Similarity-Based Virtual Screen Using Enhanced Siamese Multi-Layer Perceptron

Traditional drug development is a slow and costly process that leads to the production of new drugs. Virtual screening (VS) is a computational procedure that measures the similarity of molecules as one of its primary tasks. Many techniques for capturing the biological similarity between a test compound and a known target ligand have been established in ligand-based virtual screens (LBVSs). However, despite the good performances of the above methods compared to their predecessors, especially when dealing with molecules that have structurally homogenous active elements, they are not satisfied when dealing with molecules that are structurally heterogeneous. The main aim of this study is to improve the performance of similarity searching, especially with molecules that are structurally heterogeneous. The Siamese network will be used due to its capability to deal with complicated data samples in many fields. The Siamese multi-layer perceptron architecture will be enhanced by using two similarity distance layers with one fused layer, then multiple layers will be added after the fusion layer, and then the nodes of the model that contribute less or nothing during inference according to their signal-to-noise ratio values will be pruned. Several benchmark datasets will be used, which are: the MDL Drug Data Report (MDDR-DS1, MDDR-DS2, and MDDR-DS3), the Maximum Unbiased Validation (MUV), and the Directory of Useful Decoys (DUD). The results show the outperformance of the proposed method on standard Tanimoto coefficient (TAN) and other methods. Additionally, it is possible to reduce the number of nodes in the Siamese multilayer perceptron model while still keeping the effectiveness of recall on the same level.

Do secondary hemiepiphytes exist?

For decades, tropical ecologists distinguished primary (PH) and secondary hemiepiphytes (SH) as two structurally dependent life forms with an epiphytic phase at, respectively, the beginning or the end of their ontogeny. However, the use of these terms has been criticized repeatedly because the term “hemiepiphyte” suggests an unsubstantiated biological similarity in ontogeny, and worse, because it is often used without a qualifier, which makes unambiguous interpretation of the life history of such species impossible. In this paper, we go one step further and ask the question whether an ontogenetic trajectory as described by the term “secondary hemiepiphyte” does exist at all. We show that until now all evidence available for the three families that were traditionally listed as taxa with SHs (Araceae, Cyclanthaceae, Marcgraviaceae) falsifies such claims, but critically discuss reports of possible SHs in other families. In all these cases unambiguous conclusions about the existence of any SH are difficult, but our detailed discussion of potential candidates is meant to provide the basis for focused field studies. Irrespective of the outcome of these studies, we urge researchers to abandon the use of the term SH for the time being: Terminological issues can be discussed once there are data.

The Effect of Strontium-Substituted Hydroxyapatite Nanofibrous Matrix on Osteoblast Proliferation and Differentiation

Natural bone tissue consists primarily of bioapatite and collagen. Synthetic hydroxyapatite (HA) possesses good biocompatibility, bioactivity, and osteoconductivity due to its chemical and biological similarity to bioapatite. Hence, HA has been widely used as a bone graft, cell carrier and drug/gene delivery carrier. Moreover, strontium-substituted hydroxyapatite (SrHA) can enhance osteogenic differentiation and inhibit adipogenic differentiation of mesenchymal stem cells. Hence, SrHA has the potential to be used as a bone graft for bone regeneration. It is widely accepted that cell adhesion and most cellular activities are sensitive to the topography and molecular composition of the matrix. Electrospun polymer or polymer-bioceramic composite nanofibers have been demonstrated to enhance osteoblast differentiation. However, to date, no studies have investigated the effect of nanofibrous bioceramic matrices on osteoblasts. In this study, hydroxyapatite nanofiber (HANF) and strontium-substituted hydroxyapatite nanofiber (SrHANF) matrices were fabricated by electrospinning. The effect of the HANF components on MG63 osteoblast-like cells was evaluated by cell morphology, proliferation, alkaline phosphatase activity (ALP) and gene expression levels of RUNX2, COLI, OCN and BSP. The results showed that MG63 osteoblast-like cells exhibited higher ALP and gene expression levels of RUNX2, COLI, BSP and OCN on the SrHANF matrix than the HANF matrix. Hence, SrHANFs could enhance the differentiation of MG63 osteoblast-like cells.

Supervised biomedical semantic similarity

Semantic similarity between concepts in knowledge graphs is essential for several bioinformatics applications, including the prediction of protein-protein interactions and the discovery of associations between diseases and genes. Although knowledge graphs describe entities in terms of several perspectives (or semantic aspects), state-of-the-art semantic similarity measures are general-purpose. This can represent a challenge since different use cases for the application of semantic similarity may need different similarity perspectives and ultimately depend on expert knowledge for manual fine-tuning.We present a new approach that uses supervised machine learning to tailor aspect-oriented semantic similarity measures to fit a particular view on biological similarity or relatedness. We implement and evaluate it using different combinations of representative semantic similarity measures and machine learning methods with four biological similarity views: protein-protein interaction, protein function similarity, protein sequence similarity and phenotype-based gene similarity. The results demonstrate that our approach outperforms non-supervised methods, producing semantic similarity models that fit different biological perspectives significantly better than the commonly used manual combinations of semantic aspects. Moreover, although black-box machine learning models produce the best results, approaches such as genetic programming and linear regression still produce improved results while generating models that are interpretable.

A New Implementation of Genome Rearrangement Problem

Unsigned reverse genome rearrangement is an important part of bioinformatics research, which is widely used in biological similarity and homology analysis, revealing biological inheritance, variation, and evolution. Branch and bound, simulated annealing, and other algorithms in unsigned reverse genome rearrangement algorithm are rare in practical application because of their huge time and space consumption, and greedy algorithms are mostly used at present. By deeply analyzing the domain of unsigned reverse genome rearrangement algorithm based on greedy strategy (unsigned reverse genome rearrangement algorithm (URGRA) based on greedy strategy), the domain features are modeled, and the URGRA algorithm components are interactively designed according to the production programming method. With the support of the PAR platform, the algorithm component library of the URGRA is formally realized, and the concrete algorithm is generated by assembly, which improves the reliability of the assembly algorithm.

The similarity of inherited diseases (II): clinical and biological similarity between the phenotypic series

Background Despite being caused by mutations in different genes, diseases in the same phenotypic series are clinically similar, as reported in Part I of this study. Here, in Part II, we hypothesized that the phenotypic series too might be clinically similar. Furthermore, on the assumption that gene mutations indirectly cause clinical phenotypes by directly affecting biological functions, we hypothesized that clinically similar phenotypic series might be biologically similar as well. Methods To test these hypotheses, we generated a clinical similarity network and a set of biological similarity networks. In both types of network, the nodes represent the phenotypic series, and the edges linking the nodes indicate the similarity of the linked phenotypic series. The weight of each edge is proportional to a similarity coefficient, which depends on the clinical phenotypes and the biological features that are shared by the linked phenotypic series, in the clinical and biological similarity networks, respectively. Results After assembling and analyzing the networks, we raised the threshold for the similarity coefficient, to retain edges of progressively greater weight. This way all the networks were gradually split into fragments, composed of phenotypic series with increasingly greater degrees of similarity. Finally, by comparing the fragments from the two types of network, we defined subsets of phenotypic series with varying types and degrees of clinical and biological correlation. Conclusions Like the individual diseases, the phenotypic series too are clinically and biologically similar to each other. Furthermore, our findings unveil different modalities of correlation between the clinical manifestations and the biological features of the inherited diseases.

sn-spMF: matrix factorization informs tissue-specific genetic regulation of gene expression

Genetic regulation of gene expression, revealed by expression quantitative trait loci (eQTLs), exhibits complex patterns of tissue-specific effects. Characterization of these patterns may allow us to better understand mechanisms of gene regulation and disease etiology. We develop a constrained matrix factorization model, sn-spMF, to learn patterns of tissue-sharing and apply it to 49 human tissues from the Genotype-Tissue Expression (GTEx) project. The learned factors reflect tissues with known biological similarity and identify transcription factors that may mediate tissue-specific effects. sn-spMF, available at https://github.com/heyuan7676/ts_eQTLs, can be applied to learn biologically interpretable patterns of eQTL tissue-specificity and generate testable mechanistic hypotheses.

Effects of maternal supplementation with fully oxidised β-carotene on the reproductive performance and immune response of sows, as well as the growth performance of nursing piglets

The present study was conducted to evaluate the impact of dietary fully oxidised β-carotene (OxBC, C40H60O15) supplementation during the perinatal period on immune status and productivity in a sow model. At day 85 of pregnancy, 150 sows were allocated to one of three dietary treatments with fifty sows per treatment. The three experimental diets were supplemented with 0, 4 or 8 mg/kg OxBC in the basal diet. The feeding trial was conducted from gestation day 85 until day 21 of lactation. Dietary OxBC supplementation greatly enhanced colostrum IgM, IgA and IgG levels, and the IgM and IgG content of 14-d milk. Dietary OxBC supplementation decreased the TNF-α and IL-8 levels in colostrum, as well as the TNF-α and IL-18 levels in 14-d milk. There was also a tendency towards an increase in the soluble CD14 level in 14-d milk. Although dietary treatments did not affect average daily feed intake nor backfat thickness loss during lactation, dietary OxBC supplementation tended to enhance litter weight and individual piglet weight at weaning. There was a trend towards increased lactose concentration in 14-d milk with increasing dietary OxBC. It is concluded that dietary supplementation with OxBC during the perinatal period enhances the lactose concentration of sow milk and the immune status of sows, which is reflected by improved cytokine status and immunoglobulin concentrations in colostrum and milk, and thus tending to increase litter weight and individual piglet weight at weaning. The results also provide a scientific nutritional reference for perinatal mothers due to the biological similarity between pigs and humans.

Trade Relationships and Gene Flow at Pottery Mound Pueblo, New Mexico

Pottery Mound is a large Ancestral Puebloan site situated within the Middle Rio Grande (MRG) region of New Mexico. This article adds to our understanding of relationships between Pottery Mound, the Western Pueblos, and Mexico through use of biological distance analysis based on dental nonmetric traits. Extensive material and cultural influences, as well as migration events from Western Pueblos to Pottery Mound, have been proposed by several scholars, while others have highlighted parallels to Mexico, especially Paquimé. A total of 1,528 individuals from the U.S. Southwest and Mexico were used to examine relationships between Pottery Mound and these areas. We find no evidence of close biological similarity between Pottery Mound and the Western Pueblos or northern Mexico. Instead, the results indicate biological affinity between Pottery Mound and sites in the MRG region and Mogollon areas. This similarity suggests that although there is evidence for trade between Pottery Mound and other sites in the southwestern United States and Mesoamerica, trade may not have been accompanied by significant gene flow from those areas from which the trade goods originated. It is possible that neighboring regions, such as the Mogollon, served as intermediaries for trade between Pottery Mound and distant regions.