Predicting protein-ligand binding modes for CELPP and GC3: workflows and insight

Template-guided method for protein-ligand complex structure prediction: Application to CASP15 protein-ligand studies

Critical Assessment of Structure Prediction 15 (CASP15) added a new category of ligand prediction to promote the development of protein/RNA-ligand modeling methods, which have become important tools in modern drug discovery. A total of 22 targets were released, including 18 protein-ligand targets and 4 RNA-ligand targets. We applied our recently developed template-guided method to the protein-ligand complex structure predictions. The method combined a physicochemical, molecular docking method, and a bioinformatics-based ligand similarity method. The Protein Data Bank was scanned for template structures containing the target protein, homologous proteins, or proteins sharing a similar fold with the target protein. The binding modes of the co-bound ligands in the template structures were used to guide the complex structure prediction for the target. The CASP assessment results show that the overall performance of our method was ranked second when the top predicted model was considered for each target. Here, we analyzed our predictions in detail, and discussed the challenges including protein conformational changes, large and flexible ligands, and multiple diverse ligands in a binding pocket.

CSAlign and CSAlign-Dock: Structure alignment of ligands considering full flexibility and application to protein-ligand docking

Structure prediction of protein-ligand complexes, called protein-ligand docking, is a critical computational technique that can be used to understand the underlying principle behind the protein functions at the atomic level and to design new molecules regulating the functions. Protein-ligand docking methods have been employed in structure-based drug discovery for hit discovery and lead optimization. One of the important technical challenges in protein-ligand docking is to account for protein conformational changes induced by ligand binding. A small change such as a single side-chain rotation upon ligand binding can hinder accurate docking. Here we report an increase in docking performance achieved by structure alignment to known complex structures. First, a fully flexible compound-to-compound alignment method CSAlign is developed by global optimization of a shape score. Next, the alignment method is combined with a docking algorithm to dock a new ligand to a target protein when a reference protein-ligand complex structure is available. This alignment-based docking method, called CSAlign-Dock, showed superior performance to ab initio docking methods in cross-docking benchmark tests. Both CSAlign and CSAlign-Dock are freely available as a web server at https://galaxy.seoklab.org/csalign.

An allosteric modulator activates BK channels by perturbing coupling between Ca2+ binding and pore opening

BK type Ca²⁺-activated K⁺ channels activate in response to both voltage and Ca²⁺. The membrane-spanning voltage sensor domain (VSD) activation and Ca²⁺ binding to the cytosolic tail domain (CTD) open the pore across the membrane, but the mechanisms that couple VSD activation and Ca²⁺ binding to pore opening  are not clear. Here we show that a compound, BC5, identified from in silico screening, interacts with the CTD-VSD interface and specifically modulates the Ca²⁺ dependent activation mechanism. BC5 activates the channel in the absence of Ca²⁺ binding but Ca²⁺ binding inhibits BC5 effects. Thus, BC5 perturbs a pathway that couples Ca²⁺ binding to pore opening to allosterically affect both, which is further supported by atomistic simulations and mutagenesis. The results suggest that the CTD-VSD interaction makes a major contribution to the mechanism of Ca²⁺ dependent activation and is an important site for allosteric agonists to modulate BK channel activation.

Structural Prediction of Peptide-MHC Binding Modes

The immune system is constantly protecting its host from the invasion of pathogens and the development of cancer cells. The specific CD8⁺ T-cell immune response against virus-infected cells and tumor cells is based on the T-cell receptor recognition of antigenic peptides bound to class I major histocompatibility complexes (MHC) at the surface of antigen presenting cells. Consequently, the peptide binding specificities of the highly polymorphic MHC have important implications for the design of vaccines, for the treatment of autoimmune diseases, and for personalized cancer immunotherapy. Evidence-based machine-learning approaches have been successfully used for the prediction of peptide binders and are currently being developed for the prediction of peptide immunogenicity. However, understanding and modeling the structural details of peptide/MHC binding is crucial for a better understanding of the molecular mechanisms triggering the immunological processes, estimating peptide/MHC affinity using universal physics-based approaches, and driving the design of novel peptide ligands. Unfortunately, due to the large diversity of MHC allotypes and possible peptides, the growing number of 3D structures of peptide/MHC (pMHC) complexes in the Protein Data Bank only covers a small fraction of the possibilities. Consequently, there is a growing need for rapid and efficient approaches to predict 3D structures of pMHC complexes. Here, we review the key characteristics of the 3D structure of pMHC complexes before listing databases and other sources of information on pMHC structures and MHC specificities. Finally, we discuss some of the most prominent pMHC docking software.

Dissimilar Ligands Bind in a Similar Fashion: A Guide to Ligand Binding-Mode Prediction with Application to CELPP Studies

The molecular similarity principle has achieved great successes in the field of drug design/discovery. Existing studies have focused on similar ligands, while the behaviors of dissimilar ligands remain unknown. In this study, we developed an intercomparison strategy in order to compare the binding modes of ligands with different molecular structures. A systematic analysis of a newly constructed protein–ligand complex structure dataset showed that ligands with similar structures tended to share a similar binding mode, which is consistent with the Molecular Similarity Principle. More importantly, the results revealed that dissimilar ligands can also bind in a similar fashion. This finding may open another avenue for drug discovery. Furthermore, a template-guiding method was introduced for predicting protein–ligand complex structures. With the use of dissimilar ligands as templates, our method significantly outperformed the traditional molecular docking methods. The newly developed template-guiding method was further applied to recent CELPP studies.

Modulating the voltage sensor of a cardiac potassium channel shows antiarrhythmic effects

Cardiac arrhythmias are the most common cause of sudden cardiac death worldwide. Lengthening the ventricular action potential duration (APD), either congenitally or via pathologic or pharmacologic means, predisposes to a life-threatening ventricular arrhythmia, Torsade de Pointes. I_Ks (KCNQ1+KCNE1), a slowly activating K⁺ current, plays a role in action potential repolarization. In this study, we screened a chemical library in silico by docking compounds to the voltage-sensing domain (VSD) of the I_Ks channel. Here, we show that C28 specifically shifted I_Ks VSD activation in ventricle to more negative voltages and reversed the drug-induced lengthening of APD. At the same dosage, C28 did not cause significant changes of the normal APD in either ventricle or atrium. This study provides evidence in support of a computational prediction of I_Ks VSD activation as a potential therapeutic approach for all forms of APD prolongation. This outcome could expand the therapeutic efficacy of a myriad of currently approved drugs that may trigger arrhythmias.

A PIP2 substitute mediates voltage sensor-pore coupling in KCNQ activation

KCNQ family K⁺ channels (KCNQ1-5) in the heart, nerve, epithelium and ear require phosphatidylinositol 4,5-bisphosphate (PIP₂) for voltage dependent activation. While membrane lipids are known to regulate voltage sensor domain (VSD) activation and pore opening in voltage dependent gating, PIP₂ was found to interact with KCNQ1 and mediate VSD-pore coupling. Here, we show that a compound CP1, identified in silico based on the structures of both KCNQ1 and PIP₂, can substitute for PIP₂ to mediate VSD-pore coupling. Both PIP₂ and CP1 interact with residues amongst a cluster of amino acids critical for VSD-pore coupling. CP1 alters KCNQ channel function due to different interactions with KCNQ compared with PIP₂. We also found that CP1 returned drug-induced action potential prolongation in ventricular myocytes to normal durations. These results reveal the structural basis of PIP₂ regulation of KCNQ channels and indicate a potential approach for the development of anti-arrhythmic therapy.

Template-Based Method for Conformation Generation and Scoring for Congeneric Series of Ligands

Physics-based prediction of protein-ligand binding affinities for a congeneric series of ligands in lead optimization requires their geometries as a first step. In this paper, we report a method that uses the 3D conformation of a lead compound in complex with a protein as a template to generate conformations of a series of related analog compounds. The method uses the Maximal Common Substructure (MCS) computed between lead and analog ligands to assign coordinates for the atoms shared between the ligands. For the differing atoms, a conformation generation procedure is implemented that results in a diversity of conformations. The generated conformations are sorted using a score based on the Molecular Mechanics and Generalized Born with Solvent Accessible Surface Area contribution (MM-GBSA) method. The accuracy of the generated conformations is tested retrospectively using a cross-validation approach applied to four data sets obtained from the Drug Design Data Resource (D3R) by measuring the RMSD of the top scored conformation with respect to the crystallographic pose. The scoring ability of the method is independently assessed using data for the same protein targets to test the rank ordering ability and separating active and inactive ligands. We tested the effect of protein flexibility during structural optimization and scoring approaches with and without strain energies. Retrospective validation on data sets comprising 4 targets shows that the method outperforms random selection for all targets and outperforms a molecular weight-based null model in 3 out of 4 targets in separating active and inactive compounds. Therefore, the presented method is expected to be of utility in lead optimization for rapidly screening analog ligands and generating initial conformations for use in more detailed physics-based binding affinity prediction methods.