Robustness and modular design of the Drosophila segment polarity network

Biomolecular networks have to perform their functions robustly. A robust function may have preferences in the topological structures of the underlying network. We carried out an exhaustive computational analysis on network topologies in relation to a patterning function in Drosophila embryogenesis. We found that while the vast majority of topologies can either not perform the required function or only do so very fragilely, a small fraction of topologies emerges as particularly robust for the function. The topology adopted by Drosophila, that of the segment polarity network, is a top ranking one among all topologies with no direct autoregulation. Furthermore, we found that all robust topologies are modular--each being a combination of three kinds of modules. These modules can be traced back to three sub-functions of the patterning function and their combinations provide a combinatorial variability for the robust topologies. Our results suggest that the requirement of functional robustness drastically reduces the choices of viable topology to a limited set of modular combinations among which nature optimizes its choice under evolutionary and other biological constraints.Comment: Supplementary Information and Synopsis available at http://www.ucsf.edu/tanglab

Combinatorial Pattern Discovery Approach for the Folding Trajectory Analysis of a β-Hairpin

The study of protein folding mechanisms continues to be one of the most challenging problems in computational biology. Currently, the protein folding mechanism is often characterized by calculating the free energy landscape versus various reaction coordinates, such as the fraction of native contacts, the radius of gyration, RMSD from the native structure, and so on. In this paper, we present a combinatorial pattern discovery approach toward understanding the global state changes during the folding process. This is a first step toward an unsupervised (and perhaps eventually automated) approach toward identification of global states. The approach is based on computing biclusters (or patterned clusters)—each cluster is a combination of various reaction coordinates, and its signature pattern facilitates the computation of the Z-score for the cluster. For this discovery process, we present an algorithm of time complexity c∈RO((N + nm) log n), where N is the size of the output patterns and (n × m) is the size of the input with n time frames and m reaction coordinates. To date, this is the best time complexity for this problem. We next apply this to a β-hairpin folding trajectory and demonstrate that this approach extracts crucial information about protein folding intermediate states and mechanism. We make three observations about the approach: (1) The method recovers states previously obtained by visually analyzing free energy surfaces. (2) It also succeeds in extracting meaningful patterns and structures that had been overlooked in previous works, which provides a better understanding of the folding mechanism of the β-hairpin. These new patterns also interconnect various states in existing free energy surfaces versus different reaction coordinates. (3) The approach does not require calculating the free energy values, yet it offers an analysis comparable to, and sometimes better than, the methods that use free energy landscapes, thus validating the choice of reaction coordinates. (An abstract version of this work was presented at the 2005 Asia Pacific Bioinformatics Conference [1].

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

Further development and validation of empirical scoring functions for structure-based binding affinity prediction

New empirical scoring functions have been developed to estimate the binding affinity of a given protein-ligand complex with known three-dimensional structure. These scoring functions include terms accounting for van der Waals interaction, hydrogen bonding, deformation penalty, and hydrophobic effect. A special feature is that three different algorithms have been implemented to calculate the hydrophobic effect term, which results in three parallel scoring functions. All three scoring functions are calibrated through multivariate regression analysis of a set of 200 protein-ligand complexes and they reproduce the binding free energies of the entire training set with standard deviations of 2.2 kcal/mol, 2.1 kcal/mol, and 2.0 kcal/mol, respectively. These three scoring functions are further combined into a consensus scoring function, X-CSCORE. When tested on an independent set of 30 protein-ligand complexes, X-CSCORE is able to predict their binding free energies with a standard deviation of 2.2 kcal/mol. The potential application of X-CSCORE to molecular docking is also investigated. Our results show that this consensus scoring function improves the docking accuracy considerably when compared to the conventional force field computation used for molecular docking.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42967/1/10822_2004_Article_405419.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