Ribosomal RNA-Aminoglycoside Hygromycin B Interaction Energy Calculation within a Density Functional Theory Framework

16S rRNA methyltransferase KsgA contributes to oxidative stress and antibiotic resistance in Pseudomonas aeruginosa

Ribosomal RNA (rRNA) modifications are involved in multiple biological processes. KsgA is a 16S rRNA adenine dimethyltransferase that methylates at the adenines 1518 and 1519 (A1518/1519) positions, which are located near the ribosome decoding center. These methylations are conserved and important for ribosome biogenesis and protein translation. In this study, we demonstrated the absence of A1518/1519 methylation in the 16S rRNA of a Pseudomonas aeruginosa ksgA mutant. Biolog phenotypic microarrays were used to screen the phenotypes of the ksgA mutant against various antimicrobial agents. The loss of ksgA led to increased sensitivity to menadione, a superoxide generator, which was, at least in part, attributed to decreased in a superoxide dismutase (SOD) activity. Interestingly, the decrease in SOD activity in the ksgA mutant was linked to a decrease in the SodM protein levels, but not the sodM mRNA levels. Furthermore, the ksgA mutant strain exhibited sensitivity to hygromycin B and tylosin antibiotics. The tylosin-sensitive phenotype was correlated with decreased transcriptional levels of tufA, tufB, and tsf, which encode elongation factors. Additionally, the ksgA mutant showed resistance to kasugamycin. Collectively, these findings highlight the role of KsgA in oxidative stress responses and antibiotic sensitivity in P. aeruginosa.

A computational workflow to determine drug candidates alternative to aminoglycosides targeting the decoding center of E. coli ribosome

The global antibiotic resistance problem necessitates fast and effective approaches to finding novel inhibitors to treat bacterial infections. In this study, we propose a computational workflow to identify plausible high-affinity compounds from FDA-approved, investigational, and experimental libraries for the decoding center on the small subunit 30S of the E. coli ribosome. The workflow basically consists of two molecular docking calculations on the intact 30S, followed by molecular dynamics (MD) simulations coupled with MM-GBSA calculations on a truncated ribosome structure. The parameters used in the molecular docking suits, Glide and AutoDock Vina, as well as in the MD simulations with Desmond were carefully adjusted to obtain expected interactions for the ligand-rRNA complexes. A filtering procedure was followed, considering a fingerprint based on aminoglycoside's binding site on the 30S to obtain seven hit compounds either with different clinical usages or aminoglycoside derivatives under investigation, suggested for in vitro studies. The detailed workflow developed in this study promises an effective and fast approach for the estimation of binding free energies of large protein-RNA and ligand complexes.

Quantum mechanics insights into melatonin and analogs binding to melatonin MT1 and MT2 receptors

Melatonin receptors MT1 and MT2 are G protein-coupled receptors that mediate the effects of melatonin, a hormone involved in circadian rhythms and other physiological functions. Understanding the molecular interactions between these receptors and their ligands is crucial for developing novel therapeutic agents. In this study, we used molecular docking, molecular dynamics simulations, and quantum mechanics calculation to investigate the binding modes and affinities of three ligands: melatonin (MLT), ramelteon (RMT), and 2-phenylmelatonin (2-PMT) with both receptors. Based on the results, we identified key amino acids that contributed to the receptor-ligand interactions, such as Gln181/194, Phe179/192, and Asn162/175, which are conserved in both receptors. Additionally, we described new meaningful interactions with Gly108/Gly121, Val111/Val124, and Val191/Val204. Our results provide insights into receptor-ligand recognition's structural and energetic determinants and suggest potential strategies for designing more optimized molecules. This study enhances our understanding of receptor-ligand interactions and offers implications for future drug development.

Quantum biochemical analysis of the TtgR regulator and effectors

The recent expansion of multidrug-resistant (MDR) pathogens poses significant challenges in treating healthcare-associated infections. Although antibacterial resistance occurs by numerous mechanisms, active efflux of the drugs is a critical concern. A single species of efflux pump can produce a simultaneous resistance to several drugs. One of the best-studied efflux pumps is the TtgABC: a tripartite resistance-nodulation-division (RND) efflux pump implicated in the intrinsic antibiotic resistance in Pseudomonas putida DOT-T1E. The expression of the TtgABC gene is down-regulated by the HTH-type transcriptional repressor TtgR. In this context, by employing quantum chemistry methods based on the Density Functional Theory (DFT) within the Molecular Fragmentation with Conjugate Caps (MFCC) approach, we investigate the coupling profiles of the transcriptional regulator TtgR in complex with quercetin (QUE), a natural polyphenolic flavonoid, tetracycline (TAC), and chloramphenicol (CLM), two broad-spectrum antimicrobial agents. Our quantum biochemical computational results show the: [i] convergence radius, [ii] total binding energy, [iii] relevance (energetically) of the ligands regions, and [iv] most relevant amino acids residues of the TtgR-QUE/TAC/CLM complexes, pointing out distinctions and similarities among them. These findings improve the understanding of the binding mechanism of effectors and facilitate the development of new chemicals targeting TtgR, helping in the battle against the rise of resistance to antimicrobial drugs. These advances are crucial in the ongoing fight against rising antimicrobial drug resistance, providing hope for a future where healthcare-associated infections can be more beneficially treated.

The energy landscape of the ribosome

The ribosome is a prototypical assembly that can be used to establish general principles and techniques for the study of biological molecular machines. Motivated by the fact that the dynamics of every biomolecule is governed by an underlying energy landscape, there has been great interest to understand and quantify ribosome energetics. In the present review, we will focus on theoretical and computational strategies for probing the interactions that shape the energy landscape of the ribosome, with an emphasis on more recent studies of the elongation cycle. These efforts include the application of quantum mechanical methods for describing chemical kinetics, as well as classical descriptions to characterize slower (microsecond to millisecond) large-scale (10-100 Å) rearrangements, where motion is described in terms of diffusion across an energy landscape. Together, these studies provide broad insights into the factors that control a diverse range of dynamical processes in this assembly.

Quantum biochemical analysis of the binding interactions between a potential inhibitory drug and the Ebola viral glycoprotein

Ebola virus disease (EVD) causes outbreaks and epidemics in West Africa that persist until today. The envelope glycoprotein of Ebola virus (GP) consists of two subunits, GP1 and GP2, and plays a key role in anchoring or fusing the virus to the host cell in its active form on the virion surface. Toremifene (TOR) is a ligand that mainly acts as an estrogen receptor antagonist; however, a recent study showed a strong and efficient interaction with GP. In this context, we aimed to evaluate the energetic affinity features involved in the interaction between GP and toremifene by computer simulation techniques using the Molecular Fractionation Method with Conjugate Caps (MFCC) scheme and quantum-mechanical (QM) calculations, as well as missense mutations to assess protein stability. We identified ASP522, GLU100, TYR517, THR519, LEU186, LEU515 as the most attractive residues in the EBOV glycoprotein structure that form the binding pocket. We divided toremifene into three regions and evaluated that region i was more important than region iii and region ii for the formation of the TOR-GP1/GP2 complex, which might control the molecular remodeling process of TOR. The mutations that caused more destabilization were ARG134, LEU515, TYR517 and ARG559, while those that caused stabilization were GLU523 and ASP522. TYR517 is a critical residue for the binding of TOR, and is highly conserved among EBOV species. Our results may help to elucidate the mechanism of drug action on the GP protein of the Ebola virus and subsequently develop new pharmacological approaches against EVD.Communicated by Ramaswamy H. Sarma.

Investigation of protein-protein interactions and hotspot region on the NSP7-NSP8 binding site in NSP12 of SARS-CoV-2

Background: The RNA-dependent RNA polymerase (RdRp) complex, essential in viral transcription and replication, is a key target for antiviral therapeutics. The core unit of RdRp comprises the nonstructural protein NSP12, with NSP7 and two copies of NSP8 (NSP81 and NSP82) binding to NSP12 to enhance its affinity for viral RNA and polymerase activity. Notably, the interfaces between these subunits are highly conserved, simplifying the design of molecules that can disrupt their interaction. Methods: We conducted a detailed quantum biochemical analysis to characterize the interactions within the NSP12-NSP7, NSP12-NSP81, and NSP12-NSP82 dimers. Our objective was to ascertain the contribution of individual amino acids to these protein-protein interactions, pinpointing hotspot regions crucial for complex stability. Results: The analysis revealed that the NSP12-NSP81 complex possessed the highest total interaction energy (TIE), with 14 pairs of residues demonstrating significant energetic contributions. In contrast, the NSP12-NSP7 complex exhibited substantial interactions in 8 residue pairs, while the NSP12-NSP82 complex had only one pair showing notable interaction. The study highlighted the importance of hydrogen bonds and π-alkyl interactions in maintaining these complexes. Intriguingly, introducing the RNA sequence with Remdesivir into the complex resulted in negligible alterations in both interaction energy and geometric configuration. Conclusion: Our comprehensive analysis of the RdRp complex at the protein-protein interface provides invaluable insights into interaction dynamics and energetics. These findings can guide the design of small molecules or peptide/peptidomimetic ligands to disrupt these critical interactions, offering a strategic pathway for developing effective antiviral drugs.

RNA-ligand molecular docking: advances and challenges

With rapid advances in computer algorithms and hardware, fast and accurate virtual screening has led to a drastic acceleration in selecting potent small molecules as drug candidates. Computational modeling of RNA-small molecule interactions has become an indispensable tool for RNA-targeted drug discovery. The current models for RNA-ligand binding have mainly focused on the docking-and-scoring method. Accurate docking and scoring should tackle four crucial problems: (1) conformational flexibility of ligand, (2) conformational flexibility of RNA, (3) efficient sampling of binding sites and binding poses, and (4) accurate scoring of different binding modes. Moreover, compared with the problem of protein-ligand docking, predicting ligand binding to RNA, a negatively charged polymer, is further complicated by additional effects such as metal ion effects. Thermodynamic models based on physics-based and knowledge-based scoring functions have shown highly encouraging success in predicting ligand binding poses and binding affinities. Recently, kinetic models for ligand binding have further suggested that including dissociation kinetics (residence time) in ligand docking would result in improved performance in estimating in vivo drug efficacy. More recently, the rise of deep-learning approaches has led to new tools for predicting RNA-small molecule binding. In this review, we present an overview of the recently developed computational methods for RNA-ligand docking and their advantages and disadvantages.

In silico analysis of energy interactions between nociceptin/orfanin FQ receptor and two antagonists with potential antidepressive action

This study addresses the binding energies of NOPR-ligand complexes and presents the main amino acid residues involved in the interaction between these complexes.

Quantum Biochemical Investigation of Lys49-PLA2 from Bothrops moojeni

Envenomation via snakebites occurs largely in areas where it is harder to access the hospital. Its mortality rate and sequelae acquired by the survivors symbolize a big challenge for antivenom therapy. In particular, the homologous phospholipase A₂ (Lys49-PLA₂) proteins can induce myonecrosis and are not effectively neutralized by current treatments. Thus, by taking advantage of crystallographic structures of Bothrops moojeni Lys49-PLA₂ complexed with VRD (varespladib) and AIN (aspirin), a quantum biochemistry study based on the molecular fractionation with conjugate cap scheme within the density functional theory formalism is performed to unveil these complexes' detailed interaction energies. The calculations revealed that important interactions between ligands and the Lys49-PLA₂ pocket could occur up to a pocket radius of r = 6.5 (5.0 Å) for VRD (AIN), with the total interaction energy of the VRD ligand being higher than that of the AIN ligand, which is well-correlated with the experimental binding affinity. Furthermore, we have identified the role played by the amino acids LYS0069, LYS0049, LEU0005, ILE0009, CYS0029, GLY0030, HIS0048, PRO0018, ALA0019, CYS0045, TYR0052, TYR0022, PRO0125*, and PHE0126* (LYS0069, LYS0049, GLY0032, LEU0002, and LEU0005) in the VRD↔Lys49-PLA₂ (AIN↔Lys49-PLA₂) complex. Our simulations are a valuable tool to support the big challenge for neutralizing the damages in victims of snakebites.

Docking and scoring for nucleic acid-ligand interactions: Principles and current status

Nucleic acid (NA)-ligand interactions have crucial roles in many cellular processes and, thus, are increasingly attracting therapeutic interest in drug discovery. Molecular docking is a valuable tool for studying molecular interactions. However, because NAs differ significantly from proteins in both their physical and chemical properties, traditional docking algorithms and scoring functions for protein-ligand interactions might not be applicable to NA-ligand docking. Therefore, various sampling strategies and scoring functions for NA-ligand interactions have been developed. Here, we review the basic principles and current status of docking algorithms and scoring functions for DNA/RNA-ligand interactions. We also discuss challenges and limitations of current docking and scoring approaches.

New ethionamide boosters and EthR2: structural and energetic analysis

Ethionamide (ETH) is a high-profile drug for the treatment of patients with multidrug-resistant Mycobacterium tuberculosis and, in order to produce its inhibitory effects, it needs to be bioactivated by monooxygenase EthA. This process is under the control of the transcriptional repressors EthR and EthR2, so that their inhibition results in the boosting of ethionamide activation. Herein, through crystallographic data and computer simulations, we calculated the interaction binding energies of four inhibitors with improved in vitro potency, namely BDM76060 (PDB ID: 6HS1), BDM72201 (PDB ID: 6HRX), BDM76150 (PDB ID: 6HS2) and BDM72719 (PDB ID: 6HRY), in complexes with the transcriptional repressor EthR2, using density functional theory (DFT) within the molecular fractionation with conjugated caps (MFCC) approach. It was observed that these ligands share the same binding site within a 10.0 Å radius of the EthR2 protein; however, their structural particularities have a significant impact on the global energies of systems. The BDM72201 and BDM72719 components are weakly attached to the binding site, while BDM76060 and BDM76150 components produce stronger bonds, corroborating with experimental studies demonstrating that BDM76060 and BDM76150 are more successful in producing inhibitory effects. BDM76060 and BDM76150 have many functional groups that increase the contact surface with the protein and attract a more significant number of amino acid residues, being able to produce polarities that generate stronger interactions. In the current scenario of a growing number of cases of bacterial resistance, the obtained data can be used to guide clinical trials of these inhibitors and other inhibitors that act on the alternative EthR2 pathway, focusing on improving the activity of ethionamide, its effectiveness, a reduction in the treatment time and exposure to cytotoxic effects.

Blockade of the checkpoint PD-1 by its ligand PD-L1 and the immuno-oncological drugs pembrolizumab and nivolumab

We investigate the interaction between the programmed cell death protein 1 (PD-1) and the programmed cell death ligand 1 (PD-L1), as well as the immuno-oncological drugs pembrolizumab (PEM), and nivolumab (NIV), through quantum chemistry methods based on the Density Functional Theory (DFT) and the molecular fractionation with conjugate caps (MFCC) scheme, in order to map their hot-spot regions. Our results showed that the total interaction energy order of the three complexes is in good agreement with the experimental binding affinity order: PD-1/PEM > PD-1/NIV > PD-1/PD-L1. Besides, a detailed investigation revealed the energetically most relevant residue-residue pairs-interaction for each complex. Our computational results give a better understanding of the interaction mechanism between the protein PD-1 and its ligands (natural and inhibitors), unleashing the immune surveillance to destroy the cancer cells by decreasing their immune evasion. They are also an efficient alternative towards the development of new small-molecules and antibody-based drugs, pointing out to new treatments for cancer therapy.

Quantum binding energies of checkpoint CTLA-4 in complex with the immuno-oncological drug ipilimumab

Inhibition of the checkpoint protein CTLA-4 by the US-FDA's approved monoclonal antibody ipilimumab has delivered breakthrough therapies against a wide range of cancers, being an important issue for clinical research. To date, many structural properties of this drug have been unveiled. However, the binding energy features of the receptor CTLA-4 in complex with its drug inhibitor, based on crystallographic data, need a deeper understanding. Within this context, by employing quantum chemistry we investigate in silico the binding energy features of the checkpoint protein CTLA-4 in complex with its drug inhibitor, highlighting the most relevant residue-residue interactions, looking for new insights into the mechanisms of pathway blockade to further engineer its affinity and selectivity. Our computational results not only give a better understanding of the binding mechanisms, but also point to an efficient alternative towards the development of antibody-based drugs, leading to new treatments for cancer therapy based upon immunotherapy.

Exploring human porphobilinogen synthase metalloprotein by quantum biochemistry and evolutionary methods

Previous studies have shown the porphobilinogen synthase (PBGS) zinc-binding mechanism and its conservation among the living cells. However, the precise molecular interaction of zinc with the active center of the enzyme is unknown. In particular, quantum chemistry techniques within the density functional theory (DFT) framework have been the key methodology to describe metalloproteins, when one is looking for a compromise between accuracy and computational feasibility. Considering this, we used DFT-based models within the molecular fractionation with conjugate caps scheme to evaluate the binding energy features of zinc interacting with the human PBGS. Besides, phylogenetic and clustering analyses were successfully employed in extracting useful information from protein sequences to identify groups of conserved residues that build the ions-binding site. Our results also report a conservative assessment of the relevant amino acids, as well as the benchmark analysis of the calculation models used. The most relevant intermolecular interactions in Zn2+-PBGS are due to the amino acids CYS0122, CYS0124, CYS0132, ASP0169, SER0168, ARG0221, HIS0131, ASP0120, GLY0133, VAL0121, ARG0209, and ARG0174. Among these residues, we highlighted ASP0120, GLY0133, HIS0131, SER0168, and ARG0209 by co-occurring in all clusters generated by unsupervised clustering analysis. On the other hand, the triple cysteines at 2.5 Å from zinc (CYS0122, CYS0124, and CYS0132) have the highest energy attraction and are absent in the taxa Viridiplantae, Sar, Rhodophyta, and some Bacteria. Additionally, the performance of the DFT-based models shows that the processing time-dependence is more associated with the choice of the basis set than the exchange-correlation functional.

In silico approach of modified melanoma peptides and their immunotherapeutic potential

Melanoma is a type of skin cancer with increasing incidence worldwide and high lethality. Conventional forms of treatment are not effective in advanced cancer stages. Hence, immunotherapeutic approaches have been tested to modulate immune response against tumor cells. Some vaccine models using tumor-associated antigens (TAAs) such as glycoprotein 100 (gp100) have been studied, but their expected effectiveness has not been shown until now. Antigen immunogenicity is a crucial point to improve the immune response, and therefore mutations are inserted in peptide sequences. It is possible to understand the interactions which occur between peptides and immune system molecules through computer simulation, and this is essential in order to guide efficient vaccine models. In this work, we have calculated the interaction binding energies of crystallographic data based on modified gp100 peptides and HLA-A*0201 using density functional theory (DFT) and the molecular fractionation with conjugated caps (MFCC) approach. Our results show the most relevant residue-residue interactions, the impact of three mutations in their binding sites, and the main HLA-A*0201 amino acids for peptide-HLA binding.

Interaction energies between two antiandrogenic and one androgenic agonist receptor in the presence of a T877A mutation in prostate cancer: a quantum chemistry analysis

We provide a detailed picture of the interactions between the androgen receptor T877A mutation and three different compounds used in the treatment of prostate cancer.

Intermolecular interactions of cn-716 and acyl-KR-aldehyde dipeptide inhibitors against Zika virus

Structural representation and graphic panel showing the most relevant residues that contribute to the ZIKV NS2B–NS3–ligand complexes.