Next-Generation Molecular Discovery: From Bottom-Up In Vivo and In Vitro Approaches to In Silico Top-Down Approaches for Therapeutics Neogenesis
Protein and drug engineering comprises a major part of the medical and research industries, and yet approaches to discovering and understanding therapeutic molecular interactions in biological systems rely on trial and error. The general approach to molecular discovery involves screening large libra...
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Published in | Life (Basel, Switzerland) Vol. 12; no. 3; p. 363 |
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Main Authors | , , , , , |
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Language | English |
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Abstract | Protein and drug engineering comprises a major part of the medical and research industries, and yet approaches to discovering and understanding therapeutic molecular interactions in biological systems rely on trial and error. The general approach to molecular discovery involves screening large libraries of compounds, proteins, or antibodies, or in vivo antibody generation, which could be considered "bottom-up" approaches to therapeutic discovery. In these bottom-up approaches, a minimal amount is known about the therapeutics at the start of the process, but through meticulous and exhaustive laboratory work, the molecule is characterised in detail. In contrast, the advent of "big data" and access to extensive online databases and machine learning technologies offers promising new avenues to understanding molecular interactions. Artificial intelligence (AI) now has the potential to predict protein structure at an unprecedented accuracy using only the genetic sequence. This predictive approach to characterising molecular structure-when accompanied by high-quality experimental data for model training-has the capacity to invert the process of molecular discovery and characterisation. The process has potential to be transformed into a top-down approach, where new molecules can be designed directly based on the structure of a target and the desired function, rather than performing screening of large libraries of molecular variants. This paper will provide a brief evaluation of bottom-up approaches to discovering and characterising biological molecules and will discuss recent advances towards developing top-down approaches and the prospects of this. |
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AbstractList | Protein and drug engineering comprises a major part of the medical and research industries, and yet approaches to discovering and understanding therapeutic molecular interactions in biological systems rely on trial and error. The general approach to molecular discovery involves screening large libraries of compounds, proteins, or antibodies, or in vivo antibody generation, which could be considered “bottom-up” approaches to therapeutic discovery. In these bottom-up approaches, a minimal amount is known about the therapeutics at the start of the process, but through meticulous and exhaustive laboratory work, the molecule is characterised in detail. In contrast, the advent of “big data” and access to extensive online databases and machine learning technologies offers promising new avenues to understanding molecular interactions. Artificial intelligence (AI) now has the potential to predict protein structure at an unprecedented accuracy using only the genetic sequence. This predictive approach to characterising molecular structure—when accompanied by high-quality experimental data for model training—has the capacity to invert the process of molecular discovery and characterisation. The process has potential to be transformed into a top-down approach, where new molecules can be designed directly based on the structure of a target and the desired function, rather than performing screening of large libraries of molecular variants. This paper will provide a brief evaluation of bottom-up approaches to discovering and characterising biological molecules and will discuss recent advances towards developing top-down approaches and the prospects of this. Protein and drug engineering comprises a major part of the medical and research industries, and yet approaches to discovering and understanding therapeutic molecular interactions in biological systems rely on trial and error. The general approach to molecular discovery involves screening large libraries of compounds, proteins, or antibodies, or in vivo antibody generation, which could be considered "bottom-up" approaches to therapeutic discovery. In these bottom-up approaches, a minimal amount is known about the therapeutics at the start of the process, but through meticulous and exhaustive laboratory work, the molecule is characterised in detail. In contrast, the advent of "big data" and access to extensive online databases and machine learning technologies offers promising new avenues to understanding molecular interactions. Artificial intelligence (AI) now has the potential to predict protein structure at an unprecedented accuracy using only the genetic sequence. This predictive approach to characterising molecular structure-when accompanied by high-quality experimental data for model training-has the capacity to invert the process of molecular discovery and characterisation. The process has potential to be transformed into a top-down approach, where new molecules can be designed directly based on the structure of a target and the desired function, rather than performing screening of large libraries of molecular variants. This paper will provide a brief evaluation of bottom-up approaches to discovering and characterising biological molecules and will discuss recent advances towards developing top-down approaches and the prospects of this.Protein and drug engineering comprises a major part of the medical and research industries, and yet approaches to discovering and understanding therapeutic molecular interactions in biological systems rely on trial and error. The general approach to molecular discovery involves screening large libraries of compounds, proteins, or antibodies, or in vivo antibody generation, which could be considered "bottom-up" approaches to therapeutic discovery. In these bottom-up approaches, a minimal amount is known about the therapeutics at the start of the process, but through meticulous and exhaustive laboratory work, the molecule is characterised in detail. In contrast, the advent of "big data" and access to extensive online databases and machine learning technologies offers promising new avenues to understanding molecular interactions. Artificial intelligence (AI) now has the potential to predict protein structure at an unprecedented accuracy using only the genetic sequence. This predictive approach to characterising molecular structure-when accompanied by high-quality experimental data for model training-has the capacity to invert the process of molecular discovery and characterisation. The process has potential to be transformed into a top-down approach, where new molecules can be designed directly based on the structure of a target and the desired function, rather than performing screening of large libraries of molecular variants. This paper will provide a brief evaluation of bottom-up approaches to discovering and characterising biological molecules and will discuss recent advances towards developing top-down approaches and the prospects of this. |
Author | Trau, Matt Korbie, Darren Howard, Christopher B Locke, Warwick J Antaw, Fiach Kenny, Sophie E |
AuthorAffiliation | 1 Centre for Personalised Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner of College and Cooper Roads (Bldg 75), Brisbane, QLD 4072, Australia; sophie.kenny@uq.edu.au (S.E.K.); f.antaw@uq.edu.au (F.A.); c.howard2@uq.edu.au (C.B.H.) 2 Molecular Diagnostic Solutions, Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Building 101, Clunies Ross Street, Canberra, ACT 2601, Australia; warwick.locke@csiro.au 3 School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD 4072, Australia |
AuthorAffiliation_xml | – name: 1 Centre for Personalised Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner of College and Cooper Roads (Bldg 75), Brisbane, QLD 4072, Australia; sophie.kenny@uq.edu.au (S.E.K.); f.antaw@uq.edu.au (F.A.); c.howard2@uq.edu.au (C.B.H.) – name: 2 Molecular Diagnostic Solutions, Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Building 101, Clunies Ross Street, Canberra, ACT 2601, Australia; warwick.locke@csiro.au – name: 3 School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD 4072, Australia |
Author_xml | – sequence: 1 givenname: Sophie E surname: Kenny fullname: Kenny, Sophie E organization: Centre for Personalised Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner of College and Cooper Roads (Bldg 75), Brisbane, QLD 4072, Australia – sequence: 2 givenname: Fiach orcidid: 0000-0002-2895-3298 surname: Antaw fullname: Antaw, Fiach organization: Centre for Personalised Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner of College and Cooper Roads (Bldg 75), Brisbane, QLD 4072, Australia – sequence: 3 givenname: Warwick J surname: Locke fullname: Locke, Warwick J organization: Molecular Diagnostic Solutions, Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Building 101, Clunies Ross Street, Canberra, ACT 2601, Australia – sequence: 4 givenname: Christopher B surname: Howard fullname: Howard, Christopher B organization: Centre for Personalised Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner of College and Cooper Roads (Bldg 75), Brisbane, QLD 4072, Australia – sequence: 5 givenname: Darren orcidid: 0000-0001-5033-914X surname: Korbie fullname: Korbie, Darren organization: Centre for Personalised Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner of College and Cooper Roads (Bldg 75), Brisbane, QLD 4072, Australia – sequence: 6 givenname: Matt orcidid: 0000-0001-5516-1280 surname: Trau fullname: Trau, Matt organization: School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD 4072, Australia |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35330114$$D View this record in MEDLINE/PubMed |
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Title | Next-Generation Molecular Discovery: From Bottom-Up In Vivo and In Vitro Approaches to In Silico Top-Down Approaches for Therapeutics Neogenesis |
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