The role of experimental and computational structural approaches in 7TM drug discovery

Abstract

There is now a significant amount of structural information covering a range of 7TM protein classes (A, B, C, and F) and activation states. For these and closely related proteins, structure-based drug discovery has proven to be a powerful tool. More structural information is needed with respect to dimerization, 7TM proteins with β-arrestin to help in understanding the control of biased signaling, and full-protein structure determinations for non-class A proteins to help in understanding and controlling their functioning. Finally, the use of the existing structural information to target new sites on these proteins needs further exploration.

In silicoExploration of the Conformational Universe of GPCRs

“…This can be considered an obstacle for structure‐based drug design targeting different receptor conformations. Fortunately, there is nowadays a considerable amount of structural information covering classes A, B, C, and F of GPCRs in various activation states, although more structural information is needed with respect to the control of different receptor activation states . The rapidly growing structural and functional information for GPCRs can be applied to advance the discovery of new drugs with desired characteristics, including agonism, biased agonism or antagonism …”

“…When a 3D structure of the target is available from experimental or molecular modelling studies, high‐throughput docking is the method of choice . Nevertheless, there are relatively few X‐ray structures of GPCRs in complex with agonists available and most high‐throughput docking experiments have identified antagonists …”

Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking

“…Both components have been the subject of intensive research and development in both academic and commercial settings (Beck et al, 2017;Ciancetta and Jacobson, 2017;Hodos et al, 2016;Kim et al, 2017;Li et al, 2018;Mafud et al, 2016;Martinez-Mayorga et al, 2015;Medina-Franco et al, 2015;Morgnanesi et al, 2015;Ogungbe and Setzer, 2016;Rosano et al, 2016;Shunmugam et al, 2017;Singh and Ecker, 2018;Tan et al, 2016;Topiol and Sabio, 2015;Zhu et al, 2015). Nonetheless, computational methods for pose prediction and affinity ranking have yet to fulfill their perceived promise, as neither is yet fully reliable (Gaieb et al, 2017(Gaieb et al, , 2019Gathiaka et al, 2016;Muddana et al, 2012Muddana et al, , 2014Warren et al, 2006;Yin et al, 2017).…”

Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking

“…Both components have been the subject of intensive research and development in both academic and commercial settings. [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] Nonetheless, computational methods for pose prediction and affinity ranking have yet to fulfill their perceived promise, as neither is yet fully reliable. [45][46][47][48][49][50][51] In fact, it is surprisingly difficult even to compare the reliability of various methods in a consistent manner, and this limitation makes it correspondingly difficult to make and verify technical progress.…”