Steered Molecular Dynamics Simulation in Rational Drug Design

Searching for novel MDM2/MDMX dual inhibitors through a drug repurposing approach

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.

Chemoinformatics Study of Benzodiazepine-1, 2, 3-triazole Derivatives Targeting Butyrylcholinesterase

This study aims to assess the potential bioactivity of newly designed benzodiazepine-1,2,3-triazole derivatives using in-silico methodologies, with a primary focus on elucidating their inhibitory interactions with the butyrylcholinesterase (BuChE) enzyme, which is implicated in Alzheimer's disease. We employed multiple linear regression (MLR) methods to conduct a quantitative structure-activity relationship (QSAR) analysis on a collection of 31 benzodiazepine-1,2,3-triazole derivatives, with the goal of investigating, assessing, and predicting their activities, as well as designing novel compounds. This approach yielded highly accurate results, with coefficients of determination (R²) of 0.77 and 0.81 for the training and test datasets, respectively. Additionally, the optimized compounds were subjected to an Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis, demonstrating their potential as non-hepatotoxic agents with enhanced absorption and blood-brain barrier permeability. To further validate these findings, the most favorable docking conformations were analyzed using molecular dynamics (MD) simulations with GROMACS software, predicting the stability of the formed complexes. These simulations underscored the critical role of hydrogen bonds in stabilizing the compounds at the BuChE receptor binding site. The results hold great promise for the development of innovative benzodiazepine-1,2,3-triazole derivatives as effective BuChE inhibitors, potentially leading to therapeutic interventions for Alzheimer's disease.

Linking triphenylphosphonium cation to a bicyclic hydroquinone improves their antiplatelet effect via the regulation of mitochondrial function

Platelets are the critical target for preventing and treating pathological thrombus formation. However, despite current antiplatelet therapy, cardiovascular mortality remains high, and cardiovascular events continue in prescribed patients. In this study, first results were obtained with ortho-carbonyl hydroquinones as antiplatelet agents; we found that linking triphenylphosphonium cation to a bicyclic ortho-carbonyl hydroquinone moiety by a short alkyl chain significantly improved their antiplatelet effect by affecting the mitochondrial functioning. The mechanism of action involves uncoupling OXPHOS, which leads to an increase in mitochondrial ROS production and a decrease in the mitochondrial membrane potential and OCR. This alteration disrupts the energy production by mitochondrial function necessary for the platelet activation process. These effects are responsive to the complete structure of the compounds and not to isolated parts of the compounds tested. The results obtained in this research can be used as the basis for developing new antiplatelet agents that target mitochondria.

Treatment of flexibility of protein backbone in simulations of protein-ligand interactions using steered molecular dynamics

To ensure that an external force can break the interaction between a protein and a ligand, the steered molecular dynamics simulation requires a harmonic restrained potential applied to the protein backbone. A usual practice is that all or a certain number of protein's heavy atoms or Cα atoms are fixed, being restrained by a small force. This present study reveals that while fixing both either all heavy atoms and or all Cα atoms is not a good approach, while fixing a too small number of few atoms sometimes cannot prevent the protein from rotating under the influence of the bulk water layer, and the pulled molecule may smack into the wall of the active site. We found that restraining the Cα atoms under certain conditions is more relevant. Thus, we would propose an alternative solution in which only the Cα atoms of the protein at a distance larger than 1.2 nm from the ligand are restrained. A more flexible, but not too flexible, protein will be expected to lead to a more natural release of the ligand.

Design and Synthesis of Novel Macrocyclic Derivatives as Potent and Selective Cyclin-Dependent Kinase 7 Inhibitors

The duality of function (cell cycle regulation and gene transcription) of cyclin-dependent kinase 7 (CDK7) makes it an attractive oncology target and the discovery of CDK7 inhibitors has been a long-term pursuit by academia and pharmaceutical companies. However, achieving selective leading compounds is still difficult owing to the similarities among the ATP binding pocket. Herein, we detail the design and synthesis of a series of macrocyclic derivatives with pyrazolo[1,5-a]-1,3,5-triazine core structure as potent and selective CDK7 inhibitors. The diverse manners of macrocyclization led to distinguished selectivity profiles of the CDK family. Molecular dynamics (MD) simulation explained the binding difference between 15- and 16-membered macrocyclic compounds. Further optimization generated compound 37 exhibiting good CDK7 inhibitory activity and high selectivity over other CDKs. This work clearly demonstrated macrocyclization is a versatile method to finely tune the selectivity profile of small molecules and MD simulation can be a valuable tool in prioritizing designs of the macrocycle.

Computational screening of natural MtbDXR inhibitors for novel anti-tuberculosis compound discovery

DXR (1-deoxy-d-xylulose-5-phosphate reductoisomerase) is an essential enzyme in the Methylerythritol 4-phosphate (MEP) pathway, which is used by M. tuberculosis and a few other pathogens. This essential enzyme in the isoprenoid synthesis pathway has been previously reported as an important target for antibiotic drug design. However, till now, there is no record of any drug-like safe molecule to inhibit MtbDXR. Numerous plant species have been traditionally used for tuberculosis therapies. In this study, we selected six plant species with anti-tubercular properties. The chemoinformatic screening was performed on 352 phytochemicals from those plants against the MtbDXR protein. After molecular docking analysis, we filtered the top five compounds, CID: 5280443 (Apigenin), CID: 3220 (Emodin), CID: 5280863 (Kaempferol), CID: 5280445 (Luteolin), and CID: 6101979 (beta-Hydroxychalcone), based on binding affinity. Molecular dynamics simulations disclosed the stability of the compounds at the active site of the proteins. Finally, in silico ADME and toxicity evaluations confirmed the compounds to be effective and safe for oral administration. Thus, our findings identified three drug-like safe molecules- Apigenin, Kaempferol, and beta-Hydroxychalcone, that showed good stability in the protein's active site. The results of this computational approach may act as an initial instruction for future in vitro and in vivo testing to identify natural drug-like compounds to treat tuberculosis.Communicated by Ramaswamy H. Sarma.

Exploring the potential of phytochemicals as inhibitors of 3'-phosphoadenosine 5'-phosphosulfate synthase 1 targeting cancer therapy

3'-phosphoadenosine 5'-phosphosulfate synthase 1 (PAPSS1) is an enzyme that critically synthesises the biologically active form of sulfate (PAPS) for all sulfation reactions. The discovery of PAPSS1 as a possible drug target for cancer therapy, specifically in non-small cell lung cancer, has prompted us to investigate potential small-molecule inhibitors of PAPSS1. Here, a structure-based virtual screening method was used to search for phytochemicals in the IMPPAT database to find potential inhibitors of PAPSS1. The primary hits were selected based on their physicochemical, ADMET, and drug-like properties. Then, the binding affinities were calculated and analyzed the interactions to identify safer and more effective hits. The research identified two phytochemicals, Guggulsterone and Corylin, that exhibited significant affinity and specific interaction with the ATP-binding pocket of PAPSS1. Structural observations made by molecular docking were further accompanied by molecular dynamics (MD) simulations and principal component analysis (PCA) to examine the conformational changes and stability of PAPSS1 with the elucidated compounds Guggulsterone and Corylin. MD simulation results suggested that the binding of Guggulsterone and Corylin stabilizes the PAPSS1 structure, leading to fewer conformational changes. This implies that these compounds may be useful in developing PAPSS1 inhibitors for the therapeutic development against non-small cell lung cancer (NSCLC). This study highlights the potential of phytochemicals as PAPSS1 inhibitors and the utility of computational approaches in drug discovery.Communicated by Ramaswamy H. Sarma.

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

Tyrosine phosphorylation of recombinant hirudin increases affinity to thrombin and antithrombotic activity

Thrombin is one of the key enzymes of the blood coagulation system and a promising target for the development of anticoagulants. One of the most specific natural thrombin inhibitors is hirudin, contained in the salivary glands of medicinal leeches. The medicinal use of recombinant hirudin is limited because of the lack of sulfation on Tyr63, resulting in a 10-fold decrease in activity compared to native (sulfated) hirudin. In the present work, a set of hirudin derivatives was tested for affinity to thrombin: phospho-Tyr63, Tyr63(carboxymethyl)Phe, and Tyr63Glu mutants, which mimic Tyr63 sulfation and Gln65Glu mutant and lysine-succinylated hirudin, which enhance the overall negative charge of hirudin, as well as sulfo-hirudin and desulfo-hirudin as references. Using steered molecular dynamics simulations with subsequent umbrella sampling, phospho-hirudin was shown to exhibit the highest affinity to thrombin among all hirudin analogs, including native sulfo-hirudin; succinylated hirudin was also prospective. Phospho-hirudin exhibited the highest antithrombotic activity in in vitro assay in human plasma. Taking into account the modern methods for obtaining phospho-hirudin and succinylated hirudin, they are prospective as anticoagulants in clinical practice.

Mechanistic insights into ligand dissociation from the SARS-CoV-2 spike glycoprotein

The COVID-19 pandemic, driven by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spurred an urgent need for effective therapeutic interventions. The spike glycoprotein of the SARS-CoV-2 is crucial for infiltrating host cells, rendering it a key candidate for drug development. By interacting with the human angiotensin-converting enzyme 2 (ACE2) receptor, the spike initiates the infection of SARS-CoV-2. Linoleate is known to bind the spike glycoprotein, subsequently reducing its interaction with ACE2. However, the detailed mechanisms underlying the protein-ligand interaction remain unclear. In this study, we characterized the pathways of ligand dissociation and the conformational changes associated with the spike glycoprotein by using ligand Gaussian accelerated molecular dynamics (LiGaMD). Our simulations resulted in eight complete ligand dissociation trajectories, unveiling two distinct ligand unbinding pathways. The preference between these two pathways depends on the gate distance between two α-helices in the receptor binding domain (RBD) and the position of the N-linked glycan at N343. Our study also highlights the essential contributions of K417, N121 glycan, and N165 glycan in ligand unbinding, which are equally crucial in enhancing spike-ACE2 binding. We suggest that the presence of the ligand influences the motions of these residues and glycans, consequently reducing accessibility for spike-ACE2 binding. These findings enhance our understanding of ligand dissociation from the spike glycoprotein and offer significant implications for drug design strategies in the battle against COVID-19.

In Silico Screening of Chlorogenic Acids from Plant Sources against Human Translocase-I to Identify Competitive Inhibitors to Treat Diabetes

Chlorogenic acids (CHLs) are known to competitively bind to translocase-I (T1) of the glucose-6-phosphatase (G6 Pase) system, thereby inhibiting the transport of glucose-6-phosphate (G6P). This competitive binding results in a consequential reduction in blood sugar levels. In this study, steered molecular dynamics (SMD) simulation is employed to investigate the interaction between T1 and G6P, aiming to gain insights into the binding dynamics and diffusion process of G6P through T1. A database comprising 41 CHLs sourced from various plants was developed, subjected to minimization, and screened against T1 through conventional docking methods. The docked conformations were fed into a newly developed customized scoring method incorporating contact-based weights to assess the binding affinities that systematically rank and identify the most effective competitive inhibitors. Among the screened CHLs, 1-methoxy 3,5-dicaffeoylquinic acid, 3,4 dicaffeoyl quinic acid, and 3,4,5-tricaffeoylquinic acid stood out as the top three inhibitors, showcasing crucial atomic interactions with key residues within the binding pocket of T1, and these CHLs are sourced from readily available plants, diminishing reliance on coffee as the predominant CHL source. Along with the devised scoring function, which serves as a valuable tool for virtual screening and lead optimization in drug development, this study also marks a pioneering effort as it involves the modeling of the human translocase and unravels the mechanism of binding and diffusion of G6P within human T1, providing valuable insights into the structural prerequisites for successfully inhibiting the G6P system, laying the foundation for a rational approach to drug design. This research contributes to the progress of drug discovery strategies focused on the G6P system, presenting potential therapeutic avenues for addressing metabolic disorders linked to an impaired glucose metabolism.

Unlocking Opportunities for Mycobacterium leprae and Mycobacterium ulcerans

In the recent decade, scientific communities have toiled to tackle the emerging burden of drug-resistant tuberculosis (DR-TB) and rapidly growing opportunistic nontuberculous mycobacteria (NTM). Among these, two neglected mycobacteria species of the Acinetobacter family, Mycobacterium leprae and Mycobacterium ulcerans, are the etiological agents of leprosy and Buruli ulcer infections, respectively, and fall under the broad umbrella of neglected tropical diseases (NTDs). Unfortunately, lackluster drug discovery efforts have been made against these pathogenic bacteria in the recent decade, resulting in the discovery of only a few countable hits and majorly repurposing anti-TB drug candidates such as telacebec (Q203), P218, and TB47 for current therapeutic interventions. Major ignorance in drug candidate identification might aggravate the dramatic consequences of rapidly spreading mycobacterial NTDs in the coming days. Therefore, this Review focuses on an up-to-date account of drug discovery efforts targeting selected druggable targets from both bacilli, including the accompanying challenges that have been identified and are responsible for the slow drug discovery. Furthermore, a succinct discussion of the all-new possibilities that could be alternative solutions to mitigate the neglected mycobacterial NTD burden and subsequently accelerate the drug discovery effort is also included. We anticipate that the state-of-the-art strategies discussed here may attract major attention from the scientific community to navigate and expand the roadmap for the discovery of next-generation therapeutics against these NTDs.

Designing collagens to shed light on the multi-scale structure-function mapping of matrix disorders

Collagens are the most abundant structural proteins in the extracellular matrix of animals and play crucial roles in maintaining the structural integrity and mechanical properties of tissues and organs while mediating important biological processes. Fibrillar collagens have a unique triple helix structure with a characteristic repeating sequence of (Gly-X-Y)n. Variations within the repetitive sequence can cause misfolding of the triple helix, resulting in heritable connective tissue disorders. The most common variations are single-point missense mutations that lead to the substitution of a glycine residue with a bulkier amino acid (Gly → X). In this review, we will first discuss the importance of collagen's triple helix structure and how single Gly substitutions can impact its folding, structure, secretion, assembly into higher-order structures, and biological functions. We will review the role of "designer collagens," i.e., synthetic collagen-mimetic peptides and recombinant bacterial collagen as model systems to include Gly → X substitutions observed in collagen disorders and investigate their impact on structure and function utilizing in vitro studies. Lastly, we will explore how computational modeling of collagen peptides, especially molecular and steered molecular dynamics, has been instrumental in probing the effects of Gly substitutions on structure, receptor binding, and mechanical stability across multiple length scales.

ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization

Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.

Magnetic solid phase extraction coupled to HPLC-UV for highly sensitive analysis of mono-hydroxy polycyclic aromatic hydrocarbons in urine

BACKGROUND

As a common pollutant, the carcinogenic properties of polycyclic aromatic hydrocarbons have garnered considerable attention. Trace metabolites of polycyclic aromatic hydrocarbons can be detected in urine as a non-invasively approach to monitor the exposure level. Nonetheless, the urine samples have the disadvantages of being large in volume and containing numerous impurities. Given the growing demand to study metabolites with low abundance and potential biomarkers, there is a pressing need for a preconcentration and high-throughput technique for effectively handling complex liquid samples.

RESULTS

Polystyrene-coated magnetic nanoparticles were used to establish a novel magnetic extraction method for monohydroxy polycyclic aromatic hydrocarbons in urine samples. Polystyrene magnetic nanoparticles are an ideal absorbent for solid-phase extraction. After the material was mixed with the sample and adsorbed the target analyte, the analytes on the material were eluted and quantified using high-performance liquid chromatography. Influencing factors were optimized, and the proposed method achieved desirable sensitivity in analyzing low-abundance metabolites in large volumes of complex urine samples. The recoveries of intra-day and inter-day were 78.0-118.0 % and 81.0 %-115.0 %, respectively. The intra-day and inter-day reproducibility were less than 4.5 % and 8.6 %, respectively. The limits of detection were in the range of 0.009-0.041 ng mL-1, and the limits of quantification were in the range of 0.030-0.135 ng mL-1.

SIGNIFICANCE AND NOVELTY

The application of reusable polystyrene-coated magnetic solid-phase nanoparticles as adsorbents makes the extraction of monohydroxy polycyclic aromatic hydrocarbons from urine samples economical and environmentally benign. The proposed method is simple, sensitive, and efficient compared to existing techniques. The nanoparticles are easy to prepare, showing potential for rapid screening of complex bulk bio-samples in batches with high efficiency and low budget.

Loop Evolutionary Patterns Shape Catalytic Efficiency of TRI101/201 for Trichothecenes: Insights into Protein-Substrate Interactions

Trichothecenes are highly toxic mycotoxins produced by Fusarium fungi, while TRI101/201 family enzymes play a crucial role in detoxification through acetylation. Studies on the substrate specificity and catalytic kinetics of TRI101/201 have revealed distinct kinetic characteristics, with significant differences observed in catalytic efficiency toward deoxynivalenol, while the catalytic efficiency for T-2 toxin remains relatively consistent. In this study, we used structural bioinformatics analysis and a molecular dynamics simulation workflow to investigate the mechanism underlying the differential catalytic activity of TRI101/201. The findings revealed that the binding stability between trichothecenes and TRI101/201 hinges primarily on a hydrophobic cage structure within the binding site. An intrinsic disordered loop, termed loop cover, defined the evolutionary patterns of the TRI101/201 protein family that are categorized into four subfamilies (V1/V2/V3/M). Furthermore, the unique loop displayed different conformations among these subfamilies' structures, which served to disrupt (V1/V2/V3) or reinforce (M) the hydrophobic cages. The disrupted cages enhanced the water exposure of the hydrophilic moieties of substrates like deoxynivalenol and thereby hindered their binding to the catalytic sites of V-type enzymes. In contrast, this water exposure does not affect substrates like T-2 toxin, which have more hydrophobic substituents, resulting in a comparable catalytic efficiency of both V- and M-type enzymes. Overall, our studies provide theoretical support for understanding the catalytic mechanism of TRI101/201, which shows how an intrinsic disordered loop could impact the protein-ligand binding and suggests a direction for rational protein design in the future.

Terahertz waves regulate the mechanical unfolding of tau pre-mRNA hairpins

Intermolecular interactions, including hydrogen bonds, dominate the pairing and unpairing of nucleic acid chains in the transfer process of genetic information. The energy of THz waves just matches with the weak interactions, so THz waves may interact with biomolecules. Here, the dynamic effects of THz electromagnetic (EM) waves on the mechanical unfolding process of RNA hairpins (WT-30nt and its mutants, rHP, SARS-CoV-2, and SRV-1 SF206) are investigated using steered molecular dynamics (SMD) simulations. The results show that THz waves can either promote the unfolding of the double helix of the RNA hairpin during the initial unfolding phase (4-21.8 THz) or significantly enhance (23.8 and 25.5 THz) or weaken (37.4 and 41.2 THz) its structural stability during unfolding. Our findings have important implications for applying THz waves to regulate dynamic deconvolution processes, such as gene replication, transcription, and translation.

Computational assessment of lipid facilitated membrane permeation of vancomycin using force-probe molecular dynamic simulation

In this study the efficacy of different edible lipids for drug permeation enhancement of vancomycin through biological membrane was investigated using molecular dynamic simulation. In this regard, at first the ability of the lipids for complex formation with the drug was evaluated for number of most common edible lipids including tripalmitin (TPA), trimyristin (TMY), labrafil (LAB), glycerol monostearate (GMS), glycerol monooleate (GMO), Distearoylphosphorylethanolamine (DSPE), dipalmitoylphosphatidylethanolamine (DPPE), Dipalmitoylphosphatidylcholine (DPPC), cholesterol (CL), stearic acid (SA), palmitic acid (PA) and oleic acid (OA). Then the complexes were pulled thorough a bilayer membrane while the changes in force were probed. The results showed that besides the SA, PA and OA the other examined lipids were able to perform a perfect molecular complex with the drug. Also the results of pulling simulation revealed that the least of force was needed for drug transmittance through the membrane when it was covered by LAB, TMY and DSPE. These results indicated that these lipids can be the excellent materials of choice as permeation enhancer for preparing a proper oral formulation of vancomycin.Communicated by Ramaswamy H. Sarma.

The Energetic Landscape of Catch Bonds in TCR Interfaces

Recognition of peptide/MHC complexes by αβ TCRs has traditionally been viewed through the lens of conventional receptor-ligand theory. Recent work, however, has shown that TCR recognition and T cell signaling can be profoundly influenced and tuned by mechanical forces. One outcome of applied force is the catch bond, where TCR dissociation rates decrease (half-lives increase) when limited force is applied. Although catch bond behavior is believed to be widespread in biology, its counterintuitive nature coupled with the difficulties of describing mechanisms at the structural level have resulted in considerable mystique. In this review, we demonstrate that viewing catch bonds through the lens of energy landscapes, barriers, and the ensuing reaction rates can help demystify catch bonding and provide a foundation on which atomic-level TCR catch bond mechanisms can be built.

Machine Learning Methods for Small Data Challenges in Molecular Science

Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.

Structural Manipulations of Marine Natural Products Inspire a New Library of 3-Amino-1,2,4-Triazine PDK Inhibitors Endowed with Antitumor Activity in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the main aggressive types of cancer, characterized by late prognosis and drug resistance. Among the main factors sustaining PDAC progression, the alteration of cell metabolism has emerged to have a key role in PDAC cell proliferation, invasion, and resistance to standard chemotherapeutic agents. Taking into account all these factors and the urgency in evaluating novel options to treat PDAC, in the present work we reported the synthesis of a new series of indolyl-7-azaindolyl triazine compounds inspired by marine bis-indolyl alkaloids. We first assessed the ability of the new triazine compounds to inhibit the enzymatic activity of pyruvate dehydrogenase kinases (PDKs). The results showed that most of derivatives totally inhibit PDK1 and PDK4. Molecular docking analysis was executed to predict the possible binding mode of these derivatives using ligand-based homology modeling technique. Evaluation of the capability of new triazines to inhibit the cell growth in 2D and 3D KRAS-wild-type (BxPC-3) and KRAS-mutant (PSN-1) PDAC cell line, was carried out. The results showed the capacity of the new derivatives to reduce cell growth with a major selectivity against KRAS-mutant PDAC PSN-1 on both cell models. These data demonstrated that the new triazine derivatives target PDK1 enzymatic activity and exhibit cytotoxic effects on 2D and 3D PDAC cell models, thus encouraging further structure manipulation for analogs development against PDAC.

Computational analysis of protein-ligand interaction by targeting a cell cycle restrainer

BACKGROUND AND OBJECTIVE

The cyclin-dependent kinases 4/6 (CDK4/6) are among the most crucial controllers of the cell cycle, and their abnormal activity may induce uncontrolled cell multiplication, leading to cancers. The FDA currently approved three CDK4/6 inhibitors, however, they are associated with a variety of side effects. Thus it is required to design/develop novel potent and safe CDK4/6 inhibitors.

METHODS

In the present work, we furnished an integrated in-silico approach followed by steered molecular dynamics (SMD) simulations to identify molecules that can be developed into novel CDK4/6 inhibitors.

RESULTS

Out of thirty-two 3-methyleneisoindolin-1-one molecules we selected top three M18, M24, and M32 molecules as potential drug candidates based on their respective interaction energies. According to the robust 250 ns MD simulations and thermodynamic free energy, M24 was the best molecule in comparison to palbociclib. In SMD, M24 required ∼205.587 kJ/mol/nm external pulling force, while palbociclib needed ∼160.97 kJ/mol/nm to dissociate from the binding pocket of the CDK4.

CONCLUSIONS

The high pulling force required for M24 dissociation from the binding site denotes stronger binding with CDK4. Therefore, M24 offers the possibility of a critical starting structure in developing effective CDK4 inhibitors.

Evolution, Stability, and Applicability of Surfactant Aggregates in Targeted Delivery

Self-assembly/self-aggregation of surfactant molecules in bulk and the vicinity of a surface has been a topic of interest for decades because of its utilization in numerous modern technical applications. In this article, the results of molecular dynamics simulations are reported to investigate the self-aggregation of sodium dodecyl sulfate (SDS) at an interface of mica and water. SDS molecules starting from lower to higher surface concentrations tend to create distinct aggregated structures in the vicinity of a mica surface. The structural properties, such as density profiles, radial distribution functions, and thermodynamic properties like excess entropy and second virial coefficient, are calculated to address the bits and pieces of the self-aggregation. The change in the free energy for aggregates of varied sizes approaching the surface from the bulk aqueous solution, along with the change in their shapes during the process in terms of change in the radius of gyration and its components, is reported respectively to model a generic pathway for a surfactant-based targeted delivery system.

Steered Molecular Dynamics Simulations Study on FABP4 Inhibitors

Ordinary small molecule de novo drug design is time-consuming and expensive. Recently, computational tools were employed and proved their efficacy in accelerating the overall drug design process. Molecular dynamics (MD) simulations and a derivative of MD, steered molecular dynamics (SMD), turned out to be promising rational drug design tools. In this paper, we report the first application of SMD to evaluate the binding properties of small molecules toward FABP4, considering our recent interest in inhibiting fatty acid binding protein 4 (FABP4). FABP4 inhibitors (FABP4is) are small molecules of therapeutic interest, and ongoing clinical studies indicate that they are promising for treating cancer and other diseases such as metabolic syndrome and diabetes.

Microscopic Understanding of the Conformational Stability of the Aggregated Nonamyloid β Components of α-Synuclein

Self-association of α-synuclein peptides into oligomeric species and ordered amyloid fibrils is associated with Parkinson's disease, a progressive neurodegenerative disorder. In particular, the peptide domain formed between the residues Glu-61 (or E61) and Val-95 (or V95) of α-synuclein, typically termed the "nonamyloid β component" (NAC), is known to play critical roles in forming aggregated structures. In this work, we have employed molecular dynamics simulations to explore the conformational properties and relative stabilities of aggregated protofilaments of different orders, namely, tetramer (P(4)), hexamer (P(6)), octamer (P(8)), decamer (P(10)), dodecamer (P(12)), and tetradecamer (P(14)), formed by the NAC domains of α-synuclein. Besides, center-of-mass pulling and umbrella sampling simulation methods have also been employed to characterize the mechanistic pathway of peptide association/dissociation and the corresponding free energy profiles. Structural analysis showed that the disordered C-terminal loop and the central core regions of the peptide units lead to more flexible and distorted structures of the lower order protofilaments (P(4) and P(6)) as compared to the higher order ones. Interestingly, our calculation shows the presence of multiple distinctly populated conformational states for the lower order protofilament P(4), which may drive the oligomerization process along multiple pathways to form different polymorphic α-synuclein fibrillar structures. It is further observed that the nonpolar interaction between the peptides and the corresponding nonpolar solvation free energy play a dominant role in stabilizing the aggregated protofilaments. Importantly, our result showed that reduced cooperativity during the binding of a peptide unit beyond a critical size of the protofilament (P(12)) leads to less favorable binding free energy of a peptide.

Phosphorylation-Competent Metastable State of Escherichia coli Toxin HipA

Phosphorylation is a key post-translational modification that alters the functional state of many proteins. The Escherichia coli toxin HipA, which phosphorylates glutamyl-tRNA synthetase and triggers bacterial persistence under stress, becomes inactivated upon autophosphorylation of Ser150. Interestingly, Ser150 is phosphorylation-incompetent in the crystal structure of HipA since it is deeply buried ("in-state"), although in the phosphorylated state it is solvent exposed ("out-state"). To be phosphorylated, a minor population of HipA must exist in the phosphorylation-competent "out-state" (solvent-exposed Ser150), not detected in the crystal structure of unphosphorylated HipA. Here we report a molten-globule-like intermediate of HipA at low urea (∼4 kcal/mol unstable than natively folded HipA). The intermediate is aggregation-prone, consistent with a solvent exposed Ser150 and its two flanking hydrophobic neighbors (Val/Ile) in the "out-state". Molecular dynamics simulations showed the HipA "in-out" pathway to contain multiple free energy minima with an increasing degree of Ser150 solvent exposure with the free energy difference between the "in-state" and the metastable exposed state(s) to be ∼2-2.5 kcal/mol, with unique sets of hydrogen bonds and salt bridges associated with the metastable loop conformations. Together, the data clearly identify the existence of a phosphorylation-competent metastable state of HipA. Our results not only suggest a mechanism of HipA autophosphorylation but also add to a number of recent reports on unrelated protein systems where the common proposed mechanism for phosphorylation of buried residues is their transient exposure even without phosphorylation.

Structure-based discovery of novel α-aminoketone derivatives as dual p53-MDM2/MDMX inhibitors for the treatment of 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.

Insights into the Effects and Mechanism of Andrographolide-Mediated Recovery of Susceptibility of Methicillin-Resistant Staphylococcus aureus to β-Lactam Antibiotics

The frequent resistance associated with β-lactam antibiotics and the high frequency of mutations in β-lactamases constitute a major clinical challenge that can no longer be ignored. Andrographolide (AP), a natural active compound, has been shown to restore susceptibility to β-lactam antibiotics. Fluorescence quenching and molecular simulation showed that AP quenched the intrinsic fluorescence of β-lactamase BlaZ and stably bound to the residues in the catalytic cavity of BlaZ. Of note, AP was found to reduce the stability of the cell wall (CW) in methicillin-resistant Staphylococcus aureus (MRSA), and in combination with penicillin G (PEN), it significantly induced CW roughness and dispersion and even caused its disintegration, while the same concentration of PEN did not. In addition, transcriptome sequencing revealed that AP induced a significant stress response and increased peptidoglycan (PG) synthesis but disrupted its cross-linking, and it repressed the expression of critical genes such as mecA, blaZ, and sarA. We also validated these findings by quantitative reverse transcription-PCR (qRT-PCR). Association analysis using the GEO database showed that the alterations caused by AP were similar to those caused by mutations in the sarA gene. In summary, AP was able to restore the susceptibility of MRSA to β-lactam antibiotics, mainly by inhibiting the β-lactamase BlaZ, by downregulating the expression of critical resistance genes such as mecA and blaZ, and by disrupting CW homeostasis. In addition, restoration of susceptibility to antibiotics could be achieved by inhibiting the global regulator SarA, providing an effective solution to alleviate the problem of bacterial resistance. IMPORTANCE Increasingly, alternatives to antibiotics are being used to mitigate the rapid onset and development of bacterial resistance, and the combination of natural compounds with traditional antibiotics has become an effective therapeutic strategy. Therefore, we attempted to discover more mechanisms to restore susceptibility and effective dosing strategies. Andrographolide (AP), as a natural active ingredient, can mediate recovery of susceptibility of MRSA to β-lactam antibiotics. AP bound stably to the β-lactamase BlaZ and impaired its hydrolytic activity. Notably, AP was able to downregulate the expression of critical resistance genes such as mecA, blaZ, and sarA. Meanwhile, it disrupted the CW cross-linking and homeostasis, while the same concentration of penicillin could not. The multiple inhibitory effect of AP resensitizes intrinsically resistant bacteria to β-lactam antibiotics, effectively prolonging the use cycle of these antibiotics and providing an effective solution to reduce the dosage of antibiotics and providing a theoretical reference for the prevention and control of MRSA.

Illustrating the Effect of Small Molecules Derived from Natural Resources on Amyloid Peptides

The onset of amyloidogenic diseases is associated with the misfolding and aggregation of proteins. Despite extensive research, no effective therapeutics are yet available to treat these chronic degenerative diseases. Targeting the aggregation of disease-specific proteins is regarded as a promising new approach to treat these diseases. In the past few years, rapid progress in this field has been made in vitro, in vivo, and in silico to generate potential drug candidates, ranging from small molecules to polymers to nanoparticles. Small molecular probes, mostly those derived from natural sources, have been of particular interest among amyloid inhibitors. Here, we summarize some of the most important natural small molecular probes which can inhibit the aggregation of Aβ, hIAPP, and α-syn peptides and discuss how their binding efficacy and preference for the peptides vary with their structure and conformation. This provides a comprehensive idea of the crucial factors which should be incorporated into the future design of novel drug candidates useful for the treatment of amyloid diseases.

Current Trends and Changes in Use of Membrane Molecular Dynamics Simulations within Academia and the Pharmaceutical Industry

There has been an almost exponential increase in the use of molecular dynamics simulations in basic research and industry over the last 5 years, with almost a doubling in the number of publications each year. Many of these are focused on neurological membranes, and biological membranes in general, applied to the medical industry. A smaller portion have utilized membrane simulations to answer more basic questions related to the function of specific proteins, chemicals or biological processes. This review covers some newer studies, alongside studies from the last two decades, to determine changes in the field. Some of these are basic, while others are more profound, such as multi-component embedded membrane machinery. It is clear that many facets of the discipline remain the same, while the focus on and uses of the technology are broadening in scope and utilization as a general research tool. Analysis of recent literature provides an overview of the current methodologies, covers some of the recent trends or advances and tries to make predictions of the overall path membrane molecular dynamics will follow in the coming years. In general, the overview presented is geared towards the general scientific community, who may wish to introduce the use of these methodologies in light of these changes, making molecular dynamic simulations more feasible for general scientific or medical research.

Interactions between curcumin and human salt-induced kinase 3 elucidated from computational tools and experimental methods

Natural products are widely used for treating mitochondrial dysfunction-related diseases and cancers. Curcumin, a well-known natural product, can be potentially used to treat cancer. Human salt-induced kinase 3 (SIK3) is one of the target proteins for curcumin. However, the interactions between curcumin and human SIK3 have not yet been investigated in detail. In this study, we studied the binding models for the interactions between curcumin and human SIK3 using computational tools such as homology modeling, molecular docking, molecular dynamics simulations, and binding free energy calculations. The open activity loop conformation of SIK3 with the ketoenol form of curcumin was the optimal binding model. The I72, V80, A93, Y144, A145, and L195 residues played a key role for curcumin binding with human SIK3. The interactions between curcumin and human SIK3 were also investigated using the kinase assay. Moreover, curcumin exhibited an IC₅₀ (half-maximal inhibitory concentration) value of 131 nM, and it showed significant antiproliferative activities of 9.62 ± 0.33 µM and 72.37 ± 0.37 µM against the MCF-7 and MDA-MB-23 cell lines, respectively. This study provides detailed information on the binding of curcumin with human SIK3 and may facilitate the design of novel salt-inducible kinases inhibitors.

Advanced simulations and screening to repurposing a 3C protease inhibitor against the rupintrivir-resistant human norovirus-induced gastroenteritis

Human norovirus (HuNoV) causes acute viral gastroenteritis in all age groups, and dehydration and severe diarrhea in the elderly. The World Health Organization reports ∼1.45 million deaths from acute gastroenteritis annually in the world. Rupintrivir, an inhibitory medicine against the human rhinovirus C3 protease, has been reported to inhibit HuNoV 3C protease. However, several HuNoV 3C protease mutations have been revealed to reduce the susceptibility of HuNoV to rupintrivir. The structural details behind rupintrivir-resistance of these single-point mutations (A105V and I109V) are not still clear. Hence, in this study, a combination of computational techniques were used to determine the rupintrivir-resistance mechanism and to propose an inhibitor against wild-type and mutant HuNoV 3C protease through structure-based virtual screening. Dynamic structural results indicated the unstable binding of rupintrivir at the cleft binding site of the wild-type and mutant 3C proteases, leading to its detachment. Our findings presented that the domain II of the HuNoV 3C protease had a critical role in binding of inhibitory molecules. Binding energy computations, steered molecular dynamics and umbrella sampling simulations confirmed that amentoflavone, the novel suggested inhibitor, strongly binds to the cleft site of all protease models and has a good structural stability in the complex system along the molecular dynamic simulations. Our in silico study proposed the selected compound as a potential inhibitor against the HuNoV 3C protease. However, additional experimental and clinical studies are required to corroborate the therapeutic efficacy of the compound.

Exploring the Pivotal Components Influencing the Side Effects Induced by an Analgesic-Antitumor Peptide from Scorpion Venom on Human Voltage-Gated Sodium Channels 1.4 and 1.5 through Computational Simulation

Voltage-gated sodium channels (VGSCs, or Nav) are important determinants of action potential generation and propagation. Efforts are underway to develop medicines targeting different channel subtypes for the treatment of related channelopathies. However, a high degree of conservation across its nine subtypes could lead to the off-target adverse effects on skeletal and cardiac muscles due to acting on primary skeletal muscle sodium channel Nav1.4 and cardiac muscle sodium channel Nav1.5, respectively. For a long evolutionary process, some peptide toxins from venoms have been found to be highly potent yet selective on ion channel subtypes and, therefore, hold the promising potential to be developed into therapeutic agents. In this research, all-atom molecular dynamic methods were used to elucidate the selective mechanisms of an analgesic-antitumor β-scorpion toxin (AGAP) with human Nav1.4 and Nav1.5 in order to unravel the primary reason for the production of its adverse reactions on the skeletal and cardiac muscles. Our results suggest that the rational distribution of residues with ring structures near position 38 and positive residues in the C-terminal on AGAP are critical factors to ensure its analgesic efficacy. Moreover, the substitution for residues with benzene is beneficial to reduce its side effects.

Molecular Dynamics Simulations of the Cardiac Ryanodine Receptor Type 2 (RyR2) Gating Mechanism

Mutations in the cardiac ryanodine receptor type 2 (RyR2) have been linked to fatal cardiac arrhythmias such as catecholaminergic polymorphic ventricular tachycardia (CPVT). While many CPVT mutations are associated with an increase in Ca²⁺ leak from the sarcoplasmic reticulum, the mechanistic details of RyR2 channel gating are not well understood, and this poses a barrier in the development of new pharmacological treatments. To address this, we explore the gating mechanism of the RyR2 using molecular dynamics (MD) simulations. We test the effect of changing the conformation of certain structural elements by constructing chimera RyR2 structures that are derived from the currently available closed and open cryo-electron microscopy (cryo-EM) structures, and we then use MD simulations to relax the system. Our key finding is that the position of the S4-S5 linker (S4S5L) on a single subunit can determine whether the channel as a whole is open or closed. Our analysis reveals that the position of the S4S5L is regulated by interactions with the U-motif on the same subunit and with the S6 helix on an adjacent subunit. We find that, in general, channel gating is crucially dependent on high percent occupancy interactions between adjacent subunits. We compare our interaction analysis to 49 CPVT1 mutations in the literature and find that 73% appear near a high percent occupancy interaction between adjacent subunits. This suggests that disruption of cooperative, high percent occupancy interactions between adjacent subunits is a primary cause of channel leak and CPVT in mutant RyR2 channels.

Free energy and kinetic rate calculation via non-equilibrium molecular simulation: application to biomolecules

Non-equilibrium molecular dynamics (NEMD) simulation has been recognized as a powerful tool for examining biomolecules and provides fruitful insights into not only non-equilibrium but also equilibrium processes. We review recent advances in NEMD simulation and relevant, fundamental results of non-equilibrium statistical mechanics. We first introduce Crooks fluctuation theorem and Jarzynski equality that relate free energy difference to work done on a physical system during a non-equilibrium process. The theorems are beneficial for the analysis of NEMD trajectories. We then describe rate theory, a framework to calculate molecular kinetics from a non-equilibrium process; this theoretical framework enables us to calculate a reaction time-mean-first passage time-from NEMD trajectories. We, in turn, present recent NEMD techniques that apply an external force to a system to enhance molecular dissociation and introduce their application to biomolecules. Lastly, we show the current status of an appropriate selection of reaction coordinates for NEMD simulation.

Evolving Experimental Techniques for Structure-Based Drug Design

Structure-based drug design (SBDD) is a prominent method in rational drug development and has traditionally benefitted from the atomic models of protein targets obtained using X-ray crystallography at cryogenic temperatures. In this perspective, we highlight recent advances in the development of structural techniques that are capable of probing dynamic information about protein targets. First, we discuss advances in the field of X-ray crystallography including serial room-temperature crystallography as a method for obtaining high-resolution conformational dynamics of protein-inhibitor complexes. Next, we look at cryogenic electron microscopy (cryoEM), another high-resolution technique that has recently been used to study proteins and protein complexes that are too difficult to crystallize. Finally, we present small-angle X-ray scattering (SAXS) as a potential high-throughput screening tool to identify inhibitors that target protein complexes and protein oligomerization.

Recognition in the Domain of Molecular Chirality: From Noncovalent Interactions to Separation of Enantiomers

It is not a coincidence that both chirality and noncovalent interactions are ubiquitous in nature and synthetic molecular systems. Noncovalent interactivity between chiral molecules underlies enantioselective recognition as a fundamental phenomenon regulating life and human activities. Thus, noncovalent interactions represent the narrative thread of a fascinating story which goes across several disciplines of medical, chemical, physical, biological, and other natural sciences. This review has been conceived with the awareness that a modern attitude toward molecular chirality and its consequences needs to be founded on multidisciplinary approaches to disclose the molecular basis of essential enantioselective phenomena in the domain of chemical, physical, and life sciences. With the primary aim of discussing this topic in an integrated way, a comprehensive pool of rational and systematic multidisciplinary information is provided, which concerns the fundamentals of chirality, a description of noncovalent interactions, and their implications in enantioselective processes occurring in different contexts. A specific focus is devoted to enantioselection in chromatography and electromigration techniques because of their unique feature as "multistep" processes. A second motivation for writing this review is to make a clear statement about the state of the art, the tools we have at our disposal, and what is still missing to fully understand the mechanisms underlying enantioselective recognition.

Distal mutation V486M disrupts the catalytic activity of DPP4 by affecting the flap of the propeller domain

Dipeptidyl peptidase-4 (DPP4) plays a crucial role in regulating the bioactivity of glucagon-like peptide-1 (GLP-1) that enhances insulin secretion and pancreatic β-cell proliferation, making it a therapeutic target for type 2 diabetes. Although the crystal structure of DPP4 has been determined, its structure-function mechanism is largely unknown. Here, we examined the biochemical properties of sporadic human DPP4 mutations distal from its catalytic site, among which V486M ablates DPP4 dimerization and causes loss of enzymatic activity. Unbiased molecular dynamics simulations revealed that the distal V486M mutation induces a local conformational collapse in a β-propeller loop (residues 234-260, defined as the flap) and disrupts the dimerization of DPP4. The "open/closed" conformational transitions of the flap whereby capping the active site, are involved in the enzymatic activity of DPP4. Further site-directed mutagenesis guided by theoretical predictions verified the importance of the conformational dynamics of the flap for the enzymatic activity of DPP4. Therefore, the current studies that combined theoretical modeling and experimental identification, provide important insights into the biological function of DPP4 and allow for the evaluation of directed DPP4 genetic mutations before initiating clinical applications and drug development.

Benchmarking the ability of novel compounds to inhibit SARS-CoV-2 main protease using steered molecular dynamics simulations

BACKGROUND

The SARS-CoV-2 main protease (M^pro) is an attractive target in the COVID-19 drug development process. It catalyzes the polyprotein's translation from viral RNA and specifies a particular cleavage site. Due to the absence of identical cleavage specificity in human cell proteases, targeting M^pro with chemical compounds can obstruct the replication of the virus.

METHODS

To explore the potential binding mechanisms of 1,2,3-triazole scaffolds in comparison to co-crystallized inhibitors 11a and 11b towards M^pro, we herein utilized molecular dynamics and enhanced sampling simulation studies.

RESULTS AND CONCLUSION

All the 1,2,3-triazole scaffolds interacted with catalytic residues (Cys145 and His41) and binding pocket residues of M^pro involving Met165, Glu166, Ser144, Gln189, His163, and Met49. Furthermore, the adequate binding free energy and potential mean force of the topmost compound 3h was comparable to the experimental inhibitors 11a and 11b of M^pro. Overall, the current analysis could be beneficial in developing the SARS-CoV-2 M^pro potential inhibitors.

Unveiling the Releasing Processes of Pt(II)-Based Anticancer Drugs from Oxidized Carbon Nanohorn: An In Silico Study

About half of all cancer chemotherapies currently applied involve medication with the three worldwide approved Pt(II)-based drugs, cisplatin (cddp), carboplatin (cpx), and oxaliplatin (oxa), due to their notable antitumor activity for several cancers. However, this wide application is accompanied by severe side effects, such as nephrotoxicity, myelosuppression, and neurotoxicity, as a result of their low bioavailability and selectivity for cancer cells. To mitigate these drawbacks, the use of chemically functionalized carbon nanohorns (CNH) as nanocarriers represents a potential formulation since CNH has been noted for their biodegradability, biocompatibility, low toxicity, and cavities dimensionally compatible with small drugs. This work reports energetic and dynamic analyses of complexes formed by oxidized CNH (CNHox) and the cddp, cpx, and oxa drugs. Using unbiased molecular dynamics (MD) simulations, we show that the encapsulated formulations (cddp@CNHox, cpx@CNHox, and oxa@CNHox) were more stable by ∼11.0 kcal mol-1 than the adsorbed ones (cddp > CNHox, cpx > CNHox, and oxa > CNHox). This high stability, mainly governed by van der Waals interactions, was responsible for the drug confinement during the entire simulation time (200 ns). The biased MD simulations of the inclusion complexes confirmed the nonspontaneity of the drug release since the potentials of mean force (PMF) indicated the endergonic character of this process. Additionally, the releasing energy profiles pointed out that the free energy barrier (ΔΔG≠) for the escape from CNHox cavity follows the order oxa > cpx ∼ cddp, with the value for the oxa complex (21-26 kcal mol-1) found to be about 36 and 30% larger than those for cpx and cddp, respectively. While the approximate residence time (tres) of the oxa drug inside the CNHox cavity was 5.45 × 108 s, the same measure for the cddp and cpx drugs was 5.3 × 105 and 1.60 × 103 s. Simulations also revealed that the escape of oxa with the oxalate group facing the nanowindow was the most unfavorable process, giving tres = 1.09 × 109 s. Besides reinforcing and extending the nanovectorization of cddp, cpx, and oxa in CNHox for cancer chemotherapies, all features considered may provide interpretations for experimental data and encourage new investigations aiming to propose less aggressive treatments for oncological diseases.

Sarcorucinine-D Inhibits Cholinesterases and Calcium Channels: Molecular Dynamics Simulation and In Vitro Mechanistic Investigations

Acetylcholinesterase (AChE) inhibitors and calcium channel blockers are considered effective therapies for Alzheimer’s disease. AChE plays an essential role in the nervous system by catalyzing the hydrolysis of the neurotransmitter acetylcholine. In this study, the inhibition of the enzyme AChE by Sarcorucinine-D, a pregnane type steroidal alkaloid, was investigated with experimental enzyme kinetics and molecular dynamics (MD) simulation techniques. Kinetics studies showed that Sarcorucinine-D inhibits two cholinesterases—AChE and butyrylcholinesterase (BChE)—noncompetitively, with K_i values of 103.3 and 4.66 µM, respectively. In silico ligand-protein docking and MD simulation studies conducted on AChE predicted that Sarcorucinine-D interacted via hydrophobic interactions and hydrogen bonds with the residues of the active-site gorge of AChE. Sarcorucinine-D was able to relax contractility concentration-dependently in the intestinal smooth muscles of jejunum obtained from rabbits. Not only was the spontaneous spasmogenicity inhibited, but it also suppressed K⁺-mediated spasmogenicity, indicating an effect via the inhibition of voltage-dependent Ca²⁺ channels. Sarcorucinine-D could be considered a potential lead molecule based on its properties as a noncompetitive AChE inhibitor and a Ca²⁺ channel blocker.

Determination of Multidirectional Pathways for Ligand Release from the Receptor: A New Approach Based on Differential Evolution

Steered molecular dynamics (SMD) simulation is a powerful method in computer-aided drug design as it can be used to access the relative binding affinity with high precision but with low computational cost. The success of SMD depends on the choice of the direction along which the ligand is pulled from the receptor-binding site. In most simulations, the unidirectional pathway was used, but in some cases, this choice resulted in the ligand colliding with the complex surface of the exit tunnel. To overcome this difficulty, several variants of SMD with multidirectional pulling have been proposed, but they are not completely devoid of disadvantages. Here, we have proposed to determine the direction of pulling with a simple scoring function that minimizes the receptor–ligand interaction, and an optimization algorithm called differential evolution is used for energy minimization. The effectiveness of our protocol was demonstrated by finding expulsion pathways of Huperzine A and camphor from the binding site of Torpedo California acetylcholinesterase and P450cam proteins, respectively, and comparing them with the previous results obtained using memetic sampling and random acceleration molecular dynamics. In addition, by applying this protocol to a set of ligands bound with LSD1 (lysine specific demethylase 1), we obtained a much higher correlation between the work of pulling force and experimental data on the inhibition constant IC50 compared to that obtained using the unidirectional approach based on minimal steric hindrance.

A lesson for the maestro of the replication fork: Targeting the protein-binding interface of proliferating cell nuclear antigen for anticancer therapy

The proliferating cell nuclear antigen (PCNA) has emerged as a promising candidate for the development of novel cancer therapeutics. PCNA is a nononcogenic mediator of DNA replication that regulates a diverse range of cellular functions and pathways through a comprehensive list of protein-protein interactions. The hydrophobic binding pocket on PCNA offers an opportunity for the development of inhibitors to target various types of cancers and modulate protein-protein interactions. In the present study, we explored the binding modes and affinity of molecule I1 (standard molecule) with the previously suggested dimer interface pocket and the hydrophobic pocket present on the frontal side of the PCNA monomer. We also identified potential lead molecules from the library of in-house synthesized 3-methylenisoindolin-1-one based molecules to inhibit the protein-protein interactions of PCNA. Our results were based on robust computational methods, including molecular docking, conventional, steered, and umbrella sampling molecular dynamics simulations. Our results suggested that the standard inhibitor I1 interacts with the hydrophobic pocket of PCNA with a higher affinity than the previously suggested binding site. Also, the proposed molecules showed better or comparable binding free energies as calculated by the Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach and further validated by enhanced umbrella sampling simulations. In vitro and in vivo methods could test the computationally suggested molecules for advancement in the drug discovery pipeline.

Designing Soluble PROTACs: Strategies and Preliminary Guidelines

Solubility optimization is a crucial step to obtaining oral PROTACs. Here we measured the thermodynamic solubilities (log S) of 21 commercial PROTACs. Next, we measured BRlogD and log k_w^IAM (lipophilicity), EPSA, and Δ log k_w^IAM (polarity) and showed that lipophilicity plays a major role in governing log S, but a contribution of polarity cannot be neglected. Two-/three-dimensional descriptors calculated on conformers arising from conformational sampling and steered molecular dynamics failed in modeling solubility. Infographic tools were used to identify a privileged region of soluble PROTACs in a chemical space defined by BRlogD, log k_w^IAM and topological polar surface area, while machine learning provided a log S classification model. Finally, for three pairs of PROTACs we measured the solubility, lipophilicity, and polarity of the building blocks and identified the limits of estimating PROTAC solubility from the synthetic components. Overall, this paper provides promising guidelines for optimizing PROTAC solubility in early drug discovery programs.

Identification of acridinedione scaffolds as potential inhibitor of DENV-2 C protein: An in silico strategy to combat dengue

Dengue is a prominent viral disease transmitted by mosquitoes to humans that affects mainly tropical and subtropical countries worldwide. The global spread of dengue virus (DENV) is mainly occurred by Aedes aegypti and Aedes albopictus mosquitoes. The dengue virus serotypes-2 (DENV-2) is a widely prevalent serotype of DENV, that causes the hemorrhagic fever and bleeding in the mucosa, which can be fatal. In the life cycle of DENV-2, a structural capsid (DENV-2 C) protein forms the nucleocapsid assembly and bind to the viral progeny RNA. For DENV-2 maturation, the nucleocapsid is a vital component. We used virtual ligand screening to filter out the best in-house synthesized acridinedione analogs (DSPD molecules) that could efficiently bind to DENV-2 C protein. The molecular docking and dynamics simulations studies were performed to analyze the effect of DSPD molecules on DENV-2 C protein after binding. Our findings showed that DSPD molecules strongly interacted with DENV-2 C protein, as evident from molecular interactions and several time-dependent molecular dynamics-driven analyses. Moreover, this study was also supported by the thermodynamic binding free energy and steered molecular dynamics simulations. Therefore, we intend to suggest that the DSPD3 molecule could be used as a potential therapeutic molecule against dengue complications as compared to the cocrystallized inhibitor ST-148. However, further studies are required to demonstrate the ability of DSPD3 to induce DENV-2 C tetramer formation.

Computational Investigation of Structural Basis for Enhanced Binding of Isoflavone Analogues with Mitochondrial Aldehyde Dehydrogenase

Isoflavone compounds are potent inhibitors against mitochondrial aldehyde dehydrogenase (ALDH2) for the treatment of alcoholism and drug addiction, and an in-depth understanding of the underlying structural basis helps design new inhibitors for enhanced binding. Here, we investigated the binding poses and strengths of eight isoflavone analogues (including CVT-10216 and daidzin) with ALDH2 via computational methods of molecular docking, molecular dynamics (MD) simulation, molecular mechanics Poisson-Boltzmann surface area (MM-PBSA), steered MD, and umbrella sampling. Neither the Vina scoring of docked and MD-sampled complexes nor the nonbonded protein-inhibitor interaction energy from MD simulations is able to reproduce the relative binding strength of the inhibitors compared to experimental IC₅₀ values. Considering the solvation contribution, MM-PBSA and relatively expensive umbrella sampling yield good performance for the relative binding (free) energies. The isoflavone skeleton prefers to form π-π stacking, π-sulfur, and π-alkyl interactions with planar (Phe and Trp) or sulfur-containing (Cys and Met) residues. The enhanced inhibition of CVT-10216 originates from both end groups of the isoflavone skeleton offering strong van der Waals contacts and from the methylsulfonamide group at the 4' position by hydrogen bonding (HB) with neighboring receptor residues. These results indicate that the hydrophobic binding tunnel of ALDH2 is larger than the isoflavone skeleton in length and thus an extended hydrophobic core is likely a premise for potent inhibitors.

Advances in computer-aided drug design for type 2 diabetes

INTRODUCTION

The number of diabetic patients is increasing, posing a heavy social and economic burden worldwide. Traditional drug development technology is time-consuming and costly, and the emergence of computer-aided drug design (CADD) has changed this situation. This study reviews the applications of CADD in diabetic drug designing.

AREAS COVERED

In this article, the authors focus on the advance in CADD in diabetic drug design by elaborating the discovery, including peroxisome proliferator-activated receptor (PPAR), G protein-coupled receptor 40 (GPR40), dipeptidyl peptidase-IV (DDP-IV), protein tyrosine phosphatase 1B (PTP1B), sodium-dependent glucose transporter 2 (SGLT-2), and glucokinase (GK). Some drug discovery of these targets is related to CADD strategies.

EXPERT OPINION

There is no doubt that CADD has contributed to the discovery of novel anti-diabetic agents. However, there are still many limitations and challenges, such as lack of co-crystal complex, dynamic simulations, water, and metal ion treatment. In the near future, artificial intelligence (AI) may be a promising strategy to accelerate drug discovery and reduce costs by identifying candidates. Moreover, AlphaFold, a deep learning model that predicts the 3D structure of proteins, represents a considerable advancement in the structural prediction of proteins, especially in the absence of homologous templates for protein structures.