Cov_DOX: A Method for Structure Prediction of Covalent Protein–Ligand Bindings

Abstract

A handful of molecular docking tools have been extended to enable a covalent docking. However, all of them face the challenge brought by the covalent bond between proteins and ligands. Many covalent drug design scenarios still heavily rely on demanding crystallographic experiments for accurate binding structures. Aiming at filling the gap between covalent dockings and crystallographic experiments, we develop and validate a hybrid method, dubbed as Cov_DOX, in this work. Cov_DOX achieves an overall success rate of 81% with RMSD < 2 Å for the Top 1 pose prediction in the validation against a test set including 405 crystal structures for covalent protein–ligand complexes, covering various types of the warhead chemistry and receptors. Such accuracy is not far from the much more demanding crystallographic experiments, in sharp contrast to the performance of the covalent docking front runners (success rate: 40–60%).

Docking covalent targets for drug discovery: stimulating the computer-aided drug design community of possible pitfalls and erroneous practices

“…Nonetheless, in a covalent virtual-screening study, a researcher may select a covalent-docking program that has been reported to achieve a high success rate for his specific receptor/residue target, ligand warhead or reaction types. Hence, as a guideline to aid the docking community in choosing an ideal covalent-docking program for their virtual-screening experiments, we survey studies that are focused on comparative evaluation of the "front runner" covalent-docking tools [ 94 ].…”

“…Secondly, Wen et al assessed the performance of four covalent-docking programs (COVDOCK, MOE, ICM-Pro, and GOLD) by employing a dataset from the BCDE set, which consists of 330 diverse ligand scaffolds and 104 receptor targets [ 93 ]. Lastly, Wei and co-workers developed a hybrid covalent-docking method known as COV_DOX and compared its performance to the previously identified covalent-docking tools (MOE, ICM-Pro, COVDOCK, and GOLD) [ 94 ]. Although, COV_DOX was validated to have a higher performance rate than all other covalent-docking tools (MOE, GOLD, ICM-Pro, and COVDOCK), the hybrid method is considered "not feasible" for a virtual-screening experiment because of its very high computing time, which is as a result of its GSA (Generalized simulated annealing) quantum mechanical calculation method [ 94 ].…”

“…Lastly, Wei and co-workers developed a hybrid covalent-docking method known as COV_DOX and compared its performance to the previously identified covalent-docking tools (MOE, ICM-Pro, COVDOCK, and GOLD) [ 94 ]. Although, COV_DOX was validated to have a higher performance rate than all other covalent-docking tools (MOE, GOLD, ICM-Pro, and COVDOCK), the hybrid method is considered "not feasible" for a virtual-screening experiment because of its very high computing time, which is as a result of its GSA (Generalized simulated annealing) quantum mechanical calculation method [ 94 ]. Therefore, the comparative studies by Keserű et al [ 95 ] and Wen et al [ 93 ] on covalent-docking tools are the only focus of this review section.…”

“…In contrast, the study of Wen et al revealed that CovDock is the docking program with the best scored pose and best sampled pose for both receptor types. Overall, the comparative analysis of the data of both groups of scientists for hydrolase, transferase, and other protein types (Table 3 ) showed inconsistency and a high discrepancy of results, which may be due to the preference of test sets considered while conducting their benchmarking studies [ 94 ]. Similarly, this is also the case for the common reaction types found with ligand warhead chemistry groups, with the exception of nucleophilic substitution (Table 4 ).…”

“…The scoring function of covalent-docking systems is deficient in terms of bond formation, preventing the evaluation of ligands with varying degrees of reactivity and hence could have a negative impact on the success rate of virtual-screening campaigns in the identification of covalent inhibitors [ 92 , 99 ]. Till date, the only covalent-docking program with more accurate scoring function with very high quantum mechanical calculation is COV_DOX (81% success rate) and it is not surprising that the hybrid method performed better than all other covalent-docking front runners 40–60% in performance) [ 94 ].…”