YASARA: A Tool to Obtain Structural Guidance in Biocatalytic Investigations.

AMP-activated protein kinase (AMPK) serves as an energy sensor and is considered a promising drug target for treatment of type II diabetes and obesity. A previous report has shown that mammalian AMPK alpha1 catalytic subunit including autoinhibitory domain was inactive. To test the hypothesis that small molecules can activate AMPK through antagonizing the autoinhibition in alpha subunits, we screened a chemical library with inactive human alpha1(394) (alpha1, residues 1-394) and found a novel small-molecule activator, PT1, which dose-dependently activated AMPK alpha1(394), alpha1(335), alpha2(398), and even heterotrimer alpha1beta1gamma1. Based on PT1-docked AMPK alpha1 subunit structure model and different mutations, we found PT1 might interact with Glu-96 and Lys-156 residues near the autoinhibitory domain and directly relieve autoinhibition. Further studies using L6 myotubes showed that the phosphorylation of AMPK and its downstream substrate, acetyl-CoA carboxylase, were dose-dependently and time-dependently increased by PT1 with-out an increase in cellular AMP:ATP ratio. Moreover, in HeLa cells deficient in LKB1, PT1 enhanced AMPK phosphorylation, which can be inhibited by the calcium/calmodulin-dependent protein kinase kinases inhibitor STO-609 and AMPK inhibitor compound C. PT1 also lowered hepatic lipid content in a dose-dependent manner through AMPK activation in HepG2 cells, and this effect was diminished by compound C. Taken together, these data indicate that this small-molecule activator may directly activate AMPK via antagonizing the autoinhibition in vitro and in cells. This compound highlights the effort to discover novel AMPK activators and can be a useful tool for elucidating the mechanism responsible for conformational change and autoinhibitory regulation of AMPK.

Computational prediction and protein structure modeling have come to the aid of various biological problems in determining the structure of proteins. These technologies have revolutionized the biological world of research, allowing scientists and researchers to gain insights into their biological questions and design experimental research much more efficiently. Pathogenic Mycobacterium spp. is known to stay alive within the macrophages of its host. Mycobacterium tuberculosis is an acid-fast bacterium that is the most common cause of tuberculosis and is considered to be the main cause of resistance of tuberculosis as a leading health issue. The genome of Mycobacterium tuberculosis contains more than 4,000 genes, of which the majority are of unknown function. An attempt has been made to computationally model and dock one of its proteins, Rv1250 (MTV006.22), which is considered as an apparent drug-transporter, integral membrane protein, and member of major facilitator superfamily (MFS). The most widely used techniques, i.e., homology modeling, molecular docking, and molecular dynamics (MD) simulation in the field of structural bioinformatics, have been used in the present work to study the behavior of Rv1250 protein from M. tuberculosis. The structure of unknown TB protein, i.e., Rv1250 was retrived using homology modeling with the help of I-TASSER server. Further, one of the sites responsible for infection was identified and docking was done by using the specific Isoniazid ligand which is an inhibitor of this protein. Finally, the stability of protein model and analysis of stable and static interaction between protein and ligand molecular dynamic simulation was performed at 100 ns The designing of novel Rv1250 enzyme inhibitors is likely achievable with the use of proposed predicted model, which could be helpful in preventing the pathogenesis caused by M. tuberculosis. Finally, the MD simulation was done to evaluate the stability of the ligand for the specific protein.

As quantum chemistry plays a more and more central role in many complicated chemical problems, it has become necessary to obtain accurate results for large molecular systems. Conventional quantum chemistry methods are either too expensive to apply to large systems or too approximate for the results to be reliable, and they fail to satisfy this requirement. A variety of different approaches is being developed with the aim of achieving this goal: local correlation methods; divide-and-conquer methods; linear-scaling density functional methods based on the fast multipole and other approximations; effective potential methods; and hybrid methods. ONIOM (our N-layered integrated molecular orbital plus molecular mechanics method), developed by the authors, is a hybrid method in which a large molecular system is divided into onion-skin-like layers, and different quantum chemistry/molecular mechanics methods are used for different parts of the system; the results are combined to extrapolatively estimate the results of high-level calculation for the real system. Several applications of ONIOM will be discussed.

The number of solved G-protein-coupled receptor (GPCR) crystal structures has expanded rapidly, but most GPCR structures remain unsolved. Therefore, computational techniques, such as homology modeling, have been widely used to produce the theoretical structures of various GPCRs for structure-based drug design (SBDD). Due to the low sequence similarity shared by the transmembrane domains of GPCRs, accurate prediction of GPCR structures by homology modeling is quite challenging. In this study, angiotensin II type I receptor (AT1R) was taken as a typical case to assess the reliability of class A GPCR homology models for SBDD. Four homology models of angiotensin II type I receptor (AT1R) at the inactive state were built based on the crystal structures of CXCR4 chemokine receptor, CCR5 chemokine receptor, and δ-opioid receptor, and refined through molecular dynamics (MD) simulations and induced-fit docking, to allow for backbone and side-chain flexibility. Then, the quality of the homology models was assessed relative to the crystal structures in terms of two criteria commonly used in SBDD: prediction accuracy of ligand-binding poses and screening power of docking-based virtual screening. It was found that the crystal structures outperformed the homology models prior to any refinement in both assessments. MD simulations could generally improve the docking results for both the crystal structures and homology models. Moreover, the optimized homology model refined by MD simulations and induced-fit docking even shows a similar performance of the docking assessment to the crystal structures. Our results indicate that it is possible to establish a reliable class A GPCR homology model for SBDD through the refinement by integrating multiple molecular modeling techniques.

N‐acetylglucosamine containing compounds acting as pathogenic or symbiotic signals are perceived by plant‐specific Lysin Motif Receptor‐Like Kinases (LysM‐RLKs). The molecular mechanisms of this perception are not fully understood, notably those of lipo‐chitooligosaccharides (LCOs) produced during root endosymbioses with nitrogen‐fixing bacteria or arbuscular mycorrhizal fungi. In Medicago truncatula, we previously identified the LysM‐RLK LYR3 (MtLYR3) as a specific LCO‐binding protein. We also showed that the absence of LCO binding to LYR3 of the non‐mycorrhizal Lupinus angustifolius, (LanLYR3), was related to LysM3, which differs from that of MtLYR3 by several amino acids and, particularly, by a critical tyrosine residue absent in LanLYR3. Here, we aimed to define the LCO binding site of MtLYR3 by using molecular modelling and simulation approaches, combined with site‐directed mutagenesis and LCO binding experiments. 3D models of MtLYR3 and LanLYR3 ectodomains were built, and homology modelling and molecular dynamics (MD) simulations were performed. Molecular docking and MD simulation on the LysM3 identified potential key residues for LCO binding. We highlighted by steered MD simulations that in addition to the critical tyrosine, two other residues were important for LCO binding in MtLYR3. Substitution of these residues in LanLYR3‐LysM3 by those of MtLYR3‐LysM3 allowed the recovery of high‐affinity LCO binding in experimental radioligand‐binding assays. An analysis of selective constraints revealed that the critical tyrosine has experienced positive selection pressure and is absent in some LYR3 proteins. These findings now pave the way to uncover the functional significance of this specific evolutionary pattern.

Utilization of computational techniques and tools to introduce or reinforce knowledge of biochemistry and protein <scp>structure–function</scp> relationships

BackgroundAn important mechanism of endocrine activity is chemicals entering target cells via transport proteins and then interacting with hormone receptors such as the estrogen receptor (ER). α-Fetoprotein (AFP) is a major transport protein in rodent serum that can bind and sequester estrogens, thus preventing entry to the target cell and where they could otherwise induce ER-mediated endocrine activity. Recently, we reported rat AFP binding affinities for a large set of structurally diverse chemicals, including 53 binders and 72 non-binders. However, the lack of three-dimensional (3D) structures of rat AFP hinders further understanding of the structural dependence for binding. Therefore, a 3D structure of rat AFP was built using homology modeling in order to elucidate rat AFP-ligand binding modes through docking analyses and molecular dynamics (MD) simulations.MethodsHomology modeling was first applied to build a 3D structure of rat AFP. Molecular docking and Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) scoring were then used to examine potential rat AFP ligand binding modes. MD simulations and free energy calculations were performed to refine models of binding modes.ResultsA rat AFP tertiary structure was first obtained using homology modeling and MD simulations. The rat AFP-ligand binding modes of 13 structurally diverse, representative binders were calculated using molecular docking, (MM-GBSA) ranking and MD simulations. The key residues for rat AFP-ligand binding were postulated through analyzing the binding modes.ConclusionThe optimized 3D rat AFP structure and associated ligand binding modes shed light on rat AFP-ligand binding interactions that, in turn, provide a means to estimate binding affinity of unknown chemicals. Our results will assist in the evaluation of the endocrine disruption potential of chemicals.

Hypertension is a disease that can increase the risk of stroke, cardiovascular, and heart failure. In this letter, we investigated the potency of active compounds of Brucea javanica (BJ) in treating hypertension by using network pharmacology combined with several in silico approaches, including molecular docking, homology modeling and molecular dynamics (MD) simulations. Twenty protein targets at the intersection of BJ and hypertension were identified by using network pharmacology. We found that peroxisome proliferator‐activated receptor gamma (PPARG) was the first‐degree rank that might strongly connect with the disease. Therefore, the tertiary structure of PPARG, generated using homology modeling, was assigned as a receptor. In docking analysis, two ligands, i. e., Javanicin and Yadanziolide A, could be potential inhibitors for PPARG due to the higher binding energy score than the control (Hydrochlorothiazide). To confirm the stability of the ligand‐receptor complex in water solvent, MD simulation was performed. We found that complexes 1 and 2 reached stable structures during the simulation indicated by no significant fluctuation in the validation parameters. Furthermore, based on the binding energies calculated by Molecular Mechanics‐Generalized Born Surface Area (MM‐GBSA), we confirm that the ligands of these complexes strongly bind to the catalytic site of the receptor. This points out the potency of these complexes as promising drugs against hypertension targeting PPARG.

Introduction: Milk kefir, a fermenting milk made with kefir grains, has shown potential in promoting apoptosis, regulating the cell cycle, and reducing tumor growth in breast cancer cells. This study aimed to investigate the stability and potential of milk kefir metabolites as inhibitors of breast cancer growth by interacting with the estrogen receptor alpha (ER-α), a key protein involved in breast cancer cell proliferation. We used computational methods, specifically molecular docking simulations with AutoDock and molecular dynamics (MD) simulations with Gromacs, to analyze how these metabolites bind to ER-α. Methods: A combination of molecular docking and MD simulations was used to explore how metabolites derived from milk kefir interact with ER-α, a crucial target in breast cancer therapy. The methodology included multiple stages: preparation of target proteins, preparation and screening of the metabolites, geometry optimization, molecular docking, and MD simulations. Results: The molecular docking simulations of 43 metabolites revealed three promising candidates: 2-Methyl (S35), benzeneethanol (S42), and 2,6-dimethyl-4-heptanone (S54), with binding affinities (ΔG) of -5.08, -5.06, and -4.90 kcal/mol, respectively. MD simulations further showed that the selected metabolites stabilized the ER-α-metabolite complex, with the 2,6-dimethyl-4-heptanone (S54) metabolite demonstrating the most negative total MM-GBSA energy value (ΔG = -22.98 kcal/mol), indicating a strong and stable binding interaction. Conclusion: 2,6-Dimethyl-4-heptanone, a metabolite from milk kefir, showed promising potential as a candidate for further development as a breast cancer treatment, offering a novel alternative to conventional therapies.

The virial stress is the most commonly used definition of stress in discrete particle systems. This quantity includes two parts. The first part depends on the mass and velocity (or, in some versions, the fluctuation part of the velocity) of atomic particles, reflecting an assertion that mass transfer causes mechanical stress to be applied on stationary spatial surfaces external to an atomic‐particle system. The second part depends on interatomic forces and atomic positions, providing a continuum measure for the internal mechanical interactions between particles. Historic derivations of the virial stress include generalization from the virial theorem of Clausius (1870) for gas pressure and solution of the spatial equation of balance of momentum. The virial stress is stress‐like a measure for momentum change in space. This paper shows that, contrary to the generally accepted view, the virial stress is not a measure for mechanical force between material points and cannot be regarded as a measure for mechanical stress in any sense. The lack of physical significance is both at the individual atom level in a time‐resolved sense and at the system level in a statistical sense. It is demonstrated that the interatomic force term alone is a valid stress measure and can be identified with the Cauchy stress. The proof in this paper consists of two parts. First, for the simple conditions of rigid translation, uniform tension and tension with thermal oscillations, the virial stress yields clearly erroneous interpretations of stress. Second, the conceptual flaw in the generalization from the virial theorem for gas pressure to stress and the confusion over spatial and material equations of balance of momentum in theoretical derivations of the virial stress that led to its erroneous acceptance as the Cauchy stress are pointed out. Interpretation of the virial stress as a measure for mechanical force violates balance of momentum and is inconsistent with the basic definition of stress. The versions of the virial‐stress formula that involve total particle velocity and the thermal fluctuation part of the velocity are demonstrated to be measures of spatial momentum flow relative to, respectively, a fixed reference frame and a moving frame with a velocity equal to the part of particle velocity not included in the virial formula. To further illustrate the irrelevance of mass transfer to the evaluation of stress, an equivalent continuum (EC) for dynamically deforming atomistic particle systems is defined. The equivalence of the continuum to discrete atomic systems includes (i) preservation of linear and angular momenta, (ii) conservation of internal, external and inertial work rates, and (iii) conservation of mass. This equivalence allows fields of work‐ and momentum‐preserving Cauchy stress, surface traction, body force and deformation to be determined. The resulting stress field depends only on interatomic forces, providing an independent proof that as a measure for internal material interaction stress is independent of kinetic energy or mass transfer.

The N-terminal Hsp90 inhibitors are promising targets for cancer treatment; however, inducing the heat shock response is one of the most significant limitations. A prominent way to overcome this limitation is by inhibiting the Hsp90 C-terminal domain. In this study, a set of structure-based methods was engaged to predict the new C-terminal inhibitors. Since there was no human PDB structure of the Hsp90 C-terminal domain, homology modeling was done using the SWISS-MODEL server online. The 3D structure of the model was refined through energy minimization using molecular dynamics (MD) simulation for 10 ns. The active site of the created model was validated by novobiocin docking. Four steps of virtual screening, including HTVS, SP, XP, and QM/MM docking, were performed on the created library (151,332 compounds) based on 80% similarity to deguelin as the C-terminal inhibitor. The best-obtained compounds were introduced to MM-GBSA studies. Finally, the stability of the best compound was investigated using a 100 ns MD simulation. Four steps of virtual screening were performed on the created library. The extracted 46 compounds with the XP GlideScore of < -4.164 kcal/mol were introduced to MM-GBSA studies, and rescoring was done. The stability of compound CID_14018348, the best compound (ΔGbinding = -80.45 kcal/mol), was investigated using MD simulation. The compound CID_14018348 was identified as the most promising candidate through computational techniques; therefore, the computational methods outlined can be applied in the development of potent anticancer agents.

Two-pore domain potassium (K2P) channels play a key role in setting the membrane potential of excitable cells. Despite their role as putative targets for drugs and general anesthetics, little is known about the structure and the drug binding site of K2P channels. We describe A1899 as a potent and highly selective blocker of the K2P channel TASK-1. As A1899 acts as an open-channel blocker and binds to residues forming the wall of the central cavity, the drug was used to further our understanding of the channel pore. Using alanine mutagenesis screens, we have identified residues in both pore loops, the M2 and M4 segments, and the halothane response element to form the drug binding site of TASK-1. Our experimental data were used to validate a K2P open-pore homology model of TASK-1, providing structural insights for future rational design of drugs targeting K2P channels.

Homology modeling, molecular docking and molecular dynamics (MD) simulation methods were used to build a reliable model for A1AR (as one of the G protein-coupled receptors—GPCRs) and to explore the structural features and binding mechanism of ligands to this receptor. A model of A1AR was built and inserted in a hydrated lipid bilayer, and 20-ns MD simulation was performed to examine the stability of the best model. In this study, RG-14718 as the best A1AR agonist and bamifylline as a selective antagonist of A1AR have been docked into the active site of the A1AR. After docking, two 20-ns MD simulation was performed on the A1AR–ligand complex to explore effects of the presence of lipid membrane in the vicinity of the A1AR–ligand complex. At the end of the MD simulation, a change in the position and orientation of the ligand in the binding site was observed. This important observation indicated that the application of MD simulation after docking of ligands is useful. Thr270, His278 and Asn70 were crucial residues for hydrogen bonds with these ligands. Phe171, Glu172, Tyr271 and Ile274 were determined to be involved in ligand–receptor binding. The results obtained are in good agreement with most of the site-directed mutagenesis data reported by others. Our results show that molecular modeling and rational drug design for adenosine targets is a possible approach.

A mutant T1 lipase homology modeling, and its molecular docking and molecular dynamics simulation with fatty acids

Acute myeloid leukemia, a serious condition affecting stem cells, drives uncontrollable myeloblast proliferation, leading to accumulation. Extensive research seeks rapid, effective chemotherapeutics. A potential option is a BRD4 inhibitor, known for suppressing cell proliferation. Sulfonamide derivatives probed essential structural elements for potent BRD4 inhibitors. To achieve this goal, we employed 3D-QSAR molecular modeling techniques, including CoMFA, CoMSIA, and HQSAR models, along with molecular docking and molecular dynamics simulations. The validation of the 2D/3D QSAR models, both internally and externally, underscores their robustness and reliability. The contour plots derived from CoMFA, CoMSIA, and HQSAR analyses played a pivotal role in shaping the design of effective BRD4 inhibitors. Importantly, our findings highlight the advantageous impact of incorporating bulkier substituents on the pyridinone ring and hydrophobic/electrostatic substituents on the methoxy-substituted phenyl ring, enhancing interactions with the BRD4 target. The interaction mode of the new compounds with the BRD4 receptor (PDB ID: 4BJX) was investigated using molecular docking simulations, revealing favorable binding energies, supported by the formation of hydrogen and hydrophobic bonds with key protein residues. Moreover, these novel inhibitors exhibited good oral bioavailability and demonstrated non-toxic properties based on ADMET analysis. Furthermore, the newly designed compounds along with the most active one from series 58, underwent a molecular dynamics simulation to analyze their behavior. The simulation provided additional evidence to support the molecular docking results, confirming the sustained stability of the analyzed molecules over the trajectory. This outcome could serve as a valuable reference for designing and developing novel and effective BRD4 inhibitors. Communicated by Ramaswamy H. Sarma