Molecular Dynamics Simulation Trajectories Research Articles

Exploring Conformational Landscapes Along Anharmonic Low-Frequency Vibrations.

We aim to automatize the identification of collective variables to simplify and speed up enhanced sampling simulations of conformational dynamics in biomolecules. We focus on anharmonic low-frequency vibrations that exhibit fluctuations on time scales faster than conformational transitions but describe a path of least resistance toward structural change. A key challenge is that harmonic approximations are ill-suited to characterize these vibrations, which are observed at far-infrared frequencies and are easily excited by thermal collisions at room temperature. Here, we approached this problem with a frequency-selective anharmonic (FRESEAN) mode analysis that does not rely on harmonic approximations and successfully isolates anharmonic low-frequency vibrations from short molecular dynamics simulation trajectories. We applied FRESEAN mode analysis to simulations of alanine dipeptide, a common test system for enhanced sampling simulation protocols, and compared the performance of isolated low-frequency vibrations to conventional user-defined collective variables (here backbone dihedral angles) in enhanced sampling simulations. The comparison shows that enhanced sampling along anharmonic low-frequency vibrations not only reproduces known conformational dynamics but can even further improve the sampling of slow transitions compared to user-defined collective variables. Notably, free energy surfaces spanned by low-frequency anharmonic vibrational modes exhibit lower barriers associated with conformational transitions relative to representations in backbone dihedral space. We thus conclude that anharmonic low-frequency vibrations provide a promising path for highly effective and fully automated enhanced sampling simulations of conformational dynamics in biomolecules.

Machine-Learning-Assisted Design of Deep Eutectic Solvents Based on Uncovered Hydrogen Bond Patterns

Non-ionic deep eutectic solvents (DESs) are non-ionic designer solvents with various applications in catalysis, extraction, carbon capture, and pharmaceuticals. However, discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES formation. The search for DES relies heavily on intuition or trial-and-error processes, leading to low success rates or missed opportunities. Recognizing that hydrogen bonds (HBs) play a central role in DES formation, we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learning (ML) models to discover new DES systems. We first analyze the HB properties of 38 known DES and 111 known non-DES systems using their molecular dynamics (MD) simulation trajectories. The analysis reveals that DES systems have two unique features compared to non-DES systems: The DESs have ① more imbalance between the numbers of the two intra-component HBs and ② more and stronger inter-component HBs. Based on these results, we develop 30 ML models using ten algorithms and three types of HB-based descriptors. The model performance is first benchmarked using the average and minimal receiver operating characteristic (ROC)-area under the curve (AUC) values. We also analyze the importance of individual features in the models, and the results are consistent with the simulation-based statistical analysis. Finally, we validate the models using the experimental data of 34 systems. The extra trees forest model outperforms the other models in the validation, with an ROC-AUC of 0.88. Our work illustrates the importance of HBs in DES formation and shows the potential of ML in discovering new DESs.

Unravelling Guest Dynamics in Crystalline Molecular Organics Using 2H Solid-State NMR and Molecular Dynamics Simulation.

2H solid-state NMR and atomistic molecular dynamics (MD) simulations are used to understand the disorder of guest solvent molecules in two cocrystal solvates of the pharmaceutical furosemide. Traditional approaches to interpreting the NMR data fail to provide a coherent model of molecular behavior and indeed give misleading kinetic data. In contrast, the direct prediction of the NMR properties from MD simulation trajectories allows the NMR data to be correctly interpreted in terms of combined jump-type and libration-type motions. Time-independent component analysis of the MD trajectories provides additional insights, particularly for motions that are invisible to NMR. This allows a coherent picture of the dynamics of molecules restricted in molecular-sized cavities to be determined.

Hydrogen bonding heterogeneity correlates with protein folding transition state passage time as revealed by data sonification

Protein-protein and protein-water hydrogen bonding interactions play essential roles in the way a protein passes through the transition state during folding or unfolding, but the large number of these interactions in molecular dynamics (MD) simulations makes them difficult to analyze. Here, we introduce a state space representation and associated "rarity" measure to identify and quantify transition state passage (transit) events. Applying this representation to a long MD simulation trajectory that captured multiple folding and unfolding events of the GTT WW domain, a small protein often used as a model for the folding process, we identified three transition categories: Highway (faster), Meander (slower), and Ambiguous (intermediate). We developed data sonification and visualization tools to analyze hydrogen bond dynamics before, during, and after these transition events. By means of these tools, we were able to identify characteristic hydrogen bonding patterns associated with "Highway" versus "Meander" versus "Ambiguous" transitions and to design algorithms that can identify these same folding pathways and critical protein-water interactions directly from the data. Highly cooperative hydrogen bonding can either slow down or speed up transit. Furthermore, an analysis of protein-water hydrogen bond dynamics at the surface of WW domain shows an increase in hydrogen bond lifetime from folded to unfolded conformations with Ambiguous transitions as an outlier. In summary, hydrogen bond dynamics provide a direct window into the heterogeneity of transits, which can vary widely in duration (by a factor of 10) due to a complex energy landscape.

Computational Analysis of Activation of Dimerized Epidermal Growth Factor Receptor Kinase Using the String Method and Markov State Model.

Epidermal growth factor receptor (EGFR) activation is accompanied by dimerization. During the activation of the intracellular kinase domain, two EGFR kinases form an asymmetric dimer, and one side of the dimer (receiver) is activated. Using the string method and Markov state model (MSM), we performed a computational analysis of the structural changes in the activation of the EGFR dimer in this study. The string method reveals the minimum free-energy pathway (MFEP) from the inactive to active structure. The MSM was constructed from numerous trajectories of molecular dynamics simulations around the MFEP, which revealed the free-energy map of structural changes. In the activation of the receiver kinase, the unfolding of the activation loop (A-loop) is followed by the rearrangement of the C-helix, as observed in other kinases. However, unlike other kinases, the free-energy map of EGFR at the asymmetric dimer showed that the active state yielded the highest stability and revealed how interactions at the dimer interface induced receiver activation. As the H-helix of the activator approaches the C-helix of the receiver during activation, the A-loop unfolds. Subsequently, L782 of the receiver enters the pocket between the G- and H-helices of the activator, leading to a rearrangement of the hydrophobic residues around L782 of the receiver, which constitutes a structural rearrangement of the C-helix of the receiver from an outward to an inner position. The MSM analysis revealed long-time scale trajectories via kinetic Monte Carlo.

Unveiling the tyrosinase inhibitory potential of phenolics from Centaurium spicatum: Bridging in silico and in vitro perspectives

Phenolics, abundant in plants, constitute a significant portion of phytoconstituents consumed in the human diet. The phytochemical screening of the aerial parts of Centaurium spicatum led to the isolation of five phenolics. The anti-tyrosinase activities of the isolated compounds were assessed through a combination of in vitro experiments and multiple in silico approaches. Docking and molecular dynamics (MD) simulation techniques were utilized to figure out the binding interactions of the isolated phytochemicals with tyrosinase. The findings from molecular docking analysis revealed that the isolated phenolics were able to bind effectively to tyrosinase and potentially inhibit substrate binding, consequently diminishing the catalytic activity of tyrosinase. Among isolated compounds, cichoric acid displayed the lowest binding energy and the highest extent of polar interactions with the target enzyme. Analysis of MD simulation trajectories indicated that equilibrium was reached within 30 ns for all complexes of tyrosinase with the isolated phenolics. Among the five ligands studied, cichoric acid exhibited the lowest interaction energies, rendering its complex with tyrosinase the most stable. Considering these collective findings, cichoric acid emerges as a promising candidate for the design and development of a potential tyrosinase inhibitor. Furthermore, the in vitro anti-tyrosinase activity assay unveiled significant variations among the isolated compounds. Notably, cichoric acid exhibited the most potent inhibitory effect, as evidenced by the lowest IC50 value (7.92 ± 1.32 µg/ml), followed by isorhamnetin and gentiopicrin. In contrast, sinapic acid demonstrated the least inhibitory activity against tyrosinase, with the highest IC50 value. Moreover, cichoric acid exhibited a mixed inhibition mode against the hydrolysis of l-DOPA catalyzed by tyrosinase, with Ki value of 1.64. Remarkably, these experimental findings align well with the outcomes of docking and MD simulations, underscoring the consistency and reliability of our computational predictions with the actual inhibitory potential observed in vitro.

Study of phase transition and local order in equiatomic and nonequiatomic mixtures of HfNbTaTi under uniaxial loading from molecular dynamics simulations

We simulate an alloy of HfNbTaTi mixed in six different proportions and also of the equiatomic system under uniaxial tensile loading at 300 K. Molecular dynamics simulation trajectories are analyzed using radial distribution functions, OVITO, bond-orientational order parameters, and coordination numbers. Equiatomic and the two other alloys (Hf0.31Nb0.23Ta0.23Ti0.23 and Hf0.23Nb0.31Ta0.23Ti0.23) containing comparable fraction of elements deform similarly through the formation of an amorphous state. Two alloys rich in Nb (Hf0.17Nb0.50Ta0.16Ti0.17) and Ta (Hf0.17Nb0.16Ta0.50Ti0.17) deform similarly resulting in the formation of bcc atoms, which transform to fcc at higher loading. Finally, alloys rich in Hf (Hf0.50Nb0.16Ta0.17Ti0.17) and Ti (Hf0.17Nb0.16Ta0.17Ti0.50) deform resulting in high dislocation densities and hcp atoms. These two hcp-rich alloys also undergo strain hardening. In each mixture during loading, local orientational order of all the different elements changes similarly. Atoms prefer to pair with other atoms than to themselves during tensile loading.

Determining the Oxidation Stability of Electrolytes for Lithium-Ion Batteries Using Quantum Chemistry and Molecular Dynamics

Determining the Oxidation Stability of Electrolytes for Lithium-Ion Batteries Using Quantum Chemistry and Molecular Dynamics

Virtual screening of flavonoids as potential RIPK1 inhibitors for neurodegeneration therapy.

Global prevalence of neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease is increasing gradually, whereas approvals of successful therapeutics for central nervous system disorders are inadequate. Accumulating evidence suggests pivotal roles of the receptor-interacting serine/threonine-protein kinase 1 (RIPK1) in modulating neuroinflammation and necroptosis. Discoveries of potent small molecule inhibitors for RIPK1 with favorable pharmacokinetic properties could thus address the unmet medical needs in treating neurodegeneration. In a structure-based virtual screening, we performed site-specific molecular docking of 4,858 flavonoids against the kinase domain of RIPK1 using AutoDock Vina. We predicted physicochemical descriptors of the top ligands using the SwissADME webserver. Binding interactions of the best ligands and the reference ligand L8D were validated using replicated 500-ns Gromacs molecular dynamics simulations and free energy calculations. From Vina docking, we shortlisted the top 20 flavonoids with the highest binding affinities, ranging from -11.7 to -10.6 kcal/mol. Pharmacokinetic profiling narrowed down the list to three orally bioavailable and blood-brain-barrier penetrant flavonoids: Nitiducarpin, Pinocembrin 7-O-benzoate, and Paratocarpin J. Next, trajectories of molecular dynamics simulations of the top protein-ligand complexes were analyzed for binding interactions. The root-mean-square deviation (RMSD) was 1.191 Å (±0.498 Å), 1.725 Å (±0.828 Å), 1.923 Å (±0.942 Å), 0.972 Å (±0.155 Å) for Nitiducarpin, Pinocembrin 7-O-benzoate, Paratocarpin J, and L8D, respectively. The radius of gyration (Rg) was 2.034 nm (±0.015 nm), 2.0.39 nm (± 0.025 nm), 2.053 nm (±0.021 nm), 2.037 nm (±0.016 nm) for Nitiducarpin, Pinocembrin 7-O-benzoate, Paratocarpin J, and L8D, respectively. The solvent accessible surface area (SASA) was 159.477 nm2 (±3.021 nm2), 159.661 nm2 (± 3.707 nm2), 160.755 nm2 (±4.252 nm2), 156.630 nm2 (±3.521 nm2), for Nitiducarpin, Pinocembrin 7-O-benzoate, Paratocarpin J, and L8D complexes, respectively. Therefore, lower RMSD, Rg, and SASA values demonstrated that Nitiducarpin formed the most stable complex with the target protein among the best three ligands. Finally, 2D protein-ligand interaction analysis revealed persistent hydrophobic interactions of Nitiducarpin with the critical residues of RIPK1, including the catalytic triads and the activation loop residues, implicated in the kinase activity and ligand binding. Our target-based virtual screening identified three flavonoids as strong RIPK1 inhibitors, with Nitiducarpin exhibiting the most potent inhibitory potential. Future in vitro and in vivo studies with these ligands could offer new hope for developing effective therapeutics and improving the quality of life for individuals affected by neurodegeneration.

Benzothiazole DNA gyrase inhibitors and their conjugates with siderophore mimics: design, synthesis and evaluation.

Benzothiazole-based bacterial DNA gyrase and topoisomerase IV inhibitors are promising new antibacterial agents with potent activity against Gram-positive and Gram-negative bacterial strains. The aim of this study was to improve the uptake of these inhibitors into the cytoplasm of Gram-negative bacteria by conjugating them to the small siderophore mimics. The best conjugate 18b displayed potent Escherichia coli DNA gyrase and topoisomerase IV inhibition. The interaction analysis of molecular dynamics simulation trajectory showed the important contribution of the siderophore mimic moiety to binding affinity. By NMR spectroscopy, we demonstrated that the hydroxypyridinone moiety alone was responsible for the chelation of iron(iii). Moreover, 18b showed an enhancement of antibacterial activity against E. coli JW5503 in an iron-depleted medium, clearly indicating an increased uptake of 18b in this bacterial strain.

Representation of Protein Dynamics Disentangled by Time-Structure-Based Prior.

Representation learning (RL) is a universal technique for deriving low-dimensional disentangled representations from high-dimensional observations, aiding in a multitude of downstream tasks. RL has been extensively applied to various data types, including images and natural language. Here, we analyze molecular dynamics (MD) simulation data of biomolecules in terms of RL. Currently, state-of-the-art RL techniques, mainly motivated by the variational principle, try to capture slow motions in the representation (latent) space. Here, we propose two methods based on an alternative perspective on the disentanglement in the latent space. By disentanglement, we here mean the separation of underlying factors in the simulation data, aiding in detecting physically important coordinates for conformational transitions. The proposed methods introduce a simple prior that imposes temporal constraints in the latent space, serving as a regularization term to facilitate the capture of disentangled representations of dynamics. Comparison with other methods via the analysis of MD simulation trajectories for alanine dipeptide and chignolin validates that the proposed methods construct Markov state models (MSMs) whose implied time scales are comparable to those of the state-of-the-art methods. Using a measure based on total variation, we quantitatively evaluated that the proposed methods successfully disentangle physically important coordinates, aiding the interpretation of folding/unfolding transitions of chignolin. Overall, our methods provide good representations of complex biomolecular dynamics for downstream tasks, allowing for better interpretations of the conformational transitions.

Prediction of interface between regions of varying degrees of order or disorderness in intrinsically disordered proteins from dihedral angles

Intrinsically disordered proteins (IDPs) are proteins that do not form uniquely defined three-dimensional (3-D) structures. Experimental research on IDPs is difficult since they go against the traditional protein structure-function paradigm. Although there are several predictors of disorder based on amino acid sequences, but very limited based on the 3-D structures of proteins. Dihedral angles have a significant role in predicting protein structure because they establish a protein’s backbone, which, coupled with its side chain, establishes its overall shape. Here, we have carried out atomistic Molecular Dynamics (MD) simulations on four different proteins: one ordered protein (Monellin), two partially disordered proteins (p53-TAD and Amyloid beta (Aβ1-42) peptide), and one completely disordered protein (Histatin 5). The MD simulation trajectories for the corresponding four proteins were used to conduct dihedral angle (ϕ and ѱ) analysis. Then, the average dihedral angles for each of the residues were calculated and plotted against the residue index. We noticed steep rises or falls in the average ϕ value at certain locations in the plot. These sudden shifts in the average ϕ value reflect the interface between regions of varying degrees of order or disorderness in intrinsically disordered proteins. Using this method, the probable conformer of a protein with a higher degree of disorder can be found among the ensembles of structures sampled during the MD simulations. The results of our study offer new understandings on precisely identifying regions of various degrees of disorder in intrinsically disordered proteins. Communicated by Ramaswamy H. Sarma

SPANA: Spatial decomposition analysis for cellular-scale molecular dynamics simulations.

The rapid increase in computational power with the latest supercomputers has enabled atomistic molecular dynamics (MDs) simulations of biomolecules in biological membrane, cytoplasm, and other cellular environments. These environments often contain a million or more atoms to be simulated simultaneously. Therefore, their trajectory analyses involve heavy computations that can become a bottleneck in the computational studies. Spatial decomposition analysis (SPANA) is a set of analysis tools in the Generalized-Ensemble Simulation System (GENESIS) software package that can carry out MD trajectory analyses of large-scale biological simulations using multiple CPU cores in parallel. SPANA applies the spatial decomposition of a large biological system to distribute structural and dynamical analyses into individual CPU cores, which reduces the computational time and the memory size, significantly. SPANA opens new possibilities for detailed atomistic analyses of biomacromolecules as well as solvent water molecules, ions, and metabolites in MD simulation trajectories of very large biological systems containing more than millions of atoms in cellular environments.

Long-Time Oxygen and Superoxide Localization in Arabidopsis thaliana Cryptochrome.

Cryptochromes are proteins that are highly conserved across species and in many instances bind the flavin adenine dinucleotide (FAD) cofactor within their photolyase-homology region (PHR) domain. The FAD cofactor has multiple redox states that help catalyze reactions, and absorbs photons at about 450 nm, a feature linked to the light-related functions of cryptochrome proteins. Reactive oxygen species (ROS) are produced from redox reactions involving molecular oxygen and are involved in a myriad of biological processes. Superoxide O2•- is an exemplary ROS that may be formed through electron transfer from FAD to O2, generating an electron radical pair. Although the formation of a superoxide-FAD radical pair has been speculated, it is still unclear if the required process steps could be realized in cryptochrome. Here, we present results from molecular dynamics (MD) simulations of oxygen interacting with the PHR domain of Arabidopsis thaliana cryptochrome 1 (AtCRY1). Using MD simulation trajectories, oxygen binding locations are characterized through both the O2-FAD intermolecular distance and the local protein environment. Oxygen unbinding times are characterized through replica simulations of the bound oxygen. Simulations reveal that oxygen molecules can localize at certain sites within the cryptochrome protein for tens of nanoseconds, and superoxide molecules can localize for significantly longer. This relatively long-duration molecule binding suggests the possibility of an electron-transfer reaction leading to superoxide formation. Estimates of electron-transfer rates using the Marcus theory are performed for the identified potential binding sites. Molecular oxygen binding results are compared with recent results demonstrating long-time oxygen binding within the electron-transfer flavoprotein (ETF), another FAD binding protein.

Impact of PmrB mutations on clinical Klebsiella pneumoniae with variable colistin-susceptibilities: Structural insights and potent therapeutic solutions.

Carbapenem-resistant Klebsiella pneumoniae (CRKP) infections continue to impose high morbidity threats to hospitalized patients worldwide, limiting therapeutic options to last-resort antibiotics like colistin. However, the dynamic genomic landscape of colistin-resistant K. pneumoniae (COLR-Kp) invoked ardent exploration of underlying molecular signatures for therapeutic propositions/designs. We unveiled the structural impact of the widespread and emerging PmrB mutations involved in colistin resistance (COLR) in K. pneumoniae. In the present study, clinical isolates of K. pneumoniae expressed variable susceptibilities to colistin (>0.5 μg/mL for resistant and ≤0.25 μg/mL for susceptible) despite mutations such as T157P, G207D and T246A. The protein sequences extracted from in-house sequenced genomes were used to model mutant PmrB proteins and analyze the underlying structural alterations. The mutations were contrasted based on molecular dynamics simulation trajectories, free-energy landscapes and structural flexibility profiles. The altered backbone flexibilities can be an essential factor for mutant selection by COLR K. pneumoniae and can provide clues to deal with emerging mutants. Furthermore, PmrB having high druggability confidence (>0.99), was explored as a potential target for 1396 virtually screened FDA-approved drug candidates. Among the top-10 compounds (scores >70), amphotericin B was found to be potential candidate with high affinity (Binding energy <-8 kcal/mol) and stable interactions (RMSF <0.7 Å) against PmrB druggable pockets, despite the mutations, which encourages future adjunct therapeutic research against COLR-Kp.

Inhibitor screening for volume-sensitive LRRC8A chloride channel

Leucine-rich repeat-containing protein 8 A (LRRC8A) protein is a critical member of volume-regulated anion channels. It plays a critical roles in the regulation of cellular volume and involves in the development of diseases like osteoarthritis. Screening of lead compounds to modulate its function may provide potential therapeutics of related diseases. Here, we employ virtual screening techniques and molecular dynamics (MD) simulation to screen potential inhibitors against LRRC8A. LRRC8A was regarded as the drug target to investigate potential compounds from the ZINC15 database via molecular docking. The final compound was selected among the top 10 Autodock Vina score (–8.8 Kcal/mol) with the ZINC ID ZINC000018195627 after druggability prediction. The docked complex from the virtual screening was subjected to MD simulation to analyze the stability of the LRRC8A protein–ligand complex, with parameters including root mean square deviation, root mean square fluctuation and radius of gyration. Molecular Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) method was further employed to predict the binding free energies from MD simulation trajectory. Our study provides insightful analysis for the potential compound to modulate LRRC8A and lay the foundation of therapeutics development against osteoarthritis. Communicated by Ramaswamy H. Sarma

Development of scalable and generalizable machine learned force field for polymers

Understanding and predicting the properties of polymers is vital to developing tailored polymer molecules for desired applications. Classical force fields may fail to capture key properties, for example, the transport properties of certain polymer systems such as polyethylene glycol. As a solution, we present an alternative potential energy surface, a charge recursive neural network (QRNN) model trained on DFT calculations made on smaller atomic clusters that generalizes well to oligomers comprising larger atomic clusters or longer chains. We demonstrate the validity of the polymer QRNN workflow by modeling the oligomers of ethylene glycol. We apply two rounds of active learning (addition of new training clusters based on current model performance) and implement a novel model training approach that uses partial charges from a semi-empirical method. Our developed QRNN model for polymers produces stable molecular dynamics (MD) simulation trajectory and captures the dynamics of polymer chains as indicated by the striking agreement with experimental values. Our model allows working on much larger systems than allowed by DFT simulations, at the same time providing a more accurate force field than classical force fields which provides a promising avenue for large-scale molecular simulations of polymeric systems.

Structure-based virtual screening and fragment replacement to design novel inhibitors of Coxsackievirus A16 (CVA16)

Numerous studies have shown that hand, foot and mouth disease (HFMD) pathogen Coxsackievirus A16 (CVA16) can also cause severe neurological complications and even death. Currently, there is no effective drugs and vaccines for CVA16. Therefore, developing a drug against CVA16 has become critical. In this study, we conducted two strategies-virtual screening (VS) and fragment replacement to obtain better candidates than the known drug GPP3. Through VS, 37 candidate drugs were screened (exhibiting a lower binding energy than GPP3). After toxicity evaluations, we obtained five candidates, analysed their binding modes and found that four candidates could enter the binding pocket of the GPP3. In another strategy, we analysed the four positions in GPP3 structures by the FragRep webserver and obtained a large number of candidates after replacing different functional groups, we obtained eight candidates (that target the four positions above) with the combined binding score and synthetic accessibility evaluations. AMDock software was uniformly utilized to perform molecular docking evaluation of the candidates with binding activity superior to that of GPP3. Finally, the selected top three molecules (Lapatinib, B001 and C001) and its interaction with CAV16 were validated by molecular dynamics (MD) simulation. The results indicated that all three molecules retain inside the pocket of CAV16 receptor throughout the simulation process, and he binding energy calculated from the MD simulation trajectories also support the strong affinity of the top three molecules towards the CVA16. These results will provide new ideas and technical guidance for designing and applying CVA16 therapeutics. Communicated by Ramaswamy H. Sarma

Identification of natural potent inhibitors against Mycobacterium tuberculosis isocitrate lyase: an in silico study.

Tuberculosis (TB) is a global burden to humanity due to its adverse effects on health and society since time is not clearly defined. The existence of drug-resistant strains and the potential threat posed by latent tuberculosis act as strong impetuses for developing novel anti-tuberculosis drugs. In this study, various flavonoids were tested against the Mycobacterium tuberculosis (Mtb) Isocitrate Lyase (ICL), which has been identified as an authorised therapeutic target for treating Mtb infection. Using in silico drug discovery approach, a library of 241 flavonoid compounds was virtually screened against the binding pocket of the crystalline ligand, the VGX inhibitor, in the Mtb ICL protein. As a result, the top four flavonoids were selected based on binding score and were further considered for redocking and intermolecular contact profiling analysis. The global and local fluctuations in the protein and ligand structure were analysed using their root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values obtained from the GROMACS generated 100ns molecular dynamics (MD) simulation trajectories. The end-state binding free energy was also calculated using the MMPBSA approach for all the respective docked complexes. All four selected compounds exhibited considerable stability and affinity compared to control ligands, i.e. VGX inhibitor; however, Vaccarin showed the highest stability and affinity against the Mtb ICL protein active site, followed by the Genistin, Glabridin, and Corylin. Therefore, this study recommends selected flavonoids for in vitro and in vivo experimental studies to check their potency and efficacy against Mtb.

PyL3dMD: Python LAMMPS 3D molecular descriptors package

Molecular descriptors characterize the biological, physical, and chemical properties of molecules and have long been used for understanding molecular interactions and facilitating materials design. Some of the most robust descriptors are derived from geometrical representations of molecules, called 3-dimensional (3D) descriptors. When calculated from molecular dynamics (MD) simulation trajectories, 3D descriptors can also capture the effects of operating conditions such as temperature or pressure. However, extracting 3D descriptors from MD trajectories is non-trivial, which hinders their wide use by researchers developing advanced quantitative-structure–property-relationship models using machine learning. Here, we describe a suite of open-source Python-based post-processing routines, called PyL3dMD, for calculating 3D descriptors from MD simulations. PyL3dMD is compatible with the popular simulation package LAMMPS and enables users to compute more than 2000 3D molecular descriptors from atomic trajectories generated by MD simulations. PyL3dMD is freely available via GitHub and can be easily installed and used as a highly flexible Python package on all major platforms (Windows, Linux, and macOS). A performance benchmark study used descriptors calculated by PyL3dMD to develop a neural network and the results showed that PyL3dMD is fast and efficient in calculating descriptors for large and complex molecular systems with long simulation durations. PyL3dMD facilitates the calculation of 3D molecular descriptors using MD simulations, making it a valuable tool for cheminformatics studies.Graphical