A series of novel C-28 amino acid/amide conjugates and oligo(ethylene glycol)-modified C-2, C-3 pyrazole-fused betulinic acid derivatives were designed, synthesized, and evaluated for their inhibitory effects on RANKL-induced osteoclastogenesis. Among these, compound 18, bearing a C-28 oligo(ethylene glycol) amino amide ester linkage, exhibited the most potent inhibitory activity. It demonstrated an IC50 of 7.96 nM against RANKL-induced osteoclastogenesis in RAW264.7 cells, representing a 10-fold increase in potency compared to the XJ13 (IC50 = 0.08 μM) and achieved >50 % inhibition at 0.01 μM. Importantly, the inhibitory effects of these compounds on RANKL-induced osteoclast differentiation were not attributed to cytotoxicity, as evidenced by the minimal cytotoxicity of compound 18 at 10 μM. Mechanistic studies showed that compound 18 could dose-dependently suppress the expression of osteoclast marker genes (TRAP, CTSK) and proteins (c-Fos, MMP-9). Furthermore, in an ovariectomized (OVX) mouse model, compound 18 (10 and 20 mg/kg, intraperitoneally, i.p.) dose - dependently prevented bone loss by improving key micro-CT parameters and decreasing serum bone resorption markers (CTx). Overall, compound 18 emerged as a highly promising candidate for the treatment of RANKL-driven osteoporosis.

Antimicrobial resistance poses an urgent threat to global health, underscoring the critical need for new antibacterial drugs. Ciprofloxacin, a third-generation quinolone antibiotic, is used to treat different types of bacterial infections; however, it often results in the rapid emergence of resistance in clinical settings. Inspired by low susceptibility to antimicrobial resistance of natural antimicrobial peptides, we herein propose a host defense peptide-mimicking strategy for designing chimeric quinolone derivatives which may reduce the likelihood of antibacterial resistance. This strategy involves the incorporation of deliberately designed amphiphilic moieties into ciprofloxacin to mimic the structural characteristics and resistance-evading properties of host defense peptides. A resulting chimeric compound IPMCL-28b, carrying a rigid linker and three cationic amino acids along with a lipophilic acyl n-decanoyl tail, exhibited potent activity against a panel of multidrug-resistant bacterial strains by endowing the ciprofloxacin derivatives with additional ability to disrupt bacterial cell membranes. Molecular dynamics simulations showed that IPMCL-28b demonstrates significantly stronger disruptive interactions with cell membranes than ciprofloxacin. This compound not only demonstrated high selectivity with low hemolysis side effect, but also significantly reduced the likelihood of resistance development compared with ciprofloxacin. Excitingly, IPMCL-28b demonstrated highly enhanced in vivo antimicrobial activity against methicillin-resistant Staphylococcus aureus (MRSA) with a 99.99 % (4.4 log) reduction in skin bacterial load after a single dose. These findings highlight the potential of host defense peptides-mimicking amphiphilic ciprofloxacin derivatives to reverse antibiotic resistance and mitigate the development of antimicrobial resistance.

Poly(ethylene glycol) diacrylate (PEGDA) microneedles (MNs) are hydrogel-based devices that achieve controlled drug delivery kinetics by adjusting the crosslinking density. However, the biosafety of many crosslinking agents used to regulate crosslinking density is not ideal. To avoid crosslinking agents and simplify the preparation process, using two types of polymer homologues with different number-average molecular weights, we have successfully developed a series of PEGDA MNs with controllable crosslinking density (abbreviated as TP-X MNs). The research showed that the mechanical properties and drug release behavior of TP-X MNs could be tuned by simply controlling the weight proportion of two different PEGDA components in MNs. Ex vivo drug delivery experiments indicated that all TP-X MNs exhibited a sustained release profile, and their control range of 336-hour accumulative release rates was from 6.24% to 40.93%. Moreover, we prepared a novel dual-layer PEDGA MN, which can customize the drug loading and release rate in each layer of MN. This work demonstrates a new way to develop hydrogel MNs with adjustable crosslink density and broadens the applications of PEGDA MN in the biomedical field.

Myeloid cell leukemia-1 (MCL-1), a key anti-apoptotic protein within the BCL-2 family, is essential in regulating cell survival, particularly in cancer, where its overexpression is often linked to therapeutic resistance. This review begins with an overview of BCL-2-mediated apoptosis, highlighting the pivotal role of MCL-1 in cellular homeostasis. We then focus on the structure and function of MCL-1, elucidating how its unique structural features contribute to its function and interaction with pro-apoptotic proteins. The core of this review is a detailed structural analysis of selective MCL-1 inhibitors, tracing their development from initial discovery to stepwise optimization. We explore various classes of inhibitors, including those with distinct core structures, covalent inhibitors that reversibly/irreversibly bind to MCL-1, and innovative approaches such as metal-based inhibitors and proteolysis-targeting chimeras (PROTACs). The structural evolution of these inhibitors is discussed, with particular emphasis on the modifications that have enhanced their selectivity, potency, and pharmacokinetic profiles. Additionally, we summarize the synergistic potential of MCL-1 inhibitors when used in combination with other therapeutic agents, emphasizing their role in overcoming drug resistance. The review concludes with a discussion of current challenges in MCL-1 modulation and future perspectives, proposing alternative strategies for targeting this critical protein for cancer therapy.

Nowadays, ER alpha is considered to be a primary target for the treatment of breast cancer, and selective estrogen receptor degraders (SERDs) are emerging as promising antitumor agents. By analysing ER alpha-SERDs complexes, the pharmacophore features of SERDs and the crucial protein-ligand interactions were identified. Then, by utilizing the scaffold-hopping and bioisosteres strategy, 23 novel derivatives were designed, synthesized and biologically evaluated. Among these derivatives, A20 exhibited potent ER alpha binding affinity (IC50 = 24.0 nM), degradation ability (EC50 = 5.3 nM), excellent ER selectivity, and outstanding anti-proliferative effects on MCF-7 cells (IC50 = 0.28 nM). Further biological studies revealed that A20 could degrade ER alpha through proteasome-mediated pathway, suppress signal transduction of MCF-7 cells, and arrest the cell cycle in G1 phase. Moreover, A20 showed excellent antitumor effect (TGI = 92.98 %, 30 mg kg(-1) day(-1)) in the MCF-7 xenograft model in vivo with good safety and favorable pharmacokinetics (F = 39.6 %), making it a promising candidate for the treatment of breast cancer.

The function of the p53 protein is impaired by the overexpression of its negative regulator murine double minute 2 protein (MDM2) and homologous protein MDMX. Disruption of the p53-MDM2/MDMX interaction to restore the transcriptional function of p53 is considered a promising strategy for cancer therapy. To design dual MDM2/MDMX inhibitors, the binding modes of MDM2 or MDMX with their inhibitors are elucidated. Several hot-spot residues of MDM2 or MDMX are identified by molecular dynamics simulations, alanine scanning and MM-GBSA calculations. Then, focusing on the interaction with hot-spot residues, two series of derivatives bearing 1,3-diketone and α-aminoketone scaffolds are designed and synthesized. Among these compounds, C16 is identified as the most potent compound with low micromolar binding affinities with MDM2 and MDMX. C16 also displays moderate antiproliferative activities against MDM2-overexpressing and MDMX-overexpressing cells, with IC50 values of 0.68 μM in HCT116 cells and 0.54 μM in SH-SY5Y cells. Furthermore, C16 inhibits cell migration and invasion, reactivates the function of p53, arrests the cell cycle and induces cellular apoptosis in HCT116 and SH-SY5Y cells. Collectively, C16 can be developed as a dual MDM2 and MDMX inhibitor for cancer therapy.

The estrogen-receptor alfa (ERα) is considered pivotal for breast cancer treatment. Although selective estrogen-receptor degraders (SERDs) have been developed to induce ERα degradation and antagonism, their agonistic effect on the uterine tissue and poor pharmacokinetic properties limit further application of ERα; thus, discovering novel SERDs is necessary. The ligand preferentially interacts with several key residues of the protein (defined as hot-spot residues). Improving the interaction with hot-spot residues of ERα offers a promising avenue for obtaining novel SERDs. In this study, pharmacophore modeling, molecular mechanics/generalized Born surface area (MM/GBSA), and amino-acid mutation were combined to determine several hot-spot residues. Focusing on the interaction with these hot-spot residues, hit fragments A1-A3 and A9 were virtually screened from two fragment libraries. Finally, these hit fragments were linked to generate compounds B1-B3, and their biological activities were evaluated. Remarkably, compound B1 exhibited potent antitumor activity against MCF-7 cells (IC50 = 4.21 nM), favorable ERα binding affinity (Ki = 14.6 nM), and excellent ERα degradative ability (DC50 = 9.7 nM), which indicated its potential to evolve as a promising SERD for breast cancer treatment.

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.

Disruption of p53-MDM2/MDMX interaction by smaller inhibitors is a promising therapeutic intervention gaining tremendous interest. However, no MDM2/MDMX inhibitors have been marketed so far. Drug repurposing is a validated, practical approach to drug discovery. In this regard, we employed structure-based virtual screening in a reservoir of marketed drugs and identified nintedanib as a new MDM2/MDMX dual inhibitor. The computational structure analysis and biochemical experiments uncover that nintedanib binds MDM2/MDMX similarly to RO2443, a dual MDM2/MDMX inhibitor. Furthermore, the mechanistic study reveals that nintedanib disrupts the physical interaction of p53-MDM2/MDMX, enabling the transcriptional activation of p53 and the subsequent cell cycle arrest and growth inhibition in p53+/+ cancer cells. Lastly, structural minimisation of nintedanib yields H3 with the equivalent potency. In summary, this work provides a solid foundation for reshaping nintedanib as a valuable lead compound for the further design of MDM2/MDMX dual inhibitors.

CL-20 (2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane, also known as HNIW) is one of the most powerful energetic materials. However, its high sensitivity to environmental stimuli greatly reduces its safety and severely limits its application. In this work, ab initio based neural network potential (NNP) energy surfaces for both β-CL-20 and CL-20/TNT co-crystals were constructed. To accurately simulate the thermal decomposition processes of these two crystal systems, reactive molecular dynamics simulations based on the NNPs were performed. Many important intermediate species and their associated reaction paths during the decomposition had been identified in the simulations and the direct results on detonation temperatures of both systems were provided. The simulations also showed clearly that 2,4,6-trinitrotoluene (TNT) molecules in the co-crystal act as a buffer to slow down the chain reactions triggered by nitrogen dioxide and this effect is more significant at lower temperatures. Specifically, the addition of TNT molecules in the CL-20/TNT co-crystal introduces intermolecular hydrogen bonds between CL-20 and TNT molecules in the system, thereby increasing the thermal stability of the co-crystal. The current reactive molecular dynamics simulation is performed based on the NNP which helps in accelerating the speed of ab initio molecular dynamics (AIMD) simulation by more than 3 orders of magnitude while preserving the accuracy of density functional theory (DFT) calculations. This enabled us to perform longer-time simulations at more realistic temperatures that traditional AIMD methods cannot achieve. With the advantage of the NNP in its powerful fitting ability and transferability, the NNP-based MD simulation can be widely applied to energetic material systems.

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.

Protein–protein interaction (PPI) plays an important role in biological processes such as signal transduction, immune response and membrane transport, etc. In this work, a protein sequence-based machine learning model, SeBPPI, to predict protein–protein binding was proposed. In this model, the descriptors were generated from three pre-trained models: Unirep, ESM and TAPE. The performance of SeBPPI with these pre-trained models was evaluated on several different test datasets. The accuracy of our binary prediction model shows improvement over the existing methods. We also compared the performance of two classification heads: The Recurrent convolution neural network (RCNN) and the fully connected neural network (FNN) and found that the use of RCNN is beneficial for the overall improvement in the accuracy of the model. This study helps to improve the accuracy in sequence-based protein–protein binding predictions. The model used in this work is integrated in the web server http://www.icdrug.com/ICDrug/SeBPPI .

As an anti-tuberculosis target, DprE1 contains two flexible loops (Loop I and Loop II) which have never been exploited for developing DprE1 inhibitors. Here Leu317 in Loop II was discovered as a new functional site to combat drug-resistance in Mycobacterium strains. Based on TCA1, LZDT1 was designed to optimize the hydrophobic interaction with Leu317. A subsequent biochemical and cellular assay displayed increased potency of LZDT1 in inhibiting DprE1 and killing drug-sensitive/-resistant Mycobacterium strains. The improved activity of LZDT1 and its analogue LZDT2 against multidrug resistant tuberculosis was particularly highlighted. For LZDT1, its enhanced interaction with Leu317 also impaired the drug-insensitivity of DprE1 caused by Cys387 mutation. A new nonbenzothiazole lead (LZDT10) with reduced Cys387-dependence was further produced by optimizing interactions with Leu317, improvement directions for LZDT10 were discussed as well. Our research underscores the value of potential functional sites in disordered loops, and affords a feasible way to develop these functional sites into opportunities for drug-resistance management.

Soot is formed resulting from incomplete combustion processes of fossil fuels and is one of the most abundant specie in the space. The early stages of soot formation are central to many ongoing studies in combustion research, but its inception and growth are still elusive and highly debated. Herein, molecular dynamic simulations with ab initio based neural network potentials were carried out to simulate the reaction process leading to the growth of PAHs and soot inception from small hydrocarbon reactants. The in silico simulation provided detailed information of reaction paths that revealed critical steps leading to the formation and growth of large PAHs. And the simulation results clearly showed that the formation and growth of possible soot inceptions are achieved through a series of reactions with small PAH radicals, particularly the two-ring PAH radicals rather than by direct combination of large polynuclear hydrocarbons.

The relationship between protein sequence and its thermodynamic stability is a critical aspect of computational protein design. In this work, we present a new theoretical method to calculate the free energy change (ΔΔG) resulting from a single-point amino acid mutation to alanine in a protein sequence. The method is derived based on physical interactions and is very efficient in estimating the free energy changes caused by a series of alanine mutations from just a single molecular dynamics (MD) trajectory. Numerical calculations are carried out on a total of 547 alanine mutations in 19 diverse proteins whose experimental results are available. The comparison between the experimental ΔΔGexp and the calculated values shows a generally good correlation with a correlation coefficient of 0.67. Both the advantages and limitations of this method are discussed. This method provides an efficient and valuable tool for protein design and engineering.

A method for accurate calculation of reorganization free energy of an explicit solvent solvating a solute molecule is presented. The method relies on the knowledge of solvation free energy known either from experimental measurement or from accurate calculation. An important part of this approach lies in the calculation of entropy in solute-solvent interaction free energy using the interaction entropy method, combined with the calculation of enthalpy of solvent reorganization based on a finite number of solvent molecules near the solute molecule. This interaction entropy-solvent reorganization or IESR method enables one to compute both enthalpy and entropy components of the solvent reorganization energy from MD simulation of the solvated system. The calculated results are determined by the molecular force field only without any empirical parameter. The current method is applied to computing the reorganization energies of water solvent solvating a wide range of solute molecules including both hydrophilic and hydrophobic ones. The accuracy of the approach is indirectly verified by the excellent agreement of individual enthalpies and entropies of the solvation energies between theory and experiment.

A method for efficient prediction of the relative stability of a protein due to a single amino acid point mutation is presented. In this approach, we calculate the free energy change due to an arbitrary point mutation of a protein from a single MD trajectory of the wild type protein. The method is tested on 27 diverse protein systems with a total of 853 mutations and the calculated relative free energies show a generally good correlation with the experimental values (a correlation coefficient of 0.63). Comparison with the free energy perturbation (FEP) method and the recently developed machine learning methods on two different benchmark data sets shows that the current method is computationally efficient and also numerically reliable for predicting the changes in thermostability upon an arbitrary point mutation of a protein. A discussion is provided on how to further improve the accuracy of the method for the prediction of thermostability of proteins.

The calculation of free energy of pure and mixed solution is important in chemistry and related fields. Existing accurate methods use alchemical approaches which require multiple molecular dynamics simulations for a series of artificial intermediate states and are computationally expensive. Here we present an efficient method to calculate excess free energies (both enthalpy and entropy) of pure and mixed alcohol solutions. The present method calculates both enthalpy and entropy of the solution system directly from a single MD trajectory. We performed free energy calculations for several alcohol solutions using two different force fields for comparison with each other and with experimental results. The method is for general mixed solutions with application to mixed water-methanol and water-ethanol solutions at various degrees of mixtures. The calculated numerical results are generally in good agreement with experimental data.

We report herein the design and synthesis of a series of novel nitrobenzamide derivatives. Results reveal that many of them display considerable in vitro antitubercular activity. Four N-benzyl or N-(pyridine-2-yl)methyl 3,5-dinitrobenzamides A6, A11, C1 and C4 have not only the same excellent MIC values of <0.016μg/mL against both drug-sensitive MTB strain H37Rv and two drug-resistant clinical isolates as PBTZ169 and the lead 1, but also acceptable safety indices (SI>1500), opening a new direction for further development.

We report herein the design and synthesis of a series of novel nitrobenzamide derivatives. Results reveal that many of them display considerable in vitro antitubercular activity. Four N-benzyl or N- （pyridine-2-yl）methyl 3,5-dinitrobenzamides A6, All, Cl and C4 have not only the same excellent MIC values of＜0.016 μg/mL against both drug-sensitive MTB strain H37 Rv and two drug-resistant clinical isolates as PBTZ169 and the lead 1, but also acceptable safety indices （SI ＞ 1500）, opening a new direction for further development.