Effect of Cholesterol on C99 Dimerization: Revealed by Molecular Dynamics Simulations.

C99 is the immediate precursor for amyloid beta (Aß) and therefore is a central intermediate in the pathway that is believed to result in Alzheimer's disease (AD). It has been suggested that cholesterol is associated with C99, but the dynamic details of how cholesterol affects C99 assembly and the Aß formation remain unclear. To investigate this question, we employed coarse-grained and all-atom molecular dynamics simulations to study the effect of cholesterol and membrane composition on C99 dimerization. We found that although the existence of cholesterol delays C99 dimerization, there is no direct competition between C99 dimerization and cholesterol association. In contrast, the existence of cholesterol makes the C99 dimer more stable, which presents a cholesterol binding C99 dimer model. Cholesterol and membrane composition change the dimerization rate and conformation distribution of C99, which will subsequently influence the production of Aß. Our results provide insights into the potential influence of the physiological environment on the C99 dimerization, which will help us understand Aß formation and AD's etiology.

Bringing Structural Implications and Deep Learning-Based Drug Identification for KRAS Mutants.

Colorectal cancer is considered one of the leading causes of death that is linked with the Kirsten Rat Sarcoma (KRAS) harboring codons 13 and 61 mutations. The objective for this study is to search for clinically important codon 61 mutations and analyze how they affect the protein structural dynamics. Additionally, a deep-learning approach is used to carry out a similarity search for potential compounds that might have a comparatively better affinity. Public databases like The Cancer Genome Atlas and Genomic Data Commons were accessed for obtaining the data regarding mutations that are associated with colon cancer. Multiple analysis such as genomic alteration landscape, survival analysis, and systems biology-based kinetic simulations were carried out to predict dynamic changes for the selected mutations. Additionally, a molecular dynamics simulation of 100 ns for all the seven shortlisted codon 61 mutations have been conducted, which revealed noticeable deviations. Finally, the deep learning-based predicted compounds were docked with the KRAS 3D conformer, showing better affinity and good docking scores as compared to the already existing drugs. Taking together the outcomes of systems biology and molecular dynamics, it is observed that the reported mutations in the SII region are highly detrimental as they have an immense impact on the protein sensitive sites' native conformation and overall stability. The drugs reported in this study show increased performance and are encouraged to be used for further evaluation regarding the situation that ascends as a result of KRAS mutations.

Structural insights of catalytic mechanism in mutant pyrazinamidase of Mycobacterium tuberculosis.

Pyrazinamidase (PZase) is a member of Fe-dependent amidohydrolases that activates pyrazinamide (PZA) into active pyrazinoic acid (POA). PZA, a nicotinamide analogue, is an essential first-line drug used in Mycobacterium tuberculosis (Mtb) treatment. The active form of PZA, POA, is toxic and potently inhibits the growth of latent Mtb, which makes it possible to shorten the conventional 9-month tuberculosis treatment to 6 months. In this study, an extensive molecular dynamics simulation was carried out to the study the resistance mechanism offered by the three mutations Q10P and D12A and G97D. Our results showed that two regions Gln10-His43, Phe50-Gly75 are profoundly affected by these mutations. Among the three mutations, Q10P and D12A mutations strongly disturb the communication among the catalytic triad (Asp8, Lys98 and Cys138). The oxyanion hole is formed between the backbone nitrogen atoms of A134 and C138 which stabilizes the hydroxyl anion of nicotinamide. The D12A mutation greatly disturbs the oxyanion hole formation followed by the Q10P and G97D. Our results also showed that these mutations destabilize the interaction between Fe2+ ion and Asp49, His51, His57 and His71. The binding pocket analysis showed that these mutations increase the cavity volume, which results in loose binding of PZA. MMGBSA analyzes have shown that these mutations reduce the binding affinity to the PZA drug. Our results may provide useful information for the design of new and effective PZase inhibitors based on structural information of WT and mutant PZases.Communicated by Ramaswamy H. Sarma.

An Integrated Pan-Cancer Analysis and Structure-Based Virtual Screening of GPR15.

G protein-coupled receptor 15 (GPR15, also known as BOB) is an extensively studied orphan G protein-coupled receptors (GPCRs) involving human immunodeficiency virus (HIV) infection, colonic inflammation, and smoking-related diseases. Recently, GPR15 was deorphanized and its corresponding natural ligand demonstrated an ability to inhibit cancer cell growth. However, no study reported the potential role of GPR15 in a pan-cancer manner. Using large-scale publicly available data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases, we found that GPR15 expression is significantly lower in colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ) than in normal tissues. Among 33 cancer types, GPR15 expression was significantly positively correlated with the prognoses of COAD, neck squamous carcinoma (HNSC), and lung adenocarcinoma (LUAD) and significantly negatively correlated with stomach adenocarcinoma (STAD). This study also revealed that commonly upregulated gene sets in the high GPR15 expression group (stratified via median) of COAD, HNSC, LUAD, and STAD are enriched in immune systems, indicating that GPR15 might be considered as a potential target for cancer immunotherapy. Furthermore, we modelled the 3D structure of GPR15 and conducted structure-based virtual screening. The top eight hit compounds were screened and then subjected to molecular dynamics (MD) simulation for stability analysis. Our study provides novel insights into the role of GPR15 in a pan-cancer manner and discovered a potential hit compound for GPR15 antagonists.

STS-NLSP: A Network-Based Label Space Partition Method for Predicting the Specificity of Membrane Transporter Substrates Using a Hybrid Feature of Structural and Semantic Similarity.

Membrane transport proteins play crucial roles in the pharmacokinetics of substrate drugs, the drug resistance in cancer and are vital to the process of drug discovery, development and anti-cancer therapeutics. However, experimental methods to profile a substrate drug against a panel of transporters to determine its specificity are labor intensive and time consuming. In this article, we aim to develop an in silico multi-label classification approach to predict whether a substrate can specifically recognize one of the 13 categories of drug transporters ranging from ATP-binding cassette to solute carrier families using both structural fingerprints and chemical ontologies information of substrates. The data-driven network-based label space partition (NLSP) method was utilized to construct the model based on a hybrid of similarity-based feature by the integration of 2D fingerprint and semantic similarity. This method builds predictors for each label cluster (possibly intersecting) detected by community detection algorithms and takes union of label sets for a compound as final prediction. NLSP lies into the ensembles of multi-label classifier category in multi-label learning field. We utilized Cramér's V statistics to quantify the label correlations and depicted them via a heatmap. The jackknife tests and iterative stratification based cross-validation method were adopted on a benchmark dataset to evaluate the prediction performance of the proposed models both in multi-label and label-wise manner. Compared with other powerful multi-label methods, ML-kNN, MTSVM, and RAkELd, our multi-label classification model of NLPS-RF (random forest-based NLSP) has proven to be a feasible and effective model, and performed satisfactorily in the predictive task of transporter-substrate specificity. The idea behind NLSP method is intriguing and the power of NLSP remains to be explored for the multi-label learning problems in bioinformatics. The benchmark dataset, intermediate results and python code which can fully reproduce our experiments and results are available at https://github.com/dqwei-lab/STS.

Prediction of CYP450 Enzyme-Substrate Selectivity Based on the Network-Based Label Space Division Method.

A drug may be metabolized by multiple cytochrome P450 (CYP450) isoforms. Predicting the metabolic fate of drugs is very important to prevent drug-drug interactions in the development of novel pharmaceuticals. Prediction of CYP450 enzyme-substrate selectivity is formulized as a multilabel learning task in this study. First, we compared the performance of feature combinations based on four different categories of features, which are physiochemical property descriptors, mol2vec descriptors, extended connectivity fingerprints, and molecular access system key fingerprints on modeling. After identifying the best combination of features, we applied seven different multilabel models, which are multilabel k-nearest neighbor (ML-kNN), multilabel twin support vector machine, and five network-based label space division (NLSD)-based methods (NLSD-MLP, NLSD-XGB, NLSD-EXT, NLSD-RF, and NLSD-SVM). All of the six models (ML-kNN, NLSD-MLP, NLSD-XGB, NLSD-EXT, NLSD-RF, and NLSD-SVM) in this paper exhibit better performances than the previous work. Besides, NLSD-XGB achieves the best performance with the average top-1 prediction success of 91.1%, the average top-2 prediction success of 96.2%, and the average top-3 prediction success of 98.2%. When compared with the previous work, NLSD-XGB shows a significant improvement over 11% on top-1 in the 10 times repeated 5-fold cross-validation test and over 14% on top-1 in the 10 times repeated hold-out method. To the best of our knowledge, the network-based label space division model is first introduced in drug metabolism and performs well in this task.

Extraction of molecular features for the drug discovery targeting protein-protein interaction of Helicobacter pylori CagA and tumor suppressor protein ASSP2.

Half of the world population is infected by the Gram-negative bacterium Helicobacter pylori (H. pylori). It colonizes in the stomach and is associated with severe gastric pathologies including gastric cancer and peptic ulceration. The most virulent factor of H. pylori is the cytotoxin-associated gene A (CagA) that is injected into the host cell. CagA interacts with several host proteins and alters their function, thereby causing several diseases. The most well-known target of CagA is the tumor suppressor protein ASPP2. The subdomain I at the N-terminus of CagA interacts with the proline-rich motif of ASPP2. Here, in this study, we carried out alanine scanning mutagenesis and an extensive molecular dynamics simulation summing up to 3.8 µs to find out hot spot residues and discovered some new protein-protein interaction (PPI)-modulating molecules. Our findings are in line with previous biochemical studies and further suggested new residues that are crucial for binding. The alanine scanning showed that mutation of Y207 and T211 residues to alanine decreased the binding affinity. Likewise, dynamics simulation and molecular mechanics with generalized Born surface area (MMGBSA) analysis also showed the importance of these two residues at the interface. A four-feature pharmacophore model was developed based on these two residues, and top 10 molecules were filtered from ZINC, NCI, and ChEMBL databases. The good binding affinity of the CHEMBL17319 and CHEMBL1183979 molecules shows the reliability of our adopted protocol for binding hot spot residues. We believe that our study provides a new insight for using CagA as the therapeutic target for gastric cancer treatment and provides a platform for a future experimental study.

An unexpected dynamic binding mode between coagulation factor X and Rivaroxaban reveals importance of flexibility in drug binding.

Rivaroxaban (RIV) is a direct oral anticoagulant (DOAC) targeting activated coagulation factor X (FXa). An earlier study reported the F174A mutant of FXa resistant to a RIV-like inhibitor, Apixaban. In current study, the detailed molecular mechanism of the resistance has been explored by molecular dynamics simulations on the impaired interactions between RIV and FXa in the damaged S4 pocket of F174A mutant. Besides, an unexpected relative stable binding mode of S1'S1 was revealed, which required dynamic motions of Gln192 and Gln61 to allow the morpholinone moiety of RIV to shift into the S1' pocket and form strong interactions. These dynamic motions of RIV and critical residues might be important in drug design for direct inhibitors of coagulation factors.

Structural-dynamic insights into the H. pylori cytotoxin-associated gene A (CagA) and its abrogation to interact with the tumor suppressor protein ASPP2 using decoy peptides.

Helicobacter pylori (H. pylori) is one of the most extensively studied Gram-negative bacteria due to its implication in gastric cancer. The oncogenicity of H. pylori is associated with cytotoxin-associated gene A (CagA), which is injected into epithelial cells lining the stomach. Both the C- and N-termini of CagA are involved in the interaction with several host proteins, thereby disrupting vital cellular functions, such as cell adhesion, cell cycle, intracellular signal transduction, and cytoskeletal structure. The N-terminus of CagA interacts with the tumor-suppressing protein, apoptosis-stimulating protein of p53 (ASPP2), subsequently disrupting the apoptotic function of tumor suppressor gene p53. Here, we present the in-depth molecular dynamic mechanism of the CagA-ASPP2 interaction and highlight hot-spot residues through in silico mutagenesis. Our findings are in agreement with previous studies and further suggest other residues that are crucial for the CagA-ASPP2 interaction. Furthermore, the ASPP2-binding pocket possesses potential druggability and could be engaged by decoy peptides, identified through a machine-learning system and suggested in this study. The binding affinities of these peptides with CagA were monitored through extensive computational procedures and reported herein. While CagA is crucial for the oncogenicity of H. pylori, our designed peptides possess the potential to inhibit CagA and restore the tumor suppressor function of ASPP2.

Helix-Switch Enables C99 Dimer Transition between the Multiple Conformations.

C99 is the immediate precursor of amyloid-ß (Aß) and therefore is a central intermediate in the pathway that is believed to result in Alzheimer's disease (AD). Recent studies have shown that C99 dimerization changes the Aß ratio, but the mechanism remains unclear. Previous studies of the C99 dimer have produced controversial structure models. To address these questions, we investigated C99 dimerization using molecular dynamics (MD) simulations. A helix-switch model was revealed in the formation and transition of the C99 dimer, and six types of conformations were identified. The different conformations show differential exposures of γ-cleavage sites and insertion depths in the bilayer, which may modulate γ-cleavage of C99 and lead to different Aß levels. Our results redefine C99 dimerization, provide a framework to mediate the current controversial results, and give insights into the understanding of the relationship between C99 dimerization and Aß formation.

Dynamics Insights Into the Gain of Flexibility by Helix-12 in ESR1 as a Mechanism of Resistance to Drugs in Breast Cancer Cell Lines.

Incidents of breast cancer (BC) are on the rise on a daily basis and have proven to be the most prevelant cause of death for women in both developed and developing countries. Among total BC cases diagnosed after menopause, 70% of cases are Estrogen Receptor (ER) positive (ER-positive or ER+). Mutations in the LBD (ligand-binding domain) of the ER have recently been reported to be the major cause of resistance to potent antagonists. In this study, the experimentally reported mutations K303R, E380Q, V392I, S463P, V524E, P535H, P536H, Y537C, Y537N, Y537S, and D538G were analyzed, and the most significant mutations were shortlisted based on multiple analyses. Initial analyses, such as mCSM stability, occluded depth analysis, mCSM-binding affinity, and FoldX energy changes shortlisted only six mutations as being highly resistant. Finally, simulations of force field-based molecular dynamics (MD on wild type (WT) ERα) on six mERα variants (E380Q, S463P, Y537S, Y537C, Y537N, and D538G) were carried out to justify mechanism of the resistance. It was observed that these mutations increased the flexibility of the H12. A bonding analysis suggested that previously reported important residue His524 lost bonding upon mutation. Other parameters, such as PCA (principal component analysis), DCCM (dynamics cross-correlation), and FEL (free energy landscape), verified that the shortlisted mutations affect the H12 helix, which opens up the co-activator binding conformation. These results provide deep insight into the mechanism of relative resistance posed to fulvestrant due to mutations in breast cancer. This study will facilitate further understanding of the important aspects of designing specific and more effective drugs.

The dynamic binding of cholesterol to the multiple sites of C99: as revealed by coarse-grained and all-atom simulations.

It is generally believed that the etiology of Alzheimer's disease (AD) is closely related to the amyloid-ß polypeptides, produced from γ-secretase cleavage of C99. There is preliminary evidence that cholesterol directly activates γ-secretase cleavage of C99 through mechanisms that have not been understood so far. In this article, coarse-grained (CG) and all-atom (AT) simulations were employed to investigate the association between C99 and cholesterol, which is essential for our understanding of the role of cholesterol in the amyloidogenic pathway. Firstly, we find that both the N-terminus and the C-terminus of the C99 transmembrane domain (TMD) show interactions with cholesterol. Secondly, a multi-site dynamic cholesterol binding model was captured from the simulations, where 6 binding sites in the C99 TMD were presented. The analyses of the binding energies show that cholesterol prefers the site no. 1, 2, 4 and 5 over others. The most favorable binding energy of nearly -58.857 kJ mol-1 is from site 1, the repeat GxxxG motif. There are two pathways and two binding states of cholesterol binding to this site. Ser697 and Phe690 contribute most to the stabilization of the tightly binding state and the loosely binding state, respectively. The other binding sites described may also be potential drug targets. Thirdly, the residues GAVILMTKF, especially IVKF play a key role in this association. The C99 model appears to suggest a new mechanism for cholesterol binding. Finally, the multiple-site dynamic cholesterol binding model better explains the hypotheses that cholesterol promotes the amyloidogenic AßPP route. The GxxxA motif in the middle of the C99 transmembrane domain is completely exposed without cholesterol sheltering, which might help γ-secretase identify the cleavage sites and then promote γ-cleavage. Our results provide a detailed picture of dynamic cholesterol binding, which is crucial to our recognition of the potential influence of cholesterol on the C99 process and the etiology of AD.

Molecular dynamic simulation of the self-assembly of DAP12-NKG2C activating immunoreceptor complex.

The DAP12-NKG2C activating immunoreceptor complex is one of the multisubunit transmembrane protein complexes in which ligand-binding receptor chains assemble with dimeric signal-transducing modules through non-covalent associations in their transmembrane (TM) domains. In this work, both coarse grained and atomistic molecular dynamic simulation methods were applied to investigate the self-assembly dynamics of the transmembrane domains of the DAP12-NKG2C activating immunoreceptor complex. Through simulating the dynamics of DAP12-NKG2C TM heterotrimer and point mutations, we demonstrated that a five-polar-residue motif including: 2 Asps and 2 Thrs in DAP12 dimer, as well as 1 Lys in NKG2C TM plays an important role in the assembly structure of the DAP12-NKG2C TM heterotrimer. Furthermore, we provided clear evidences to exclude the possibility that another NKG2C could stably associate with the DAP12-NKG2C heterotrimer. Based on the simulation results, we proposed a revised model for the self-assembly of DAP12-NKG2C activating immunoreceptor complex, along with a plausible explanation for the association of only one NKG2C with a DAP12 dimer.