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      Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives

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          Abstract

          The discovery of new pharmaceutical drugs is one of the preeminent tasks—scientifically, economically, and socially—in biomedical research. Advances in informatics and computational biology have increased productivity at many stages of the drug discovery pipeline. Nevertheless, drug discovery has slowed, largely due to the reliance on small molecules as the primary source of novel hypotheses. Natural products (such as plant metabolites, animal toxins, and immunological components) comprise a vast and diverse source of bioactive compounds, some of which are supported by thousands of years of traditional medicine, and are largely disjoint from the set of small molecules used commonly for discovery. However, natural products possess unique characteristics that distinguish them from traditional small molecule drug candidates, requiring new methods and approaches for assessing their therapeutic potential. In this review, we investigate a number of state-of-the-art techniques in bioinformatics, cheminformatics, and knowledge engineering for data-driven drug discovery from natural products. We focus on methods that aim to bridge the gap between traditional small-molecule drug candidates and different classes of natural products. We also explore the current informatics knowledge gaps and other barriers that need to be overcome to fully leverage these compounds for drug discovery. Finally, we conclude with a “road map” of research priorities that seeks to realize this goal.

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Cancer immunotherapy: moving beyond current vaccines.

            Great progress has been made in the field of tumor immunology in the past decade, but optimism about the clinical application of currently available cancer vaccine approaches is based more on surrogate endpoints than on clinical tumor regression. In our cancer vaccine trials of 440 patients, the objective response rate was low (2.6%), and comparable to the results obtained by others. We consider here results in cancer vaccine trials and highlight alternate strategies that mediate cancer regression in preclinical and clinical models.
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              The coming of age of de novo protein design.

              There are 20(200) possible amino-acid sequences for a 200-residue protein, of which the natural evolutionary process has sampled only an infinitesimal subset. De novo protein design explores the full sequence space, guided by the physical principles that underlie protein folding. Computational methodology has advanced to the point that a wide range of structures can be designed from scratch with atomic-level accuracy. Almost all protein engineering so far has involved the modification of naturally occurring proteins; it should now be possible to design new functional proteins from the ground up to tackle current challenges in biomedicine and nanotechnology.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                30 April 2019
                2019
                : 10
                : 368
                Affiliations
                [1] 1Department of Biomedical Informatics, Columbia University , New York, NY, United States
                [2] 2Department of Systems Biology, Columbia University , New York, NY, United States
                [3] 3Department of Medicine, Columbia University , New York, NY, United States
                [4] 4Data Science Institute, Columbia University , New York, NY, United States
                Author notes

                Edited by: Dana C. Crawford, Case Western Reserve University, United States

                Reviewed by: Brett K. Beaulieu-Jones, Harvard Medical School, United States; Harry Hochheiser, University of Pittsburgh, United States; Ellen L. Palmer, Case Western Reserve University, United States

                *Correspondence: Nicholas P. Tatonetti npt2105@ 123456cumc.columbia.edu

                This article was submitted to Applied Genetic Epidemiology, a section of the journal Frontiers in Genetics

                Article
                10.3389/fgene.2019.00368
                6503039
                31114606
                e0ad593e-80a3-4411-bf81-f75e0d1bee63
                Copyright © 2019 Romano and Tatonetti.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 12 December 2018
                : 05 April 2019
                Page count
                Figures: 1, Tables: 1, Equations: 1, References: 169, Pages: 16, Words: 14693
                Funding
                Funded by: National Institutes of Health 10.13039/100000002
                Categories
                Genetics
                Review

                Genetics
                drug discovery,methods,cheminformatics,bioinformatics,ontologies,translation,natural products
                Genetics
                drug discovery, methods, cheminformatics, bioinformatics, ontologies, translation, natural products

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