Jose G Almeida1, Antonio J Preto1, Panagiotis I Koukos2, Alexandre M J J Bonvin2, Irina S Moreira3. 1. CNC - Center for Neuroscience and Cell Biology, Rua Larga, FMUC, Polo I, 1°andar, Universidade de Coimbra, 3004-517, Coimbra, Portugal. 2. Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, Padualaan 8, 3584CH, The Netherlands. 3. CNC - Center for Neuroscience and Cell Biology, Rua Larga, FMUC, Polo I, 1°andar, Universidade de Coimbra, 3004-517, Coimbra, Portugal; Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, Padualaan 8, 3584CH, The Netherlands. Electronic address: irina.moreira@cnc.uc.pt.
Abstract
BACKGROUND: Membrane proteins (MPs) play diverse and important functions in living organisms. They constitute 20% to 30% of the known bacterial, archaean and eukaryotic organisms' genomes. In humans, their importance is emphasized as they represent 50% of all known drug targets. Nevertheless, experimental determination of their three-dimensional (3D) structure has proven to be both time consuming and rather expensive, which has led to the development of computational algorithms to complement the available experimental methods and provide valuable insights. SCOPE OF REVIEW: This review highlights the importance of membrane proteins and how computational methods are capable of overcoming challenges associated with their experimental characterization. It covers various MP structural aspects, such as lipid interactions, allostery, and structure prediction, based on methods such as Molecular Dynamics (MD) and Machine-Learning (ML). MAJOR CONCLUSIONS: Recent developments in algorithms, tools and hybrid approaches, together with the increase in both computational resources and the amount of available data have resulted in increasingly powerful and trustworthy approaches to model MPs. GENERAL SIGNIFICANCE: Even though MPs are elementary and important in nature, the determination of their 3D structure has proven to be a challenging endeavor. Computational methods provide a reliable alternative to experimental methods. In this review, we focus on computational techniques to determine the 3D structure of MP and characterize their binding interfaces. We also summarize the most relevant databases and software programs available for the study of MPs.
BACKGROUND: Membrane proteins (MPs) play diverse and important functions in living organisms. They constitute 20% to 30% of the known bacterial, archaean and eukaryotic organisms' genomes. In humans, their importance is emphasized as they represent 50% of all known drug targets. Nevertheless, experimental determination of their three-dimensional (3D) structure has proven to be both time consuming and rather expensive, which has led to the development of computational algorithms to complement the available experimental methods and provide valuable insights. SCOPE OF REVIEW: This review highlights the importance of membrane proteins and how computational methods are capable of overcoming challenges associated with their experimental characterization. It covers various MP structural aspects, such as lipid interactions, allostery, and structure prediction, based on methods such as Molecular Dynamics (MD) and Machine-Learning (ML). MAJOR CONCLUSIONS: Recent developments in algorithms, tools and hybrid approaches, together with the increase in both computational resources and the amount of available data have resulted in increasingly powerful and trustworthy approaches to model MPs. GENERAL SIGNIFICANCE: Even though MPs are elementary and important in nature, the determination of their 3D structure has proven to be a challenging endeavor. Computational methods provide a reliable alternative to experimental methods. In this review, we focus on computational techniques to determine the 3D structure of MP and characterize their binding interfaces. We also summarize the most relevant databases and software programs available for the study of MPs.
Authors: Siewert J Marrink; Valentina Corradi; Paulo C T Souza; Helgi I Ingólfsson; D Peter Tieleman; Mark S P Sansom Journal: Chem Rev Date: 2019-01-09 Impact factor: 72.087