Prediction of Drug-Likeness Using Deep Autoencoder Neural Networks

Due to diverse reasons, most drug candidates cannot eventually become marketed drugs. Developing reliable computational methods for prediction of drug-likeness of candidate compounds is of vital importance to improve the success rate of drug discovery and development. In this study, we used a fully connected neural networks (FNN) to construct drug-likeness classification models with deep autoencoder to initialize model parameters. We collected datasets of drugs (represented by ZINC World Drug), bioactive molecules (represented by MDDR and WDI), and common molecules (represented by ZINC All Purchasable and ACD). Compounds were encoded with MOLD2 two-dimensional structure descriptors. The classification accuracies of drug-like/non-drug-like model are 91.04% on WDI/ACD databases, and 91.20% on MDDR/ZINC, respectively. The performance of the models outperforms previously reported models. In addition, we develop a drug/non-drug-like model (ZINC World Drug vs. ZINC All Purchasable), which distinguishes drugs and common compounds, with a classification accuracy of 96.99%. Our work shows that by using high-latitude molecular descriptors, we can apply deep learning technology to establish state-of-the-art drug-likeness prediction models

Activation of Glutathione Peroxidase 4 as a Novel Anti-inflammatory Strategy

The anti-oxidative enzyme, glutathione peroxidase 4 (GPX4), helps to promote inflammation resolution by eliminating oxidative species produced by the arachidonic acid (AA) metabolic network. Up-regulating its activity has been proposed as a promising strategy for inflammation intervention. In the present study, we aimed to study the effect of GPX4 activator on the AA metabolic network and inflammation related pathways. Using combined computational and experimental screen, we identified a novel compound that can activate the enzyme activity of GPX4 by more than two folds. We further assessed its potential in a series of cellular assays where GPX4 was demonstrated to play a regulatory role. We are able to show that GPX4 activation suppressed inflammatory conditions such as oxidation of AA and NF-κB pathway activation. We further demonstrated that this GPX4 activator can decrease the intracellular ROS level and suppress ferroptosis. Our study suggests that GPX4 activators can be developed as anti-inflammatory or cyto-protective agent in lipid-peroxidation-mediated diseases

Structure-based method for analyzing protein–protein interfaces

Hydrogen bond, hydrophobic and vdW interactions are the three major non-covalent interactions at protein–protein interfaces. We have developed a method that uses only these properties to describe interactions between proteins, which can qualitatively estimate the individual contribution of each interfacial residue to the binding and gives the results in a graphic display way. This method has been applied to analyze alanine mutation data at protein–protein interfaces. A dataset containing 13 protein–protein complexes with 250 alanine mutations of interfacial residues has been tested. For the 75 hot-spot residues (ΔΔ G ≥1.5 kcal mol -1 ), 66 can be predicted correctly with a success rate of 88%. In order to test the tolerance of this method to conformational changes upon binding, we utilize a set of 26 complexes with one or both of their components available in the unbound form. The difference of key residues exported by the program is 11% between the results using complexed proteins and those from unbound ones. As this method gives the characteristics of the binding partner for a particular protein, in-depth studies on protein–protein recognition can be carried out. Furthermore, this method can be used to compare the difference between protein–protein interactions and look for correlated mutation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47876/1/894_2003_Article_168.pd

Identification of Functional Subclasses in the DJ-1 Superfamily Proteins

Genomics has posed the challenge of determination of protein function from sequence and/or 3-D structure. Functional assignment from sequence relationships can be misleading, and structural similarity does not necessarily imply functional similarity. Proteins in the DJ-1 family, many of which are of unknown function, are examples of proteins with both sequence and fold similarity that span multiple functional classes. THEMATICS (theoretical microscopic titration curves), an electrostatics-based computational approach to functional site prediction, is used to sort proteins in the DJ-1 family into different functional classes. Active site residues are predicted for the eight distinct DJ-1 proteins with available 3-D structures. Placement of the predicted residues onto a structural alignment for six of these proteins reveals three distinct types of active sites. Each type overlaps only partially with the others, with only one residue in common across all six sets of predicted residues. Human DJ-1 and YajL from Escherichia coli have very similar predicted active sites and belong to the same probable functional group. Protease I, a known cysteine protease from Pyrococcus horikoshii, and PfpI/YhbO from E. coli, a hypothetical protein of unknown function, belong to a separate class. THEMATICS predicts a set of residues that is typical of a cysteine protease for Protease I; the prediction for PfpI/YhbO bears some similarity. YDR533Cp from Saccharomyces cerevisiae, of unknown function, and the known chaperone Hsp31 from E. coli constitute a third group with nearly identical predicted active sites. While the first four proteins have predicted active sites at dimer interfaces, YDR533Cp and Hsp31 both have predicted sites contained within each subunit. Although YDR533Cp and Hsp31 form different dimers with different orientations between the subunits, the predicted active sites are superimposable within the monomer structures. Thus, the three predicted functional classes form four different types of quaternary structures. The computational prediction of the functional sites for protein structures of unknown function provides valuable clues for functional classification

Dynamic Simulations on the Arachidonic Acid Metabolic Network

Drug molecules not only interact with specific targets, but also alter the state and function of the associated biological network. How to design drugs and evaluate their functions at the systems level becomes a key issue in highly efficient and low–side-effect drug design. The arachidonic acid metabolic network is the network that produces inflammatory mediators, in which several enzymes, including cyclooxygenase-2 (COX-2), have been used as targets for anti-inflammatory drugs. However, neither the century-old nonsteriodal anti-inflammatory drugs nor the recently revocatory Vioxx have provided completely successful anti-inflammatory treatment. To gain more insights into the anti-inflammatory drug design, the authors have studied the dynamic properties of arachidonic acid (AA) metabolic network in human polymorphous leukocytes. Metabolic flux, exogenous AA effects, and drug efficacy have been analyzed using ordinary differential equations. The flux balance in the AA network was found to be important for efficient and safe drug design. When only the 5-lipoxygenase (5-LOX) inhibitor was used, the flux of the COX-2 pathway was increased significantly, showing that a single functional inhibitor cannot effectively control the production of inflammatory mediators. When both COX-2 and 5-LOX were blocked, the production of inflammatory mediators could be completely shut off. The authors have also investigated the differences between a dual-functional COX-2 and 5-LOX inhibitor and a mixture of these two types of inhibitors. Their work provides an example for the integration of systems biology and drug discovery

Structure-Templated Predictions of Novel Protein Interactions from Sequence Information

The multitude of functions performed in the cell are largely controlled by a set of carefully orchestrated protein interactions often facilitated by specific binding of conserved domains in the interacting proteins. Interacting domains commonly exhibit distinct binding specificity to short and conserved recognition peptides called binding profiles. Although many conserved domains are known in nature, only a few have well-characterized binding profiles. Here, we describe a novel predictive method known as domain–motif interactions from structural topology (D-MIST) for elucidating the binding profiles of interacting domains. A set of domains and their corresponding binding profiles were derived from extant protein structures and protein interaction data and then used to predict novel protein interactions in yeast. A number of the predicted interactions were verified experimentally, including new interactions of the mitotic exit network, RNA polymerases, nucleotide metabolism enzymes, and the chaperone complex. These results demonstrate that new protein interactions can be predicted exclusively from sequence information

DNA binding mechanism revealed by high resolution crystal structure of Arabidopsis thaliana WRKY1 protein

WRKY proteins, defined by the conserved WRKYGQK sequence, are comprised of a large superfamily of transcription factors identified specifically from the plant kingdom. This superfamily plays important roles in plant disease resistance, abiotic stress, senescence as well as in some developmental processes. In this study, the Arabidopsis WRKY1 was shown to be involved in the salicylic acid signaling pathway and partially dependent on NPR1; a C-terminal domain of WRKY1, AtWRKY1-C, was constructed for structural studies. Previous investigations showed that DNA binding of the WRKY proteins was localized at the WRKY domains and these domains may define novel zinc-binding motifs. The crystal structure of the AtWRKY1-C determined at 1.6 Å resolution has revealed that this domain is composed of a globular structure with five β strands, forming an antiparallel β-sheet. A novel zinc-binding site is situated at one end of the β-sheet, between strands β4 and β5. Based on this high-resolution crystal structure and site-directed mutagenesis, we have defined and confirmed that the DNA-binding residues of AtWRKY1-C are located at β2 and β3 strands. These results provided us with structural information to understand the mechanism of transcriptional control and signal transduction events of the WRKY proteins

Finding multiple target optimal intervention in disease-related molecular network

Drugs against multiple targets may overcome the many limitations of single targets and achieve a more effective and safer control of the disease. Numerous high-throughput experiments have been performed in this emerging field. However, systematic identification of multiple drug targets and their best intervention requires knowledge of the underlying disease network and calls for innovative computational methods that exploit the network structure and dynamics. Here, we develop a robust computational algorithm for finding multiple target optimal intervention (MTOI) solutions in a disease network. MTOI identifies potential drug targets and suggests optimal combinations of the target intervention that best restore the network to a normal state, which can be customer designed. We applied MTOI to an inflammation-related network. The well-known side effects of the traditional non-steriodal anti-inflammatory drugs and the recently recalled Vioxx were correctly accounted for in our network model. A number of promising MTOI solutions were found to be both effective and safer