MD Simulation Investigation on the Binding Process of Smoke-Derived Germination Stimulants to Its Receptor

Computational Simulation Study of Potential Inhibition of c-Met Kinase Receptor by Phenoxy pyridine Derivatives: Based on QSAR, Molecular Docking, Molecular Dynamics

The mesenchymal-epithelial transition factor (c-Met) is a tyrosine kinase receptor protein, and excessive cell transformation can lead to cancer. Therefore, there is an urgent need to develop novel receptor tyrosine kinase inhibitors by inhibiting the activity of c-Met protein. In this study, 41 compounds are selected from the reported literature, and the interactions between phenoxy pyridine derivatives and tumor-associated proteins are systematically investigated using a series of computer-assisted drug design (CADD) methods, aiming to predict potential c-Met inhibitors with high activity. The Topomer CoMFA (q2=0.620, R2=0.837) and HQSAR (q2=0.684, R2=0.877) models demonstrate a high level of robustness. Further internal and external validation assessments show high applicability and accuracy. Based on the results of the Topomer CoMFA model, structural fragments with higher contribution values are identified and randomly combined using a fragment splice technique, result in a total of 20 compounds with predicted activities higher than the template molecules. Molecular docking results show that these compounds have good interactions and van der Waals forces with the target proteins. The results of molecular dynamics and ADMET predictions indicate that compounds Y4, Y5, and Y14 have potential as c-Met inhibitors. Among them, compound Y14 exhibits superior stability with a binding free energy of -165.18 KJ/mol. These studies provide a reference for the future design and development of novel compounds with c-Met inhibitory activity.

Design of vilazodone-donepezil chimeric derivatives as acetylcholinesterase inhibitors by QSAR, molecular docking and molecular dynamics simulations

Alzheimer's disease (AD) is a disease that affects the cognitive abilities of older adults, and it is one of the biggest global medical challenges of the 21st century. Acetylcholinesterase (AChE) can increase acetylcholine concentrations and improve cognitive function in patients, and is a potential target to develop small molecule inhibitors for the treatment of Alzheimer's disease (AD). In this study, 29 vilazodone-donepezil chimeric derivatives are systematically studied using 3D-QSAR modeling, and a robust and reliable Topomer CoMFA model was obtained with: q2 = 0.720, r2 = 0.991, F = 287.234, N = 6, and SEE = 0.098. Based on the established model and combined with the ZINC20 database, 33 new compounds with ideal inhibitory activity are successfully designed. Molecular docking and ADMET property prediction also show that these newly designed compounds have a good binding ability to the target protein and can meet the medicinal conditions. Subsequently, four new compounds with good comprehensive ability are selected for molecular dynamics simulation, and the simulation results confirm that the newly designed compounds have a certain degree of reliability and stability. This study provides guidance for vilazodone-donepezil chimeric derivatives as a potential AChE inhibitor and has certain theoretical value.

Discovery of novel BRD4-BD2 inhibitors via in silico approaches: QSAR techniques, molecular docking, and molecular dynamics simulations

Bromodomain-containing protein 4(BRD4) plays an important role in the occurrence and development of various malignant tumors, which has attracted the attention of scientific research institutions and pharmaceutical companies. The structural modification of most currently available BRD4 inhibitors is relatively simple, but the drug effectiveness is limited. Research has found that the inhibition of BD1 may promote the differentiation of oligodendrocyte progenitor cell; however, the inhibition of BD2 will not cause this outcome. Therefore, newly potential drugs which target BRD4-BD2 need further research. Herein, we initially built QSAR models out of 49 compounds using HQSAR, CoMFA, CoMSIA, and Topomer CoMFA technology. All of the models have shown suitable reliabilities (q2 = 0.778, 0.533, 0.640, 0.702, respectively) and predictive abilities (r2pred = 0.716, 0.6289, 0.6153, 0.7968, respectively) for BRD4-BD2 inhibitors. On the basis of QSAR results and the search of the R-group in the topomer search module, we designed 20 new compounds with high activity that showed appropriate docking score and suitable ADMET. Docking studies and MD simulation were carried out to reveal the amino acid residues (Asn351, Cys347, Tyr350, Pro293, and Asp299) at the active site of BRD4-BD2. Free energy calculations and free energy landscapes verified the stable binding results and indicated stable conformations of the complexes. These theoretical studies provide guidance and theoretical basis for designing and developing novel BRD4-BD2 inhibitors.

Hazardous Shortcuts in Standard Binding Free Energy Calculations

Calculating the standard binding free energies of protein-protein and protein-ligand complexes from atomistic molecular dynamics simulations in explicit solvent is a problem of central importance in computational biophysics. A rigorous strategy for carrying out such calculations is the so-called "geometrical route". In this method, two molecular objects are progressively separated from one another in the presence of orientational and conformational restraints serving to control the change in configurational entropy that accompanies the dissociation process, thereby allowing the computations to converge within simulations of affordable length. Although the geometrical route provides a rigorous theoretical framework, a tantalizing computational shortcut consists of simply leaving out such orientational and conformational degrees of freedom during the separation process. Here the accuracy and convergence of the two approaches are critically compared in the case of two protein-ligand complexes (Abl kinase-SH3:p41 and MDM2-p53:NVP-CGM097) and three protein-protein complexes (pig insulin dimer, SARS-CoV-2 spike RBD:ACE2, and CheA kinase-P2:CheY). The results of the simulations that strictly follow the geometrical route match the experimental standard binding free energies within chemical accuracy. In contrast, simulations bereft of geometrical restraints converge more poorly, yielding inconsistent results that are at variance with the experimental measurements. Furthermore, the orientational and positional time correlation functions of the protein in the unrestrained simulations decay over several microseconds, a time scale that is far longer than the typical simulation times of the geometrical route, which explains why those simulations fail to sample the relevant degrees of freedom during the separation process of the complexes.

Application of molecular dynamics simulation in biomedicine

Molecular dynamics (MD) simulation has been widely used in the field of biomedicine to study the conformational transition of proteins caused by mutation or ligand binding/unbinding. It provides some perspectives those are difficult to find in traditional biochemical or pathological experiments, for example, detailed effects of mutations on protein structure and protein-protein/ligand interaction at the atomic level. In this review, a broad overview on conformation changes and drug discovery by MD simulation is given. We first discuss the preparation of protein structure for MD simulation, which is a key step that determines the accuracy of the simulation. Then, we summarize the applications of commonly used force fields and MD simulations in scientific research. Finally, enhanced sampling methods and common applications of these methods are introduced. In brief, MD simulation is a powerful tool and it can be used to guide experimental study. The combination of MD simulation and experimental techniques is an a priori means to solve the biomedical problems and give a deep understanding on the relationship between protein structure and function.

Integration of machine learning with computational structural biology of plants

Computational structural biology of proteins has developed rapidly in recent decades with the development of new computational tools and the advancement of computing hardware. However, while these techniques have widely been used to make advancements in human medicine, these methods have seen less utilization in the plant sciences. In the last several years, machine learning methods have gained popularity in computational structural biology. These methods have enabled the development of new tools which are able to address the major challenges that have hampered the wide adoption of the computational structural biology of plants. This perspective examines the remaining challenges in computational structural biology and how the development of machine learning techniques enables more in-depth computational structural biology of plants.

In Silico Investigation on KAR Signaling Reveals the Significant Dynamic Change of Its Receptor's Structure

Karrikins (KARs) have been identified as a class of smoke-derived plant growth regulators widely functioning among angiosperms. However, little is known about the mechanism by which these molecules trigger the relevant signal transduction. In this research, conventional molecular dynamics simulations were used to investigate the dynamical behavior of the apo- and holo-forms of the KAR receptor KAI2. The results show that the dynamic binding conformation of KAR₁ in the active site is not completely consistent with that in the static crystal and is largely affected by the residue segment of the receptor, Tyr150-Asn180. The binding of the ligand with KAI2 changes the distribution of the electrostatic potential near the active site and drives the conformational transition of the Tyr150-Asn180 segment with strong internal positive correlation. A "dual induction" signaling mechanism is proposed in view of the present calculations. Our work paves way for in-depth understanding of the KAR signal transduction mechanism and sheds light on further experimental and theoretical exploration.

Dual Role of Strigolactone Receptor Signaling Partner in Inhibiting Substrate Hydrolysis

Plant branch and root growth relies on metabolism of the strigolactone (SL) hormone. The interaction between the SL molecule, Oryza sativa DWARF14 (D14) SL receptor, and D3 F-box protein has been shown to play a critical role in SL perception. Previously, it was believed that D3 only interacts with the closed form of D14 to induce downstream signaling, but recent experiments indicate that D3, as well as its C-terminal helix (CTH), can interact with the open form as well to inhibit strigolactone signaling. Two hypotheses for the CTH induced inhibition are that either the CTH affects the conformational ensemble of D14 by stabilizing catalytically inactive states or the CTH interacts with SLs in a way that prevents them from entering the binding pocket. In this study, we have performed molecular dynamics (MD) simulations to assess the validity of these hypotheses. We used an apo system with only D14 and the CTH to test the active site conformational stability and a holo system with D14, the CTH, and an SL molecule to test the interaction between the SL and CTH. Our simulations show that the CTH affects both active site conformation and the ability of SLs to move into the binding pocket. In the apo system, the CTH allosterically stabilized catalytic residues into their inactive conformation. In the holo system, significant interactions between SLs and the CTH hindered the ability of SLs to enter the D14 binding pocket. These two mechanisms account for the observed decrease in SL binding to D14 and subsequent ligand hydrolysis in the presence of the CTH.

Activation Mechanism of Strigolactone Receptors and Its Impact on Ligand Selectivity between Host and Parasitic Plants

Parasitic weeds such as Striga have led to significant losses in agricultural productivity worldwide. These weeds use the plant hormone strigolactone as a germination stimulant. Strigolactone signaling involves substrate hydrolysis followed by a conformational change of the receptor to a "closed" or "active" state that associates with a signaling partner, MAX2/D3. Crystal structures of active and inactive AtD14 receptors have helped elucidate the structural changes involved in activation. However, the mechanism by which the receptor activates remains unknown. The ligand dependence of AtD14 activation has been disputed by mutagenesis studies showing that enzymatically inactive receptors are able to associate with MAX2 proteins. Furthermore, activation differences between strigolactone receptor in Striga, ShHTL7, and AtD14 could contribute to the high sensitivity to strigolactones exhibited by parasitic plants. Using molecular dynamics simulations, we demonstrate that both AtD14 and ShHTL7 could adopt an active conformation in the absence of ligand. However, ShHTL7 exhibits a higher population in the inactive apo state as compared to the AtD14 receptor. We demonstrate that this difference in inactive state population is caused by sequence differences between their D-loops and interactions with the catalytic histidine that prevent full binding pocket closure in ShHTL7. These results indicate that ligand hydrolysis would enhance the active state population by destabilizing the inactive state in ShHTL7 as compared to AtD14. We also show that the mechanism of activation is more concerted in AtD14 than in ShHTL7 and that the main barrier to activation in ShHTL7 is closing of the binding pocket.

Role of substrate recognition in modulating strigolactone receptor selectivity in witchweed

Witchweed, or Striga hermonthica, is a parasitic weed that destroys billions of dollars' worth of crops globally every year. Its germination is stimulated by strigolactones exuded by its host plants. Despite high sequence, structure, and ligand-binding site conservation across different plant species, one strigolactone receptor in witchweed, ShHTL7, uniquely exhibits a picomolar EC50 for downstream signaling. Previous biochemical and structural analyses have hypothesized that this unique ligand sensitivity can be attributed to a large binding pocket volume in ShHTL7 resulting in enhanced ability to bind substrates, but additional structural details of the substrate-binding process would help explain its role in modulating the ligand selectivity. Using long-timescale molecular dynamics simulations, we demonstrate that mutations at the entrance of the binding pocket facilitate a more direct ligand-binding pathway to ShHTL7, whereas hydrophobicity at the binding pocket entrance results in a stable “anchored” state. We also demonstrate that several residues on the D-loop of AtD14 stabilize catalytically inactive conformations. Finally, we show that strigolactone selectivity is not modulated by binding pocket volume. Our results indicate that while ligand binding is not the sole modulator of strigolactone receptor selectivity, it is a significant contributing factor. These results can be used to inform the design of selective antagonists for strigolactone receptors in witchweed.

Molecular dynamics investigation on the interaction of human angiotensin-converting enzyme with tetrapeptide inhibitors

Angiotensin-converting enzyme (ACE) is a well-known zinc metalloenzyme whose physiological functions are vital to blood pressure regulation and management of hypertension. The development of more efficient peptide inhibitors is of great significance for the prevention and treatment of hypertension. In this research, molecular dynamics (MD) simulations were implemented to study the specific binding mechanism and interaction between human ACE (hACE) and tetrapeptides, YIHP, YKHP, YLVR, and YRHP. The calculation of relative binding free energy on the one hand verified that YLVR, an experimentally identified inhibitor, has a stronger inhibitory effect and, on the other hand, indicated that YRHP is the "best" inhibitor with the strongest binding affinity. Inspection of atomic interactions discriminated the specific binding mode of each tetrapeptide inhibitor with hACE and explained the difference of their affinity. Moreover, in-depth analysis of the MD production trajectories, including clustering, principal component analysis, and dynamic network analysis, determined the dynamic correlation between tetrapeptides and hACE and obtained the communities' distribution of a protein-ligand complex. The present study provides essential insights into the binding mode and interaction mechanism of the hACE-peptide complex, which paves a path for designing effective anti-hypertensive peptides.

Interaction Mechanism of the Germination Stimulants Karrikins and Their Receptor ShKAI2iB

The significance of karrikins (KARs) in plant physiology opens a door for their application in the agricultural production. As the first event of the whole signaling pathway, the binding of smoke-derived signal molecules KARs to the receptor protein KAI2 triggers the germination of the primary dormant seeds of all angiosperms, not just the "fire-prone" taxa. In the present study, all-atom molecular dynamics simulations, along with the accurate estimation of the ligand-receptor binding free energy, were used to investigate the atomic level interaction of all the members of the KARs family (from KAR₁ to KAR₆) with the receptor ShKAI2iB, an intermediate-evolving KAI2 from Striga hermonthica. The calculated binding energy value of KAR₁ to ShKAI2iB, -5.64 kcal/mol, is in good agreement with the available experimental data, -5.67 kcal/mol. The further analysis of the detailed interaction between each KAR and the protein reveals the primary reasons for the difference of the affinity of the diverse ligands with the receptor and displays the regional characteristics of the protein's active site. Our research will not only provide clues for the study of equivalent endogenous phytohormone, but also contribute to the development of synthetic germinating chemicals.

In-silico analysis of the strigolactone ligand-receptor system

Strigolactones (SLs) are a diverse class of butenolide-bearing plant hormones associated with several processes of major agricultural concern. SLs initiate symbiosis between plants and arbuscular mycorrhizal fungi, cause germination of crop-devastating parasitic plants, and inhibit shoot branching in vascular plants. SLs are perceived by dual receptor-hydrolase proteins, and capturing the intact ligand inside the receptor remains a key challenge for structural biologists. In addition, many discovered SLs are hard to obtain and too unstable to work with. In a computer-based approach, we investigated the interaction of 20 different SL molecules with nine crystal structures of SL receptors. Our results suggest an important role of the active site for ligand binding and orientation, and that the parasitic plant Striga hermonthica has developed both promiscuous and type-specific SL receptors as part of its host recognition strategy.