A statistical rescoring scheme for protein-ligand docking: Consideration of entropic effect

Improving docking and virtual screening performance using AlphaFold2 multi-state modeling for kinases

Structure-based virtual screening (SBVS) is a crucial computational approach in drug discovery, but its performance is sensitive to structural variations. Kinases, which are major drug targets, exemplify this challenge due to active site conformational changes caused by different inhibitor types. Most experimentally determined kinase structures have the DFGin state, potentially biasing SBVS towards type I inhibitors and limiting the discovery of diverse scaffolds. We introduce a multi-state modeling (MSM) protocol for AlphaFold2 (AF2) kinase structures using state-specific templates to address these challenges. Our comprehensive benchmarks evaluate predicted model qualities, binding pose prediction accuracy, and hit compound identification through ensemble SBVS. Results demonstrate that MSM models exhibit comparable or improved structural accuracy compared to standard AF2 models, enhancing pose prediction accuracy and effectively capturing kinase-ligand interactions. In virtual screening experiments, our MSM approach consistently outperforms standard AF2 and AF3 modeling, particularly in identifying diverse hit compounds. This study highlights the potential of MSM in broadening kinase inhibitor discovery by facilitating the identification of chemically diverse inhibitors, offering a promising solution to the structural bias problem in kinase-targeted drug discovery.

Integrated Approach Including Docking, MD Simulations, and Network Analysis Highlights the Action Mechanism of the Cardiac hERG Activator RPR260243

hERG is a voltage-gated potassium channel involved in the heart contraction whose defections are associated with the cardiac arrhythmia Long QT Syndrome type 2. The activator RPR260243 (RPR) represents a possible candidate to pharmacologically treat LQTS2 because it enhances the opening of the channel. However, the molecular detail of its action mechanism remains quite elusive. Here, we address the problem using a combination of docking, molecular dynamics simulations, and network analysis. We show that the drug preferably binds at the interface between the voltage sensor and the pore, enhancing the canonical activation path and determining a whole-structure rearrangement of the channel that slightly impairs inactivation.

Calculation of Crystal-Solution Dissociation Constants

The calculation of dissociation constants is an important problem in molecular biophysics. For such a calculation, it is important to correctly calculate both terms of the binding free energy; that is, the enthalpy and entropy of binding. Both these terms can be computed using molecular dynamics simulations, but this approach is very computationally expensive, and entropy calculations are especially slow. We develop an alternative very fast method of calculating the binding entropy and dissociation constants. The main part of our approach is based on the evaluation of movement ranges of molecules in the bound state. Then, the range of molecular movements in the bound state (here, in molecular crystals) is used for the calculation of the binding entropies and, then (using, in addition, the experimentally measured sublimation enthalpies), the crystal-to-vapor dissociation constants. Previously, we considered the process of the reversible sublimation of small organic molecules from crystals to vapor. In this work, we extend our approach by considering the dissolution of molecules, in addition to their sublimation. Similar to the sublimation case, our method shows a good correlation with experimentally measured dissociation constants at the dissolution of crystals.

Sublimation Entropy and Dissociation Constants Prediction by Quantitative Evaluation of Molecular Mobility in Crystals

Prediction of binding free energies (or dissociation constants) is a crucial challenge for computational biochemistry. One of the main problems here consists in fast and accurate evaluation of binding entropy, which is far more time-consuming than evaluation of binding enthalpy. Here, we offer a fast and rather accurate approach to evaluate the sublimation entropy (i.e., entropy of binding of a vapor molecule to a crystal, taken with the opposite sign) from the average range of molecular movements in the solid state. To estimate this range (and the corresponding amplitude), we considered equilibrium sublimation of small organic molecules from molecular crystals. The calculations were based on experimental data on the sublimation enthalpy, pressure of saturated vapor, and structural characteristics of the molecule in question. The resulting average amplitude (0.17 ± 0.01 Å) of molecular movements in crystals was used to predict sublimation entropies and dissociation constants for sublimation of 28 molecular crystals. The results of these predictions are in close agreement with experimental values.

Scoring Functions of Protein-Ligand Interactions

Scoring function of protein-ligand interactions is used to recognize the “native” binding pose of a ligand on the protein and to predict the binding affinity, so that the active small molecules can be discriminated from the non-active ones. Scoring function is widely used in computationally molecular docking and structure-based drug discovery. The development and improvement of scoring functions have broad implications in pharmaceutical industry and academic research. During the past three decades, much progress have been made in methodology and accuracy for scoring functions, and many successful cases have be witnessed in virtual database screening. In this chapter, the authors introduced the basic types of scoring functions and their derivations, the commonly-used evaluation methods and benchmarks, as well as the underlying challenges and current solutions. Finally, the authors discussed the promising directions to improve and develop scoring functions for future molecular docking-based drug discovery.

Structural modeling of G-protein coupled receptors: An overview on automatic web-servers

Despite the significant efforts and discoveries during the last few years in G protein-coupled receptor (GPCR) expression and crystallization, the receptors with known structures to date are limited only to a small fraction of human GPCRs. The lack of experimental three-dimensional structures of the receptors represents a strong limitation that hampers a deep understanding of their function. Computational techniques are thus a valid alternative strategy to model three-dimensional structures. Indeed, recent advances in the field, together with extraordinary developments in crystallography, in particular due to its ability to capture GPCRs in different activation states, have led to encouraging results in the generation of accurate models. This, prompted the community of modelers to render their methods publicly available through dedicated databases and web-servers. Here, we present an extensive overview on these services, focusing on their advantages, drawbacks and their role in successful applications. Future challenges in the field of GPCR modeling, such as the predictions of long loop regions and the modeling of receptor activation states are presented as well.

Other Related Techniques

With the advances in computational resources, there is an increasing urge among the computational researchers to make the in silico approaches fast, convenient, reproducible, acceptable, and sensible ones. Along with the typical two-dimensional (2D) and three-dimensional (3D) quantitative structure–activity relationship (QSAR) methods, approaches like pharmacophore, structure-based docking studies, and combinations of ligand- and structure-based approaches like comparative residue interaction analysis (CoRIA) and comparative binding energy analysis (COMBINE) have gained a significant popularity in the computational drug design process. A pharmacophore can be developed either in a ligand-based method, by superposing a set of active molecules and extracting common chemical features which are vital for their bioactivity; or in a structure-based manner, by probing probable interaction points between the macromolecular target and ligands. The interaction of protein and ligand molecules with each other is one of the interesting studies in modern molecular biology and molecular recognition. This interaction can well be explained with the conceptof a docking study to show how a molecule can bind to another molecule to exert the bioactivity. Docking and pharmacophore are non-QSAR approaches in in silico drug design that can support the QSAR findings. Approaches like CoRIA and COMBINE can use information generated from the ligand–receptor complexes to extract the critical clue concerning the types of significant interaction at the level of both the receptor and the ligand. Employing the abovementioned ligand- and structure-based methodologies and chemical libraries, virtual screening (VS) emerged as an important tool in the quest to develop novel drug compounds. VS serves as an efficient computational tool that integrates structural data with lead optimization as a cost-effective approach to drug discovery.

Photoreactive "nanorulers" detect a novel conformation of full length HDAC3-SMRT complex in solution

Histone deacetylase 3 (HDAC3) is a promising epigenetic drug target for multiple therapeutic applications. Direct interaction between the Deacetylase Activating Domain of the silencing mediator for retinoid or thyroid-hormone receptors (SMRT-DAD) is required for activation of enzymatic activity of HDAC3. The structure of this complex and the nature of interactions with HDAC inhibitors in solution are unknown. Using novel photoreactive HDAC probes, "nanorulers", we determined the distance between the catalytic site of the full-length HDAC3 and SMRT-DAD in solution at physiologically relevant conditions and found it to be substantially different from that predicted by the X-ray model with a Δ379-428 aa truncated HDAC3. Further experiments indicated that in solution this distance might change in response to chemical stimuli, while the enzymatic activity remained unaffected. These observations were further validated by Saturation Transfer Difference (STD) NMR experiments. We propose that the observed changes in the distance are an important part of the histone code that remains to be explored. Mapping direct interactions and distances between macromolecules with such "nanorulers" as a function of cellular events facilitates better understanding of basic biology and ways for its manipulation in a cell- and tissue-specific manner.

Docking challenge: protein sampling and molecular docking performance

Computational tools are essential in the drug design process, especially in order to take advantage of the increasing numbers of solved X-ray and NMR protein-ligand structures. Nowadays, molecular docking methods are routinely used for prediction of protein-ligand interactions and to aid in selecting potent molecules as a part of virtual screening of large databases. The improvements and advances in computational capacity in the past decade have allowed for further developments in molecular docking algorithms to address more complicated aspects such as protein flexibility. The effects of incorporation of active site water molecules and implicit or explicit solvation of the binding site are other relevant issues to be addressed in the docking procedures. Using the right docking algorithm at the right stage of virtual screening is most important. We report a staged study to address the effects of various aspects of protein flexibility and inclusion of active site water molecules on docking effectiveness to retrieve (and to be able to predict) correct ligand poses and to rank docked ligands in relation to their biological activity for CHK1, ERK2, LpxC, and UPA. We generated multiple conformers for the ligand and compared different docking algorithms that use a variety of approaches to protein flexibility, including rigid receptor, soft receptor, flexible side chains, induced fit, and multiple structure algorithms. Docking accuracy varied from 1% to 84%, demonstrating that the choice of method is important.

Automatic modeling of mammalian olfactory receptors and docking of odorants

We present a procedure that (i) automates the homology modeling of mammalian olfactory receptors (ORs) based on the six three-dimensional (3D) structures of G protein-coupled receptors (GPCRs) available so far and (ii) performs the docking of odorants on these models, using the concept of colony energy to score the complexes. ORs exhibit low-sequence similarities with other GPCR and current alignment methods often fail to provide a reliable alignment. Here, we use a fold recognition technique to obtain a robust initial alignment. We then apply our procedure to a human OR that we have previously functionally characterized. The analysis of the resulting in silico complexes, supported by receptor mutagenesis and functional assays in a heterologous expression system, suggests that antagonists dock in the upper part of the binding pocket whereas agonists dock in the narrow lower part. We propose that the potency of agonists in activating receptors depends on their ability to establish tight interactions with the floor of the binding pocket. We developed a web site that allows the user to upload a GPCR sequence, choose a ligand in a library and obtain the 3D structure of the free receptor and ligand-receptor complex (http://genome.jouy.inra.fr/GPCRautomodel).

PHOENIX: a scoring function for affinity prediction derived using high-resolution crystal structures and calorimetry measurements

Binding affinity prediction is one of the most critical components to computer-aided structure-based drug design. Despite advances in first-principle methods for predicting binding affinity, empirical scoring functions that are fast and only relatively accurate are still widely used in structure-based drug design. With the increasing availability of X-ray crystallographic structures in the Protein Data Bank and continuing application of biophysical methods such as isothermal titration calorimetry to measure thermodynamic parameters contributing to binding free energy, sufficient experimental data exists that scoring functions can now be derived by separating enthalpic (ΔH) and entropic (TΔS) contributions to binding free energy (ΔG). PHOENIX, a scoring function to predict binding affinities of protein-ligand complexes, utilizes the increasing availability of experimental data to improve binding affinity predictions by the following: model training and testing using high-resolution crystallographic data to minimize structural noise, independent models of enthalpic and entropic contributions fitted to thermodynamic parameters assumed to be thermodynamically biased to calculate binding free energy, use of shape and volume descriptors to better capture entropic contributions. A set of 42 descriptors and 112 protein-ligand complexes were used to derive functions using partial least-squares for change of enthalpy (ΔH) and change of entropy (TΔS) to calculate change of binding free energy (ΔG), resulting in a predictive r2 (r(pred)2) of 0.55 and a standard error (SE) of 1.34 kcal/mol. External validation using the 2009 version of the PDBbind "refined set" (n = 1612) resulted in a Pearson correlation coefficient (R(p)) of 0.575 and a mean error (ME) of 1.41 pK(d). Enthalpy and entropy predictions were of limited accuracy individually. However, their difference resulted in a relatively accurate binding free energy. While the development of an accurate and applicable scoring function was an objective of this study, the main focus was evaluation of the use of high-resolution X-ray crystal structures with high-quality thermodynamic parameters from isothermal titration calorimetry for scoring function development. With the increasing application of structure-based methods in molecular design, this study suggests that using high-resolution crystal structures, separating enthalpy and entropy contributions to binding free energy, and including descriptors to better capture entropic contributions may prove to be effective strategies toward rapid and accurate calculation of binding affinity.

Advances and challenges in protein-ligand docking

Molecular docking is a widely-used computational tool for the study of molecular recognition, which aims to predict the binding mode and binding affinity of a complex formed by two or more constituent molecules with known structures. An important type of molecular docking is protein-ligand docking because of its therapeutic applications in modern structure-based drug design. Here, we review the recent advances of protein flexibility, ligand sampling, and scoring functions—the three important aspects in protein-ligand docking. Challenges and possible future directions are discussed in the Conclusion.

Docking ligands into flexible and solvated macromolecules. 5. Force-field-based prediction of binding affinities of ligands to proteins

We report herein our efforts in the development of three empirical scoring functions with application in protein-ligand docking. A first scoring function was developed from 209 crystal structures of protein-ligand complexes and a second one from 946 cross-docked complexes. Tuning of the coefficients for the different terms making up these functions was performed by an iterative approach to optimize the correlations between observed activities and calculated scores. A third scoring function was developed from libraries of known actives and decoys docked to six different protein conformational ensembles. In the latter case, the tuning of the coefficients was performed so as to optimize the area under the curve of a receiver operating characteristic (ROC) for the discrimination of actives and inactives. The newly developed scoring functions were next assessed on independent sets of protein-ligand complexes for their ability to predict binding affinities and to discriminate actives from inactives. In the first validation the first function, which was trained on active compounds only, performed as well as other commonly used ones. On a high-throughput virtual screening validation on five protein conformational ensembles, the third scoring function that included data from inactive compounds performed significantly better. This validation showed that the inclusion of data from inactive compounds is critical for performance in virtual high-throughput screening applications.

Automated site preparation in physics-based rescoring of receptor ligand complexes

Hydrogen atoms are not typically observable in X-ray crystal structures, but inferring their locations is often important in structure-based drug design. In addition, protonation states of the protein can change in response to ligand binding, as can the orientations of OH groups, a subtle form of "induced fit." We implement and evaluate an automated procedure for optimizing polar hydrogens in protein-binding sites in complex with ligands. Specifically, we apply the previously described Independent Cluster Decomposition Algorithm (ICDA) algorithm (Li et al., Proteins 2007;66:824-837), which assigns the ionization states of titratable residues, the amide orientations of Asn/Gln side chains, the imidazole ring orientation in His, and the orientations of OH/SH groups, in a unified algorithm. We test the utility of this method for identifying nativelike ligand poses using 247 protein-ligand complexes from an established database of docked decoys. Pose selection is performed with a physics-based scoring function based on a molecular mechanics energy function and a Generalized Born implicit solvent model. The use of the ICDA receptor preparation protocol, implemented with no knowledge of the native ligand pose, increases the accuracy of pose selection significantly, with the average RMSD over all complexes decreasing from 2.7 to 1.5 A when applying ICDA. Large improvements are seen for specific classes of binding sites with titratable groups, such as aspartyl proteases.

Comparative assessment of scoring functions on a diverse test set

Scoring functions are widely applied to the evaluation of protein-ligand binding in structure-based drug design. We have conducted a comparative assessment of 16 popular scoring functions implemented in main-stream commercial software or released by academic research groups. A set of 195 diverse protein-ligand complexes with high-resolution crystal structures and reliable binding constants were selected through a systematic nonredundant sampling of the PDBbind database and used as the primary test set in our study. All scoring functions were evaluated in three aspects, that is, "docking power", "ranking power", and "scoring power", and all evaluations were independent from the context of molecular docking or virtual screening. As for "docking power", six scoring functions, including GOLD::ASP, DS::PLP1, DrugScore(PDB), GlideScore-SP, DS::LigScore, and GOLD::ChemScore, achieved success rates over 70% when the acceptance cutoff was root-mean-square deviation < 2.0 A. Combining these scoring functions into consensus scoring schemes improved the success rates to 80% or even higher. As for "ranking power" and "scoring power", the top four scoring functions on the primary test set were X-Score, DrugScore(CSD), DS::PLP, and SYBYL::ChemScore. They were able to correctly rank the protein-ligand complexes containing the same type of protein with success rates around 50%. Correlation coefficients between the experimental binding constants and the binding scores computed by these scoring functions ranged from 0.545 to 0.644. Besides the primary test set, each scoring function was also tested on four additional test sets, each consisting of a certain number of protein-ligand complexes containing one particular type of protein. Our study serves as an updated benchmark for evaluating the general performance of today's scoring functions. Our results indicate that no single scoring function consistently outperforms others in all three aspects. Thus, it is important in practice to choose the appropriate scoring functions for different purposes.

Computational evaluation of protein-small molecule binding

Determining protein-small molecule binding affinity is a key component of present-day rational drug discovery. To circumvent the time, labor, and materials costs associated with experimental protein-small molecule binding assays, a variety of structure-based computational methods have been developed for determining protein-small molecule binding affinities. These methods can be placed in one of two classes: accurate but slow (Class 1), and fast but approximate (Class 2). Class 1 methods, which explicitly take into account protein flexibility and include an atomic-level description of solvation, are capable of quantitatively reproducing experimental protein-small molecule absolute binding free energies. However, Class 1 computational requirements make screening thousands to millions of small molecules against a protein, as required for rational drug design, infeasible for the foreseeable future. Class 2 methods, on the contrary, are sufficiently fast to perform such inhibitor screening, yet they suffer from limited descriptions of protein flexibility and solvation, which in turn limit their ability to select and rank-order small molecules by computed binding affinities. This review presents an overview of Class 1 and Class 2 methods, and avenues of research in Class 2 methods aimed at bringing them closer to Class 1 accuracy.