Computational and structural biotechnology journal
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Comput Struct Biotechnol J · Jan 2021
Antioxidants and pentoxifylline as coadjuvant measures to standard therapy to improve prognosis of patients with pneumonia by COVID-19.
The type 2 coronavirus causes severe acute respiratory syndrome (SARS-CoV-2) and produces pneumonia with pulmonary alveolar collapse. In some cases it also causes sepsis and septic shock. There is no specific treatment for coronavirus disease 2019 (COVID-19). ⋯ The different antioxidants reversed this alteration at the end of the treatment. The treatment with antioxidant supplements such as Vit C, E, NAC, and MT plus Px could decelerate the aggressive and lethal development of COVID-19. Antioxidant therapy can be effective in this pandemia since it improves the survival scores including SOFA, Apache II, SAPS II, COVIDGRAM, GCS by lowering the LPO, IL-6, CRP, PCT and increasing systemic TAC and NO2 -.
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Comput Struct Biotechnol J · Jan 2021
ReviewThe language of proteins: NLP, machine learning & protein sequences.
Natural language processing (NLP) is a field of computer science concerned with automated text and language analysis. In recent years, following a series of breakthroughs in deep and machine learning, NLP methods have shown overwhelming progress. Here, we review the success, promise and pitfalls of applying NLP algorithms to the study of proteins. ⋯ We present methods for encoding the information of proteins as text and analyzing it with NLP methods, reviewing classic concepts such as bag-of-words, k-mers/n-grams and text search, as well as modern techniques such as word embedding, contextualized embedding, deep learning and neural language models. In particular, we focus on recent innovations such as masked language modeling, self-supervised learning and attention-based models. Finally, we discuss trends and challenges in the intersection of NLP and protein research.
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Comput Struct Biotechnol J · Jan 2020
Unlocking COVID therapeutic targets: A structure-based rationale against SARS-CoV-2, SARS-CoV and MERS-CoV Spike.
There are no approved target therapeutics against SARS-CoV-2 or other beta-CoVs. The beta-CoV Spike protein is a promising target considering the critical role in viral infection and pathogenesis and its surface exposed features. We performed a structure-based strategy targeting highly conserved druggable regions resulting from a comprehensive large-scale sequence analysis and structural characterization of Spike domains across SARSr- and MERSr-CoVs. ⋯ These sites/residues exhibit advantageous structural features for targeted molecular and pharmacological modulation. This study establishes the Spike as a promising anti-CoV target using an approach with a potential higher resilience to resistance development and directed to a broad spectrum of Beta-CoVs, including the new SARS-CoV-2 responsible for COVID-19. This research also provides a structure-based rationale for the design and discovery of chemical inhibitors, antibodies or other therapeutic modalities successfully targeting the Beta-CoV Spike protein.
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Comput Struct Biotechnol J · Jan 2020
ReviewProbing infectious disease by single-cell RNA sequencing: Progresses and perspectives.
The increasing application of single-cell RNA sequencing (scRNA-seq) technology in life science and biomedical research has significantly increased our understanding of the cellular heterogeneities in immunology, oncology and developmental biology. This review will summarize the development of various scRNA-seq technologies; primarily discussing the application of scRNA-seq on infectious diseases, and exploring the current development, challenges, and potential applications of scRNA-seq technology in the future.
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Comput Struct Biotechnol J · Jan 2020
Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model.
The infection of a novel coronavirus found in Wuhan of China (SARS-CoV-2) is rapidly spreading, and the incidence rate is increasing worldwide. Due to the lack of effective treatment options for SARS-CoV-2, various strategies are being tested in China, including drug repurposing. In this study, we used our pre-trained deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI) to identify commercially available drugs that could act on viral proteins of SARS-CoV-2. ⋯ However, in our prediction, they may also bind to the replication complex components of SARS-CoV-2 with an inhibitory potency with Kd < 1000 nM. In addition, we also found that several antiviral agents, such as Kaletra (lopinavir/ritonavir), could be used for the treatment of SARS-CoV-2. Overall, we suggest that the list of antiviral drugs identified by the MT-DTI model should be considered, when establishing effective treatment strategies for SARS-CoV-2.