borax that is added plays a decisive role in the ultimate performance of the actuator. The HA is able to perform rapid, reversible, and sustainable bidirectional self-rolling deformation actuated by heat- and moisture-triggered swelling/deswelling of PVA–DEEDA–borax gel, transforming into 2D/3D tube, wave and cross shapes through designing the assembly of actuators. The significant difference in the swelling behavior between PVA–DEEDA–borax and BOPP generates enough force to actuate the performance of the hydrogels as soft artificial muscle and self-adapted smart manipulator with capability of lifting masses much heavier than the actuator devices, showing potential applications in small-scale soft robotics.

Deep learning-based methods have been extensively developed to improve scoring performance in structure-based drug discovery. Extending multitask deep networks in addressing pharmaceutical problems shows remarkable improvements over single task network. Recently, grid featurization has been introduced to convert protein-ligand complex co-ordinates into fingerprints with the advantage of incorporating inter- and intra-molecular information. The combination of grid featurization with multitask deep networks would hold great potential to boost the scoring performance. We examined the performance of three novel multitask deep networks (standard multitask, bypass, and progressive network) in reproducing the binding affinities of protein-ligand complexes in comparison with AutoDock Vina docking and MM/GBSA method. Among five evaluated methods, progressive network combined with grid featurization provided the best Pearson correlation coefficient (0.74) and least mean absolute average error (0.98) for the overall scoring performance. Moreover, all networks increased screening ability for the re-docking pose and progressive network even achieved AUC of 0.87 over 0.52 of AutoDock Vina. Our results demonstrated that progressive network combined with grid featurization would be one powerful rescoring approach to strengthen screening results after obtaining protein-ligand complex in the conventional docking software.

The single deuterium substitution in porphycene leads to an asymmetric molecular geometry, which may affect the double proton transfer process in the porphycene molecule. In this study, we applied an enhanced QM/MM method called SITS-QM/MM to investigate hydrogen/deuterium (H/D) isotope effects on the double proton transfer in porphycene. Distance changes in SITS-QM/MM molecular dynamics simulations suggested that the deuterium substituted porphycene adopted the stepwise double proton transfer mechanism. The structural analysis and the free energy shifts of double proton transfer process indicated that the asymmetric isotopic substitution subtly compressed the covalent hydrogen bonds and may alter the original transition state location.

Free energy calculations for chemical reactions with a steep energy barrier require well defined reaction coordinates (RCs). However, when multiple parallel channels exist along selected RC, the application of conventional enhanced samplings is difficult to generate correct sampling within limited simulation time and thus cannot give correct prediction about the favorable pathways, the relative stability of multiple products or intermediates. Here, we implement the selective integrated tempering sampling (SITS) method with quantum mechanical and molecular mechanical (QM/MM) potential to investigate the chemical reactions in solution. The combined SITS-QM/MM scheme is used to identify possible reaction paths, intermediate and product states, and the free energy profiles for the different reaction paths. Two double proton transfer reactions were studied to validate the implemented method and simulation protocol, from which the independent and correlated proton transfer processes are identified in two representative systems, respectively. This protocol can be generalized to various kinds of chemical reactions for both academic studies and industry applications, such as in exploration and optimization of potential reactions in DNA encoded compound library and halogen or deuterium substitution of the hit discovery and lead optimization stages of drug design via providing a better understanding of the reaction mechanism along the designed chemical reaction pathways.

Molecular recognition towards peptides with high affinity and sequence selectivity by synthetic macrocyclic receptors is a great challenge in supramolecular chemistry. In this work, we investigate the molecular recognition of tripeptides containing N-terminal phenylalanine (Phe, F) by synthetic macrocyclic receptor Cucurbit[7]uril (CB[7]) in aqueous solution by molecular dynamics simulations. We found that among twenty amino acids (AAs), the binding of CB[7] to Phe is the strongest. Based on that a series of tripeptides containing N-terminal Phe (with the second residue Gly (G) fixed) are designed to explore the effects of adjacent AA residues on the binding to CB[7]. It is indicated that, in contrast to FGG, only when the 3rd-residue is Glu (E), Lys (K), or Arg (R), the binding affinity with CB[7] can reach the nanomolar level. For the most prominent FGE, the binding free energy with CB[7] is −12.8 kcal/mol, with association constant (Ka) of 2.1 × 109 M−1. It is surprising that once reversing the sequence order of the 2nd-residue and 3rd-residue from FGX to FXG (X = E, K, R), or inserting a second Gly in the middle of FGX, such as FGGX, the binding affinity is significantly changed, with Ka decreased more than 3 orders of magnitude. These results predicted the high binding affinity and sequence selectivity of CB[7] to containing N-terminal Phe peptides, which are beneficial to predict the recognition sites of specific proteins/peptides and to design host-guest complexes with high affinity.

Here, we investigated the secondary structure transformation for a design peptide, which has both the α-helix (PDB ID code 2DX3) and β-hairpin (PDB ID code 2DX4) structures in aqueous solution. We show that the transformation between α-helix and β-hairpin structures can be sampled efficiently using the enhanced sampling method based on integrated tempering without the definition of reaction coordinates. The reliable and smooth two-dimensional potential of mean force surfaces of the conformation space can be obtained efficiently, which has been used to propose the probable pathways for the transformation of the α-helix and β-hairpin structures. Our simulation results revealed the efficiency, and further suggested the general applicability of integrated tempering sampling method into complex biomolecule processes without prior structure knowledge.

Although photocatalysis has attracted tremendous research interest, there still remains critical challenges (e.g., low visible-light quantum efficiency, organic media, etc.), especially for selective hydrogenation of nitroarenes. Herein, we design and synthesize the first confined photocatalyst by introducing the nanospace of double-shelled hollow silica sphere as a photocatalytic nanoreactor to promote the hydrogenation reaction with the fast reaction kinetics. This photocatalyst exhibits excellent activity, selectivity, and recyclability. Especially, superior selectivity (>99%) is achieved when used for the hydrogenation of nitroarenes under visible-light irradiation in pure water medium. Both experimental and theoretical simulation results indicate that the Ag/Ag2O structure and confined nanospace of the photocatalyst greatly increase the contact probability between photogenerated atomic hydrogen and nitroarenes. Additionally, corresponding anilines are obtained almost quantitatively towards the hydrogenation of nitroarenes in pure water medium at room temperature. Therefore, this work provides a rational design concept of highly efficient visible-light photocatalyst for green chemistry industry.

Canonical molecular dynamics simulations are performed to investigate the behavior of single-chain and multiple-chain poly(ethylene glycol) (PEG) contained within a cubic framework spanned by polyethylene (PE) chains. This simple model is the first of its kind to study the chemical physics of polymer-threaded organic frameworks, which are materials with potential applications in catalysis and separation processes. For a single-chain 9-mer, 14-mer, and 18-mer in a small framework, the PEG will interact strongly with the framework and assume a more linear geometry chain with an increased radius of gyration Rg compared to that of a large framework. The interaction between PEG and the framework decreases with increasing mesh size in both vacuum and water. In the limit of a framework with an infinitely large cavity (infinitely long linkers), PEG behavior approaches simulation results without a framework. The solvation of PEG is simulated by adding explicit TIP3P water molecules to a 6-chain PEG 14-mer aggregate confined in a framework. The 14-mer chains are readily solvated and leach out of a large 2.6 nm mesh framework. There are fewer water-PEG interactions in a small 1.0 nm mesh framework, as indicated by a smaller number of hydrogen bonds. The PEG aggregate, however, still partially dissolves but is retained within the 1.0 nm framework. The preliminary results illustrate the effectiveness of the simple model in studying polymer-threaded framework materials and in optimizing polymer or framework parameters for high performance.

Although energy barriers can be efficiently crossed in the reaction coordinate (RC) guided sampling, this type of method suffers from identification of the correct RCs or requirements of high dimensionality of the defined RCs for a given system. If only the approximate RCs with significant barriers are used in the simulations, hidden energy barriers with small to medium height would exist in other degrees of freedom (DOFs) relevant to the target process and consequently cause the problem of insufficient sampling. To address the sampling in this so-called hidden barrier situation, here we propose an effective approach to combine temperature accelerated molecular dynamics (TAMD), an efficient RC-guided sampling method, with the integrated tempering sampling (ITS), a generalized ensemble sampling method. In this combined ITS-TAMD method, the sampling along the major RCs with high energy barriers is guided by TAMD and the sampling of the rest of the DOFs with lower but not negligible barriers is enhanced by ITS. The performance of ITS-TAMD to three systems in the processes with hidden barriers has been examined. In comparison to the standalone TAMD or ITS approach, the present hybrid method shows three main improvements. (1) Sampling efficiency can be improved at least five times even if in the presence of hidden energy barriers. (2) The canonical distribution can be more accurately recovered, from which the thermodynamic properties along other collective variables can be computed correctly. (3) The robustness of the selection of major RCs suggests that the dimensionality of necessary RCs can be reduced. Our work shows more potential applications of the ITS-TAMD method as the efficient and powerful tool for the investigation of a broad range of interesting cases.