Project description:Aztreonam is a Gram-negative bacteria-targeting synthetic monobactam antibiotic. Human serum albumin (HSA) plays an important role in the transference of pharmaceuticals, hormones, and fatty acids, along with other compounds, determining their biodistribution and physiological fate. Using several biophysical and in silico approaches, we studied the interaction of aztreonam with HSA under physiological environments in this study. Results confirm the formation of HSA-aztreonam complex where aztreonam showed moderate affinity towards HSA. A static mode of quenching was confirmed from the steady state fluorescence data. FRET findings also showed that there was a significant feasibility of energy transfer between HSA and aztreonam. Site marker displacement experimental conclusion suggested the binding site of aztreonam was the sub-domain IB of HSA. Circular dichroic spectroscopic analysis suggested that aztreonam interaction decreases the α-helical content of HSA. Changes in microenvironment were studied through synchronous fluorescence data. According to molecular docking results, the HSA-aztreonam complex is mostly maintained by non-covalent forces, with a binding energy of 7.7 kcal mol-1. The presence of a hydrogen bond, van der Waal interaction, and pi-anion interaction in the binding process, as well as conformational changes in HSA after binding with aztreonam, are all confirmed by molecular dynamic simulation.
Project description:COVID-19 has emerged as the most serious international pandemic in early 2020 and the lack of comprehensive knowledge in the recognition and transmission mechanisms of this virus hinders the development of suitable therapeutic strategies. The specific recognition during the binding of the spike glycoprotein (S protein) of coronavirus to the angiotensin-converting enzyme 2 (ACE2) in the host cell is widely considered the first step of infection. However, detailed insights on the underlying mechanism of dynamic recognition and binding of these two proteins remain unknown. In this work, molecular dynamics simulation and binding free energy calculation were carried out to systematically compare and analyze the receptor-binding domain (RBD) of six coronavirus' S proteins. We found that affinity and stability of the RBD from SARS-CoV-2 under the binding state with ACE2 are stronger than those of other coronaviruses. The solvent-accessible surface area (SASA) and binding free energy of different RBD subunits indicate an "anchor-locker" recognition mechanism involved in the binding of the S protein to ACE2. Loop 2 (Y473-F490) acts as an anchor for ACE2 recognition, and Loop 3 (G496-V503) locks ACE2 at the other nonanchoring end. Then, the charged or long-chain residues in the β-sheet 1 (N450-F456) region reinforce this binding. The proposed binding mechanism was supported by umbrella sampling simulation of the dissociation process. The current computational study provides important theoretical insights for the development of new vaccines against SARS-CoV-2.
Project description:As one of the crucial targets of epigenetics, histone lysine-specific demethylase 1 (LSD1) is significant in the occurrence and development of various tumors. Although several irreversible covalent LSD1 inhibitors have entered clinical trials, the large size and polarity of the FAD-binding pocket and undesired toxicity have focused interest on developing reversible LSD1 inhibitors. In this study, targeting the substrate-binding pocket of LSD1, structure-based and ligand-based virtual screenings were adopted to expand the potential novel structures with molecular docking and pharmacophore model strategies, respectively. Through drug-likeness evaluation, ADMET screening, molecular dynamics simulations, and binding free energy screening, we screened out one and four hit compounds from the databases of 2,029,554 compounds, respectively. Generally, these hit compounds can be divided into two categories, amide (Lig2 and Comp2) and 1,2,4-triazolo-4,3-α-quinazoline (Comp3, Comp4, Comp7). Among them, Comp4 exhibits the strongest binding affinity. Finally, the binding mechanisms of the hit compounds were further calculated in detail by the residue free energy decomposition. It was found that van der Waals interactions contribute most to the binding, and FAD is also helpful in stabilizing the binding and avoiding off-target effects. We believe this work not only provides a solid theoretical foundation for the design of LSD1 substrate reversible inhibitors, but also expands the diversity of parent nucleus, offering new insights for synthetic chemists.
Project description:BackgroundSARS coronavirus main proteinase (SARS CoVMpro) is an important enzyme for the replication of Severe Acute Respiratory Syndrome virus. The active site region of SARS CoVMpro is divided into 8 subsites. Understanding the binding mode of SARS CoVMpro with a specific substrate is useful and contributes to structural-based drug design. The purpose of this research is to investigate the binding mode between the SARS CoVMpro and two octapeptides, especially in the region of the S3 subsite, through a molecular docking and molecular dynamics (MD) simulation approach.ResultsThe one turn alpha-helix chain (residues 47-54) of the SARS CoVMpro was directly involved in the induced-fit model of the enzyme-substrate complex. The S3 subsite of the enzyme had a negatively charged region due to the presence of Glu47. During MD simulations, Glu47 of the enzyme was shown to play a key role in electrostatic bonding with the P3Lys of the octapeptide.ConclusionMD simulations were carried out on the SARS CoVMpro-octapeptide complex. The hypothesis proposed that Glu47 of SARS CoVMpro is an important residue in the S3 subsite and is involved in binding with P3Lys of the octapeptide.
Project description:Under the oxidative stress condition, the small RNA (sRNA) OxyS that acts as essential post-transcriptional regulators of gene expression is produced and plays a regulatory function with the assistance of the RNA chaperone Hfq protein. Interestingly, experimental studies found that the N48A mutation of Hfq protein could enhance the binding affinity with OxyS while resulting in the defection of gene regulation. However, how the Hfq protein interacts with sRNA OxyS and the origin of the stronger affinity of N48A mutation are both unclear. In this paper, molecular dynamics (MD) simulations were performed on the complex structure of Hfq and OxyS to explore their binding mechanism. The molecular mechanics generalized born surface area (MM/GBSA) and interaction entropy (IE) method were combined to calculate the binding free energy between Hfq and OxyS sRNA, and the computational result was correlated with the experimental result. Per-residue decomposition of the binding free energy revealed that the enhanced binding ability of the N48A mutation mainly came from the increased van der Waals interactions (vdW). This research explored the binding mechanism between Oxys and chaperone protein Hfq and revealed the origin of the strong binding affinity of N48A mutation. The results provided important insights into the mechanism of gene expression regulation affected by protein mutations.
Project description:Ubiquinone is an important cofactor that plays vital and diverse roles in many biological processes. Ubiquinone-binding proteins (UBPs) are receptor proteins that dock with ubiquinones. Analyzing and identifying UBPs via a computational approach will provide insights into the pathways associated with ubiquinones. In this work, we were the first to propose a UBPs predictor (UBPs-Pred). The optimal feature subset selected from three categories of sequence-derived features was fed into the extreme gradient boosting (XGBoost) classifier, and the parameters of XGBoost were tuned by multi-objective particle swarm optimization (MOPSO). The experimental results over the independent validation demonstrated considerable prediction performance with a Matthews correlation coefficient (MCC) of 0.517. After that, we analyzed the UBPs using bioinformatics methods, including the statistics of the binding domain motifs and protein distribution, as well as an enrichment analysis of the gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway.
Project description:The prevalence of Alzheimer's disease (AD) has been a major health concern for a long time. Despite recent progress, there is still a strong need to develop effective disease-modifying therapies. Several drugs have already been approved to retard the progression of AD-related symptoms; however, there is a need to develop an effective carrier system for the delivery of drugs to combat such diseases. In recent years, various biological macromolecules, including proteins, have been used as carriers for drug delivery. Irisin is a beneficial hormone in such diseases, including AD and related pathologies. Herein, the interaction mechanism of irisin with AD drugs such as memantine, galantamine, and fluoxetine is investigated. Fluorescence studies revealed that the above drugs bind to irisin with significant affinity, with fluoxetine having the highest binding affinity. Isothermal titration calorimetry (ITC) complemented the spontaneous binding of these drugs with irisin, delineating various associated thermodynamic and binding parameters. Molecular docking further validated the fluorescence and ITC results and unfolded the mechanism that hydrogen bonding governs the binding of fluoxetine to irisin with a significant binding score, i.e., -6.3 kcal/mol. We believe that these findings provide a promising solution to fight against AD as well as a platform for further research to utilize irisin in the drug-delivery system for an effective therapeutic strategy.
Project description:Mutations that arise in HIV-1 protease after exposure to various HIV-1 protease inhibitors have proved to be a difficult aspect in the treatment of HIV. Mutations in the binding pocket of the protease can prevent the protease inhibitor from binding to the protein effectively. In the present study, the crystal structures of 68 HIV-1 proteases complexed with one of the nine FDA approved protease inhibitors from the Protein Data Bank (PDB) were analyzed by (a) identifying the mutational changes with the aid of a developed mutation map and (b) correlating the structure of the binding pockets with the complexed inhibitors. The mutations of each crystal structure were identified by comparing the amino acid sequence of each structure against the HIV-1 wild-type strain HXB2. These mutations were visually presented in the form of a mutation map to analyze mutation patterns corresponding to each protease inhibitor. The crystal structure mutation patterns of each inhibitor (in vitro) were compared against the mutation patterns observed in in vivo data. The in vitro mutation patterns were found to be representative of most of the major in vivo mutations. We then performed a data mining analysis of the binding pockets from each crystal structure in terms of their chemical descriptors to identify important structural features of the HIV-1 protease protein with respect to the binding conformation of the HIV-1 protease inhibitors. Data mining analysis is performed using several classification techniques: Random Forest (RF), linear discriminant analysis (LDA), and logistic regression (LR). We developed two hybrid models, RF-LDA and RF-LR. Random Forest is used as a feature selection proxy, reducing the descriptor space to a few of the most relevant descriptors determined by the classifier. These descriptors are then used to develop the subsequent LDA, LR, and hierarchical classification models. Clustering analysis of the binding pockets using the selected descriptors used to produce the optimal classification models reveals conformational similarities of the ligands in each cluster. This study provides important information in understanding the structural features of HIV-1 protease which cannot be studied by other existing in vivo genomic data sets.
Project description:Pleckstrin homology (PH) domains share low sequence identities but extremely conserved structures. They have been found in many proteins for cellular signal-dependent membrane targeting by binding inositol phosphates to perform different physiological functions. In order to understand the sequence-structure relationship and binding specificities of PH domains, quantum mechanical (QM) calculations and sequence-based combined with structure-based binding analysis were employed in our research. In the structural aspect, the binding specificities were shown to correlate with the hydropathy characteristics of PH domains and electrostatic properties of the bound inositol phosphates. By comparing these structure properties with sequence-based profiles of physicochemical properties, PH domains can be classified into four functional subgroups according to their binding specificities and affinities to inositol phosphates. The method not only provides a simple and practical paradigm to predict binding specificities for functional genomic research but also gives new insight into the understanding of the basis of diseases with respect to PH domain structures.
Project description:Computational solvent mapping globally samples the surface of target proteins using molecular probes-small molecules or functional groups-to identify potentially favorable binding positions. The method is based on X-ray and NMR screening studies showing that the binding sites of proteins also bind a large variety of fragment-sized molecules. We have developed the multistage mapping algorithm FTMap (available as a server at http://ftmap.bu.edu/ ) based on the fast Fourier transform (FFT) correlation approach. Identifying regions of low free energy rather than individual low energy conformations, FTMap reproduces the available experimental mapping results. Applications to a variety of proteins show that the probes always cluster in important subsites of the binding site, and the amino acid residues that interact with many probes also bind the specific ligands of the protein. The "consensus" sites at which a number of different probes cluster are likely to be "druggable" sites, capable of binding drug-size ligands with high affinity. Due to its sensitivity to conformational changes, the method can also be used for comparing the binding sites in different structures of a protein.