Modeling Binding with Large Conformational Changes: Key Points in Ensemble-Docking Approaches

An in silico investigation of Kv2.1 potassium channel: Model building and inhibitors binding sites analysis

Kv2.1 is widely expressed in brain, and inhibiting Kv2.1 is a potential strategy to prevent cell death and achieve neuroprotection in ischemic stroke. Herein, an in silico model of Kv2.1 tetramer structure was constructed by employing the AlphaFold-Multimer deep learning method to facilitate the rational discovery of Kv2.1 inhibitors. GaMD was utilized to create an ion transporting trajectory, which was analyzed with HMM to generate multiple representative receptor conformations. The binding site of RY785 and RY796(S) under the P-loop was defined with Fpocket program together with the competitive binding electrophysiology assay. The docking poses of the two inhibitors were predicted with the aid of the semi-empirical quantum mechanical calculation, and the IGMH results suggested that Met375, Thr376, and Thr377 of the P-helix and Ile405 of the S6 segment made significant contributions to the binding affinity. These results provided insights for rational molecular design to develop novel Kv2.1 inhibitors.

Structural basis of dimerization of chemokine receptors CCR5 and CXCR4

G protein-coupled receptors (GPCRs) are prominent drug targets responsible for extracellular-to-intracellular signal transduction. GPCRs can form functional dimers that have been poorly characterized so far. Here, we show the dimerization mechanism of the chemokine receptors CCR5 and CXCR4 by means of an advanced free-energy technique named coarse-grained metadynamics. Our results reproduce binding events between the GPCRs occurring in the minute timescale, revealing a symmetric and an asymmetric dimeric structure for each of the three investigated systems, CCR5/CCR5, CXCR4/CXCR4, and CCR5/CXCR4. The transmembrane helices TM4-TM5 and TM6-TM7 are the preferred binding interfaces for CCR5 and CXCR4, respectively. The identified dimeric states differ in the access to the binding sites of the ligand and G protein, indicating that dimerization may represent a fine allosteric mechanism to regulate receptor activity. Our study offers structural basis for the design of ligands able to modulate the formation of CCR5 and CXCR4 dimers and in turn their activity, with therapeutic potential against HIV, cancer, and immune-inflammatory diseases.

Worth the Weight: Sub-Pocket EXplorer (SubPEx), a Weighted Ensemble Method to Enhance Binding-Pocket Conformational Sampling

Structure-based virtual screening (VS) is an effective method for identifying potential small-molecule ligands, but traditional VS approaches consider only a single binding-pocket conformation. Consequently, they struggle to identify ligands that bind to alternate conformations. Ensemble docking helps address this issue by incorporating multiple conformations into the docking process, but it depends on methods that can thoroughly explore pocket flexibility. We here introduce Sub-Pocket EXplorer (SubPEx), an approach that uses weighted ensemble (WE) path sampling to accelerate binding-pocket sampling. As proof of principle, we apply SubPEx to three proteins relevant to drug discovery: heat shock protein 90, influenza neuraminidase, and yeast hexokinase 2. SubPEx is available free of charge without registration under the terms of the open-source MIT license: http://durrantlab.com/subpex/.

Worth the weight: Sub-Pocket EXplorer (SubPEx), a weighted-ensemble method to enhance binding-pocket conformational sampling

Structure-based virtual screening (VS) is an effective method for identifying potential small-molecule ligands, but traditional VS approaches consider only a single binding-pocket conformation. Consequently, they struggle to identify ligands that bind to alternate conformations. Ensemble docking helps address this issue by incorporating multiple conformations into the docking process, but it depends on methods that can thoroughly explore pocket flexibility. We here introduce Sub-Pocket EXplorer (SubPEx), an approach that uses weighted ensemble (WE) path sampling to accelerate binding-pocket sampling. As proof of principle, we apply SubPEx to three proteins relevant to drug discovery: heat shock protein 90, influenza neuraminidase, and yeast hexokinase 2. SubPEx is available free of charge without registration under the terms of the open-source MIT license: http://durrantlab.com/subpex/.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Preliminary modelling as the first stage of targeted organic synthesis

The review aims to present a classification and applicability analysis of methods for preliminary molecular modelling for targeted organic, catalytic and biocatalytic synthesis. The following three main approaches are considered as a primary classification of the methods: modelling of the target – ligand coordination without structural information on both the target and the resulting complex; calculations based on experimentally obtained structural information about the target; and dynamic simulation of the target – ligand complex and the reaction mechanism with calculation of the free energy of the reaction. The review is meant for synthetic chemists to be used as a guide for building an algorithm for preliminary modelling and synthesis of structures with specified properties.

The bibliography includes 353 references.

Metadynamics-Based Approaches for Modeling the Hypoxia-Inducible Factor 2α Ligand Binding Process

Several methods based on enhanced-sampling molecular dynamics have been proposed for studying ligand binding processes. Here, we developed a protocol that combines the advantages of steered molecular dynamics (SMD) and metadynamics. While SMD is proposed for investigating possible unbinding pathways of the ligand and identifying the preferred one, metadynamics, with the path collective variable (PCV) formalism, is suggested to explore the binding processes along the pathway defined on the basis of SMD, by using only two CVs. We applied our approach to the study of binding of two known ligands to the hypoxia-inducible factor 2α, where the buried binding cavity makes simulation of the process a challenging task. Our approach allowed identification of the preferred entrance pathway for each ligand, highlighted the features of the bound and intermediate states in the free-energy surface, and provided a binding affinity scale in agreement with experimental data. Therefore, it seems to be a suitable tool for elucidating ligand binding processes of similar complex systems.

Large-Scale Virtual Screening Against the MET Kinase Domain Identifies a New Putative Inhibitor Type

By using an ensemble-docking strategy, we undertook a large-scale virtual screening campaign in order to identify new putative hits against the MET kinase target. Following a large molecular dynamics sampling of its conformational space, a set of 45 conformers of the kinase was retained as docking targets to take into account the flexibility of the binding site moieties. Our screening funnel started from about 80,000 chemical compounds to be tested in silico for their potential affinities towards the kinase binding site. The top 100 molecules selected—thanks to the molecular docking results—were further analyzed for their interactions, and 25 of the most promising ligands were tested for their ability to inhibit MET activity in cells. F0514-4011 compound was the most efficient and impaired this scattering response to HGF (Hepatocyte Growth Factor) with an IC50 of 7.2 μM. Interestingly, careful docking analysis of this molecule with MET suggests a possible conformation halfway between classical type-I and type-II MET inhibitors, with an additional region of interaction. This compound could therefore be an innovative seed to be repositioned from its initial antiviral purpose towards the field of MET inhibitors. Altogether, these results validate our ensemble docking strategy as a cost-effective functional method for drug development.

Highly Flexible Ligand Docking: Benchmarking of the DockThor Program on the LEADS-PEP Protein-Peptide Data Set

Protein-peptide interactions play a crucial role in many cellular and biological functions, which justify the increasing interest in the development of peptide-based drugs. However, predicting experimental binding modes and affinities in protein-peptide docking remains a great challenge for most docking programs due to some particularities of this class of ligands, such as the high degree of flexibility. In this paper, we present the performance of the DockThor program on the LEADS-PEP data set, a benchmarking set composed of 53 diverse protein-peptide complexes with peptides ranging from 3 to 12 residues and with up to 51 rotatable bonds. The DockThor performance for pose prediction on redocking studies was compared with some state-of-the-art docking programs that were also evaluated on the LEADS-PEP data set, AutoDock, AutoDock Vina, Surflex, GOLD, Glide, rDock, and DINC, as well as with the task-specific docking protocol HPepDock. Our results indicate that DockThor could dock 40% of the cases with an overall backbone RMSD below 2.5 Å when the top-scored docking pose was considered, exhibiting similar results to Glide and outperforming other protein-ligand docking programs, whereas rDock and HPepDock achieved superior results. Assessing the docking poses closest to the crystal structure (i.e., best-RMSD pose), DockThor achieved a success rate of 60% in pose prediction. Due to the great overall performance of handling peptidic compounds, the DockThor program can be considered as suitable for docking highly flexible and challenging ligands, with up to 40 rotatable bonds. DockThor is freely available as a virtual screening Web server at https://www.dockthor.lncc.br/ .

Holo-like and Druggable Protein Conformations from Enhanced Sampling of Binding Pocket Volume and Shape

Understanding molecular recognition of small molecules by proteins in atomistic detail is key for drug design. Molecular docking is a widely used computational method to mimic ligand-protein association in silico. However, predicting conformational changes occurring in proteins upon ligand binding is still a major challenge. Ensemble docking approaches address this issue by considering a set of different conformations of the protein obtained either experimentally or from computer simulations, e.g., molecular dynamics. However, holo structures prone to host (the correct) ligands are generally poorly sampled by standard molecular dynamics simulations of the apo protein. In order to address this limitation, we introduce a computational approach based on metadynamics simulations called ensemble docking with enhanced sampling of pocket shape (EDES) that allows holo-like conformations of proteins to be generated by exploiting only their apo structures. This is achieved by defining a set of collective variables that effectively sample different shapes of the binding site, ultimately mimicking the steric effect due to the ligand. We assessed the method on three challenging proteins undergoing different extents of conformational changes upon ligand binding. In all cases our protocol generates a significant fraction of structures featuring a low RMSD from the experimental holo geometry. Moreover, ensemble docking calculations using those conformations yielded in all cases native-like poses among the top-ranked ones.

Free Energy Profile for Penetration of Pittsburgh Compound-B into the Amyloid β Fibril

The amyloid β (Aβ) fibril is a hallmark of Alzheimer's disease (AD) and has therefore served as an important target for early diagnosis of AD. The Pittsburgh Compound-B (PiB) is one of the most famous positron emission tomography (PET) tracers commonly used for in vivo detection of Aβ fibrils. Many theoretical studies have predicted the existence of various core binding sites with different microenvironments for probes binding to the Aβ fibril. However, little attention has been devoted to how the probes actually penetrate into the different core binding sites. In this study, an integrated molecular modeling scheme is used to study the penetration of PiB into the core binding sites of the Aβ_1-42 fibril structure recently obtained by cryogenic electron microscopy. We find that there are two core binding sites for PiB with dramatic differences in cavity size and microenvironment properties, and furthermore that the penetration of PiB into site-1 is energetically prohibitive, whereas the penetration into site-2 is much more favorable. Therefore, the binding capacity at site-2 may be larger than that at site-1 despite its lower binding affinity. Our results thus suggest that site-2 may be a major binding site for PiB binding to Aβ fibril and emphasize the importance to adopt a full dynamical picture when studying tracer-fibril binding problems in general, something that in turn can be used to guide the development of tracers with higher affinity and selectivity for the Aβ fibril.

Molecular modeling simulation studies reveal new potential inhibitors against HPV E6 protein

High-risk strains of human papillomavirus (HPV) have been identified as the etiologic agent of some anogenital tract, head, and neck cancers. Although prophylactic HPV vaccines have been approved; it is still necessary a drug-based treatment against the infection and its oncogenic effects. The E6 oncoprotein is one of the most studied therapeutic targets of HPV, it has been identified as a key factor in cell immortalization and tumor progression in HPV-positive cells. E6 can promote the degradation of p53, a tumor suppressor protein, through the interaction with the cellular ubiquitin ligase E6AP. Therefore, preventing the formation of the E6-E6AP complex is one of the main strategies to inhibit the viability and proliferation of infected cells. Herein, we propose an in silico pipeline to identify small-molecule inhibitors of the E6-E6AP interaction. Virtual screening was carried out by predicting the ADME properties of the molecules and performing ensemble-based docking simulations to E6 protein followed by binding free energy estimation through MM/PB(GB)SA methods. Finally, the top-three compounds were selected, and their stability in the E6 docked complex and their effect in the inhibition of the E6-E6AP interaction was corroborated by molecular dynamics simulation. Therefore, this pipeline and the identified molecules represent a new starting point in the development of anti-HPV drugs.

Efficiency of Stratification for Ensemble Docking Using Reduced Ensembles

Molecular docking can account for receptor flexibility by combining the docking score over multiple rigid receptor conformations, such as snapshots from a molecular dynamics simulation. Here, we evaluate a number of common snapshot selection strategies using a quality metric from stratified sampling, the efficiency of stratification, which compares the variance of a selection strategy to simple random sampling. We also extend the metric to estimators of exponential averages (which involve an exponential transformation, averaging, and inverse transformation) and minima. For docking sets of over 500 ligands to four different proteins of varying flexibility, we observe that, for estimating ensemble averages and exponential averages, many clustering algorithms have similar performance trends: for a few snapshots (less than 25), medoids are the most efficient, while, for a larger number, optimal (the allocation that minimizes the variance) and proportional (to the size of each cluster) allocation become more efficient. Proportional allocation appears to be the most consistently efficient for estimating minima.

Simulation-Guided Design of Cytochrome P450 for Chemo- and Regioselective Macrocyclic Oxidation

Engineering high chemo-, regio-, and stereoselectivity is a prerequisite for enzyme usage in organic synthesis. Cytochromes P450 can oxidize a broad range of substrates, including macrocycles, which are becoming popular scaffolds for therapeutic agents. However, a large conformational space explored by macrocycles not only reduces the selectivity of oxidation but also impairs computational enzyme design strategies based on docking and molecular dynamics (MD) simulations. We present a novel design workflow that uses enhanced-sampling Hamiltonian replica exchange (HREX) MD and focuses on quantifying the substrate binding for suggesting the mutations to be made. This computational approach is applied to P450 BM3 with the aim to shift regioselectively toward one of the numerous possible positions during β-cembrenediol oxidation. The predictions are experimentally tested and the resulting product distributions validate our design strategy, as single mutations led up to 5-fold regioselectivity increases. We thus conclude that the HREX-MD-based workflow is a promising tool for the identification of positions for mutagenesis aiming at P450 enzymes with improved regioselectivity.