Protein design: from computer models to artificial intelligence. (24th May 2017)

• Record Type: Journal Article
• Authors: Paladino, Antonella; Marchetti, Filippo; Rinaldi, Silvia; Colombo, Giorgio
• Is Part Of: Wiley interdisciplinary reviews. Volume 7:Number 5(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-05-24
• Subjects: Chemistry, Physical and theoretical -- Periodicals; Cheminformatics -- Periodicals; Biochemistry -- Periodicals; 541.220285
• DOI: 10.1002/wcms.1318
• Languages: English
• ISSNs: 1759-0876

Abstract

Abstract : The rational design of novel biomolecules with desired functional properties is one of the most fascinating challenges in science, with implications at the fundamental and practical levels. From the fundamental point of view, the design of proteins able to support nonnatural reactivities represents the decisive test on our understanding of the molecular mechanisms through which biomolecules operate. From the practical point of view, new designs may open the way to applications in a wide variety of fields, ranging from health to life science, and from catalysis to material sciences. During the past decades, we have witnessed an amazing transition in the application of computational methods to protein and enzyme design, from simple fold prediction to ab initio structural design. Herein, we review key areas and fundamental aspects of research in the design of protein structures, interactions, and reactivities. We also provide our perspective on the exciting range of developments that are made possible by the integration of innovations in hardware, software, and theory, while keeping an eye on the applications, challenges, and opportunities that can open up in many different domains of science. WIREs Comput Mol Sci 2017, 7:e1318. doi: 10.1002/wcms.1318 This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods Abstract : Design of proteins with desired functions is within reach thanks to the combination of computational biology and computer learning.