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BioChem
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Computational Analysis of Proteomes and Genomes
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Computational Analysis of Proteomes and Genomes

A special issue of BioChem (ISSN 2673-6411).

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 13552

Special Issue Editor

Dr. Irina S. Moreira

E-Mail Website
Guest Editor
1. Life Sciences Department, Coimbra University, 3000-456 Coimbra, Portugal
2. CNC - Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal
Interests: data science; drug discovery; deep learning; computational chemistry; structural biology; protein–protein complexes; modeling; GPCRs; functional selectivity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,


The world is in urgent need of new cutting-edge computational tools that can create useful knowledge from information that is being published quickly and in a fragmented manner, delivering more tailored therapies against key multifactorial diseases. Despite the vast scientific and technological advances in drug research and development (R&D), as well as the amount of time and funding involved, we are witnessing a steady decline in pharmaceutical productivity in the last decades due to: i) lack of safety—high toxicity and many side effects are still significant reasons why a large percentage of drugs fail at pre-clinical and clinical stages; ii) patient-related constraints—human biological complexity and high genomic variability lead to different responses to therapy at both the individual and population level; iii) target-related issues—target identification and validation are the main reasons for project withdrawal in early stages, mainly due to the lack of understanding of the complexity and multifactorial character of infectious diseases. We need better-informed decisions in drug development, which means more robust and faster identification of the best targets and drug candidates to advance to preclinical and clinical trials. It is time to take advantage of the big boom of data availability, as well as the existence of powerful and cheaper software/hardware that allows the high performance of top in silico algorithms to enhance and accelerate scientific discoveries in this area.


As such, this Special Issue welcomes original research articles, short communications, and review papers. Potential topics include, but are not limited to, computational omics (proteomics, genomics, transcriptomics, metagenomics, and metabolomics) and their associated methodologies, data mining applied to different biological settings, phylogenetic and systematic analysis, network analysis, next-generation sequencing analysis, etc.

Dr. Irina S. Moreira
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 submissions that pass pre-check are 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. BioChem is an international peer-reviewed open access quarterly 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 1000 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

  • proteomics
  • genomics
  • transcriptomics
  • metagenomics
  • metabolomics
  • artificial intelligence
  • big data
  • biophysics
  • network analysis
  • phylogenetic analysis
  • NGS analysis

Published Papers (3 papers)

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Research

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13 pages, 10280 KiB  
Article
De Novo Drug Design Using Artificial Intelligence Applied on SARS-CoV-2 Viral Proteins ASYNT-GAN
by Ivan Jacobs and Manolis Maragoudakis
BioChem 2021, 1(1), 36-48; https://0-doi-org.brum.beds.ac.uk/10.3390/biochem1010004 - 05 Apr 2021
Cited by 7 | Viewed by 5340
Abstract
Computer-assisted de novo design of natural product mimetics offers a viable strategy to reduce synthetic efforts and obtain natural-product-inspired bioactive small molecules, but suffers from several limitations. Deep learning techniques can help address these shortcomings. We propose the generation of synthetic molecule structures [...] Read more.
Computer-assisted de novo design of natural product mimetics offers a viable strategy to reduce synthetic efforts and obtain natural-product-inspired bioactive small molecules, but suffers from several limitations. Deep learning techniques can help address these shortcomings. We propose the generation of synthetic molecule structures that optimizes the binding affinity to a target. To achieve this, we leverage important advancements in deep learning. Our approach generalizes to systems beyond the source system and achieves the generation of complete structures that optimize the binding to a target unseen during training. Translating the input sub-systems into the latent space permits the ability to search for similar structures, and the sampling from the latent space for generation. Full article
(This article belongs to the Special Issue Computational Analysis of Proteomes and Genomes)
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18 pages, 11313 KiB  
Article
A Novel FACS-Based Workflow for Simultaneous Assessment of RedOx Status, Cellular Phenotype, and Mitochondrial Genome Stability
by Patrick M. McTernan, Paige S. Katz, Constance Porretta, David A. Welsh and Robert W. Siggins
BioChem 2021, 1(1), 1-18; https://0-doi-org.brum.beds.ac.uk/10.3390/biochem1010001 - 02 Apr 2021
Viewed by 2792
Abstract
Intracellular reduction-oxidation (RedOx) status mediates a myriad of critical biological processes. Importantly, RedOx status regulates the differentiation of hematopoietic stem and progenitor cells (HSPCs), mesenchymal stromal cells (MSCs) and maturation of CD8+ T Lymphocytes. In most cells, mitochondria are the greatest contributors of [...] Read more.
Intracellular reduction-oxidation (RedOx) status mediates a myriad of critical biological processes. Importantly, RedOx status regulates the differentiation of hematopoietic stem and progenitor cells (HSPCs), mesenchymal stromal cells (MSCs) and maturation of CD8+ T Lymphocytes. In most cells, mitochondria are the greatest contributors of intracellular reactive oxygen species (ROS). Excess ROS leads to mitochondrial DNA (mtDNA) damage and protein depletion. We have developed a fluorescence-activated cell sorting (FACS)-based protocol to simultaneously analyze RedOx status and mtDNA integrity. This simultaneous analysis includes measurements of ROS (reduced glutathione (GSH)), ATP5H (nuclear encoded protein), MTCO1 (mitochondrial DNA encoded protein), and cell surface markers to allow discrimination of different cell populations. Using the ratio of MTCO1 to ATP5H median fluorescence intensity (MFI), we can gain an understanding of mtDNA genomic stability, since MTCO1 levels are decreased when mtDNA becomes significantly damaged. Furthermore, this workflow can be optimized for sorting cells, using any of the above parameters, allowing for downstream quantification of mtDNA genome copies/nucleus by quantitative PCR (qPCR). This unique methodology can be used to enhance analyses of the impacts of pharmacological interventions, as well as physiological and pathophysiological processes on RedOx status along with mitochondrial dynamics in most cell types. Full article
(This article belongs to the Special Issue Computational Analysis of Proteomes and Genomes)
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Review

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21 pages, 777 KiB  
Review
The Treasury Chest of Text Mining: Piling Available Resources for Powerful Biomedical Text Mining
by Nícia Rosário-Ferreira, Catarina Marques-Pereira, Manuel Pires, Daniel Ramalhão, Nádia Pereira, Victor Guimarães, Vítor Santos Costa and Irina Sousa Moreira
BioChem 2021, 1(2), 60-80; https://0-doi-org.brum.beds.ac.uk/10.3390/biochem1020007 - 27 Jul 2021
Cited by 6 | Viewed by 4410
Abstract
Text mining (TM) is a semi-automatized, multi-step process, able to turn unstructured into structured data. TM relevance has increased upon machine learning (ML) and deep learning (DL) algorithms’ application in its various steps. When applied to biomedical literature, text mining is named biomedical [...] Read more.
Text mining (TM) is a semi-automatized, multi-step process, able to turn unstructured into structured data. TM relevance has increased upon machine learning (ML) and deep learning (DL) algorithms’ application in its various steps. When applied to biomedical literature, text mining is named biomedical text mining and its specificity lies in both the type of analyzed documents and the language and concepts retrieved. The array of documents that can be used ranges from scientific literature to patents or clinical data, and the biomedical concepts often include, despite not being limited to genes, proteins, drugs, and diseases. This review aims to gather the leading tools for biomedical TM, summarily describing and systematizing them. We also surveyed several resources to compile the most valuable ones for each category. Full article
(This article belongs to the Special Issue Computational Analysis of Proteomes and Genomes)
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