Learning probabilistic models of hydrogen bond stability from molecular dynamics simulation trajectories

Insights on molecular modeling and supramolecular arrangement of bilastine polymorphs

CONTEXT

The advancement in the development of second-generation drugs in the field of antihistamines represents a significant milestone in the management of allergic diseases, targeting the effects of histamine. The efficacy of bilastine in treating allergic disorders has sparked interest in investigating its polymorphism, a crucial property that impacts quality, safety, and effectiveness as per regulatory guidelines. This study examines the polymorphism of bilastine, focusing on two crystalline forms labeled as Form I and Form II. Utilizing advanced analytical techniques, the research explores the structural characteristics and molecular interactions within these forms. Geometric parameters, such as bond lengths, bond angles, and torsion angles, are examined to comprehend molecular conformations and crystal packing arrangements. Hydrogen bonding, covalent bonds, and van der Waals forces contribute to the unique supramolecular arrangements in these forms. This study provides a significant contribution to understanding bilastine's polymorphism, offering critical insights to researchers and regulatory bodies to ensure the quality, efficacy, and safety of antihistamine products.

METHODS

The molecular conformation of two bilastine forms was obtained through DFT with the exchange-correlation functional M06-2X and the 6-311 +  + G(d,p) basis set, and the results were compared with the experimental X-ray. The atomic coordinates were obtained directly from the crystalline structures, and charge transfer was also investigated using frontier molecular orbitals (HOMO and LUMO), and MEP map in order to evaluate the energies associated with charge transfers and regions of high electron affinity. The geometric and topological parameters and intermolecular interactions in the crystals were analyzed using Hirshfeld Surface.

Molecular interaction and MD-simulations: investigation of Sizofiran as a promising anti-cancer agent targeting eIF4E in colorectal cancer

CRC has a major global health impact due to high mortality rates. CRC shows high expression of eukaryotic translation initiation factor (eIF4E) protein, the rapid development of lung, bladder, colon, prostate, breast, head, and neck cancer is attributed to the dysregulation of eIF4E making an important target for treatment. Targeting eIF4E-mediated translation is a promising anti-cancer strategy. Many organic compounds that inhibit eIF4E are being studied clinically. The compound Sizofiran has emerged as a promising eIF4E inhibitor candidate, but its exact mechanism of action is unclear. In an effort to close this discrepancy by clarifying the mechanism of the interactions between phytochemical substances and eIF4E, molecular docking and dynamics studies were conducted. Molecular docking studies found Sizofiran (- 12.513 kcal/mol) has the most affinity eIF4E binding energy out of 93 phytochemicals, 5 current drugs, and 4 known inhibitors. This positions it as a top eIF4E inhibitor candidate. An alignment of eIF4E protein sequences from multiple pathogens revealed that the glutamate103 interacting residues are evolutionarily conserved across the different eIF4E proteins. Further insights from 100 ns of MD simulations supported Sizofiran having superior stability and eIF4E inhibition compared to reference compounds. Designed Sizofiran-related compounds showed better activity than the current drugs such as Camptosar, Sorafenib, Regorafenib, Doxorubicin, and Kenpaullone, indicating strong potential to suppress CRC progression by targeting eIF4E. This research aims to significantly aid development of improved eIF4E-targeting drugs for cancer treatment.

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Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00206-3.

Unraveling the Role of Hydrogen Bonds in Thrombin via Two Machine Learning Methods

Hydrogen bonds play a critical role in the folding and stability of proteins, such as proteins and nucleic acids, by providing strong and directional interactions. They help to maintain the secondary and 3D structure of proteins, and structural changes in these molecules often result from the formation or breaking of hydrogen bonds. To gain insights into these hydrogen bonding networks, we applied two machine learning models - a logistic regression model and a decision tree model - to study four variants of thrombin: wild-type, ΔK9, E8K, and R4A. Our results showed that both models have their unique advantages. The logistic regression model highlighted potential key residues (GLU295) in thrombin's allosteric pathways, while the decision tree model identified important hydrogen bonding motifs. This information can aid in understanding the mechanisms of folding in proteins and has potential applications in drug design and other therapies. The use of these two models highlights their usefulness in studying hydrogen bonding networks in proteins.

DSF inactivator RpfB homologous FadD upregulated in Bradyrhizobium japonicum under iron limiting conditions

Phytopathogenic bacteria Xanthomonas campestris pv. campestris (Xcc) causes black rot and other plant diseases. Xcc senses diffusible signal factor (DSF) as a quorum-sensing (QS) signal that mediates mainly iron uptake and virulence. RpfB deactivates DSF in this DSF-QS circuit. We examined differential gene expression profiles of Bradyrhizobium japonicum under low versus high iron conditions and found that fadD and irr were upregulated under low iron (log2 fold change 0.825 and 1.716, respectively). In addition to having similar protein folding patterns and functional domain similarities, FadD shared 58% sequence similarity with RpfB of Xcc. The RpfB-DSF and FadD-DSF complexes had SWISSDock molecular docking scores of - 8.88 kcal/mol and - 9.85 kcal/mol, respectively, and the 100 ns molecular dynamics simulation results were in accord with the docking results. However, significant differences were found between the binding energies of FadD-DSF and RpfB-DSF, indicating possible FadD-dependent DSF turnover. The protein-protein interaction network showed that FadD connected indirectly with ABC transporter permease (ABCtp), which was also upregulated (log2 fold change 5.485). We speculate that the low iron condition may be a mimetic environmental stimulus for fadD upregulation in B. japonicum to deactivate DSF, inhibit iron uptake and virulence of DSF-producing neighbors. This finding provides a new option of using B. japonicum or a genetically improved B. japonicum as a potential biocontrol agent against Xcc, with the added benefit of plant growth-promoting properties.

Integrated approach for studying bioactive compounds from Cladosporium spp. against estrogen receptor alpha as breast cancer drug target

Cladosporium spp. have been reported for their great diversity of secondary metabolites which represent as a prominent base material for verifying the biological activities. Several bioactive compounds which have antimicrobial, cytotoxic, quorum sensing inhibitory and phytotoxic activities have been isolated from Cladosporium species. Most of them are still needed to be explored for their anticancer properties. Therefore, the present study is focused on screening and identifying the bioactive compounds of Cladosporium spp. for their anticancer activity via the integrated approaches of Molecular Docking (MD), Molecular Dynamics Simulation (MDS) and Density Functional Theory (DFT) studies. A total of 123 bioactive compounds of Cladosporium spp. were explored for their binding affinity with the selected breast cancer drug target receptor such as estrogen receptor alpha (PDB:6CBZ). The Molecular Docking studies revealed that amongst the bioactive compounds screened, Altertoxin X and Cladosporol H showed a good binding affinity of - 10.5 kcal/mol and - 10.3 kcal/mol, respectively, with the estrogen receptor alpha when compared to the reference compound (17[Formula: see text]-Estradiol: - 10.2 kcal/mol). The MDS study indicated the stable binding patterns and conformation of the estrogen receptor alpha-Altertoxin X complex in a stimulating environment. In addition, in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) study suggested that Altertoxin X has a good oral bioavailability with a high LD[Formula: see text] value of 2.375 mol/kg and did not cause any hepatotoxicity and skin sensitization. In summary, the integrated approaches revealed that Altertoxin X possesses a promising anticancer activity and could serve as a new therapeutic drug for breast cancer treatment.

Discovery of new chemotypes of dual 5-HT2A/D2 receptor antagonists with a strategy of drug design methodologies

Aim: Through the application of structure- and ligand-based methods, the authors aimed to create an integrative approach to developing a computational protocol for the rational drug design of potent dual 5-HT_2A/D₂ receptor antagonists without off-target activities on H₁ receptors. Materials & methods: Molecular dynamics and virtual docking methods were used to identify key interactions of the structurally diverse antagonists in the binding sites of the studied targets, and to generate their bioactive conformations for further 3D-quantitative structure-activity relationship modeling. Results & conclusion: Toward the goal of finding multi-potent drugs with a more effective and safer profile, the obtained results led to the design of a new set of dual antagonists and opened a new perspective on the therapy for complex brain diseases.

In Silico study for acyclovir, ganciclovir and its derivatives to fight the COVID-19: Molecular docking, DFT calculations, ADME and td-Molecular dynamics simulations

In the present work, we have designed three molecules, acyclovir (A), ganciclovir (G) and derivative of hydroxymethyl derivative of ganciclovir (CH₂OH of G, that is D) and investigated their biological potential against the Mpro of nCoV via in silico studies. Further, density functional theory (DFT) calculations of A, G and D were performed using Gaussian 16 on applying B3LYP under default condition to collect the information for the delocalization of electron density in their optimized geometry. Authors have also calculated various energies including free energy of A, G and D in Hartree per particle. It can be seen that D has the least free energy. As mentioned, the molecular docking of the A, G and D against the Mpro of nCoV was performed using iGemdock, an acceptable computational tool and the interaction has been studied in the form of physical data, that is, binding energy for A, G and D were calculated in kcal/mol. It can be seen the D showed effective binding, that is, maximum inhibition that A and G. For a better understanding for the inhibition of the Mpro of nCoV by A, G and D, temperature dependent molecular dynamics simulations were performed. Different trajectories like RMSD, RMSF, Rg and hydrogen bond were extracted and analyzed. The results of molecular docking of A, G and D corroborate with the td-MD simulations and hypothesized that D could be a promising candidate to inhibit the activity of Mpro of nCoV.

Rational design of hyper-glycosylated human follicle-stimulating hormone analogs (a bioinformatics approach)

N-glycosylation is a complex mechanism in which the carbohydrate molecules bind to the Asn amino acid in the N-glycan consensus sequence (AsnXxxThr/Ser sequon, where Xxx is any residue, excluding Pro). Introduction of additional N-linked glycosylation site into proposed location in the protein causes to its hyper-glycosylation and can enhance the protein characteristics to provide promising prospects in treatment. Glycoengineering is a favorably used strategy to design and generate hyper-glycosylated variants. In this research, human follicle-stimulating hormone (HuFSH) was considered to identify appropriate positions for adding novel N-glycan sites. A rational computational strategy was applied to predict functional/structural variations induced through changes in polypeptide chain. We analyzed the amino acid chain of FSH to find out the proper locations to introduce asparagine and/or threonine for creating novel N-glycan positions. This analysis resulted in the recognition of 40 possible N-glycosylation positions, and then the eight adequate ones were chosen for additional investigation. The model validation techniques were used to examine 3-dimensional structures of the chosen mutant proteins. Finally, 2 mutants with a further glycan site were recommended as eligible FSH hyper-glycosylated analogs, which may be regarded for subsequent experimental studies. Our in silico approach may decrease tedious and time-wasting laboratory researches of the mutants.Communicated by Ramaswamy H. Sharma.

Drug design targeting active posttranslational modification protein isoforms

Modern drug design aims to discover novel lead compounds with attractable chemical profiles to enable further exploration of the intersection of chemical space and biological space. Identification of small molecules with good ligand efficiency, high activity, and selectivity is crucial toward developing effective and safe drugs. However, the intersection is one of the most challenging tasks in the pharmaceutical industry, as chemical space is almost infinity and continuous, whereas the biological space is very limited and discrete. This bottleneck potentially limits the discovery of molecules with desirable properties for lead optimization. Herein, we present a new direction leveraging posttranslational modification (PTM) protein isoforms target space to inspire drug design termed as "Post-translational Modification Inspired Drug Design (PTMI-DD)." PTMI-DD aims to extend the intersections of chemical space and biological space. We further rationalized and highlighted the importance of PTM protein isoforms and their roles in various diseases and biological functions. We then laid out a few directions to elaborate the PTMI-DD in drug design including discovering covalent binding inhibitors mimicking PTMs, targeting PTM protein isoforms with distinctive binding sites from that of wild-type counterpart, targeting protein-protein interactions involving PTMs, and hijacking protein degeneration by ubiquitination for PTM protein isoforms. These directions will lead to a significant expansion of the biological space and/or increase the tractability of compounds, primarily due to precisely targeting PTM protein isoforms or complexes which are highly relevant to biological functions. Importantly, this new avenue will further enrich the personalized treatment opportunity through precision medicine targeting PTM isoforms.

SNP mutations occurring in thyroid hormone receptor influenced individual susceptibility to triiodothyronine: Molecular dynamics and site-directed mutagenesis approaches

The increasing evidences have suggested that expression of single nucleotide polymorphisms (SNP) coded thyroid hormone receptors (THR) generally are associated with individual susceptibility to chemicals. In the present research, multiple molecular dynamics simulations on four SNP mutants (G332R, T337Δ, G345R, and G347E) were performed to investigate the structural and dynamical altering, which could lead to a binding capability variation to triiodothyronine (T3). It proved the structures of two SNP mutants (G345R and T337Δ) occurring in the THR proteins had experienced conformational change to a great extend, which also led to a significant decreasing in binding ability with T3. In addition, two mutates (G345R and G347E) and wild type THR proteins were expressed and purified based on site-directed mutagenesis technology to test their binding abilities with T3 by fluorescence experiments. The fluorescence quenching efficiencies of two mutates displayed that the conjugation with T3 decreased with a significant rate in G345R system and a little rate in G347E system compared with its wild type. It was consistent with the molecular dynamic research that the SNP mutations did change structures of THR protein, and thereby decreased the binding behavior of T3 at different extent. The overall molecular-level look at the protein structure may provide the structural basis to explain how one amino acid change can create a ripple effect on the protein structures and eventually affect the binding affinity of the ligands, which maybe the first stage to understand how SNP mutation results in individual difference in susceptibility to variant chemicals.

Computational analysis of C-reactive protein for assessment of molecular dynamics and interaction properties

Serum C-reactive protein (CRP) is used as a marker of inflammation in several diseases including autoimmune disease and cardiovascular disease. CRP, a member of the pentraxin family, is comprised of five identical subunits. CRP has diverse ligand-binding properties which depend upon different structural states of CRP. However, little is known about the molecular dynamics and interaction properties of CRP. In this study, we used SAPS, SCRATCH protein predictor, PDBsum, ConSurf, ProtScale, Drawhca, ASAView, SCide and SRide server and performed comprehensive analyses of molecular dynamics, protein-protein and residue-residue interactions of CRP. We used 1GNH.pdb file for the crystal structure of human CRP which generated two pentamers (ABCDE and FGHIJ). The number of residues involved in residue-residue interactions between A-B, B-C, C-D, D-E, F-G, G-H, H-I, I-J, A-E and F-J subunits were 12, 11, 10, 11, 12, 11, 10, 11, 10 and 10, respectively. Fifteen antiparallel β sheets were involved in β-sheet topology, and five β hairpins were involved in forming the secondary structure. Analysis of hydrophobic segment distribution revealed deviations in surface hydrophobicity at different cavities present in CRP. Approximately 33 % of all residues were involved in the stabilization centers. We show that the bioinformatics tools can provide a rapid method to predict molecular dynamics and interaction properties of CRP. Our prediction of molecular dynamics and interaction properties of CRP combined with the modeling data based on the known 3D structure of CRP is helpful in designing stable forms of CRP mutants for structure-function studies of CRP and may facilitate in silico drug design for therapeutic targeting of CRP.