Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities

The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2nd-level, 3rd-level, 4th-level, and 5th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels

Analysis and Prediction of the Metabolic Stability of Proteins Based on Their Sequential Features, Subcellular Locations and Interaction Networks

The metabolic stability is a very important idiosyncracy of proteins that is related to their global flexibility, intramolecular fluctuations, various internal dynamic processes, as well as many marvelous biological functions. Determination of protein's metabolic stability would provide us with useful information for in-depth understanding of the dynamic action mechanisms of proteins. Although several experimental methods have been developed to measure protein's metabolic stability, they are time-consuming and more expensive. Reported in this paper is a computational method, which is featured by (1) integrating various properties of proteins, such as biochemical and physicochemical properties, subcellular locations, network properties and protein complex property, (2) using the mRMR (Maximum Relevance & Minimum Redundancy) principle and the IFS (Incremental Feature Selection) procedure to optimize the prediction engine, and (3) being able to identify proteins among the four types: “short”, “medium”, “long”, and “extra-long” half-life spans. It was revealed through our analysis that the following seven characters played major roles in determining the stability of proteins: (1) KEGG enrichment scores of the protein and its neighbors in network, (2) subcellular locations, (3) polarity, (4) amino acids composition, (5) hydrophobicity, (6) secondary structure propensity, and (7) the number of protein complexes the protein involved. It was observed that there was an intriguing correlation between the predicted metabolic stability of some proteins and the real half-life of the drugs designed to target them. These findings might provide useful insights for designing protein-stability-relevant drugs. The computational method can also be used as a large-scale tool for annotating the metabolic stability for the avalanche of protein sequences generated in the post-genomic age

Prediction of Protein Domain with mRMR Feature Selection and Analysis

The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28–40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine

Coherent coupling between radio frequency, optical, and acoustic waves in piezo-optomechanical circuits

The interaction of optical and mechanical modes in nanoscale optomechanical systems has been widely studied for applications ranging from sensing to quantum information science. Here, we develop a platform for cavity optomechanical circuits in which localized and interacting 1550 nm photons and 2.4 GHz phonons are combined with photonic and phononic waveguides. Working in GaAs facilitates manipulation of the localized mechanical mode either with a radio frequency field through the piezo-electric effect, or optically through the strong photoelastic effect. We use this to demonstrate a novel acoustic wave interference effect, analogous to coherent population trapping in atomic systems, in which the coherent mechanical motion induced by the electrical drive can be completely cancelled out by the optically-driven motion. The ability to manipulate cavity optomechanical systems with equal facility through either photonic or phononic channels enables new device and system architectures for signal transduction between the optical, electrical, and mechanical domains

Inter-calibration of a proposed new primary reference standard AA-ETH Zn for zinc isotopic analysis

We have prepared a large volume of pure, concentrated and homogenous zinc standard solution. This new standard solution is intended to be used as a primary reference standard for the zinc isotope community, and to serve as a replacement for the nearly exhausted current reference standard, the so-called JMC-Lyon Zn. The isotopic composition of this new zinc standard (AA-ETH Zn) has been determined through an inter-laboratory calibration exercise, calibrated against the existing JMC-Lyon standard, as well as the certified Zn reference standard IRMM-3702. The data show that the new standard is isotopically indistinguishable from the IRMM-3702 zinc standard, with a weighted δ66/64Zn value of 0.28 ± 0.02‰ relative to JMC-Lyon. We suggest that this new standard be assigned a δ66/64Zn value of +0.28‰ for reporting of future Zn isotope data, with the rationale that all existing published Zn isotope data are presented relative to the JMC-Lyon standard. Therefore our proposed presentation allows for a direct comparison with all previously published data, and that are directly traceable to a certified reference standard, IRMM-3702 Zn. This standard will be made freely available to all interested labs through contact with the corresponding author

Predicting Transcriptional Activity of Multiple Site p53 Mutants Based on Hybrid Properties

As an important tumor suppressor protein, reactivate mutated p53 was found in many kinds of human cancers and that restoring active p53 would lead to tumor regression. In this work, we developed a new computational method to predict the transcriptional activity for one-, two-, three- and four-site p53 mutants, respectively. With the approach from the general form of pseudo amino acid composition, we used eight types of features to represent the mutation and then selected the optimal prediction features based on the maximum relevance, minimum redundancy, and incremental feature selection methods. The Mathew's correlation coefficients (MCC) obtained by using nearest neighbor algorithm and jackknife cross validation for one-, two-, three- and four-site p53 mutants were 0.678, 0.314, 0.705, and 0.907, respectively. It was revealed by the further optimal feature set analysis that the 2D (two-dimensional) structure features composed the largest part of the optimal feature set and maybe played the most important roles in all four types of p53 mutant active status prediction. It was also demonstrated by the optimal feature sets, especially those at the top level, that the 3D structure features, conservation, physicochemical and biochemical properties of amino acid near the mutation site, also played quite important roles for p53 mutant active status prediction. Our study has provided a new and promising approach for finding functionally important sites and the relevant features for in-depth study of p53 protein and its action mechanism

Accurate Prediction of Protein Structural Class

Because of the increasing gap between the data from sequencing and structural genomics, the accurate prediction of the structural class of a protein domain solely from the primary sequence has remained a challenging problem in structural biology. Traditional sequence-based predictors generally select several sequence features and then feed them directly into a classification program to identify the structural class. The current best sequence-based predictor achieved an overall accuracy of 74.1% when tested on a widely used, non-homologous benchmark dataset 25PDB. In the present work, we built a multiple linear regression (MLR) model to convert the 440-dimensional (440D) sequence feature vector extracted from the Position Specific Scoring Matrix (PSSM) of a protein domain to a 4-dimensinal (4D) structural feature vector, which could then be used to predict the four major structural classes. We performed 10-fold cross-validation and jackknife tests of the method on a large non-homologous dataset containing 8,244 domains distributed among the four major classes. The performance of our approach outperformed all of the existing sequence-based methods and had an overall accuracy of 83.1%, which is even higher than the results of those predicted secondary structure-based methods

Helical Chirality: a Link between Local Interactions and Global Topology in DNA

DNA supercoiling plays a major role in many cellular functions. The global DNA conformation is however intimately linked to local DNA-DNA interactions influencing both the physical properties and the biological functions of the supercoiled molecule. Juxtaposition of DNA double helices in ubiquitous crossover arrangements participates in multiple functions such as recombination, gene regulation and DNA packaging. However, little is currently known about how the structure and stability of direct DNA-DNA interactions influence the topological state of DNA. Here, a crystallographic analysis shows that due to the intrinsic helical chirality of DNA, crossovers of opposite handedness exhibit markedly different geometries. While right-handed crossovers are self-fitted by sequence-specific groove-backbone interaction and bridging Mg2+ sites, left-handed crossovers are juxtaposed by groove-groove interaction. Our previous calculations have shown that the different geometries result in differential stabilisation in solution, in the presence of divalent cations. The present study reveals that the various topological states of the cell are associated with different inter-segmental interactions. While the unstable left-handed crossovers are exclusively formed in negatively supercoiled DNA, stable right-handed crossovers constitute the local signature of an unusual topological state in the cell, such as the positively supercoiled or relaxed DNA. These findings not only provide a simple mechanism for locally sensing the DNA topology but also lead to the prediction that, due to their different tertiary intra-molecular interactions, supercoiled molecules of opposite signs must display markedly different physical properties. Sticky inter-segmental interactions in positively supercoiled or relaxed DNA are expected to greatly slow down the slithering dynamics of DNA. We therefore suggest that the intrinsic helical chirality of DNA may have oriented the early evolutionary choices for DNA topology

Classification and Analysis of Regulatory Pathways Using Graph Property, Biochemical and Physicochemical Property, and Functional Property

Given a regulatory pathway system consisting of a set of proteins, can we predict which pathway class it belongs to? Such a problem is closely related to the biological function of the pathway in cells and hence is quite fundamental and essential in systems biology and proteomics. This is also an extremely difficult and challenging problem due to its complexity. To address this problem, a novel approach was developed that can be used to predict query pathways among the following six functional categories: (i) “Metabolism”, (ii) “Genetic Information Processing”, (iii) “Environmental Information Processing”, (iv) “Cellular Processes”, (v) “Organismal Systems”, and (vi) “Human Diseases”. The prediction method was established trough the following procedures: (i) according to the general form of pseudo amino acid composition (PseAAC), each of the pathways concerned is formulated as a 5570-D (dimensional) vector; (ii) each of components in the 5570-D vector was derived by a series of feature extractions from the pathway system according to its graphic property, biochemical and physicochemical property, as well as functional property; (iii) the minimum redundancy maximum relevance (mRMR) method was adopted to operate the prediction. A cross-validation by the jackknife test on a benchmark dataset consisting of 146 regulatory pathways indicated that an overall success rate of 78.8% was achieved by our method in identifying query pathways among the above six classes, indicating the outcome is quite promising and encouraging. To the best of our knowledge, the current study represents the first effort in attempting to identity the type of a pathway system or its biological function. It is anticipated that our report may stimulate a series of follow-up investigations in this new and challenging area

Identification of Colorectal Cancer Related Genes with mRMR and Shortest Path in Protein-Protein Interaction Network

One of the most important and challenging problems in biomedicine and genomics is how to identify the disease genes. In this study, we developed a computational method to identify colorectal cancer-related genes based on (i) the gene expression profiles, and (ii) the shortest path analysis of functional protein association networks. The former has been used to select differentially expressed genes as disease genes for quite a long time, while the latter has been widely used to study the mechanism of diseases. With the existing protein-protein interaction data from STRING (Search Tool for the Retrieval of Interacting Genes), a weighted functional protein association network was constructed. By means of the mRMR (Maximum Relevance Minimum Redundancy) approach, six genes were identified that can distinguish the colorectal tumors and normal adjacent colonic tissues from their gene expression profiles. Meanwhile, according to the shortest path approach, we further found an additional 35 genes, of which some have been reported to be relevant to colorectal cancer and some are very likely to be relevant to it. Interestingly, the genes we identified from both the gene expression profiles and the functional protein association network have more cancer genes than the genes identified from the gene expression profiles alone. Besides, these genes also had greater functional similarity with the reported colorectal cancer genes than the genes identified from the gene expression profiles alone. All these indicate that our method as presented in this paper is quite promising. The method may become a useful tool, or at least plays a complementary role to the existing method, for identifying colorectal cancer genes. It has not escaped our notice that the method can be applied to identify the genes of other diseases as well