DOX: A new computational protocol for accurate prediction of the protein–ligand binding structures

Abstract

Molecular docking techniques have now been widely used to predict the protein-ligand binding modes, especially when the structures of crystal complexes are not available. Most docking algorithms are able to effectively generate and rank a large number of probable binding poses. However, it is hard for them to accurately evaluate these poses and identify the most accurate binding structure. In this study, we first examined the performance of some docking programs, based on a testing set made of 15 crystal complexes with drug statins for the human 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR). We found that most of the top ranking HMGR-statin binding poses, predicted by the docking programs, were energetically unstable as revealed by the high theoretical-level calculations, which were usually accompanied by the large deviations from the geometric parameters of the corresponding crystal binding structures. Subsequently, we proposed a new computational protocol, DOX, based on the joint use of molecular Docking, ONIOM, and eXtended ONIOM (XO) methods to predict the accurate binding structures for the protein-ligand complexes of interest. Our testing results demonstrate that the DOX protocol can efficiently predict accurate geometries for all 15 HMGR-statin crystal complexes without exception. This study suggests a promising computational route, as an effective alternative to the experimental one, toward predicting the accurate binding structures, which is the prerequisite for all the deep understandings of the properties, functions, and mechanisms of the protein-ligand complexes.

XO-PBC: An Accurate and Efficient Method for Molecular Crystals

“…We use model j d to denote the derived models defined by the corresponding intersection terms in the IEP equation, where level j d is determined, in a similar way as in ONIOM, by the lowest level among all the primitive models used to derive model j d . We emphasize here again that, to go beyond the standard ONIOM scheme, XO allows different models to overlap with each other, and to be treated at different levels of theory, leading to an accurate yet efficient treatment of large molecular systems, as represented by good modellings of zeolites, polypeptides, cyclodextrins, protein–ligand interactions, anion−π interacting systems, etc. ,,− …”

A Cloud Computing Platform for Scalable Relative and Absolute Binding Free Energy Prediction: New Opportunities and Challenges for Drug Discovery

“…Many in-silico methods, including both rule-based physical models and data-based machine learning (ML) or artificial intelligence (AI) models, have been adopted in drug discovery projects for binding affinity prediction with continuously improved performance. [6][7][8][9][10][11][12][13][14][15][16] Among these computational methods, free energy perturbation (FEP) has attracted increasing attention for binding affinity predictions between candidate compounds and their biological target due to its reliable performance in accuracy and efficiency. FEP employs a series of well-defined alchemical states to change the system from one real ligand to another one (the relative binding free energy method, RBFE) or from the target-ligand complex to the separated target and ligand state (the absolute binding free energy method, ABFE).…”

Conformation Search Across Multiple-Level Potential-Energy Surfaces (CSAMP): A Strategy for Accurate Prediction of Protein–Ligand Binding Structures

“…Lack of information on crystallographic binding structures can result in failure on virtual screening or modification and optimization of the potential lead structures. A useful alternative is provided by molecular docking methods, − although there is always a trade-off from accuracy to computational speediness and cheapness. , Recently, , it has been noticed that the docking algorithm for conformation search is sufficiently good to generate a set of binding structures which encompasses the reference crystal structure; nevertheless, the employed empirical scoring functions often fail to acknowledge the “right poses” and credit a significantly worse binding pose as the optimal docking result.…”