Prediction of drug-target interactions and drug repositioning via network-based inference

Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 μM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning.     

Graph neural network approaches for drug-target interactions

Developing new drugs remains prohibitively expensive, time-consuming, and often involves safety issues. Accurate prediction of drug-target interactions (DTIs) can guide the drug discovery process and thus facilitate drug development. Non-Euclidian data such as drug-like molecule structures, key pocket residue structures, and protein interaction networks can be represented effectively using graphs. Therefore, the emerging graph neural network has been rapidly applied to predict DTIs, and proved effective in finding repositioning drugs and accelerating drug discovery. In this review, we provide a brief overview of deep neural networks used in DTI models. Then, we summarize the database required for DTI prediction, followed by a comprehensive introduction of applications of graph neural networks for DTI prediction. We also highlight current challenges and future directions to guide the further development of this field.     

高产表面活性素的重组枯草芽孢杆菌构建及培养优化

表面活性素是一种新型生物表面活性剂,因其具有良好的表面活性、可生物降解及抗菌活性,在石油开采、医药、农业和食品化妆品等领域具有广阔的应用前景。高产表面活性素菌株的获得和发酵过程优化是其商业化生产的关键。文中考察了脂肪酸合成途径对表面活性素合成的影响,强化脂肪酸生物合成关键基因以及该途径全部基因分别构建了高产表面活性素枯草芽孢杆菌BacillussubtilisTHBS-2和THBS-8,并对发酵过程中氨基酸种类及添加量、诱导剂异丙基-β-D-硫代半乳糖苷(IPTG)添加时间和添加量等条件对产物合成的影响进行考察,获得优化的两阶段前体添加方案:发酵3 h,加入IPTG和L-亮氨酸,使其终浓度分别为1.25 mmol/L、5 g/L;发酵24 h,添加L-亮氨酸(终浓度5 g/L)和浓缩培养基5 mL。优化条件下,枯草芽孢杆菌THBS-2摇瓶发酵48 h,表面活性素产量高达24 g/L;30 L发酵罐中发酵68 h,产物产量最高达到34 g/L。研究结果为表面活性素的工业化生产及应用奠定基础。     

Psychedelic-inspired drug discovery using an engineered biosensor

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An entropy-based statistic for genomewide association studies

Efficient genotyping methods and the availability of a large collection of single-nucleotide polymorphisms provide valuable tools for genetic studies of human disease. The standard chi2 statistic for case-control studies, which uses a linear function of allele frequencies, has limited power when the number of marker loci is large. We introduce a novel test statistic for genetic association studies that uses Shannon entropy and a nonlinear function of allele frequencies to amplify the differences in allele and haplotype frequencies to maintain statistical power with large numbers of marker loci. We investigate the relationship between the entropy-based test statistic and the standard chi2 statistic and show that, in most cases, the power of the entropy-based statistic is greater than that of the standard chi2 statistic. The distribution of the entropy-based statistic and the type I error rates are validated using simulation studies. Finally, we apply the new entropy-based test statistic to two real data sets, one for the COMT gene and schizophrenia and one for the MMP-2 gene and esophageal carcinoma, to evaluate the performance of the new method for genetic association studies. The results show that the entropy-based statistic obtained smaller P values than did the standard chi2 statistic.     

Predicting drug metabolism-dependent toxicity for humans with a genetically engineered cell battery

This paper covers the presentation of an invited lecture - the FRAME Annual Lecture - given in London on 8 November 2006. Investigating the metabolism of chemicals in general, and of drugs and pollutants in particular, is of key importance to understanding pharmacological and toxicological effects. Over more than 15 years, the genes encoding the enzymes involved, have been individually cloned and expressed after gene transfer into V79 Chinese hamster cells, yielding a collection of cell lines - the so called V79 Cell Battery. With this technology, it has become possible to study the relevant enzymes individually, thus avoiding complex in vivo situations. By cloning genes from different species, including humans, species-species comparison became possible, yielding results of immediate predictive value for humans. Since V79 cells had already been approved by the OECD for toxicity studies since the 1980s, the metabolically competent V79 cell lines are of even greater value, as metabolism and toxicity testing are linked in the very same cells in a highly defined fashion. The results obtained so far with the genetically engineered V79 cell lines justify their acceptance as alternatives to animal experimentation in drug development and in the toxicity testing of chemicals, serving the goals of the Three Rs and, in particular, the most important R: Replacement.     

Mining significant substructure pairs for interpreting polypharmacology in drug-target network

A current key feature in drug-target network is that drugs often bind to multiple targets, known as polypharmacology or drug promiscuity. Recent literature has indicated that relatively small fragments in both drugs and targets are crucial in forming polypharmacology. We hypothesize that principles behind polypharmacology are embedded in paired fragments in molecular graphs and amino acid sequences of drug-target interactions. We developed a fast, scalable algorithm for mining significantly co-occurring subgraph-subsequence pairs from drug-target interactions. A noteworthy feature of our approach is to capture significant paired patterns of subgraph-subsequence, while patterns of either drugs or targets only have been considered in the literature so far. Significant substructure pairs allow the grouping of drug-target interactions into clusters, covering approximately 75% of interactions containing approved drugs. These clusters were highly exclusive to each other, being statistically significant and logically implying that each cluster corresponds to a distinguished type of polypharmacology. These exclusive clusters cannot be easily obtained by using either drug or target information only but are naturally found by highlighting significant substructure pairs in drug-target interactions. These results confirm the effectiveness of our method for interpreting polypharmacology in drug-target network.     

Metabolic pathway optimization for biosynthesis of 1,2,4-butanetriol from xylose by engineered Escherichia coli

1,2,4-Butanetriol (BT) and related derivatives have been widely used in many fields, especially in the military and in medicine. In this paper, we systematically optimized the BT biosynthetic pathway. We first investigated the activities of various NADH dependent aldehyde reductases (ALRs), which catalyze the fourth reaction in the four-step pathway for BT production from xylose in E. coli, and found that a combination of multiple endogenous enzymes catalyzed aldehyde reduction in the BT production bioprocess and that YqhD in E. coli was a main ALR for BT production. In addition, ADH2 from Saccharomyces cerevisiae can effectively catalyze 3,4-dihydroxybutanal to BT. Also, YjhG was identified as the major xylonate dehydratase and was co-overexpressed with YqhD, resulting in an improvement of BT production by 30%. Moreover, we identified and eliminated the competing branch pathway by inactivating 2-keto acid reductases (yiaE). Finally, the combination of these approaches led to BT production of 5.1 g/L. In summary, our study provides insights into the biosynthetic pathway for BT production, demonstrates an effective strategy to enhance BT production, and paves the way toward in-depth research on BT biosynthesis.     

Rhizosphere engineering for sustainable crop production: entropy-based insights

There is a growing interest in exploring interactions at root–soil interface in natural and agricultural ecosystems, but an entropy-based understanding of these dynamic rhizosphere processes is lacking. We have developed a new conceptual model of rhizosphere regulation by localized nutrient supply using thermodynamic entropy. Increased nutrient-use efficiency is achieved by rhizosphere management based on self-organization and minimized entropy via equilibrium attractors comprising (i) optimized root strategies for nutrient acquisition and (ii) improved information exchange related to root–soil–microbe interactions. The cascading effects through different hierarchical levels amplify the underlying processes in plant–soil system. We propose a strategy for manipulating rhizosphere dynamics and improving nutrient-use efficiency by localized nutrient supply with minimization of entropy to underpin sustainable food/feed/fiber production.     

An entropy-based approach for testing genetic epistasis underlying complex diseases

The genetic basis of complex diseases is expected to be highly heterogeneous, with complex interactions among multiple disease loci and environment factors. Due to the multi-dimensional property of interactions among large number of genetic loci, efficient statistical approach has not been well developed to handle the high-order epistatic complexity. In this article, we introduce a new approach for testing genetic epistasis in multiple loci using an entropy-based statistic for a case-only design. The entropy-based statistic asymptotically follows a χ² distribution. Computer simulations show that the entropy-based approach has better control of type I error and higher power compared to the standard χ² test. Motivated by a schizophrenia data set, we propose a method for measuring and testing the relative entropy of a clinical phenotype, through which one can test the contribution or interaction of multiple disease loci to a clinical phenotype. A sequential forward selection procedure is proposed to construct a genetic interaction network which is illustrated through a tree-based diagram. The network information clearly shows the relative importance of a set of genetic loci on a clinical phenotype. To show the utility of the new entropy-based approach, it is applied to analyze two real data sets, a schizophrenia data set and a published malaria data set. Our approach provides a fast and testable framework for genetic epistasis study in a case-only design.     

A new DNA sequence entropy-based Kullback-Leibler algorithm for gene clustering

Information theory is a branch of mathematics that overlaps with communications, biology, and medical engineering. Entropy is a measure of uncertainty in the set of information. In this study, for each gene and its exons sets, the entropy was calculated in orders one to four. Based on the relative entropy of genes and exons, Kullback-Leibler divergence was calculated. After obtaining the Kullback-Leibler distance for genes and exons sets, the results were entered as input into 7 clustering algorithms: single, complete, average, weighted, centroid, median, and K-means. To aggregate the results of clustering, the AdaBoost algorithm was used. Finally, the results of the AdaBoost algorithm were investigated by GeneMANIA prediction server to explore the results from gene annotation point of view. All calculations were performed using the MATLAB Engineering Software (2015). Following our findings on investigating the results of genes metabolic pathways based on the gene annotations, it was revealed that our proposed clustering method yielded correct, logical, and fast results. This method at the same that had not had the disadvantages of aligning allowed the genes with actual length and content to be considered and also did not require high memory for large-length sequences. We believe that the performance of the proposed method could be used with other competitive gene clustering methods to group biologically relevant set of genes. Also, the proposed method can be seen as a predictive method for those genes bearing up weak genomic annotations.     

T-T base mismatches enhance drug binding at the branch site in a four-arm DNA junction.

Base mismatches--non Watson-Crick pairing between bases--can arise in duplex DNA as a consequence of mutational events or by recombination. In a duplex, the sequence of the two bases involved, and those flanking the site of mismatch, determines the local structure and extent of destabilization of the helix. Base mismatches can arise also in recombination of nonhomologous strands, and their occurrence in Holliday recombination intermediates can influence the outcome of general or specialized recombination events. We have previously reported that the branch site in a DNA junction can interact selectively with a variety of ligands. Here we describe the thermodynamics of junctions containing T-T mismatches flanking the branch and show that these structures bind methidium and other intercalators with higher affinity than junctions lacking mismatches.     

A Synthetic Biology Project - Developing a single-molecule device for screening drug-target interactions

This review describes a European-funded project in the area of Synthetic Biology. The project seeks to demonstrate the application of engineering techniques and methodologies to the design and construction of a biosensor for detecting drug-target interactions at the single-molecule level. Production of the proteins required for the system followed the principle of previously described "bioparts" concepts (a system where a database of biological parts - promoters, genes, terminators, linking tags and cleavage sequences - is used to construct novel gene assemblies) and cassette-type assembly of gene expression systems (the concept of linking different "bioparts" to produce functional "cassettes"), but problems were quickly identified with these approaches. DNA substrates for the device were also constructed using a cassette-system. Finally, micro-engineering was used to build a magnetoresistive Magnetic Tweezer device for detection of single molecule DNA modifying enzymes (motors), while the possibility of constructing a Hall Effect version of this device was explored. The device is currently being used to study helicases from Plasmodium as potential targets for anti-malarial drugs, but we also suggest other potential uses for the device.     

Several appropriate background distributions for entropy-based protein sequence conservation measures

Amino acid background distribution is an important factor for entropy-based methods which extract sequence conservation information from protein multiple sequence alignments (MSAs). However, MSAs are usually not large enough to allow a reliable observed background distribution. In this paper, we propose two new estimations of background distribution. One is an integration of the observed background distribution and the position-specific residue distribution, and the other is a normalized square root of observed background frequency. To validate these new background distributions, they are applied to the relative entropy model to find catalytic sites and ligand binding sites from protein MSAs. Experimental results show that they are superior to the observed background distribution in predicting functionally important residues.     

Improvement and optimization of two engineered phage resistance mechanisms in Lactococcus lactis

Homologous replication module genes were identified for four P335 type phages. DNA sequence analysis revealed that all four phages exhibited more than 90% DNA homology for at least two genes, designated rep2009 and orf17. One of these genes, rep2009, codes for a putative replisome organizer protein and contains an assumed origin of phage DNA replication (ori2009), which was identical for all four phages. DNA fragments representing the ori2009 sequence confer a phage-encoded resistance (Per) phenotype on lactococcal hosts when they are supplied on a high-copy-number vector. Furthermore, cloning multiple copies of the ori2009 sequence was found to increase the effectiveness of the Per phenotype conferred. A number of antisense plasmids targeting specific genes of the replication module were constructed. Two separate plasmids targeting rep2009 and orf17 were found to efficiently inhibit proliferation of all four phages by interfering with intracellular phage DNA replication. These results represent two highly effective strategies for inhibiting bacteriophage proliferation, and they also identify a novel gene, orf17, which appears to be important for phage DNA replication. Furthermore, these results indicate that although the actual mechanisms of DNA replication are very similar, if not identical, for all four phages, expression of the replication genes is significantly different in each case.     

Mammalian mismatches in nucleotide metabolism: implications for xenotransplantation

Acute humoral rejection (AHR) limits the clinical application of animal organs for xenotransplantation. Mammalian disparities in nucleotide metabolism may contribute significantly to the microvascular component in AHR; these, however remain ill-defined. We evaluated the extent of species-specific differences in nucleotide metabolism. HPLC analysis was performed on venous blood samples (nucleotide metabolites) and heart biopsies (purine enzymes) from wild type mice, rats, pigs, baboons, and human donors. Ecto-5′-nucleotidase (E5′N) activities were 4-fold lower in pigs and baboon hearts compared to human and mice hearts while rat activity was highest. Similar differences between pigs and humans were also observed with kidneys and endothelial cells. More than 10-fold differences were observed with other purine enzymes. AMP deaminase (AMPD) activity was exceptionally high in mice but very low in pig and baboon hearts. Adenosine deaminase (ADA) activity was highest in baboons. Adenosine kinase (AK) activity was more consistent across different species. Pig blood had the highest levels of hypoxanthine, inosine and adenine. Human blood uric acid concentration was almost 100 times higher than in other species studied. We conclude that species-specific differences in nucleotide metabolism may affect compatibility of pig organs within a human metabolic environment. Furthermore, nucleotide metabolic mismatches may affect clinical relevance of animal organ transplant models. Supplementation of deficient precursors or application of inhibitors of nucleotide metabolism (e.g., allopurinol) or transgenic upregulation of E5'N may overcome some of these differences.