Special Issue "Big Data Analysis in Biomolecular Research, Bioinformatics, and Systems Biology with Complex Networks and Multi-Label Machine Learning Models"

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: 31 July 2020.

Special Issue Editor

Special Issue Information

Dear Colleagues,

Modern experimental techniques used in biomolecular research produce a large amount of data. These techniques include like next-generation sequencing, molecular NMR, iNMR imaging, 2DE and MS in proteomics, and EEG in neurosciences. The data produced, sometimes coined big data, have been collected in public databases online (e.g., ChEMBL, GeneBank, PDB, PubChem, KEGG, NLM, and AIDSvu). The big data sets may give important clues for knowledge discovery, translational research, and personalized medicine if we can analyze them properly. This in turn may result in the development of new applications for omics, drug discovery, vaccine design, biomarkers discovery, neurosciences, and biomedical engineering, etc.

However, most of these big data sets present certain features that difficult the analysis. We can summarize may of these problems, shortly, as big data = 5V + C data features. The 5Vs include problems with data volume, veracity, variability, velocity, and value. The C refers to the complexity of data due to in part of a high number of interconnections among variables in the complex systems studied. This is due to the existence of big data sets forming complex networks in Systems Biology. Examples of these complex networks are due to multiple drugs interacting with different target proteins (drug-target networks), protein–protein interactions networks (PINS), gene–gene regulatory networks (GRN), etc.

In this context, we may need complex network analysis tools to capture the complexity of the data and lulti-label machine learning (ML) algorithms to find predictive models for these data about systems with multiple biological properties (IC50, Ki, Km, LD50, etc.) and multiple labels (drugs, proteins, cell lines, tissues, brain regions, organisms, populations, etc.).

Last but not the least, the use of all these computational techniques to process biomolecular data becomes even more important if we develop computational biomedical engineering systems for translational and personalized medicine. In consequence, ML algorithms have to merge data from preclinical assays (as in ChEMBL databases) with data from clinical assays with personal data information. In this sense, the use of the previous data analysis tools in biomolecular sciences has to consider the legal aspects relevant to personal data protection, software copyright, etc., as well; see, e.g., GDPR in Europe, REACH, and OECD regulations.

Consequently, in this new Issue we propose to open a forum for the publication of technical aspects and new applications or results (software, databases, cheminformatic models, machine learning algorithms, and complex network tools) and the discussion of the ethical and legal implications of these tools as well.

The present Special Issue is also associated with MOL2NET-05, the International Conference on Multidisciplinary Sciences, ISSN: 2624-5078, MDPI SciForum, Basel, Switzerland, 2019. The conference has its HQs in University of Basque Country (UPV/EHU) and is supported by Professors of Ikerbasque: Basque Foundation for Sciences, Harvard Medicine School, UNC Chapel Hill, EMBL-EBI United Kingdom, CNAM Paris, Miami Dade College (MDC), University of Coruña (UDC), etc. The MOL2NET series is hosting more than 10 workshops with in-person and/or online participation every year in universities in the USA, Europe, Brazil, China, India, etc. In addition, the conference hosts the USEDAT: USA-Europe Data Analysis Training School, focused on training students worldwide in data analysis, with an emphasis in cheminformatics. The members of the committee have also guest edited other Special Issues in multiple MDPI journals. Please see the link of the conference at https://mol2net-05.sciforum.net/

We especially encourage submissions of papers from colleagues worldwide to the conference (short communications) and complete versions (full papers) to the present Special Issue. Prof. Dr. Humbert González-Díaz

Prof. Dr. Humbert González-Díaz
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomolecules is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data
  • bioinformatics
  • complex networks
  • systems biology
  • machine learning
  • cheminformatics
  • QSAR

Published Papers (2 papers)

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Research

Open AccessArticle
Dynamical Rearrangement of Human Epidermal Growth Factor Receptor 2 upon Antibody Binding: Effects on the Dimerization
Biomolecules 2019, 9(11), 706; https://doi.org/10.3390/biom9110706 - 05 Nov 2019
Abstract
Human epidermal growth factor 2 (HER2) is a ligand-free tyrosine kinase receptor of the HER family that is overexpressed in some of the most aggressive tumours. Although it is known that HER2 dimerization involves a specific region of its extracellular domain, the so-called [...] Read more.
Human epidermal growth factor 2 (HER2) is a ligand-free tyrosine kinase receptor of the HER family that is overexpressed in some of the most aggressive tumours. Although it is known that HER2 dimerization involves a specific region of its extracellular domain, the so-called “dimerization arm”, the mechanism of dimerization inhibition remains uncertain. However, uncovering how antibody interactions lead to inhibition of HER2 dimerization is of key importance in understanding its role in tumour progression and therapy. Herein, we employed several computational modelling techniques for a molecular-level understanding of the interactions between HER and specific anti-HER2 antibodies, namely an antigen-binding (Fab) fragment (F0178) and a single-chain variable fragment from Trastuzumab (scFv). Specifically, we investigated the effects of antibody-HER2 interactions on the key residues of “dimerization arm” from molecular dynamics (MD) simulations of unbound HER (in a total of 1 µs), as well as ScFv:HER2 and F0178:HER2 complexes (for a total of 2.5 µs). A deep surface analysis of HER receptor revealed that the binding of specific anti-HER2 antibodies induced conformational changes both in the interfacial residues, which was expected, and in the ECDII (extracellular domain), in particular at the “dimerization arm”, which is critical in establishing protein–protein interface (PPI) interactions. Our results support and advance the knowledge on the already described trastuzumab effect on blocking HER2 dimerization through synergistic inhibition and/or steric hindrance. Furthermore, our approach offers a new strategy for fine-tuning target activity through allosteric ligands. Full article
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Open AccessArticle
A Computational Toxicology Approach to Screen the Hepatotoxic Ingredients in Traditional Chinese Medicines: Polygonum multiflorum Thunb as a Case Study
Biomolecules 2019, 9(10), 577; https://doi.org/10.3390/biom9100577 - 07 Oct 2019
Abstract
In recent years, liver injury induced by Traditional Chinese Medicines (TCMs) has gained increasing attention worldwide. Assessing the hepatotoxicity of compounds in TCMs is essential and inevitable for both doctors and regulatory agencies. However, there has been no effective method to screen the [...] Read more.
In recent years, liver injury induced by Traditional Chinese Medicines (TCMs) has gained increasing attention worldwide. Assessing the hepatotoxicity of compounds in TCMs is essential and inevitable for both doctors and regulatory agencies. However, there has been no effective method to screen the hepatotoxic ingredients in TCMs available until now. In the present study, we initially built a large scale dataset of drug-induced liver injuries (DILIs). Then, 13 types of molecular fingerprints/descriptors and eight machine learning algorithms were utilized to develop single classifiers for DILI, which resulted in 5416 single classifiers. Next, the NaiveBayes algorithm was adopted to integrate the best single classifier of each machine learning algorithm, by which we attempted to build a combined classifier. The accuracy, sensitivity, specificity, and area under the curve of the combined classifier were 72.798, 0.732, 0.724, and 0.793, respectively. Compared to several prior studies, the combined classifier provided better performance both in cross validation and external validation. In our prior study, we developed a herb-hepatotoxic ingredient network and a herb-induced liver injury (HILI) dataset based on pre-clinical evidence published in the scientific literature. Herein, by combining that and the combined classifier developed in this work, we proposed the first instance of a computational toxicology to screen the hepatotoxic ingredients in TCMs. Then Polygonum multiflorum Thunb (PmT) was used as a case to investigate the reliability of the approach proposed. Consequently, a total of 25 ingredients in PmT were identified as hepatotoxicants. The results were highly consistent with records in the literature, indicating that our computational toxicology approach is reliable and effective for the screening of hepatotoxic ingredients in Pmt. The combined classifier developed in this work can be used to assess the hepatotoxic risk of both natural compounds and synthetic drugs. The computational toxicology approach presented in this work will assist with screening the hepatotoxic ingredients in TCMs, which will further lay the foundation for exploring the hepatotoxic mechanisms of TCMs. In addition, the method proposed in this work can be applied to research focused on other adverse effects of TCMs/synthetic drugs. Full article
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