Binding Free Energy Calculations Research Articles

Uncovering potential CDK9 inhibitors from natural compound databases through docking-based virtual screening and MD simulations.

Cyclin-dependent kinase 9 (CDK9) plays a significant role in gene regulation and RNA polymerase II transcription under basal and stimulated conditions. The upregulation of transcriptional homeostasis by CDK9 leads to various malignant tumors and therefore acts as a valuable drug target in addressing cancer incidences. Ongoing drug development endeavors targeting CDK9 have yielded numerous clinical candidate molecules currently undergoing investigation as potential CDK9 modulators, though none have yet received Food and Drug Administration (FDA) approval. In this study, we employ in silico approaches including the molecular docking and molecular dynamics simulations for the virtual screening over the natural compounds library to identify novel promising selective CDK9 inhibitors. The compounds derived from the initial virtual screening were subsequently employed for molecular dynamics simulations and binding free energy calculations to study the compound's stability under virtual physiological conditions. The first-generation CDK inhibitor Flavopiridol was used as a reference to compare with our novel hit compound as a CDK9 antagonist. The 500-ns molecular dynamics simulation and binding free energy calculation showed that two natural compounds showed better binding affinity and interaction mode with CDK9 receptors over the reference Flavopiridol. They also showed reasonable figures in the predicted absorption, distribution, metabolism, excretion, and toxicity (ADMET) calculations as well as in computational cytotoxicity predictions. Therefore, we anticipate that the proposed scaffolds could contribute to developing potential and selective CDK9 inhibitors subjected to further validations.

Discovery and Characterization of Panaxatriol as a Novel Thrombin Inhibitor from Panax notoginseng Using a Combination of Computational and Experimental Approaches.

Thrombin is a crucial enzyme in the coagulation cascade, and inhibitors of thrombin have been extensively studied as potential antithrombotic agents. The objective of this study was to identify natural inhibitors of thrombin from Panax notoginseng and evaluate their biological activity in vitro and binding characteristics. A combined approach involving molecular docking, thrombin inhibition assays, surface plasmon resonance, and molecular dynamics simulation was utilized to identify natural thrombin inhibitors. The results demonstrated that panaxatriol directly inhibits thrombin, with an IC50 of 10.3 µM. Binding studies using surface plasmon resonance revealed that panaxatriol interacts with thrombin, with a KD value of 7.8 µM. Molecular dynamics analysis indicated that the thrombin-panaxatriol system reached equilibrium rapidly with minimal fluctuations, and the calculated binding free energy was - 23.8 kcal/mol. The interaction between panaxatriol and thrombin involves the amino acid residues Glu146, Glu192, Gly216, Gly219, Tyr60A, and Trp60D. This interaction provides a mechanistic basis for further optimizing panaxatriol as a thrombin inhibitor. Our study has shown that panaxatriol serves as a direct thrombin inhibitor, laying the groundwork for further research and development of novel thrombin inhibitors.

Massively parallel free energy calculations for in silico affinity maturation of designed miniproteins.

Computational protein design efforts continue to make remarkable advances, yet the discovery of high-affinity binders typically requires large-scale experimental screening of site-saturated mutant (SSM) libraries. Here, we explore how massively parallel free energy methods can be used for in silico affinity maturation of de novo designed binding proteins. Using an expanded ensemble (EE) approach, we perform exhaustive relative binding free energy calculations for SSM variants of three miniproteins designed to bind influenza A H1 hemagglutinin by Chevalier et al. (2017). We compare our predictions to experimental ΔΔ G values inferred from a Bayesian analysis of the high-throughput sequencing data, and to state-of-the-art predictions made using the Flex ddG Rosetta protocol. A systematic comparison reveals prediction accuracies around 2 kcal/mol, and identifies net charge changes, large numbers of alchemical atoms, and slow side chain conformational dynamics as key contributors to the uncertainty of the EE predictions. Flex ddG predictions are more accurate on average, but highly conservative. In contrast, EE predictions can better classify stabilizing and destabilizing mutations. We also explored the ability of SSM scans to rationalize known affinity-matured variants containing multiple mutations, which are non-additive due to epistatic effects. Simple electrostatic models fail to explain non-additivity, but observed mutations are found at positions with higher Shannon entropies. Overall, this work suggests that simulation-based free energy methods can provide predictive information for in silico affinity maturation of designed miniproteins, with many feasible improvements to the efficiency and accuracy within reach.

Replica Exchange of Expanded Ensembles: A Generalized Ensemble Approach with Enhanced Flexibility and Parallelizability.

Generalized ensemble methods such as Hamiltonian replica exchange (HREX) and expanded ensemble (EE) have been shown effective in free energy calculations for various contexts, given their ability to circumvent free energy barriers via nonphysical pathways defined by states with different modified Hamiltonians. However, both HREX and EE methods come with drawbacks, such as limited flexibility in parameter specification or the lack of parallelizability for more complicated applications. To address this challenge, we present the method of replica exchange of expanded ensembles (REXEE), which integrates the principles of HREX and EE methods by periodically exchanging coordinates of EE replicas sampling different yet overlapping sets of alchemical states. With the solvation free energy calculation of anthracene and binding free energy calculation of the CB7-10 binding complex, we show that the REXEE method achieves the same level of accuracy in free energy calculations as the HREX and EE methods, while offering enhanced flexibility and parallelizability. Additionally, we examined REXEE simulations with various setups to understand how different exchange frequencies and replica configurations influence the sampling efficiency in the fixed-weight phase and the weight convergence in the weight-updating phase. The REXEE approach can be further extended to support asynchronous parallelization schemes, allowing looser communications between larger numbers of loosely coupled processors such as cloud computing and therefore promising much more scalable and adaptive executions of alchemical free energy calculations. All algorithms for the REXEE method are available in the Python package ensemble_md, which offers an interface for REXEE simulation management without modifying the source code in GROMACS.

Biocompatible ionic liquid self-assembled transdermal delivery system (P-M IL/AKE) for significantly improved skin photoaging and reduced wrinkles

Most peptide drugs have good biological activity and safety, but their stability and permeability are poor, limiting their applications in the field of local drug delivery. Snake venom peptide (AKE) is a tripeptide with good anti-wrinkle effects, but its weak esterophilicity, poor penetration, and low bioavailability limit its full anti-wrinkle potential. To address this issue, a bioactive ionic liquid (P-M IL) was prepared and an efficient self-assembled peptide drug delivery system was constructed through AKE loading, which increased the cumulative absorption of AKE in the skin by more than five-fold, resulting in good biocompatibility. Further, P-M IL/AKE can significantly increase collagen protein secretion by human fibroblasts, improved skin photoaging and reduce the areas and volume of wrinkle in the eye tail lines. In addition, for the first time, quantum chemical calculations, homology modeling, molecular docking and binding free energy calculations were used to analyze the structural stability of the system, construct the 3D model, explore the interaction mode and reveal the theoretical basis of anti-wrinkle effect. Therefore, this supramolecular self-assembled transdermal drug delivery system has potential advantages, providing a novel approach for the highly efficient and non-invasive delivery of peptide drugs.

Modulation of the intermolecular interactions of cucurbit[7]uril with phenylalanine derivatives by the functional groups

Abstract Intermolecular interactions in 1:1 inclusion complexes of cucurbit[7]uril (CB[7]) with phenylalanine derivatives were investigated by density functional theory (B97-D) calculations. For each complex, 2 optimized geometries were found: conformer C in which a guest molecule resided in the center of CB[7], and conformer E where a guest molecule was present at the edge of CB[7]. The order of energy differences between these conformers agreed well with previously reported differences in the affinity of guest molecules for CB[7]. Molecular dynamics simulations of the complexes showed that the guest molecules in CB[7] had different stabilities, and the calculated binding free energies between them also qualitatively agreed with the experimental results. Pair-interaction energy decomposition analyses of the complexes at FMO-MP2/6-31G(d) and aug-cc-pVDZ level of theory were performed using snapshot structures at 500 ns of simulations. The dispersion interaction characterized the interaction, and the order of total interaction energies between the guest molecules and CB[7] was also associated with the experimental results. A significant difference in electrostatic interaction energies was observed in conformer E, which was correlated with the stability of the guest molecules at the edge of CB[7]. The balance between the stabilities of these conformers was correlated with the affinity of guest molecules for CB[7].

Bidirectional path-based non-equilibrium simulations for binding free energy

Estimating free energy is a fundamental computational challenge, especially in complex biological systems characterised by numerous degrees of freedom. In this study, we investigate the potential of leveraging non-equilibrium free energy estimators within path-based approaches; this offers an appealing feature of inherent parallelism. Building upon our prior work on protein-ligand binding free energy calculations, we develop its non-equilibrium counterpart. We begin by validating our computational strategy on a simple toy model and then extend our analysis to the well-established trypsin-benzamidine complex, serving as a benchmark system. Subsequently, we apply this method to a more intricate, relevant pharmaceutical system to evaluate the performance of our computational pipeline on this complex system. Our results not only demonstrate the feasibility of this approach but also shed light on potential limitations. Furthermore, we showcase the capabilities of the Jarzynski and Crooks estimators employed in our study.

Probing Dual Covalent Irreversible Inhibition of EGFR/FGFR4 by Electrophilic-Based Natural Compounds to Overcome Resistance and Enhance Combination Therapeutic Potentials and Management of Hepatocellular Carcinoma (HCC).

Hepatocellular carcinoma (HCC) is one of the most prevalent cancer types in the world and accounts for the majority of cases of primary liver cancer. A crucial part of the carcinogenesis of HCC involves aberrant stimulation of the FGF19-FGFR4 signaling pathway. Therefore, FGFR4 inhibition has become a strategic therapeutic approach for the treatment of HCC. However, the clinical treatment procedure is significantly hampered by the prevalence of kinase inhibitors resistance. It was recently established that the activation of EGFR signaling was found to be one of the primary mechanisms mediating the acquired resistance to FGFR4 inhibitors, moreover, sensitivity to FGFR4 inhibitors was effectively restored by inhibiting EGFR. These results provide compelling evidence that dual inhibition of EGFR and FGFR4 could represent a viable therapeutic approach to overcome resistance, hence enhanced management of HCC. To this end, we proposed a dual irreversible inhibition strategy through covalent binding by naturally occurring electrophilic warhead-bearing compounds (curcumin, deoxyelephantopin, eupalmerin acetate, syringolin A and andrographolide) to covalently target both EGFR and FGFR4 through cysteine residues, Cys797 and Cys552, respectively. Covalent docking and covalent molecular dynamics (MM/MDcov) simulations combined with thermodynamic binding free energy calculations were performed, and the results were compared against known potent and selective covalent EGFR and FGFR4 inhibitors with available X-ray crystal structures, Afatinib and BLU9931, respectively. Curcumin, deoxyelephantopin, eupalmerin acetate, syringolin A, and andrographolide showed relative binding free energies of -22.85, -17.14, -12.98, -21.81, and - 19.00kcal/mol against EGFR and - 41.06, -29.45, -24.76, -40.11, and - 37.55kcal/mol against FGFR4, respectively. The mechanisms of binding were emphasized by hydrogen bonding and binding forces analysis as well as active site physicochemical profiling. The findings of this study identified that curcumin, syringolin A and andrographolide-but not eupalmerin acetate or deoxyelephantopin -could be viable dual EGFR and FGFR4 covalent irreversible inhibitors and could be implemented in HCC combination therapy protocols alone or in conjunction with other chemotherapeutic agents. Investigations of this study conclusively indicate dual blockade of EGFR and FGFR4 may be a promising future therapeutic strategy for enhanced management of HCC.

Crystal structures of coronaviral main proteases in complex with the non-covalent inhibitor X77

Main proteases (Mpros) are a class of conserved cysteine hydrolases among coronaviruses and play a crucial role in viral replication. Therefore, Mpros are ideal targets for the development of pan-coronavirus drugs. X77, previously developed against SARS-CoV Mpro, was repurposed as a non-covalent tight binder inhibitor against SARS-CoV-2 Mpro during COVID-19 pandemic. Many novel inhibitors with favorable efficacy have been discovered using X77 as a reference, suggesting that X77 could be a valuable scaffold for drug design. However, the broad-spectrum performance of X77 and underlying mechanism remain less understood. Here, we reported the crystal structures of Mpros from SARS-CoV-2, SARS-CoV, and MERS-CoV, and several Mpro mutants from SARS-CoV-2 variants bound to X77. A detailed analysis of these structures revealed key structural determinants essential for interaction and elucidated the binding modes of X77 with different coronaviral Mpros. The potencies of X77 against these investigated Mpros were further evaluated through molecular dynamic simulation and binding free energy calculation. These data provide molecular insights into broad-spectrum inhibition against coronaviral Mpros by X77 and the similarities and differences of X77 when bound to various Mpros, which will promote X77-based design of novel antivirals with broad-spectrum efficacy against different coronaviruses and SARS-CoV-2 variants.

Camptothecin and its Analogues as Putative Bruton Tyrosine Kinase (BTK) Inhibitors in Cancer Therapy - Biophysical Simulations of Wild Type and C481S Mutation

Camptothecin (CPT) and its derivatives are extracted from the Chinese tree Camptotheca acuminata and have been long known for their significant antitumor activity. The widely reported primary mechanism of their anti-cancer activity is through inhibition of topoisomerase I (Topo I), a key anticancer target. However, there is a lack of research on other possible mechanisms of action of CPT and its analogues (CPT/analogues). In this report, we investigated the potential inhibitory mechanism of CPT/derivatives against Bruton’s tyrosine kinase (BTK) ‒ a crucial protein for regulating various cellular functions and is essential for B-cell growth and cyclical division. The binding mechanism of CPT and its analogues (Irinotecan, Diflomotecan, and Topotecan) against BTK was investigated using molecular docking, molecular dynamics simulations and thermodynamic binding free energy calculations. NRX-0492, a potent orally active degrader of both wild-type and mutant BTK, was used as a reference/control inhibitor. We also included a set of active binders (actives) and non-binders (inactives) to distinguish between effective binders and false positives to ensure the validity of our results. Binding affinity analysis suggested that CPT and its analogues could potentially bind to BTK comparably to NRX-0492. The studied compounds demonstrated stable binding with the protein throughout the simulation time via hydrogen bonds, π-π interactions, van der Waals forces and π-sulfur interactions. Irinotecan exhibited a binding affinity of ΔGbinding = –42.4±5.3 kcal/mol, closely matching that of NRX-0492 (ΔGbinding = –48.8±3.4 kcal/mol). Furthermore, the activity of CPT/derivatives was examined against the C481S mutant. Although the activity was slightly decreased due to the mutation, the compounds retained a promising binding affinity when compared to the results of NRX-0492. The findings of this study provide foundation for future experimental investigation of unexplored potential anti-cancer mechanism of CPT/derivatives and offer a new perspective on repurposing of natural products in cancer therapy.

Structure-Guided Antiviral Peptides Identification Targeting the HIV-1 Integrase.

HIV-1 integrase (IN), a major protein in the HIV life cycle responsible for integrating viral cDNA into the host DNA, represents a promising drug target. Small peptides have emerged as antiviral therapeutics for HIV because of their facile synthesis, highly selective nature, and fewer side effects. However, selecting the best candidates from a vast pool of peptides is a daunting task. In this study, multistep virtual screening was employed to identify potential peptides from a list of 280 HIV inhibitory peptides. Initially, 80 peptides were selected based on their minimum inhibitory concentrations (MIC). Then, molecular docking was performed to evaluate their binding scores compared to HIP000 and HIP00N which are experimentally validated HIV-1 integrase binding peptides that were used as a positive and negative control, respectively. The top-scoring docked complexes, namely, IN-HIP1113, IN-HIP1140, IN-HIP1142, IN-HIP678, IN-HIP776, and IN-HIP777, were subjected to initial 500 ns molecular dynamics (MD) simulations. Subsequently, HIP776, HIP777, and HIP1142 were selected for an in-depth mechanistic study of peptide interactions, with multiple simulations conducted for each complex spanning one microsecond. Independent simulations of the peptides, along with comparisons to the bound state, were performed to elucidate the conformational dynamics of the peptides. These peptides exhibit strong interactions with specific residues, as revealed by snapshot interaction analysis. Notably, LYS159, LYS156, VAL150, and GLU69 residues are prominently involved in these interactions. Additionally, residue-based binding free energy (BFE) calculations highlight the significance of HIS67, GLN148, GLN146, and SER147 residues within the binding pocket. Furthermore, the structure-activity relationship (SAR) analysis demonstrated that aromatic amino acids and the overall volume of peptides are the two major contributors to the docking scores. The best peptides will be validated experimentally by incorporating SAR properties, aiming to develop them as therapeutic agents and structural models for future peptide-based HIV-1 drug design, addressing the urgent need for effective HIV treatments.

Ligand Binding Affinity Prediction for Membrane Proteins with Alchemical Free Energy Calculation Methods.

Alchemical relative binding free energy (ΔΔG) calculations have shown high accuracy in predicting ligand binding affinity and have been used as important tools in computer-aided drug discovery and design. However, there has been limited research on the application of ΔΔG methods to membrane proteins despite the fact that these proteins represent a significant proportion of drug targets, play crucial roles in biological processes, and are implicated in numerous diseases. In this study, to predict the binding affinity of ligands to G protein-coupled receptors (GPCRs), we employed two ΔΔG calculation methods: thermodynamic integration (TI) with AMBER and the alchemical transfer method (AToM) with OpenMM. We calculated ΔΔG values for 53 transformations involving four class A GPCRs and evaluated the performance of AMBER-TI and AToM-OpenMM. In addition, we conducted tests using different numbers of windows and varying simulation times to achieve reliable ΔΔG results and to optimize resource utilization. Overall, both AMBER-TI and AToM-OpenMM show good agreement with the experimental data. Our results validate the applicability of AMBER-TI and AToM-OpenMM for optimization of lead compounds targeting membrane proteins.

Automated Absolute Binding Free Energy Calculation Workflow for Drug Discovery.

Absolute binding free energies play a crucial role in drug development, particularly as part of the lead discovery process. In recent work, we showed how in silico predictions directly could support drug development by ranking and recommending favorable ideas over unfavorable ones. Here, we demonstrate a Python workflow that enables the calculation of ABFEs with minimal manual input effort, such as the receptor PDB and ligand SDF files, and outputs a .tsv file containing the ranked ligands and their corresponding binding free energies. The implementation uses Snakemake to structure and control the execution of tasks, allowing for dynamic control of parameters and execution patterns. We provide an example of a benchmark system that demonstrates the effectiveness of the automated workflow.

Molecular insight into the role of isoflavones targeting estrogen β receptor in diabetic wound healing pathway: A computational and in-vitro studies

This study aims to investigate the modulation of ER-β by phytoestrogens (isoflavones) in target diabetic wound healing using computational tools and in vitro studies. One of the primary limitations associated with delayed wound healing is inhibiting antioxidant pathways mediated by estrogen receptor-β (ER-β). In this study, we screened 50 isoflavones against the active pocket of ER-β (5TOA.pdb). Through the XP docking score, we evaluated the binding pattern and dynamic changes of the top 5 isoflavones having the highest docking scores, binding energy, and interactions with ER-β. Further, in vitro, MTT assay was performed on the L929 cell line, followed by scratch assay, DPPH assay and glucose uptake assay. The XP (Extra precision) docking score of the top 5 isoflavones were S-Equol −9.90, Genistein −9.71, Icaritin (ICT) −9.92, Biochanin A −8.97, and Formononetin −8.87 respectively when compared with standard estradiol −10.61 when docked with ER-β. The binding free energy values (ΔG bind) of the top 5 isoflavones were S-Equol (−27.15 kcal/mol), Genistein (−41.49 kcal/mol), ICT (−46.56 kcal/mol) Biochanin A (−37.01 kcal/Mol) and Formononetin (−42.96 kcal/Mol) respectively when compared with standard estradiol (−97.15 kcal/mol). Based on the XP glide score and free binding energy calculations, ICT was subjected to 100 ns MD simulation, confirming the stability of ICT with ER-β. The in-vitro results showed no cytotoxicity found up to 125 µM concentration, revealing normal proliferation of fibroblasts reduced total ROS production and prevented cell glucose uptake. These results highlighted the interaction potential of ICT for targeting ER-β, antioxidant, and anti-diabetic activity for diabetic wound healing.

Molecular Dynamics Simulations and Binding Free Energy Calculations to Discover New Insights into NLRP3 Inhibitors

Background: Inflammation is an immunological reaction against an aggressor agent. NLRP3 inflammasome is a component of the immune system, which, when excessively activated, results in several inflammatory diseases, making it an attractive target for discovering antiinflammatory drugs. Computer-Aided Drug Design (CADD) techniques are powerful tools used to search for new drugs in less time and financial cost. Recently, studies demonstrated the CADD methods to discover information about NLRP3 inhibitors MCC950 and NP3-146. In addition, the discovery of GDC-2394 and its evaluation in clinical trials instigate new studies to find binding modes and structural attributes that can used in drug design works against this target. Objectives: Here, molecular modeling methods were used to discover the significant interactions of GDC-2394, MCC950, and NP3-146 with NLRP3 to obtain helpful information in drug design compared to other inhibitors. Methods: Molecular docking was performed using GOLD software. The best complexes were submitted into molecular dynamics simulations using GROMACS software, and the MM-PBSA was used to provide the free binding energy, which was performed using the tool g_mmpbsa compiled in GROMACS. Results: The RMSD, RMSF, Rg, SASA, and H-bond plots showed that the compound was stable during MD simulation time (100 ns) for GDC-2394. The PCA analysis for all compounds verified similar variance of the complex with the inhibitors to the apo-NLRP3, indicative of stability. DCCM analysis showed the best correlation in residues 134 - 371 region, which contains critical amino acids from the binding site (Ala227, Ala228, and Arg578), besides the newly identified residues. Using MMPBSA to provide the binding free energy, it was observed that the high affinity of the drugs against NLRP3 is related to the lower rigidity of the structure. Furthermore, we identified the critical residues Phe575, Pro352, Tyr632, and Met661 related to the coupling process. result: Then, RMSD, RMSF, Rg, SASA, and H-bond plots showed that the compound was stable during MD simulation time (100 ns) for GDC-2394. The PCA analysis for all compounds verified similar variance of the complex with the inhibitors to the apo-NLRP3, indicative of stability. Through DCCM analysis, the best correlation was observed in residues 134 - 371 region, which contains critical amino acids from the binding site (Ala227, Ala228, and Arg578), besides the newly identified residues. Using MM-PBSA to provide the binding free energy, it was observed that the high affinity of the drugs against NLRP3 is related to the lower rigidity of the structure. Furthermore, we identified the critical residues Phe575, Pro352, Tyr632, and Met661 related to the coupling process. Conclusion: Thus, these discoveries may contribute to the development of new anti-inflammatory drugs, such as NLRP3 inhibitors.

Conformational Properties of Poly(A)-Binding Protein Complexed with Poly(A) RNA.

Microscopic understanding of protein-RNA interactions is important for different biological activities, such as RNA transport, translation, splicing, silencing, etc. Polyadenine (Poly(A)) binding proteins (PABPs) make up a class of regulatory proteins that play critical roles in protecting the poly(A) tails of cellular mRNAs from nuclease degradation. In this work, we performed molecular dynamics simulations to investigate the conformational modifications of human PABP protein and poly(A) RNA that occur during complexation. It is demonstrated that the intermediate linker domain of the protein transforms from a disordered coil-like structure to a helical form during the recognition process, leading to the formation of the complex. On the other hand, disordered collapsed coil-like RNA on complexation has been found to transform into a rigid extended conformation. Importantly, the binding free energy calculation showed that the thermodynamic stability of the complex is primarily guided by favorable hydrophobic interactions between the protein and the RNA.

Unveiling potent inhibitors for schistosomiasis through ligand-based drug design, molecular docking, molecular dynamics simulations and pharmacokinetics predictions.

Schistosomiasis is a neglected tropical disease which imposes a considerable and enduring impact on affected regions, leading to persistent morbidity, hindering child development, diminishing productivity, and imposing economic burdens. Due to the emergence of drug resistance and limited management options, there is need to develop additional effective inhibitors for schistosomiasis. In view of this, quantitative structure-activity relationship studies, molecular docking, molecular dynamics simulations, drug-likeness and pharmacokinetics predictions were applied to 39 Schistosoma mansoni Thioredoxin Glutathione Reductase (SmTGR) inhibitors. The chosen QSAR model demonstrated robust statistical parameters, including an R2 of 0.798, R2adj of 0.767, Q2cv of 0.681, LOF of 0.930, R2test of 0.776, and cR2p of 0.746, confirming its reliability. The most active derivative (compound 40) was identified as a lead candidate for the development of new potential non-covalent inhibitors through ligand-based design. Subsequently, 12 novel compounds (40a-40l) were designed with enhanced anti-schistosomiasis activity and binding affinity. Molecular docking studies revealed strong and stable interactions, including hydrogen bonding, between the designed compounds and the target receptor. Molecular dynamics simulations over 100 nanoseconds and MM-PBSA free binding energy (ΔGbind) calculations validated the stability of the two best-designed molecules. Furthermore, drug-likeness and pharmacokinetics prediction analyses affirmed the potential of these designed compounds, suggesting their promise as innovative agents for the treatment of schistosomiasis.

Engineering the catechol 1,2-dioxygenase from Arthrobacter sp. with improved activity toward 4-alkyl substituted catechols

Catechol 1,2-dioxygenase (CAT) is a key enzyme in the cis, cis-muconic acid biosynthesis pathways. However, CAT demonstrates limited catalytic ability to the substrate 4-alkyl substituted catechols, and its application in biocatalysis remains underexplored. Here, a CAT from Arthrobacter sp. CGMCC 3584 (ArCAT) was overexpressed in Escherichia coli for the first time. Subsequently, the structure-guided semi-rational engineering of ArCAT was employed to generate small and precise mutation libraries, and the mutant I206V exhibited the best catalytic ability. Molecular docking analysis revealed that the mutant I206V provided additional hydrophobic interactions and hydrogen bonds with the substrates in the catalytic activity pocket compared with the wild type (WT) for both 4-ethylcatechol and 4-n-propylcatechol. Furthermore, binding free energy calculations showed that the mutant I206V had a better binding affinity to 4-ethylcatechol or to 4-n-propylcatechol compared to the WT, in good agreement with the results of kinetic parameters. Finally, in small-scale biotransformation based on crude enzymes, the mutant I206V enhanced the overall conversion rates of 4-ethylcatechol and 4-n-propylcatechol by 15.28 % and 25.55 %, respectively, compared to the WT. This study presented a smart strategy for improving ArCAT activity, and revealed the potential application of ArCAT in biological production of muconic acid analogs from branched catechols.

Current State of Open Source Force Fields in Protein-Ligand Binding Affinity Predictions.

In drug discovery, the in silico prediction of binding affinity is one of the major means to prioritize compounds for synthesis. Alchemical relative binding free energy (RBFE) calculations based on molecular dynamics (MD) simulations are nowadays a popular approach for the accurate affinity ranking of compounds. MD simulations rely on empirical force field parameters, which strongly influence the accuracy of the predicted affinities. Here, we evaluate the ability of six different small-molecule force fields to predict experimental protein-ligand binding affinities in RBFE calculations on a set of 598 ligands and 22 protein targets. The public force fields OpenFF Parsley and Sage, GAFF, and CGenFF show comparable accuracy, while OPLS3e is significantly more accurate. However, a consensus approach using Sage, GAFF, and CGenFF leads to accuracy comparable to OPLS3e. While Parsley and Sage are performing comparably based on aggregated statistics across the whole dataset, there are differences in terms of outliers. Analysis of the force field reveals that improved parameters lead to significant improvement in the accuracy of affinity predictions on subsets of the dataset involving those parameters. Lower accuracy can not only be attributed to the force field parameters but is also dependent on input preparation and sampling convergence of the calculations. Especially large perturbations and nonconverged simulations lead to less accurate predictions. The input structures, Gromacs force field files, as well as the analysis Python notebooks are available on GitHub.

Computational Analysis of Albaflavenone Interaction with SlMAPK1 for Drought Resistance in Tomato.

As global agricultural challenges intensify, particularly drought stress, the exploration of innovative strategies for crop resilience has become crucial. This study focuses on the role of the microbial endophyte metabolite Albaflavenone in enhancing drought resistance in tomato (Solanum lycopersicum L.) through the activation of the SlMAPK1 protein in the MAPK pathway. To computationally analyze the interaction between Albaflavenone and SlMAPK1 and to elucidate the potential enhancement of drought tolerance in tomato plants through this interaction. We utilized molecular docking, homology modeling, and molecular dynamics simulations to investigate the binding affinities and interaction dynamics between SlMAPK1 and Albaflavenone. Functional network analysis was employed to examine protein-protein interactions within the MAPK pathway, while the MM-GBSA method was used to calculate binding free energies. Our computational analyses revealed that Albaflavenone exhibited a high binding affinity to SlMAPK1 with a binding energy of - 8.9kcal/mol. Molecular dynamics simulations showed this interaction significantly stabilized SlMAPK1, suggesting enhanced activity. Specifically, the root mean square deviation (RMSD) of the Albaflavenone-SlMAPK1 complex stabilized at around 3.1Å, while the root mean square fluctuations (RMSF) indicated consistent amino acid conformations. Additionally, the radius of gyration (Rg) analysis demonstrated minimal variance, suggesting a compact and stable protein-ligand complex. The significant binding affinity between Albaflavenone and SlMAPK1 highlights the potential of leveraging plant-microbe interactions in developing sustainable agricultural practices. These findings also demonstrate the effectiveness of computational methods in dissecting complex biological interactions, contributing to a deeper understanding of plant resilience strategies against environmental stresses.