G protein coupled receptors (GPCRs) are the most important drug targets for modern drug discovery. GPCRs are involved in different cell signalling including inflammation, neuron transmission, cell growth and many others. Almost all human diseases and disorders are related to GPCRs and their signalling. GPCRs have become popular targets for modern drug discovery and about 40% of the marketed drugs are targeting them.

The recent international GPCR-DOCK 2021 took place in December, 2021. The purpose of this event is to validate the accuracy of modern computational methods of the model predictions for GPCRs and their ligand binding. There were 45 groups from all over the world joint this contest. Five new GPCR targets, including GPR139, NPY1R, APJ, NMUR2 and kOR receptors, as well as their ligand were provided to predict their 3D models as well as their binding modes. More than 800 models in all were submitted for this contest. The results of this global event were announced during April 13-14 in the 7th iHuman Forum symposium. The cofounder of AlphaMol Dr. Yuan was invited to give a presentation in this event to share our successful story.

Prof. Irina Kufareva from UCSD, who is an organizer of GPCR-DOCK 2021, concluded that the difficulty of modelling for GPCR-DOCK 2021 is: GPR139

Based on the newly resolved GPCR structures, comparisons were also made between the  predictions of AlphaMol and AlphaFold2, as well as that with RosettaGPCR. comparison results indicated that the predictions from our team were much more accurate than both AlphaFold2 and RosettaGPCR , of which both models were obtained from their official deposit website.

A news feature article, which is entitled "What's next for AlphaFold and the AI protein-folding revolution" has been published on April 13, 2022 in Nature. In this paper,  Prof. Brian Roth, who is a structural biologist and pharmacologist at the University of North Carolina at Chapel Hill, stated the following:

... "AlphaFold isn’t always that accurate. Of the several dozen GPCR structures his lab has solved, but not yet published, he says, “about half the time, the AlphaFold structures are fairly good, and half the time they’re more or less useless for our purposes”. In some instances, he says, AlphaFold labels predictions with high confidence, but experimental structures show that it is wrong. Even when the software gets it right, it cannot model how a protein would look when bound to a drug or other small molecule (ligand), which can substantially alter the structure. Such caveats make Roth wonder how useful AlphaFold will be for drug discovery." ...

One major reason for why AlphaFold2 doesn't perform perfectly for GPCR could be: there are about 190 K structures of macromolecule were deposited in the PDB database, of which the proportion of membrane protein is only about 4%. Most of the current resolved structures were soluble proteins  which occupied about 91%. The rests were nucleotide.  AlphaFold2 is an AI-based tool which highly depends on the size of training data. However, the number of resolved membrane protein structures are only about 6500 in all. Our new algorithm is not pure AI-based and overcome this weakness.

Prediction comparisons were made among AlphaMol, AlphaFold2 and RosettaGPCRs for the new five GPCR targets from GPCR-DOCK 2021. The models from AlphaMol have much smaller RMSD of GPCR backbones and transmembrane (TM) helices  with experimental structures than that from both AlphaFold2 and RosettaGPCRs.  Smaller RMSD indicates higher accuracy.