Binding Affinity Of Complex Research Articles

Synthesis, Urease Inhibitory Activity, Molecular Docking, Dynamics, MMGBSA and DFT Studies of Schiff Bases Bearing Benzimidazole Scaffold.

In this study, eleven hydrazone-Schiff bases bearing benzimidazole moiety were synthesized successfully via three step reactions and structures of these products were deduced by HR-ESI-MS, 1H-, and 13C-NMR spectroscopic techniques. Lastly, these derivatives were tested for their in vitro urease inhibitory potential. Six compounds among the series attributed excellent inhibition with IC50 values of 7.20 ± 0.59 to 19.61 ± 1.10 µM better than the reference drug thiourea (IC50 = 22.12 ± 1.20 µM). Similarly, three derivatives showed significant while two compounds showed less inhibitory effects against the urease enzyme. The molecular docking analysis was carried out to reveal the binding modes and types of interaction taking place between protein (urease) and synthesized compounds. The Density Functional Theory (DFT) calculations were performed at B3LYP/6-311++G(d,p) to check the structure stability. For the account of intramolecular interaction, the DFT-D3 and Reduced Density Gradient (RDG) analysis were performed. Furthermore, the chemical nature of all compounds was explored by TD-DFT method using CAM-B3LYP functional with 6-311++G(d,p) basis set. The dynamic simulation as well as MMGBSA studies validated the binding affinity and stability of the ligand receptor complex, displaying main interactions contributing in the biological activity of the product derivatives.

Improved Rapid Equilibrium Dialysis-Mass Spectrometry (RED-MS) Method for Measuring Small Molecule-Protein Complex Binding Affinities in Solution.

Rapid equilibrium dialysis (RED) is predominantly used for the characterization of drug absorption, distribution, metabolism, and excretion (ADME) properties in plasma and biological fluids. We describe herein improvements in the use of RED in conjunction with mass spectrometry (RED-MS) to enable robust binding affinity measurements of small molecules for recombinant proteins and complexes from a single dialysis data set. The affinities calculated from RED-MS correlated well with measurements by both surface plasmon resonance (SPR) and affinity selection mass spectrometry (AS-MS). The method was particularly useful for quantifying the binding of small molecules to large protein complexes that were not amendable by common biophysical characterization techniques. Compound pooling and integration with automated liquid handling increased assay throughput and enabled the analysis of hundreds of measurements per week. RED-MS offers a viable option for measuring compound binding in solution and may facilitate small molecule affinity optimization toward difficult-to-drug protein complexes.

New Pd(II)-pincer type complexes as potential antitumor drugs: synthesis, nucleophilic substitution reactions, DNA/HSA interaction, molecular docking study and cytotoxic activity.

Two new complexes of Pd(II), [Pd(L1)Cl]Cl (Pd1) and [Pd(L2)Cl]Cl (Pd2), (where L1 = N2,N6-bis(5-methylthiazol-2-yl)pyridine-2,6-dicarboxamide and L2 = N2,N6-di(benzo[d]thiazol-2-yl)pyridine-2.6-dicarboxamide) were synthesized. Characterization of the complexes was performed using elemental analysis, IR, 1H NMR spectroscopy and MALDI-TOF mass spectrometry. The nucleophilic substitution reactions of complexes with L-Methionine (L-Met), L-Cysteine (L-Cys) and guanosine-5'-monophosphate (5'-GMP) were studied by stopped-flow method at physiological conditions (pH = 7.2 and 37 °C). Complex Pd1 was more reactive than Pd2 in all studied reactions, while the order of reactivity of the selected ligands was: L-Met > L-Cys > 5'-GMP. The interaction of complexes with calf thymus-DNA (CT-DNA) was studied by Uv-Vis absorption and fluorescence emission spectroscopy. Competitive binding studies with intercalative agent ethidium bromide (EB) and minor groove binder Hoechst 33258 were performed as well. Both complexes interacted with DNA through intercalation and minor groove binding, where the latter was preferred. Additionally, the interaction of Pd1 and Pd2 complexes with human serum albumin (HSA) was studied employing fluorescence quenching spectroscopy. The results indicate a moderate binding affinity of complexes, with slightly stronger binding of the Pd1. Fluorescence competition experiments with site-markers (eosin Y and ibuprofen) for HSA were used to locate the binding site of Pd1 to the HSA. Additionally, the interaction with DNA and HSA was studied by molecular docking and the revealed results were in good agreement with the experimentally obtained ones. Pd1 complex exhibited cytotoxicity toward human (HCT116) and mouse cell lines (CT26) of colorectal cancer, mouse (4T1) and human (MDA-MB468) breast cancer lines and non-cancerous mouse mesenchymal stem cells (mMSC). In addition, Pd1 complex demonstrated significant selectivity towards cancer cells over non-cancerous mMSC, indicating a high potential to eliminate malignant cells without affecting normal cells. It induced apoptosis in CT26 cells, effectively arrested the cell cycle in the S phase, and selectively down-regulated cyclin D and cyclin E. Moreover, it can alter the expression of cell cycle regulators by increasing p21 and decreasing p-AKT. These findings confirm its ability to disrupt key tumor cell survival signals and suggest that the Pd1 complex is a potent candidate for effective cancer treatment.

Discovery of Highly Bioactive Peptides through Hierarchical Structural Information and Molecular Dynamics Simulations.

Peptide drugs play an essential role in modern therapeutics, but the computational design of these molecules is hindered by several challenges. Traditional methods like molecular docking and molecular dynamics (MD) simulation, as well as recent deep learning approaches, often face limitations related to computational resource demands, complex binding affinity assessments, extensive data requirements, and poor model interpretability. Here, we introduce PepHiRe, an innovative methodology that utilizes the hierarchical structural information in peptide sequences and employs a novel strategy called Ladderpath, rooted in algorithmic information theory, to rapidly generate and enhance the efficiency and clarity of novel peptide design. We applied PepHiRe to develop BH3-like peptide inhibitors targeting myeloid cell leukemia-1, a protein associated with various cancers. By analyzing just eight known bioactive BH3 peptide sequences, PepHiRe effectively derived a hierarchy of subsequences used to create new BH3-like peptides. These peptides underwent screening through MD simulations, leading to the selection of five candidates for synthesis and subsequent in vitro testing. Experimental results demonstrated that these five peptides possess high inhibitory activity, with IC50 values ranging from 28.13 ± 7.93 to 167.42 ± 22.15 nM. Our study explores a white-box model driven technique and a structured screening pipeline for identifying and generating novel peptides with potential bioactivity.

In-silico and in-vitro studies to identify potential inhibitors of SARS-CoV-2 spike protein from Omani medicinal plants

In the quest for novel therapeutic agents against SARS-CoV-2, the proposed study explores the potential of traditional Omani medicinal plants, focusing on the efficacy of natural ligands against the virus's Spike protein. Among 437 identified medicinal plants across Oman, 47 species that are documented for their traditional use in treating respiratory infections, with 30 species' ligands available were chosen for analysis. Molecular docking was performed using Autodock Vina on these ligands, yielding 406 unique ligands post-duplication removal. The binding affinities of target-ligand complexes were precisely determined, ranking them by interaction strength. This process identified Corilagin, a phytochemical from the Acalypha indica plant (locally known as Aeyan Al Aqrada), as the most promising inhibitor. Subsequent analyses using GROMACS for molecular dynamics simulation confirmed its binding stability and interaction dynamics of the Corilagin-protein complex. The in-vitro studies further validated Corilagin's inhibitory effect on SARS-CoV-2, demonstrating a remarkable 92 % inhibition at 0.5 mM concentration. Dilution studies to ascertain the IC50 value revealed Corilagin's high potency at a micromolar level (IC50 = 2.15 ± 0.13 μM), underscoring its potential as a drug candidate for SARS-CoV-2 treatment. These findings highlight the significance of ethnomedicine and in-silico methodologies in drug discovery, offering promising directions for future antiviral research.

Synthesis and Anticancer Evaluation of Tryptanthrin‐1,2,3‐Triazole Hybrids

ABSTRACTSynthesis of 18 tryptanthrin‐triazole hybrid molecules by employing Cu(I)‐catalyzed azide‐alkyne [3 + 2] cycloaddition (CuAAC) between tryptanthrin oxime O‐propargyl ether and aromatic azides is described here. The exclusive formation of E‐triazoles was confirmed by theoretical studies using the M06‐2x/6–311++G(d,p) level. From the synthesized triazoles, four of them have been selected and were subjected to in vitro anticancer activity studies against selected cell lines. Furthermore, in silico studies have been conducted for the most active compound, 5h, and it suggested that various noncovalent interactions and one conventional hydrogen bond enhance the stability of the complex (binding affinity = −11.29 kcal/mol). ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) studies also prove the increased biological potency of 5h.

Synthesis, crystal structure, DNA/protein interactions and cytotoxicity studies of tridentate ligand based Cu(II) complexes with various amine co-ligands

The present study utilizes a tridentate ligand H2L (1) derived as a condensation product of dehydroacetic acid and 2-furoic acid hydrazide for the synthesis of a series of copper complexes, namely [Cu(L)(bipy)] (3), [Cu(L)(4,4′-Me2-bipy)] (4), [Cu(L)(6,6′-Me2-bipy)] (5), [Cu(L)(phen)] (6), [Cu(L)(2,9-Me2-phen)] (7) [Cu(L)(pyrazino[2,3]phen)] (8) [Cu(L)(2,2′-dipyridylamine)] (9) [Cu(L)(diphenylmethanamine)] (10), obtained by reacting Cu(acetate)2 with the doubly deprotonated ligand and a range of co-ligand amines. All the synthesized compounds were fully structurally characterized using IR, 1H NMR, HRMS and single-crystal X-ray diffraction studies. The thermal stability and decomposition pattern was investigated using TGA studies. The bio-efficacy of the developed ligand and its complexes was evaluated in anti-cancer assay against SKOV3 cell lines. Copper complexes have been extensively studied for their potential to generate anticancer properties. Most complexes comprise mixed ligands, such as N-N-chelating heterocycles like 2,2’-bipyridine (bpy) and 1,10 phenanthroline (phen), chosen for their chelating and intercalative characteristics. Complex 7 displayed the highest anti-cancer activity with an IC50 value of 0.8 µM compared to standard drug doxorubicin. The impact of complex 7 on DNA fragmentation was also assessed through agarose gel electrophoresis, which indicated that complex 7 promotes observable DNA fragmentation. The serum binding affinity of the complex 7 was also investigated against BSA and HSA protein. The strong binding affinity of complex [Cu(L)(2,9-Me2-phen)] (7) with BSA/HSA and its better stability compared to the other investigated complexes were deduced based on its biological activity. The impact of varying concentrations of complex 7 on BSA and HSA was investigated using absorption spectroscopy, revealing the presence of a dynamic quenching mechanism. In silico molecular dynamics and docking, approaches have been utilized to validate the empirical data derived from serum binding studies.

Enhancing protein-ligand binding affinity prediction through sequential fusion of graph and convolutional neural networks.

Predicting protein-ligand binding affinity is a crucial and challenging task in structure-based drug discovery. With the accumulation of complex structures and binding affinity data, various machine-learning scoring functions, particularly those based on deep learning, have been developed for this task, exhibiting superiority over their traditional counterparts. A fusion model sequentially connecting a graph neural network (GNN) and a convolutional neural network (CNN) to predict protein-ligand binding affinity is proposed in this work. In this model, the intermediate outputs of the GNN layers, as supplementary descriptors of atomic chemical environments at different levels, are concatenated with the input features of CNN. The model demonstrates a noticeable improvement in performance on CASF-2016 benchmark compared to its constituent CNN models. The generalization ability of the model is evaluated by setting a series of thresholds for ligand extended-connectivity fingerprint similarity or protein sequence similarity between the training and test sets. Masking experiment reveals that model can capture key interaction regions. Furthermore, the fusion model is applied to a virtual screening task for a novel target, PI5P4Kα. The fusion strategy significantly improves the ability of the constituent CNN model to identify active compounds. This work offers a novel approach to enhancing the accuracy of deep learning models in predicting binding affinity through fusion strategies.

Rational synthesis of Azo ligands and copper complexes: Insights into potential therapeutic agents

In the pursuit of innovation, four distinct mono Azo dyes D1-D4 were synthesized. These dyes were crafted through a coupling reaction involving 1-naphthol, resorcinol and salicylic acid with diazonium salts derived from a diverse array of aromatic amines, including aniline and sulphanilic acid. Subsequently, they are transformed into copper Azo complexes, namely (D1CuC-D4CuC). The structural underpinnings of these mono Azo dyes and their corresponding copper complexes were unveiled using an arsenal of analytical techniques: FTIR, 1H NMR, 13C NMR, and UV-visible spectroscopy which unequivocally confirmed their synthesis. A computational protein-ligand docking approach was used to assess the binding affinity of ligands and metal complexes to an anti-oxidant receptor protein (PDB ID:2HCK) which showed excellent binding interactions. The findings from an in vitro antioxidant activity using the DPPH scavenging method exhibited: Complexes > Ligands. As an antioxidant agent, the synthesized compounds may prove to be a great therapeutic option. This research introduces a novel approach by utilizing Azo dyes as ligands, leveraging their exceptional electronic tunability and stability to form innovative copper complexes. Their pharmacological applications will be explored, potentially yielding drugs with improved efficacy and selectivity.

Machine Learning Predicts 1-[(4-Fluorophenyl) Methyl] Indole-2,3- Dione as Drug Lead for Peptide Deformylase in Plasmodium falciparum

Malaria continues to be a serious illness for world health. Deadly parasites that cause malaria infect female Anopheles mosquitoes and bite humans are the disease's vector. Since Plasmodium falciparum is the deadliest of the five Plasmodium species and research indicates that medicines for malaria are increasingly revealing drug-resistance mechanisms, it is imperative that new and effective medications be developed. This project aims to identify and produce a novel therapeutic lead against a P. falciparum target that has been validated. Target2Scan, a programmed tool, was employed in this investigation to find potential therapeutic targets for P. falciparum peptide deformylase. After the tool received the target signature, it ran a BLAST methodology to find targets that resembled peptide deformylase. To ascertain the binding affinities of protein-ligand complexes, molecular docking was performed using PyRx and CBDock. Of the 12,561 compounds produced, 11,304 were used as the training set and 1,256 as the test set. Six ligands were produced by machine learning as potential therapeutic leads, and using molecular docking, 1-[(4-fluorophenyl) methyl] indole-2,3-dione showed the greatest biding effect on peptide deformylase when compared to 5-chloro-1-(2-phenylethyl) indole-2,3-dione. The higher binding affinity of 1-[(4-fluorophenyl) methyl] indole-2,3-dione over 5-chloro-1-(2-phenylethyl) indole-2,3-dione and other ligands on peptide deformylase suggests that it has the ability to suppress P. falciparum pepide deformylase activity.

Commensal HPVs Have Evolved to Be More Immunogenic Compared with High-Risk α-HPVs.

Commensal human papillomaviruses (HPVs) are responsible for persistent asymptomatic infection in the human population by maintaining low levels of the episomal genome in the stratified epithelia. Herein, we examined the immunogenicity of cutaneotropic HPVs that are commonly found in the skin. Using an in silico platform to determine human leukocyte antigen (HLA)-peptide complex binding affinity, we observed that early genes of cutaneotropic HPV types within the same species can generate multiple conserved, homologous peptides that bind with high affinity to HLA class I alleles. Interestingly, we discovered that commensal β, γ, μ, and ν HPVs contain significantly more immunogenic peptides compared with α-HPVs, which include high-risk, oncogenic HPV types. Our findings indicate that commensal HPV proteins have evolved to generate peptides that better complement their host's HLA repertoire. Promoting higher control by host T cell immunity in this way could be a mechanism by which HPVs achieve widespread asymptomatic colonization in humans. This work supports the role of commensal HPVs as immunogenic targets within epithelial cells, which may contribute to the immune regulation of the skin and mucosa.

Experimental and molecular modelling demonstration of effective DNA and protein binding as well as anticancer potential of two mononuclear Cu(II) and Co(II) complexes with isothiocyanate and azide as anionic residues

In the present study, one mononuclear Cu(II) [CuL(SCN)] (1) and one mononuclear Co(II) [CoLN3] (2) complexes, with a Schiff base ligand (HL) formed by condensation of 2-picolylamine and salicylaldehyde, have been successfully developed and structurally characterized. The square planer geometry of both complexes is fulfilled by the coordination of one deprotonated ligand and one ancillary ligand SCN−(1) or N3−(2) to the metal centre. Binding affinities of both complexes with deoxyribonucleic acid (DNA) and human serum albumin (HSA) are investigated using several biophysical and spectroscopic techniques. High values of the macromolecule-complex binding constants and other results confirm the effectiveness of both complexes towards binding with DNA and HSA. The determined values of the thermodynamic parameters support spontaneous interactions of both complexes with HSA, while fluorescence displacement and DNA melting studies establish groove-binding interactions with DNA for both complexes 1 and 2. The molecular modelling study validates the experimental findings. Both complexes are subjected to an MTT test establishing the anticancer property of complex 1 with lower risk to normal cells, confirmed by the IC50 values of the complex for HeLa cancer cells and HEK normal cells. Finally, a nuclear staining analysis reveals that the complexes have caused apoptotic cell death.

A theoretical approach on ADMET properties of an azo-ester based fluorophore (AEF), and it's energetics, binding stability and molecular interactions with select globular proteins

Molecular docking (Mol.Doc) approach of an azo-ester based fluorophore (AEF) with widely studied proteins like Human serum Albumin (HSA), Bovine serum Albumin (BSA), Beta lactoglobin (βLG) and Ovalbumin (OVA) were carried out. The binding affinity and strength of AEF-HSA complex is due to hydrogen-bonding (h-bonding) and hydrophobic interactions (predominantly attributed to pi-alkyl). AEF and HSA acts as h-bonding acceptor as well as donor. The energetically favored conformers of AEF-HSA complex are governed and stabilized by polar as well as non-polar amino acids. On the contrary, the pattern observed in all the conformers of AEF-BSA, AEF- βLG and AEF-OVA are energetically least favored (+ve ∆G) compared to that of HSA. The least binding affinity of AEF is towards OVA (Binding energy (BE) +581.15 Kcalmol−1 followed by βLG (+55.11) and BSA (+12.12) . Though BSA and HSA are structurally similar to each other, they vary in the binding stability with AEF. This is attributed to several unfavorable interactions that destabilize AEF-BSA complex which was not resulted in the complex existing between AEF-HSA. The energetically least stable complexes (AEF-BSA, AEF-βLG and AEF-OVA) are predominantly governed by hydrophobic interactions. However, several h-bonding interactions along with pi-sigma/pi-pi/pi-alkyl interactions result in destabilization of the above complexes. Interestingly, AEF-HSA complex stability is attributed to fewer number of hydrophobic interactions along with h-bonding interactions. The h-bonding interaction governs the stability of the complex which is the driving force. Docking studies illustrates that the binding of amino acids (AAs) in various subdomains play a significant role on the binding nature. The stability of AEF-HSA over other protein complexes in terms of BE is emphasized in the study. The energetically stable sites and sub-domains of AEF with HSA and BSA establish the site selective and site-specific nature of AEF with proteins. In silico studies provide an excellent and easier approach in establishing the molecular interactions existing between AEF with globular proteins. ADMET parameters of the guest molecule calculated exemplifies that AEF compound is less toxic and possesses high oral bioavailability. Based on the binding efficiency of AEF with albumins, the ADMET properties and drug likeliness approach of AEF provides an information on the application towards proteins in the concept of medicine and chemistry.

Synthesis, characterization, and antibacterial effectiveness of gemifloxacin C-3 modified amide analogs: A theoretical and experimental approach

In search of a newer, efficient, and adaptable antimicrobial solution to combat the resistance that has developed due to the widespread extensive and in appropriate use of antibiotics. Seven new amide analogs of Gemifloxacin (G-D01-G-D07), an important member of the fluoroquinolone (FQ's) was synthesized by modifying the C-3 position in a quick and facile way, using Schotten Baumann reaction approach exhibiting highly efficient yields and short reaction times. Their in-silico pharmacokinetic and toxicity studies were determined by SwissADME and Protox-II webserver, and found that all the compounds obeys the lipinski's rule and didn't exhibit any vital organ toxicity. The introduction of selected amines to form amide analogs (G-D01-G-D07) was assured by their spectroscopic characterization, structural stability studies by DFT parameters and Microbiological assay evaluates these synthesized analogs as potent antibacterial and antifungal agents against studied species. At the B3LYP/6–311 G level of theory, the molecular structures, reactivity, and electronic characteristics of candidate compounds exhibiting the most promising antibacterial actions were examined, and their quantum mechanical parameters were calculated. The energy gaps of all the synthesized compounds were found in ranges between 0.1088 and 0.1091 eV, indicating good softness, thermal and kinetic stability. This can be correlated by the activity of these analogs against Klebsiella pneumonia, and Citrobacter freundii, where synthesized derivatives G-D06 and G-D03, G-D04, and G-D07 exhibit activities more than the parent drug. The binding affinity of complexes revealed by docking was found in the range between -14.31 and -16.20 kcal/ mol, which indicates their high antibacterial potential against one of the major targets Klebsiella pneumoniae cytidine deaminase CDA protein by using AutoDock Vina and MD simulations by GROMACS, confirming them as potential antibacterial agents.

Gene network-based and ensemble modeling-based selection of tumor-associated antigens with a predicted low risk of tissue damage for targeted immunotherapy

BackgroundTumor-associated antigens and their derived peptides constitute an opportunity to design off-the-shelf mainline or adjuvant anti-cancer immunotherapies for a broad array of patients. A performant and rational antigen selection pipeline...

Advancing Ligand Docking through Deep Learning: Challenges and Prospects in Virtual Screening.

Molecular docking, also termed ligand docking (LD), is a pivotal element of structure-based virtual screening (SBVS) used to predict the binding conformations and affinities of protein-ligand complexes. Traditional LD methodologies rely on a search and scoring framework, utilizing heuristic algorithms to explore binding conformations and scoring functions to evaluate binding strengths. However, to meet the efficiency demands of SBVS, these algorithms and functions are often simplified, prioritizing speed over accuracy.The emergence of deep learning (DL) has exerted a profound impact on diverse fields, ranging from natural language processing to computer vision and drug discovery. DeepMind's AlphaFold2 has impressively exhibited its ability to accurately predict protein structures solely from amino acid sequences, highlighting the remarkable potential of DL in conformation prediction. This groundbreaking advancement circumvents the traditional search-scoring frameworks in LD, enhancing both accuracy and processing speed and thereby catalyzing a broader adoption of DL algorithms in binding pose prediction. Nevertheless, a consensus on certain aspects remains elusive.In this Account, we delineate the current status of employing DL to augment LD within the VS paradigm, highlighting our contributions to this domain. Furthermore, we discuss the challenges and future prospects, drawing insights from our scholarly investigations. Initially, we present an overview of VS and LD, followed by an introduction to DL paradigms, which deviate significantly from traditional search-scoring frameworks. Subsequently, we delve into the challenges associated with the development of DL-based LD (DLLD), encompassing evaluation metrics, application scenarios, and physical plausibility of the predicted conformations. In the evaluation of LD algorithms, it is essential to recognize the multifaceted nature of the metrics. While the accuracy of binding pose prediction, often measured by the success rate, is a pivotal aspect, the scoring/screening power and computational speed of these algorithms are equally important given the pivotal role of LD tools in VS. Regarding application scenarios, early methods focused on blind docking, where the binding site is unknown. However, recent studies suggest a shift toward identifying binding sites rather than solely predicting binding poses within these models. In contrast, LD with a known pocket in VS has been shown to be more practical. Physical plausibility poses another significant challenge. Although DLLD models often achieve higher success rates compared to traditional methods, they may generate poses with implausible local structures, such as incorrect bond angles or lengths, which are disadvantageous for postprocessing tasks like visualization. Finally, we discuss the future perspectives for DLLD, emphasizing the need to improve generalization ability, strike a balance between speed and accuracy, account for protein conformation flexibility, and enhance physical plausibility. Additionally, we delve into the comparison between generative and regression algorithms in this context, exploring their respective strengths and potential.

Understanding the Reversible Binding of a Multichain Protein-Protein Complex through Free-Energy Calculations.

We demonstrate that the binding affinity of a multichain protein-protein complex, insulin dimer, can be accurately predicted using a streamlined route of standard binding free-energy calculations. We find that chains A and C, which do not interact directly during binding, stabilize the insulin monomer structures and reduce the binding affinity of the two monomers, therefore enabling their reversible association. Notably, we confirm that although classical methods can estimate the binding affinity of the insulin dimer, conventional molecular dynamics, enhanced sampling algorithms, and classical geometrical routes of binding free-energy calculations may not fully capture certain aspects of the role played by the noninteracting chains in the binding dynamics. Therefore, this study not only elucidates the role of noninteracting chains in the reversible binding of the insulin dimer but also offers a methodological guide for investigating the reversible binding of multichain protein-protein complexes utilizing streamlined free-energy calculations.

AMPK Suppression Due to Obesity Drives Oocyte mtDNA Heteroplasmy via ATF5-POLG Axis.

Due to the exclusive maternal transmission, oocyte mitochondrial dysfunction reduces fertility rates, affects embryonic development, and programs offspring to metabolic diseases. However, mitochondrial DNA (mtDNA) are vulnerable to mutations during oocyte maturation, leading to mitochondrial nucleotide variations (mtSNVs) within a single oocyte, referring to mtDNA heteroplasmy. Obesity (OB) accounts for more than 40% of women at the reproductive age in the USA, but little is known about impacts of OB on mtSNVs in mature oocytes. It is found that OB reduces mtDNA content and increases mtSNVs in mature oocytes, which impairs mitochondrial energetic functions and oocyte quality. In mature oocytes, OB suppresses AMPK activity, aligned with an increased binding affinity of the ATF5-POLG protein complex to mutated mtDNA D-loop and protein-coding regions. Similarly, AMPK knockout increases the binding affinity of ATF5-POLG proteins to mutated mtDNA, leading to the replication of heteroplasmic mtDNA and impairing oocyte quality. Consistently, AMPK activation blocks the detrimental impacts of OB by preventing ATF5-POLG protein recruitment, improving oocyte maturation and mitochondrial energetics. Overall, the data uncover key features of AMPK activation in suppressing mtSNVs, and improving mitochondrial biogenesis and oocyte maturation in obese females.

Directional ΔG Neural Network (DrΔG-Net): A Modular Neural Network Approach to Binding Free Energy Prediction.

The protein-ligand binding free energy is a central quantity in structure-based computational drug discovery efforts. Although popular alchemical methods provide sound statistical means of computing the binding free energy of a large breadth of systems, they are generally too costly to be applied at the same frequency as end point or ligand-based methods. By contrast, these data-driven approaches are typically fast enough to address thousands of systems but with reduced transferability to unseen systems. We introduce DrΔG-Net (or simply Dragnet), an equivariant graph neural network that can blend ligand-based and protein-ligand data-driven approaches. It is based on a 3D fingerprint representation of the ligand alone and in complex with the protein target. Dragnet is a global scoring function to predict the binding affinity of arbitrary protein-ligand complexes, but can be easily tuned via transfer learning to specific systems or end points, performing similarly to common 2D ligand-based approaches in these tasks. Dragnet is evaluated on a total of 28 validation proteins with a set of congeneric ligands derived from the Binding DB and one custom set extracted from the ChEMBL Database. In general, a handful of experimental binding affinities are sufficient to optimize the scoring function for a particular protein and ligand scaffold. When not available, predictions from physics-based methods such as absolute free energy perturbation can be used for the transfer learning tuning of Dragnet. Furthermore, we use our data to illustrate the present limitations of data-driven modeling of binding free energy predictions.

Theoretical Insights into Different Complexation Modes of Dioxovanadium(V) Compounds with Pyridoxal Semicarbazone/Thiosemicarbazone/S-Methyl-iso-thiosemicarbazone Ligands.

Vanadium complexes have gained considerable attention as biologically active compounds. In this contribution, three previously reported dioxovanadium(V) complexes with pyridoxal semicarbazone, thiosemicarbazone, and S-methyl-iso-thiosemicarbazone ligands are theoretically examined. The intermolecular stabilization interactions within crystallographic structures were investigated by Hirshfeld surface analysis. These experimental structures were optimized at the B3LYP-D3BJ/6-311++G(d,p)(H,C,N,O,S)/def2-TZVP(V) level of theory, and crystallographic and optimized bond lengths and angles were compared. High correlation coefficients and low mean absolute errors between these two data sets proved that the selected level of theory was appropriate for the description of the system. The changes in structures and stability were examined by adding explicit solvent molecules. The Quantum Theory of Atoms in Molecules (QTAIM) was employed to analyze the intramolecular interactions with special emphasis on the effect of substituents. A good correlation between electron density/Laplacian and interatomic distance was found. Through molecular docking simulations towards Bovine Serum Albumin (BSA), the binding affinity of complexes was further investigated. The spontaneity of binding in the active position of BSA was shown. Further experimental studies on this class of compounds are advised.