The role of artificial intelligence and data science in nanoparticles development: a review

Artificial intelligence has revolutionized many sectors with unparalleled predictive capabilities supported by machine learning (ML). So far, this tool has not been able to provide the same level of development in pharmaceutical nanotechnology. This review discusses the current data science methodologies related to polymeric drug-loaded nanoparticle production from an innovative multidisciplinary perspective while considering the strictest data science practices. Several methodological and data interpretation flaws were identified by analyzing the few qualified ML studies. Most issues lie in following appropriate analysis steps, such as cross-validation, balancing data, or testing alternative models. Thus, better-planned studies following the recommended data science analysis steps along with adequate numbers of experiments would change the current landscape, allowing the exploration of the full potential of ML.

Hit discovery of potential CDK8 inhibitors and analysis of amino acid mutations for cancer therapy through computer-aided drug discovery

Cyclin-dependent kinase 8 (CDK8) has emerged as a promising target for inhibiting cancer cell function, intensifying efforts towards the development of CDK8 inhibitors as potential cancer therapeutics. Mutations in CDK8, a protein kinase, are also implicated as a primary factor associated with tumor formation. In this study, we identified potential inhibitors through virtual screening for CDK8 and single amino acid mutations in CDK8, namely D173A (Aspartate 173 mutate to Alanine), D189N (Aspartate 189 mutate to Asparagine), T196A (Threonine 196 mutate to Alanine) and T196D (Threonine 196 mutate to Aspartate). Four databases (CHEMBEL, ZINC, MCULE, and MolPort) containing 65,209,131 molecules have been searched to identify new inhibitors for CDK8 and its single mutations. In the first step, structure-based pharmacophore modeling in the Pharmit server was used to select the compounds to know the inhibitors. Then molecules with better predicted drug-like molecule properties were selected. The final filter used to select more effective inhibitors among the previously selected molecules was molecular docking. Finally, 13 hits for CDK8, 11 hits for D173A, 11 hits for D189N, 15 hits for T196A, and 12 hits for T196D were considered potential inhibitors. A majority of the virtual screening hits exhibited satisfactorily predict pharmacokinetic characteristics and toxicity properties.

Augmenting a training dataset of the generative diffusion model for molecular docking with artificial binding pockets

This study introduces the PocketCFDM generative diffusion model, aimed at improving the prediction of small molecule poses in the protein binding pockets. The model utilizes a novel data augmentation technique, involving the creation of numerous artificial binding pockets that mimic the statistical patterns of non-bond interactions found in actual protein-ligand complexes. An algorithmic method was developed to assess and replicate these interaction patterns in the artificial binding pockets built around small molecule conformers. It is shown that the integration of artificial binding pockets into the training process significantly enhanced the model's performance. Notably, PocketCFDM surpassed DiffDock in terms of non-bond interaction and steric clash numbers, and the inference speed. Future developments and optimizations of the model are discussed. The inference code and final model weights of PocketCFDM are accessible publicly via the GitHub repository: https://github.com/vtarasv/pocket-cfdm.git.

The Application of MD Simulation to Lead Identification, Vaccine Design, and Structural Studies in Combat against Leishmaniasis - A Review

Drug discovery, vaccine design, and protein interaction studies are rapidly moving toward the routine use of molecular dynamics simulations (MDS) and related methods. As a result of MDS, it is possible to gain insights into the dynamics and function of identified drug targets, antibody-antigen interactions, potential vaccine candidates, intrinsically disordered proteins, and essential proteins. The MDS appears to be used in all possible ways in combating diseases such as cancer, however, it has not been well documented as to how effectively it is applied to infectious diseases such as Leishmaniasis. As a result, this review aims to survey the application of MDS in combating leishmaniasis. We have systematically collected articles that illustrate the implementation of MDS in drug discovery, vaccine development, and structural studies related to Leishmaniasis. Of all the articles reviewed, we identified that only a limited number of studies focused on the development of vaccines against Leishmaniasis through MDS. Also, the PCA and FEL studies were not carried out in most of the studies. These two were globally accepted utilities to understand the conformational changes and hence it is recommended that this analysis should be taken up in similar approaches in the future.

In silico identification of novel ACE and DPP-IV inhibitory peptides derived from buffalo milk proteins and evaluation of their inhibitory mechanisms

The capacity of buffalo milk proteins to release bioactive peptides was evaluated and novel bioactive peptides were identified. The sequential similarity between buffalo milk proteins and their cow counterparts was analysed. Buffalo milk proteins were simulated to yield theoretical peptides via in silico proteolysis. The potential of selected proteins to release specific bioactive peptides was evaluated by the A value obtained from the BIOPEP-UWM database (Minkiewicz et al. in Int J Mol Sci 20(23):5978, 2019). Buffalo milk protein is a suitable precursor to produce bioactive peptides, particularly dipeptidyl peptidase IV (DPP-IV) and angiotensin I-converting enzyme (ACE) inhibitory peptides. Two novel ACE inhibitory peptides (KPW and RGP) and four potential DPP-IV inhibitory peptides (RGP, KPW, FPK and KFTW) derived from in silico proteolysis of buffalo milk proteins were screened using different integrated bioinformatic approaches (PeptideRanker, Innovagen, peptide-cutter and molecular docking). The Lineweaver-Burk plots showed that KPW (IC₅₀ = 136.28 ± 10.77 μM) and RGP (104.72 ± 8.37 μM) acted as a competitive inhibitor against ACE. Similarly, KFTW (IC₅₀ = 873.92 ± 32.89 μM) was also a competitive inhibitor of DPP-IV, while KPW and FPK (82.52 ± 10.37 and 126.57 ± 8.45 μM, respectively) were mixed-type inhibitors. It should be emphasized that this study does not involve any clinical trial.

Targeting human thymidylate synthase: Ensemble-based virtual screening for drug repositioning and the role of water

A drug repositioning computational approach was carried to search inhibitors for human thymidylate synthase. An ensemble-based virtual screening of FDA-approved drugs showed the drugs Imatinib, Lumacaftor and Naldemedine to be likely candidates for repurposing. The role of water in the drug-receptor interactions was revealed by the application of an extended AutoDock scoring function that included the water forcefield. The binding affinity scores when hydrated ligands were docked were improved in the drugs considered. Further binding free energy calculations based on the Molecular Mechanics Poisson-Boltzmann Surface Area method revealed that Imatinib, Lumacaftor and Naldemedine scored -130.7 ± 28.1, -210.6 ± 29.9 and -238.0 ± 25.4 kJ/mol, respectively, showing good binding affinity for the candidates considered. Overall, the analysis of the molecular dynamics trajectory of the receptor-drug complexes revealed stable structures for Imatinib, Lumacaftor and Naldemedine, for the entire simulation time.

In vitro and in silico pharmaceutical activities of the methylated cyclic pentapeptide, persipeptides

AIMS

The persipeptides were recognized as a promising source of multiple pharmaceutical activities which were revealed following structure-activity prediction and examination in experimental analysis.

METHODS AND RESULTS

The profile of toxicity, antioxidant, anti-inflammatory, anti-diabetic and anti-ageing activity of persipeptides and the crude extract were evaluated experimentally. The pure Persipeptide A and B revealed a moderate xanthine oxidase inhibition activity at the concentration of 10 µg/ml. Persipeptide exhibited α-glucosidase inhibition activity (~10% inhibition) and less than 2% tyrosinase inhibition activity at the concentration of 10 µg/ml. The extract exhibited the inhibition of less than 2% acetylcholine esterase inhibition activity, but the pure persipeptide showed 6%-14% inhibition activity at the concentration of 10 µg/ml. The molecular docking analysis revealed that the activities of Persipeptide A and B are due to interaction with xanthin oxidase, α-amylase, α-glucosidase, tyrosinase and acetylcholine esterase enzymes.

CONCLUSIONS

The persipeptides showed a similar inhibition rate with positive control that might imply its potential as an anti-diabetic and anti-gout compound among. Only acetylcholine esterase inhibition of persipeptide was higher than the extract. The interacting amino acids of the molecules with different targets show that persipeptides might have antioxidant, anti-inflammatory, anti-diabetic, anti-ageing activity and even other potential pharmaceutical activities that were not investigated in this study.

SIGNIFICANCE AND IMPACT OF THE STUDY

This report was presented to find some new pharmaceutical activities of Persipeptide A and B including the α-glucosidase inhibition activity as a molecular target of diabetes mellitus. Persipeptides also exhibited an effective inhibition of xanthine oxidase (XO) which can be a drug-like candidate in the treatment of diseases associated with XO like gout. The binding values indicated the interaction of persipeptides with these enzymes.

Shared Binding Mode of Perrottetinene and Tetrahydrocannabinol Diastereomers inside the CB1 Receptor May Incentivize Novel Medicinal Drug Design: Findings from an in Silico Assay

In recent years, therapeutic compounds derived from phytocannabinoids have brought renewed attention to the benefits they offer to ameliorate chronic disease symptoms. Among cannabinoids, tetrahydrocannabinol (THC) is a well-known component of the Cannabis plant, whose active principles have been studied through the years. Another psychoactive phytocannabinoid, derived from liverworts Radula, perrottetinene (PET), has created interest, especially as a pharmaceutical product and for its legal recreational use. Unfortunately, so far, the interaction mode of these compounds at the type 1 cannabinoid receptors (CB1R) binding site remains unknown, and no experimental three-dimensional structure in complex with THC or PET is available in the Protein Data Bank. Today, many computational methodologies can assist in this crusade and help unveil how these molecules bind, based on the already known pose of a structurally similar compound. In this work, we aim to elucidate the binding mode of THC and PET molecules in both cis and trans conformers, using a combination of several computational methodologies, including molecular docking, molecular dynamics, free energy calculations, and protein-energy network studies. We found that THC and PET interact similarly with the CB1R, in a different conformation depending on the considered diastereomer. We have observed that cis ligands adopted a half-chair conformation of the cycle ring containing the dimethyl group, assuming an axial or equatorial conformation producing a different induced fitting of the surrounding residues compared with trans ligands, with higher interaction energy than the trans conformer. For PET, we have seen that Trp-279 and Trp-356 have a marked influence on the binding. After binding, Trp-279 accommodates its side chain to better interact with the PET's terminal phenyl group, disturbing CB1R residues communication. The interaction with Trp-356 might impair the activation of CB1R and can influence the binding of PET as a partial agonist. Understanding the PET association with CB1R from a molecular perspective can offer a glimpse of preventing potential toxicological or recreational effects since it is an attractive lead for drug development with fewer side effects than trans-THC.

Recent advances in computational methods for biosensor design

Biosensors are analytical tools with a great application in healthcare, food quality control, and environmental monitoring. They are of considerable interest to be designed by using cost-effective and efficient approaches. Designing biosensors with improved functionality or application in new target detection has been converted to a fast-growing field of biomedicine and biotechnology branches. Experimental efforts have led to valuable successes in the field of biosensor design; however, some deficiencies restrict their utilization for this purpose. Computational design of biosensors is introduced as a promising key to eliminate the gap. A set of reliable structure prediction of the biosensor segments, their stability, and accurate descriptors of molecular interactions are required to computationally design biosensors. In this review, we provide a comprehensive insight into the progress of computational methods to guide the design and development of biosensors, including molecular dynamics simulation, quantum mechanics calculations, molecular docking, virtual screening, and a combination of them as the hybrid methodologies. By relying on the recent advances in the computational methods, an opportunity emerged for them to be complementary or an alternative to the experimental methods in the field of biosensor design.