Computational Biology and Chemistry

With food, a huge variety of biological material gets into the human digestive tract, which the body uses for life support. The variety of food material entering the gastrointestinal tract, especially at the molecular level, cannot be distinguished from endogenous metabolites and these exogenous compounds can significantly alter the body's metabolism. Such compounds include plant miRNAs, which are indistinguishable from endogenous human miRNAs in physicochemical properties. Recently, publications have appeared about the ingestion of exogenous miRNAs into the human body, and therefore it is necessary to know how these miRNAs can be useful or harmful. In the absence of the expected effect of plant miRNAs on all human genes, one cannot be afraid of this effect. Therefore, it is necessary to clarify the degree of influence of exogenous plant miRNAs on the expression of human genes, since it is not known in advance what consequences can occur when plant miRNAs enters the human body. A huge amount of research does not allow experiments with all human genes and all plant miRNAs, so we have studied the effect of plant miRNAs on human genes using computer methods. The MirTarget program used by us with high efficiency determines the quantitative characteristics of the interaction of plant miRNAs with animal mRNAs. Supplemental data contain information that would occupy a large volume in the text of the article. But this information is necessary for understanding and confirming the conclusions of the work.

Using the PathMolD-AB software, four datasets of protein folding trajectories were produced as case studies. Four proteins were simulated, one artificially created and three real-world proteins with a growing number of amino acids, the detailed as follows: 13FIBO: it has 13 amino acids, and was artificially created by \cite{stillinger1995collective}, by distributing the hydrophobic amino acids according to the Fibonacci sequence. 2GB1 (http://10.2210/pdb2GB1/pdb): this protein is in the group of the G proteins, which exerts signal transduction functions. The dysfunction of this protein is linked to diseases such as schizophrenia in humans; 1PLC (http://10.2210/pdb1PLC/pdb): this protein performs the function of electron transportation, which is related to the process of energy production in the cell. Its functional impairment results in cell death; 5NAZ (https://www.rcsb.org/structure/5NAZ): this is a globular structural protein of collagen, and it is related to the Goodpasture's and Alport's syndromes. Link for the Dataset of Spatiotemporal Pathways of Protein Folding: https://mega.nz/#F!C5QkHQ6A!Ng2xowc2hVPoHHiSB7ww-w For each protein, a dataset was generated with 1,000 (for 13FIBO, 2GB1 and 1PLC) or 500 (for 5NAZ) different pathways. Due to the length of the last protein, fewer simulations were done. As mentioned before, all simulations start with structures randomly initialized in the 3D space, to achieve a high diversity of pathways, each one leading to the native conformation of the protein. To guarantee a reliable stabilization of the native structure, the maximum number of time-steps (t_{max}) for the simulations of the 13FIBO, 2GB1 and 1PLC proteins were set to 3x 10^6 iterations and 1x 10^8 for the 5NAZ protein. Consequently, for standardizing the number of spatiotemporal states per pathway in each dataset, the step_size for 13FIBO, 2GB1 and 1PLC were 3000, and 8000 for the 5NAZ. For each pathway, 1,000 folding states were recorded.

The data file contains miRNA, mRNA expression data and clinical data of Breast invasive carcinoma (BRCA)

Three different molecular alignment methods were used for the development of 3D-QSAR models.(1) docking-based alignment (DBA); (2) pharmacophore-based alignment (PBA) and (3) co-crystallized conformer-based alignment (CCBA).

DNA sequence alignment of oak sequences.

Aptamer configurations predicted by SimRNA; input and output of docking each of them to Ang2

We include the gene sets that are used in the experimental part of our research paper.

all the clusters sets generated by K means clustering algorithm in weka tools, and the drug-drug interaction networks generation data and results of networks are provided along with the code, which was used to identify the number of clusters

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