Target flexibility: an emerging consideration in drug discovery and design

Assessment of Four Theoretical Approaches to Predict Protein Flexibility in the Crystal Phase and Solution

In this paper, we evaluated the ability of four coarse-grained methods to predict protein flexible regions with potential biological importance, UNRES-flex, UNRES-DSSP-flex (based on the united residue model of polypeptide chains without and with secondary structure restraints, respectively), CABS-flex (based on the C-α, C-β, and side chain model), and nonlinear rigid block normal mode analysis (NOLB) with a set of 100 protein structures determined by NMR spectroscopy or X-ray crystallography, with all secondary structure types. End regions with high fluctuations were excluded from analysis. The Pearson and Spearman correlation coefficients were used to quantify the conformity between the calculated and experimental fluctuation profiles, the latter determined from NMR ensembles and X-ray B-factors, respectively. For X-ray structures (corresponding to proteins in a crowded environment), NOLB resulted in the best agreement between the predicted and experimental fluctuation profiles, while for NMR structures (corresponding to proteins in solution), the ranking of performance is CABS-flex > UNRES-DSSP-flex > UNRES-flex > NOLB; however, CABS-flex sometimes exaggerated the extent of small fluctuations, as opposed to UNRES-DSSP-flex.

Increased throughput in methods for simulating protein ligand binding and unbinding

By incorporating full flexibility and enabling the quantification of crucial parameters such as binding free energies and residence times, methods for investigating protein-ligand binding and unbinding via molecular dynamics provide details on the involved mechanisms at the molecular level. While these advancements hold promise for impacting drug discovery, a notable drawback persists: their relatively time-consuming nature limits throughput. Herein, we survey recent implementations which, employing a blend of enhanced sampling techniques, a clever choice of collective variables, and often machine learning, strive to enhance the efficiency of new and previously reported methods without compromising accuracy. Particularly noteworthy is the validation of these methods that was often performed on systems mirroring real-world drug discovery scenarios.

Synergizing drug repurposing and target identification for neurodegenerative diseases

Despite dedicated research efforts, the absence of disease-curing remedies for neurodegenerative diseases (NDDs) continues to jeopardize human society and stands as a challenge. Drug repurposing is an attempt to find new functionality of existing drugs and take it as an opportunity to discourse the clinically unmet need to treat neurodegeneration. However, despite applying this approach to rediscover a drug, it can also be used to identify the target on which a drug could work. The primary objective of target identification is to unravel all the possibilities of detecting a new drug or repurposing an existing drug. Lately, scientists and researchers have been focusing on specific genes, a particular site in DNA, a protein, or a molecule that might be involved in the pathogenesis of the disease. However, the new era discusses directing the signaling mechanism involved in the disease progression, where receptors, ion channels, enzymes, and other carrier molecules play a huge role. This review aims to highlight how target identification can expedite the whole process of drug repurposing. Here, we first spot various target-identification methods and drug-repositioning studies, including drug-target and structure-based identification studies. Moreover, we emphasize various drug repurposing approaches in NDDs, namely, experimental-based, mechanism-based, and in silico approaches. Later, we draw attention to validation techniques and stress on drugs that are currently undergoing clinical trials in NDDs. Lastly, we underscore the future perspective of synergizing drug repurposing and target identification in NDDs and present an unresolved question to address the issue.

An optimized purification protocol for enzymatically synthesized S-adenosyl-L-methionine (SAM) for applications in solution state infrared spectroscopic studies

S-adenosyl-L-methionine (SAM) is an abundant biomolecule used by methyltransferases to regulate a wide range of essential cellular processes such as gene expression, cell signaling, protein functions, and metabolism. Despite considerable effort, there remain many specificity challenges associated with designing small molecule inhibitors for methyltransferases, most of which exhibit off-target effects. Interestingly, NMR evidence suggests that SAM undergoes conformeric exchange between several states when free in solution. Infrared spectroscopy can detect different conformers of molecules if present in appreciable populations. When SAM is noncovalently bound within enzyme active sites, the nature and the number of different conformations of the molecule are likely to be altered from when it is free in solution. If there are unique structures or different numbers of conformers between different methyltransferase active sites, solution-state information may provide promising structural leads to increase inhibitor specificity for a particular methyltransferase. Toward this goal, frequencies measured in SAM's infrared spectra must be assigned to the motions of specific atoms via isotope incorporation at discrete positions. The incorporation of isotopes into SAM's structure can be accomplished via an established enzymatic synthesis using isotopically labeled precursors. However, published protocols produced an intense and highly variable IR signal which overlapped with many of the signals from SAM rendering comparison between isotopes challenging. We observed this intense absorption to be from co-purifying salts and the SAM counterion, producing a strong, broad signal at 1100 cm-1. Here, we report a revised SAM purification protocol that mitigates the contaminating salts and present the first IR spectra of isotopically labeled CD3-SAM. These results provide a foundation for isotopic labeling experiments of SAM that will define which atoms participate in individual molecular vibrations, as a means to detect specific molecular conformations.

Unsupervised deep learning for molecular dynamics simulations: a novel analysis of protein-ligand interactions in SARS-CoV-2 Mpro

Molecular dynamics (MD) simulations, which are central to drug discovery, offer detailed insights into protein-ligand interactions. However, analyzing large MD datasets remains a challenge. Current machine-learning solutions are predominantly supervised and have data labelling and standardisation issues. In this study, we adopted an unsupervised deep-learning framework, previously benchmarked for rigid proteins, to study the more flexible SARS-CoV-2 main protease (Mpro). We ran MD simulations of Mpro with various ligands and refined the data by focusing on binding-site residues and time frames in stable protein conformations. The optimal descriptor chosen was the distance between the residues and the center of the binding pocket. Using this approach, a local dynamic ensemble was generated and fed into our neural network to compute Wasserstein distances across system pairs, revealing ligand-induced conformational differences in Mpro. Dimensionality reduction yielded an embedding map that correlated ligand-induced dynamics and binding affinity. Notably, the high-affinity compounds showed pronounced effects on the protein's conformations. We also identified the key residues that contributed to these differences. Our findings emphasize the potential of combining unsupervised deep learning with MD simulations to extract valuable information and accelerate drug discovery.

Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods

The therapeutic approach of targeted protein degradation (TPD) is gaining momentum due to its potentially superior effects compared with protein inhibition. Recent advancements in the biotech and pharmaceutical sectors have led to the development of compounds that are currently in human trials, with some showing promising clinical results. However, the use of computational tools in TPD is still limited, as it has distinct characteristics compared with traditional computational drug design methods. TPD involves creating a ternary structure (protein-degrader-ligase) responsible for the biological function, such as ubiquitination and subsequent proteasomal degradation, which depends on the spatial orientation of the protein of interest (POI) relative to E2-loaded ubiquitin. Modeling this structure necessitates a unique blend of tools initially developed for small molecules (e.g., docking) and biologics (e.g., protein-protein interaction modeling). Additionally, degrader molecules, particularly heterobifunctional degraders, are generally larger than conventional small molecule drugs, leading to challenges in determining drug-like properties like solubility and permeability. Furthermore, the catalytic nature of TPD makes occupancy-based modeling insufficient. TPD consists of multiple interconnected yet distinct steps, such as POI binding, E3 ligase binding, ternary structure interactions, ubiquitination, and degradation, along with traditional small molecule properties. A comprehensive set of tools is needed to address the dynamic nature of the induced proximity ternary complex and its implications for ubiquitination. In this Perspective, we discuss the current state of computational tools for TPD. We start by describing the series of steps involved in the degradation process and the experimental methods used to characterize them. Then, we delve into a detailed analysis of the computational tools employed in TPD. We also present an integrative approach that has proven successful for degrader design and its impact on project decisions. Finally, we examine the future prospects of computational methods in TPD and the areas with the greatest potential for impact.

HINT, a code for understanding the interaction between biomolecules: a tribute to Donald J. Abraham

A long-lasting goal of computational biochemists, medicinal chemists, and structural biologists has been the development of tools capable of deciphering the molecule-molecule interaction code that produces a rich variety of complex biomolecular assemblies comprised of the many different simple and biological molecules of life: water, small metabolites, cofactors, substrates, proteins, DNAs, and RNAs. Software applications that can mimic the interactions amongst all of these species, taking account of the laws of thermodynamics, would help gain information for understanding qualitatively and quantitatively key determinants contributing to the energetics of the bimolecular recognition process. This, in turn, would allow the design of novel compounds that might bind at the intermolecular interface by either preventing or reinforcing the recognition. HINT, hydropathic interaction, was a model and software code developed from a deceptively simple idea of Donald Abraham with the close collaboration with Glen Kellogg at Virginia Commonwealth University. HINT is based on a function that scores atom-atom interaction using LogP, the partition coefficient of any molecule between two phases; here, the solvents are water that mimics the cytoplasm milieu and octanol that mimics the protein internal hydropathic environment. This review summarizes the results of the extensive and successful collaboration between Abraham and Kellogg at VCU and the group at the University of Parma for testing HINT in a variety of different biomolecular interactions, from proteins with ligands to proteins with DNA.

Deep Generation Model Guided by the Docking Score for Active Molecular Design

A deep generation model, as a novel drug design and discovery tool, shows obvious advantages in generating compounds with novel backbones and has been applied successfully in the field of drug discovery. However, it is still a challenge to generate molecules with expected properties, especially high activity. Here, to obtain compounds both with novelty and high activity to a target, we proposed a conditional molecular generation model COMG by considering the docking score and 3D pharmacophore matching during molecular generation. The proposed model was based on the conditional variational autoencoder architecture constrained by the pharmacophore matching score. During Bayesian optimization, the docking score was applied to enhance the target relevance of generated compounds. Furthermore, to overcome the problem of high structural similarity caused by Bayesian optimization, the idea of the scaffold memory unit was also introduced. The evaluation results of COMG show that our model not only can improve the structural diversity of generated molecules but also can effectively improve the proportion of target-related drug-active molecules. The obtained results indicate that our proposed model COMG is a useful drug design tool.

Modeling with Alternate Locations in X-ray Protein Structures

In many molecular modeling applications, the standard procedure is still to handle proteins as single, rigid structures. While the importance of conformational flexibility is widely known, handling it remains challenging. Even the crystal structure of a protein usually contains variability exemplified in alternate side chain orientations or backbone segments. This conformational variability is encoded in PDB structure files by so-called alternate locations (AltLocs). Most modeling approaches either ignore AltLocs or resolve them with simple heuristics early on during structure import. We analyzed the occurrence and usage of AltLocs in the PDB and developed an algorithm to automatically handle AltLocs in PDB files enabling all structure-based methods using rigid structures to take the alternative protein conformations described by AltLocs into consideration. A respective software tool named AltLocEnumerator can be used as a structure preprocessor to easily exploit AltLocs. While the amount of data makes it difficult to show impact on a statistical level, handling AltLocs has a substantial impact on a case-by-case basis. We believe that the inspection and consideration of AltLocs is a very valuable approach in many modeling scenarios.

A General Picture of Cucurbit[8]uril Host–Guest Binding: Recalibrating Bonded Interactions

Atomic-level understanding of the dynamic feature of host–guest interactions remains a central challenge in supramolecular chemistry. The remarkable guest binding behavior of the Cucurbiturils family of supramolecular containers makes them promising drug carriers. Among Cucurbit[n]urils, Cucurbit[8]uril (CB8) has an intermediate portal size and cavity volume. It can exploit almost all host–guest recognition motifs formed by this host family. In our previous work, an extensive computational investigation of the binding of seven commonly abused and structurally diverse drugs to the CB8 host was performed, and a general dynamic binding picture of CB8-guest interactions was obtained. Further, two widely used fixed-charge models for drug-like molecules were investigated and compared in great detail, aiming at providing guidelines in choosing an appropriate charge scheme in host-guest modelling. Iterative refitting of atomic charges leads to improved binding thermodynamics and the best root-mean-squared deviation from the experimental reference is 2.6 kcal/mol. In this work, we focus on a thorough evaluation of the remaining parts of classical force fields, i.e., the bonded interactions. The widely used general Amber force fields are assessed and refitted with generalized force-matching to improve the intra-molecular conformational preference, and thus the description of inter-molecular host–guest interactions. The interaction pattern and binding thermodynamics show a significant dependence on the modelling parameters. The refitted system-specific parameter set improves the consistency of the modelling results and the experimental reference significantly. Finally, combining the previous charge-scheme comparison and the current force-field refitting, we provide general guidelines for the theoretical modelling of host–guest binding.

Structure of a 28.5 kDa duplex-embedded G-quadruplex system resolved to 7.4 Å resolution with cryo-EM

Genomic regions with high guanine content can fold into non-B form DNA four-stranded structures known as G-quadruplexes (G4s). Extensive in vivo investigations have revealed that promoter G4s are transcriptional regulators. Little structural information exists for these G4s embedded within duplexes, their presumed genomic environment. Here, we report the 7.4 Å resolution structure and dynamics of a 28.5 kDa duplex-G4-duplex (DGD) model system using cryo-EM, molecular dynamics, and small-angle X-ray scattering (SAXS) studies. The DGD cryo-EM refined model features a 53° bend induced by a stacked duplex-G4 interaction at the 5' G-tetrad interface with a persistently unstacked 3' duplex. The surrogate complement poly dT loop preferably stacks onto the 3' G-tetrad interface resulting in occlusion of both 5' and 3' tetrad interfaces. Structural analysis shows that the DGD model is quantifiably more druggable than the monomeric G4 structure alone and represents a new structural drug target. Our results illustrate how the integration of cryo-EM, MD, and SAXS can reveal complementary detailed static and dynamic structural information on DNA G4 systems.

CADD, AI and ML in drug discovery: A comprehensive review

Computer-aided drug design (CADD) is an emerging field that has drawn a lot of interest because of its potential to expedite and lower the cost of the drug development process. Drug discovery research is expensive and time-consuming, and it frequently took 10-15 years for a drug to be commercially available. CADD has significantly impacted this area of research. Further, the combination of CADD with Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies to handle enormous amounts of biological data has reduced the time and cost associated with the drug development process. This review will discuss how CADD, AI, ML, and DL approaches help identify drug candidates and various other steps of the drug discovery process. It will also provide a detailed overview of the different in silico tools used and how these approaches interact.

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.

Binding pocket stabilization by high-throughput screening of yeast display libraries

Protein dynamics have a great influence on the binding pockets of some therapeutic targets. Flexible protein binding sites can result in transient binding pocket formation which might have a negative impact on drug screening efforts. Here, we describe a protein engineering strategy with FK506-binding protein 51 (FKBP51) as a model protein, which is a promising target for stress-related disorders. High-throughput screening of yeast display libraries of FKBP51 resulted in the identification of variants exhibiting higher affinity binding of conformation-specific FKBP51 selective inhibitors. The gene libraries of a random mutagenesis and site saturation mutagenesis of the FK1 domain of FKBP51 encoding sequence were used to create a yeast surface display library. Fluorescence-activated cell sorting for FKBP51 variants that bind conformation-specific fluorescently labeled ligands with high affinity allowed for the identification of 15 different protein variants with improved binding to either, or both FKBP51-specific ligands used in the screening, with improved affinities up to 34-fold compared to the wild type. These variants will pave the way to a better understanding of the conformational flexibility of the FKBP51 binding pocket and may enable the isolation of new selective ligands that preferably and selectively bind the active site of the protein in its open conformation state.

Recognition of a Flexible Protein Loop in Taspase 1 by Multivalent Supramolecular Tweezers

Many natural proteins contain flexible loops utilizing well-defined complementary surface regions of their interacting partners and usually undergo major structural rearrangements to allow perfect binding. The molecular recognition of such flexible structures is still highly challenging due to the inherent conformational dynamics. Notably, protein-protein interactions are on the other hand characterized by a multivalent display of complementary binding partners to enhance molecular affinity and specificity. Imitating this natural concept, we here report the rational design of advanced multivalent supramolecular tweezers that allow addressing two lysine and arginine clusters on a flexible protein surface loop. The protease Taspase 1, which is involved in cancer development, carries a basic bipartite nuclear localization signal (NLS) and thus interacts with Importin α, a prerequisite for proteolytic activation. Newly established synthesis routes enabled us to covalently fuse several tweezer molecules into multivalent NLS ligands. The resulting bi- up to pentavalent constructs were then systematically compared in comprehensive biochemical assays. In this series, the stepwise increase in valency was robustly reflected by the ligands' gradually enhanced potency to disrupt the interaction of Taspase 1 with Importin α, correlated with both higher binding affinity and inhibition of proteolytic activity.

In silico testing of flavonoids as potential inhibitors of protease and helicase domains of dengue and Zika viruses

Background

Dengue and Zika are two major vector-borne diseases. Dengue causes up to 25,000 deaths and nearly a 100 million cases worldwide per year, while the incidence of Zika has increased in recent years. Although Zika has been associated to fetal microcephaly and Guillain-Barré syndrome both it and dengue have common clinical symptoms such as severe headache, retroocular pain, muscle and join pain, nausea, vomiting, and rash. Currently, vaccines have been designed and antivirals have been identified for these diseases but there still need for more options for treatment. Our group previously obtained some fractions from medicinal plants that blocked dengue virus (DENV) infection in vitro. In the present work, we explored the possible targets by molecular docking a group of molecules contained in the plant fractions against DENV and Zika virus (ZIKV) NS3-helicase (NS3-hel) and NS3-protease (NS3-pro) structures. Finally, the best ligands were evaluated by molecular dynamic simulations.

Methods

To establish if these molecules could act as wide spectrum inhibitors, we used structures from four DENV serotypes and from ZIKV. ADFR 1.2 rc1 software was used for docking analysis; subsequently molecular dynamics analysis was carried out using AMBER20.

Results

Docking suggested that 3,5-dicaffeoylquinic acid (DCA01), quercetin 3-rutinoside (QNR05) and quercetin 3,7-diglucoside (QND10) can tightly bind to both NS3-hel and NS3-pro. However, after a molecular dynamics analysis, tight binding was not maintained for NS3-hel. In contrast, NS3-pro from two dengue serotypes, DENV3 and DENV4, retained both QNR05 and QND10 which converged near the catalytic site. After the molecular dynamics analysis, both ligands presented a stable trajectory over time, in contrast to DCA01. These findings allowed us to work on the design of a molecule called MOD10, using the QND10 skeleton to improve the interaction in the active site of the NS3-pro domain, which was verified through molecular dynamics simulation, turning out to be better than QNR05 and QND10, both in interaction and in the trajectory.

Discussion

Our results suggests that NS3-hel RNA empty binding site is not a good target for drug design as the binding site located through docking is too big. However, our results indicate that QNR05 and QND10 could block NS3-pro activity in DENV and ZIKV. In the interaction with these molecules, the sub-pocket-2 remained unoccupied in NS3-pro, leaving opportunity for improvement and drug design using the quercetin scaffold. The analysis of the NS3-pro in complex with MOD10 show a molecule that exerts contact with sub-pockets S1, S1', S2 and S3, increasing its affinity and apparent stability on NS3-pro.

Ensemble learning from ensemble docking: revisiting the optimum ensemble size problem

Despite considerable advances obtained by applying machine learning approaches in protein–ligand affinity predictions, the incorporation of receptor flexibility has remained an important bottleneck. While ensemble docking has been used widely as a solution to this problem, the optimum choice of receptor conformations is still an open question considering the issues related to the computational cost and false positive pose predictions. Here, a combination of ensemble learning and ensemble docking is suggested to rank different conformations of the target protein in light of their importance for the final accuracy of the model. Available X-ray structures of cyclin-dependent kinase 2 (CDK2) in complex with different ligands are used as an initial receptor ensemble, and its redundancy is removed through a graph-based redundancy removal, which is shown to be more efficient and less subjective than clustering-based representative selection methods. A set of ligands with available experimental affinity are docked to this nonredundant receptor ensemble, and the energetic features of the best scored poses are used in an ensemble learning procedure based on the random forest method. The importance of receptors is obtained through feature selection measures, and it is shown that a few of the most important conformations are sufficient to reach 1 kcal/mol accuracy in affinity prediction with considerable improvement of the early enrichment power of the models compared to the different ensemble docking without learning strategies. A clear strategy has been provided in which machine learning selects the most important experimental conformers of the receptor among a large set of protein–ligand complexes while simultaneously maintaining the final accuracy of affinity predictions at the highest level possible for available data. Our results could be informative for future attempts to design receptor-specific docking-rescoring strategies.

Protein-Protein Docking: Past, Present, and Future

The biological significance of proteins attracted the scientific community in exploring their characteristics. The studies shed light on the interaction patterns and functions of proteins in a living body. Due to their practical difficulties, reliable experimental techniques pave the way for introducing computational methods in the interaction prediction. Automated methods reduced the difficulties but could not yet replace experimental studies as the field is still evolving. Interaction prediction problem being critical needs highly accurate results, but none of the existing methods could offer reliable performance that can parallel with experimental results yet. This article aims to assess the existing computational docking algorithms, their challenges, and future scope. Blind docking techniques are quite helpful when no information other than the individual structures are available. As more and more complex structures are being added to different databases, information-driven approaches can be a good alternative. Artificial intelligence, ruling over the major fields, is expected to take over this domain very shortly.

Combination of pose and rank consensus in docking-based virtual screening: the best of both worlds

The use of high-throughput docking (HTD) in the drug discovery pipeline is today widely established. In spite of methodological improvements in docking accuracy (pose prediction), scoring power, ranking power, and screening power in HTD remain challenging. In fact, pose prediction is of critical importance in view of the pose-dependent scoring process, since incorrect poses will necessarily decrease the ranking power of scoring functions. The combination of results from different docking programs (consensus scoring) has been shown to improve the performance of HTD. Moreover, it has been also shown that a pose consensus approach might also result in database enrichment. We present a new methodology named Pose/Ranking Consensus (PRC) that combines both pose and ranking consensus approaches, to overcome the limitations of each stand-alone strategy. This approach has been developed using four docking programs (ICM, rDock, Auto Dock 4, and PLANTS; the first one is commercial, the other three are free). We undertook a thorough analysis for the best way of combining pose and rank strategies, and applied the PRC to a wide range of 34 targets sampling different protein families and binding site properties. Our approach exhibits an improved systematic performance in terms of enrichment factor and hit rate with respect to either pose consensus or consensus ranking alone strategies at a lower computational cost, while always ensuring the recovery of a suitable number of ligands. An analysis using four free docking programs (replacing ICM by Auto Dock Vina) displayed comparable results.

The new methodology named Pose/Ranking Consensus (PRC) combines both pose and ranking consensus strategies. It displays an enhanced performance in terms of enrichment factor and hit rate, ensuring the recovery of a suitable number of ligands.

Identification of Potential Binders of Mtb Universal Stress Protein (Rv1636) Through an in silico Approach and Insights Into Compound Selection for Experimental Validation

Millions of deaths caused by Mycobacterium tuberculosis (Mtb) are reported worldwide every year. Treatment of tuberculosis (TB) involves the use of multiple antibiotics over a prolonged period. However, the emergence of resistance leading to multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) is the most challenging aspect of TB treatment. Therefore, there is a constant need to search for novel therapeutic strategies that could tackle the growing problem of drug resistance. One such strategy could be perturbing the functions of novel targets in Mtb, such as universal stress protein (USP, Rv1636), which binds to cAMP with a higher affinity than ATP. Orthologs of these proteins are conserved in all mycobacteria and act as “sink” for cAMP, facilitating the availability of this second messenger for signaling when required. Here, we have used the cAMP-bound crystal structure of USP from Mycobacterium smegmatis, a closely related homolog of Mtb, to conduct a structure-guided hunt for potential binders of Rv1636, primarily employing molecular docking approach. A library of 1.9 million compounds was subjected to virtual screening to obtain an initial set of ~2,000 hits. An integrative strategy that uses the available experimental data and consensus indications from other computational analyses has been employed to prioritize 22 potential binders of Rv1636 for experimental validations. Binding affinities of a few compounds among the 22 prioritized compounds were tested through microscale thermophoresis assays, and two compounds of natural origin showed promising binding affinities with Rv1636. We believe that this study provides an important initial guidance to medicinal chemists and biochemists to synthesize and test an enriched set of compounds that have the potential to inhibit Mtb USP (Rv1636), thereby aiding the development of novel antitubercular lead candidates.

Docking Paradigm in Drug Design

Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins.

The Picornavirus Precursor 3CD Has Different Conformational Dynamics Compared to 3Cpro and 3Dpol in Functionally Relevant Regions

Viruses have evolved numerous strategies to maximize the use of their limited genetic material, including proteolytic cleavage of polyproteins to yield products with different functions. The poliovirus polyprotein 3CD is involved in important protein-protein, protein-RNA and protein-lipid interactions in viral replication and infection. It is a precursor to the 3C protease and 3D RNA-dependent RNA polymerase, but has different protease specificity, is not an active polymerase, and participates in other interactions differently than its processed products. These functional differences are poorly explained by the known X-ray crystal structures. It has been proposed that functional differences might be due to differences in conformational dynamics between 3C, 3D and 3CD. To address this possibility, we conducted nuclear magnetic resonance spectroscopy experiments, including multiple quantum relaxation dispersion, chemical exchange saturation transfer and methyl spin-spin relaxation, to probe conformational dynamics across multiple timescales. Indeed, these studies identified differences in conformational dynamics in functionally important regions, including enzyme active sites, and RNA and lipid binding sites. Expansion of the conformational ensemble available to 3CD may allow it to perform additional functions not observed in 3C and 3D alone despite having nearly identical lowest-energy structures.

Identification of viable TCDD access pathways to human AhR PAS-B ligand binding domain

Unintentionally released in the environment as by-products of industrial activities, dioxins, exemplified by 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), represent a primary concern for human health. Exposure to these chemicals is known to produce a broad spectrum of adverse effects, including cancer. The main mechanism of action of TCDD in humans involves binding to the Aryl hydrocarbon Receptor (AhR). Although qualitatively established, TCDD capture by the AhR remains poorly characterized at the molecular level. Starting from a recently developed structural model of the human AhR PAS-B domain, in this work we attempt the identification of viable TCDD access pathways to the human AhR ligand binding domain by means of molecular dynamics. Based on the result of metadynamics simulations, we identify two main regions that may potentially serve as access paths for TCDD. For each path, we characterize the residues closely interacting with TCDD, thereby suggesting a possible mechanism for TCDD capture. Our results are reviewed and discussed in the light of the available information about Human AhR structure and functions.

Hierarchical Graph Representation of Pharmacophore Models

For the investigation of protein-ligand interaction patterns, the current accessibility of a wide variety of sampling methods allows quick access to large-scale data. The main example is the intensive use of molecular dynamics simulations applied to crystallographic structures which provide dynamic information on the binding interactions in protein-ligand complexes. Chemical feature interaction based pharmacophore models extracted from these simulations, were recently used with consensus scoring approaches to identify potentially active molecules. While this approach is rapid and can be fully automated for virtual screening, additional relevant information from such simulations is still opaque and so far the full potential has not been entirely exploited. To address these aspects, we developed the hierarchical graph representation of pharmacophore models (HGPM). This single graph representation enables an intuitive observation of numerous pharmacophore models from long MD trajectories and further emphasizes their relationship and feature hierarchy. The resulting interactive depiction provides an easy-to-apprehend tool for the selection of sets of pharmacophores as well as visual support for analysis of pharmacophore feature composition and virtual screening results. Furthermore, the representation can be adapted to include information involving interactions between the same protein and multiple different ligands. Herein, we describe the generation, visualization and use of HGPMs generated from MD simulations of two x-ray crystallographic derived structures of the human glucokinase protein in complex with allosteric activators. The results demonstrate that a large number of pharmacophores and their relationships can be visualized in an interactive, efficient manner, unique binding modes identified and a combination of models derived from long MD simulations can be strategically prioritized for VS campaigns.

Dynamic-shared Pharmacophore Approach as Tool to Design New Allosteric PRC2 Inhibitors, Targeting EED Binding Pocket

The Polycomb Repressive complex 2 (PRC2) maintains a repressive chromatin state and silences many genes, acting as methylase on histone tails. This enzyme was found overexpressed in many types of cancer. In this work, we have set up a Computer-Aided Drug Design approach based on the allosteric modulation of PRC2. In order to minimize the possible bias derived from using a single set of coordinates within the protein-ligand complex, a dynamic workflow was developed. In details, molecular dynamic was used as tool to identify the most significant ligand-protein interactions from several crystallized protein structures. The identified features were used for the creation of dynamic pharmacophore models and docking grid constraints for the design of new PRC2 allosteric modulators. Our protocol was retrospectively validated using a dataset of active and inactive compounds, and the results were compared to the classic approaches, through ROC curves and enrichment factor. Our approach suggested some important interaction features to be adopted for virtual screening performance improvement.

Virtual screening of potentially endocrine-disrupting chemicals against nuclear receptors and its application to identify PPARγ-bound fatty acids

Nuclear receptors (NRs) are key regulators of energy homeostasis, body development, and sexual reproduction. Xenobiotics binding to NRs may disrupt natural hormonal systems and induce undesired adverse effects in the body. However, many chemicals of concerns have limited or no experimental data on their potential or lack-of-potential endocrine-disrupting effects. Here, we propose a virtual screening method based on molecular docking for predicting potential endocrine-disrupting chemicals (EDCs) that bind to NRs. For 12 NRs, we systematically analyzed how multiple crystal structures can be used to distinguish actives and inactives found in previous high-throughput experiments. Our method is based on (i) consensus docking scores from multiple structures at a single functional state (agonist-bound or antagonist-bound), (ii) multiple functional states (agonist-bound and antagonist-bound), and (iii) multiple pockets (orthosteric site and alternative sites) of these NRs. We found that the consensus enrichment from multiple structures is better than or comparable to the best enrichment from a single structure. The discriminating power of this consensus strategy was further enhanced by a chemical similarity-weighted scoring scheme, yielding better or comparable enrichment for all studied NRs. Applying this optimized method, we screened 252 fatty acids against peroxisome proliferator-activated receptor gamma (PPARγ) and successfully identified 3 previously unknown fatty acids with Kd = 100-250 μM including two furan fatty acids: furannonanoic acid (FNA) and furanundecanoic acid (FUA), and one cyclopropane fatty acid: phytomonic acid (PTA). These results suggested that the proposed method can be used to rapidly screen and prioritize potential EDCs for further experimental evaluations.

SAMPL7 TrimerTrip host-guest binding poses and binding affinities from spherical-coordinates-biased simulations

Host-guest binding remains a major challenge in modern computational modelling. The newest 7th statistical assessment of the modeling of proteins and ligands (SAMPL) challenge contains a new series of host-guest systems. The TrimerTrip host binds to 16 structurally diverse guests. Previously, we have successfully employed the spherical coordinates as the collective variables coupled with the enhanced sampling technique metadynamics to enhance the sampling of the binding/unbinding event, search for possible binding poses and calculate the binding affinities in all three host-guest binding cases of the 6th SAMPL challenge. In this work, we report a retrospective study on the TrimerTrip host-guest systems by employing the same protocol to investigate the TrimerTrip host in the SAMPL7 challenge. As no binding pose is provided by the SAMPL7 host, our simulations initiate from randomly selected configurations and are proceeded long enough to obtain converged free energy estimates and search for possible binding poses. The calculated binding affinities are in good agreement with the experimental reference, and the obtained binding poses serve as a nice starting point for end-point or alchemical free energy calculations. Note that as the work is performed after the close of the SAMPL7 challenge, we do not participate in the challenge and the results are not formally submitted to the SAMPL7 challenge.

Capturing the Flexibility of a Protein-Ligand Complex: Binding Free Energies from Different Enhanced Sampling Techniques

Enhanced sampling techniques are a promising approach to obtain reliable binding free-energy profiles for flexible protein-ligand complexes from molecular dynamics (MD) simulations. To put four popular enhanced sampling techniques to a biologically relevant and challenging test, we studied the partial dissociation of an antigenic peptide from the Major Histocompatibility Complex I (MHC I) HLA-B*35:01 to systematically investigate the performance of umbrella sampling (US), replica exchange with solute tempering 2 (REST2), bias exchange umbrella sampling (BEUS, or replica-exchange umbrella sampling), and well-tempered metadynamics (MTD). With regard to the speed of sampling and convergence, the peptide-MHC I complex (pMHC I) under study showcases intrinsic strengths and weaknesses of the four enhanced sampling techniques used. We found that BEUS can best handle the sampling challenges that arise from the coexistence of an enthalpically and an entropically stabilized free-energy minimum in the pMHC I under study. These findings might also be relevant for other flexible biomolecular systems with competing enthalpically and entropically stabilized minima.

Structural And Computational Perspectives of Selectively Targeting Mutant Proteins

Diseases are often caused by mutant proteins. Many drugs have limited effectiveness and/or toxic side effects because of a failure to selectively target the disease-causing mutant variant, rather than the functional wild type protein. Otherwise, the drugs may even target different proteins with similar structural features. Designing drugs that successfully target mutant proteins selectively represents a major challenge. Decades of cancer research have led to an abundance of potential therapeutic targets, often touted to be "master regulators". For many of these proteins, there are no FDA-approved drugs available; for others, off-target effects result in dose-limiting toxicity. Cancer-related proteins are an excellent medium to carry the story of mutant-specific targeting, as the disease is both initiated and sustained by mutant proteins; furthermore, current chemotherapies generally fail at adequate selective distinction. This review discusses some of the challenges associated with selective targeting from a structural biology perspective, as well as some of the developments in algorithm approach and computational workflow that can be applied to address those issues. One of the most widely researched proteins in cancer biology is p53, a tumor suppressor. Here, p53 is discussed as a specific example of a challenging target, with contemporary drugs and methodologies used as examples of burgeoning successes. The oncogene KRAS, which has been described as "undruggable", is another extensively investigated protein in cancer biology. This review also examines KRAS to exemplify progress made towards selective targeting of diseasecausing mutant proteins. Finally, possible future directions relevant to the topic are discussed.

SAMPL6 host-guest binding affinities and binding poses from spherical-coordinates-biased simulations

Host-guest binding is a challenging problem in computer simulation. The prediction of binding affinities between hosts and guests is an important part of the statistical assessment of the modeling of proteins and ligands (SAMPL) challenges. In this work, the volume-based variant of well-tempered metadynamics is employed to calculate the binding affinities of the host-guest systems in the SAMPL6 challenge. By biasing the spherical coordinates describing the relative position of the host and the guest, the initial-configuration-induced bias vanishes and all possible binding poses are explored. The agreement between the predictions and the experimental results and the observation of new binding poses indicate that the volume-based technique serves as a nice candidate for the calculation of binding free energies and the search of the binding poses.

Molecular Docking Using Quantum Mechanical-Based Methods

Computational methods are a powerful and consolidated tool in the early stage of the drug lead discovery process. Among these techniques, high-throughput molecular docking has proved to be extremely useful in identifying novel bioactive compounds within large chemical libraries. In the docking procedure, the predominant binding mode of each small molecule within a target binding site is assessed, and a docking score reflective of the likelihood of binding is assigned to them. These methods also shed light on how a given hit could be modified in order to improve protein-ligand interactions and are thus able to guide lead optimization. The possibility of reducing time and cost compared to experimental approaches made this technology highly appealing. Due to methodological developments and the increase of computational power, the application of quantum mechanical methods to study macromolecular systems has gained substantial attention in the last decade. A quantum mechanical description of the interactions involved in molecular association of biomolecules may lead to better accuracy compared to molecular mechanics, since there are many physical phenomena that cannot be correctly described within a classical framework, such as covalent bond formation, polarization effects, charge transfer, bond rearrangements, halogen bonding, and others, that require electrons to be explicitly accounted for. Considering the fact that quantum mechanics-based approaches in biomolecular simulation constitute an active and important field of research, we highlight in this work the recent developments of quantum mechanical-based molecular docking and high-throughput docking.

Discovery of selective farnesoid X receptor agonists for the treatment of hyperlipidemia from traditional Chinese medicine based on virtual screening and in vitro validation

Farnesoid X receptor (FXR), a bile acid receptor, has important roles in maintaining bile acid and cholesterol homeostasis, which is an attractive target for hyperlipidemia. Present study aimed to discover potential selective FXR agonists over G-protein coupled bile acid receptor 1 (GPBAR1, TGR5) from traditional Chinese medicine (TCM) by using virtual screening, in vitro studies and molecular dynamics simulation (MD). Ligand-based pharmacophore model for FXR was firstly built to screen FXR agonists from the Traditional Chinese Medicine Database (TCMD). Then, 21 FXR crystal structures were clustered in two types and two representative structures (PDB ID: 3OMM and 3P89) were, respectively, used to carry out molecular docking to refine the screened result. Moreover, the pharmacophore model for GPBAR1 was built to screen selective FXR agonists with no activity on GPBAR1. A set of 24 candidate selective FXR agonists which fitvalue of FXR pharmacophore model and docking score of 3OMM and 3P89 were in the top 100 and cannot match the pharmacophore model for GPBAR1 were obtained. By the lipid-lowering activity test in HepG2 cell lines, Arctigenin was identified to be potential selective FXR agonist with the activity of 20 μmol·L^-1. After down-regulating FXR, Arctigenin could increase the mRNA of FXR while exerted no effect on the mRNA of GPBAR1. MD was further used to interpret the mechanism of Arctigenin with the representative structures. This research provided a new screening procedure for finding selective candidate compounds and appropriate docking models of a target by considering the structure diversity of PDB structures, which was applied to discovery novel selective FXR agonists to treat hyperlipidemia.Communicated by Ramaswamy H. Sarma.

The In Silico Drug Discovery Toolbox: Applications in Lead Discovery and Optimization

BACKGROUND

Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug.

METHODS

In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan.

RESULTS

A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given.

CONCLUSION

The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs.

An investigation of structural stability in protein-ligand complexes reveals the balance between order and disorder

The predominant view in structure-based drug design is that small-molecule ligands, once bound to their target structures, display a well-defined binding mode. However, structural stability (robustness) is not necessary for thermodynamic stability (binding affinity). In fact, it entails an entropic penalty that counters complex formation. Surprisingly, little is known about the causes, consequences and real degree of robustness of protein-ligand complexes. Since hydrogen bonds have been described as essential for structural stability, here we investigate 469 such interactions across two diverse structure sets, comprising of 79 drug-like and 27 fragment ligands, respectively. Completely constricted protein-ligand complexes are rare and may fulfill a functional role. Most complexes balance order and disorder by combining a single anchoring point with looser regions. 25% do not contain any robust hydrogen bond and may form loose structures. Structural stability analysis reveals a hidden layer of complexity in protein-ligand complexes that should be considered in ligand design.

Discovering New Casein Kinase 1d Inhibitors with an Innovative Molecular Dynamics Enabled Virtual Screening Workflow

The value of including protein flexibility in structure-based drug design (SBDD) is widely documented, and currently, molecular dynamics (MD) simulations represent a powerful tool to investigate protein dynamics. Yet, the inclusion of MD-derived information in pre-existing SBDD workflows is still far from trivial. We recently published an integrated MD-FLAP (Fingerprints for Ligands and Proteins) approach combining MD, clustering and Linear Discriminant Analysis (LDA) for enhancing accuracy, efficacy, and for protein conformational selection in virtual screening (VS) campaigns. Here we prospectively applied the MD-FLAP workflow to discover novel chemotypes inhibiting the Casein Kinase 1 delta (CSNK1D) enzyme. We first obtained a VS model able to separate active from inactive compounds, with a global AUC of 0.9 and a partial ROC enrichment at 0.5% of 0.18, and use it to mine the internal Pfizer screening database. Seven active molecules sharing a phenyl-indazole scaffold, not yet reported among CSNK1D inhibitors, were found. The most potent inhibitor showed an IC₅₀ of 134 nM.

Exponential consensus ranking improves the outcome in docking and receptor ensemble docking

Consensus-scoring methods are commonly used with molecular docking in virtual screening campaigns to filter potential ligands for a protein target. Traditional consensus methods combine results from different docking programs by averaging the score or rank of each molecule obtained from individual programs. Unfortunately, these methods fail if one of the docking programs has poor performance, which is likely to occur due to training-set dependencies and scoring-function parameterization. In this work, we introduce a novel consensus method that overcomes these limitations. We combine the results from individual docking programs using a sum of exponential distributions as a function of the molecule rank for each program. We test the method over several benchmark systems using individual and ensembles of target structures from diverse protein families with challenging decoy/ligand datasets. The results demonstrate that the novel method outperforms the best traditional consensus strategies over a wide range of systems. Moreover, because the novel method is based on the rank rather than the score, it is independent of the score units, scales and offsets, which can hinder the combination of results from different structures or programs. Our method is simple and robust, providing a theoretical basis not only for molecular docking but also for any consensus strategy in general.

Determination of Ligand Binding Epitope Structures Using Polarization Transfer from Hyperpolarized Ligands

Drug discovery processes require the determination of the protein binding site structure, which can be achieved via nuclear magnetic resonance (NMR) spectroscopy. While traditional NMR spectroscopy suffers from low sensitivity, NMR signals can be significantly enhanced through hyperpolarization of nuclear spins. Here, folic acid is hyperpolarized by dissolution dynamic nuclear polarization (D-DNP). Polarization transfer to dihydrofolate reductase is compared to signal evolution predicted for docking-derived structures. The results demonstrate that a scoring function derived from the experimental data improves the ranking of structures. With data from six methyl groups, Spearman's correlation coefficient of the experimental scoring function to the root-mean-square deviation from a reference structure is 0.88 for five individually addressed ligand protons and 0.59 for the entire ligand, while the same correlation coefficient of the energy calculated from docking alone is 0.49. D-DNP NMR-derived ranking, therefore, is capable of determining the ligand structure with a small number of individually addressed source spins.

Molecular modelling and competitive inhibition of a Mycobacterium tuberculosis multidrug-resistance efflux pump

Tuberculosis is a major cause of mortality and morbidity in developing countries, and the emergency of multidrug and extensive drug resistance cases is an utmost issue on the control of the disease. Despite the efforts on the development of new antibiotics, eventually there will be strains resistant to them as well. Efflux plays an important role in the evolution of resistance in Mycobacterium tuberculosis. Tap is an important efflux pump associated with tuberculosis resistant to isoniazid, rifampicine and ofloxacin and with multidrug resistance. The development of efflux inhibitors for Tap could raise the effectiveness of second line drugs and reduce the duration of the current treatment. Therefore the objective of this study is to build a reliable molecular model of Tap efflux pump and test the possible competitive inhibition between efflux inhibitors and antibiotics in the optimized structure. We built twenty five Tap models with molecular modelling to elect the best according to the results of the validation analysis. The elected model went through to a 100 ns molecular dynamics simulation in a lipid bilayer, and the resulting optimized structure was used in docking studies to test if the used efflux inhibitors may act via competitive inhibition on antibiotics. The validation results pointed the model built by Phyre2 as the closest to a possible native Tap structure, and therefore it was the elected model. RSMD analysis revealed the model is stable, where the predicted binding site stabilized between 15 and 20 ns, maintaining the RMSD at around 0.35 Å throughout the molecular dynamics simulation in a lipid bilayer. Therefore this model is reliable and can also be used for further studies. The docking studies showed a possibility of competitive inhibition by NUNL02 on ofloxacin and bedaquiline, and by verapamil on ofloxacin and rifampicin. This presents the possibility that NUNL02 and verapamil are possible inhibitors of Tap efflux and highlights the importance of including efflux inhibitors as adjuvants to the tuberculosis therapy, as it indicates a possible extrusion of ofloxacin, rifampicin and bedaquilin by Tap.

A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model

BACKGROUND

In the rational drug design process, an ensemble of conformations obtained from a molecular dynamics simulation plays a crucial role in docking experiments. Some studies have found that Fully-Flexible Receptor (FFR) models predict realistic binding energy accurately and improve scoring to enhance selectiveness. At the same time, methods have been proposed to reduce the high computational costs involved in considering the explicit flexibility of proteins in receptor-ligand docking. This study introduces a novel method to optimize ensemble docking-based experiments by reducing the size of an InhA FFR model at docking runtime and scaling docking workflow invocations on cloud virtual machines.

RESULTS

First, in order to find the most affordable cost-benefit pool of virtual machines, we evaluated the performance of the docking workflow invocations in different configurations of Azure instances. Second, we validated the gains obtained by the proposed method based on the quality of the Reduced Fully-Flexible Receptor (RFFR) models produced using AutoDock4.2. The analyses show that the proposed method reduced the model size by approximately 50% while covering at least 86% of the best docking results from the 74 ligands tested. Third, we tested our novel method using AutoDock Vina, a different docking software, and showed the positive accuracy achieved in the resulting RFFR models. Finally, our results demonstrated that the method proposed optimized ensemble docking experiments and is applicable to different docking software. In addition, it detected new binding modes, which would be unreachable if employing only the rigid structure used to generate the InhA FFR model.

CONCLUSIONS

Our results showed that the selective method is a valuable strategy for optimizing ensemble docking-based experiments using different docking software. The RFFR models produced by discarding non-promising snapshots from the original model are accurately shaped for a larger number of ligands, and the elapsed time spent in the ensemble docking experiments are considerably reduced.

Targeting secondary protein complexes in drug discovery: studying the druggability and chemical biology of the HSP70/BAG1 complex

A non-nucleotide FP-probe was designed to study the mechanism of action and druggability of the secondary HSP70/BAG1 complex.

Proteins typically carry out their biological functions as multi-protein complexes, which can significantly affect the affinity of small-molecule inhibitors. HSP70 is an important target in oncology, so to study its chemical biology and the drug discovery potential of the HSP70/BAG1 complex, we designed a high-affinity non-nucleotide fluorescence polarisation probe.

Fast Dynamic Docking Guided by Adaptive Electrostatic Bias: The MD-Binding Approach

Engineering chemical entities to modify how pharmaceutical targets function, as it is done in drug design, requires a good understanding of molecular recognition and binding. In this context, the limitations of statically describing bimolecular recognition, as done in docking/scoring, call for insightful and efficient dynamical investigations. On the experimental side, the characterization of dynamical binding processes is still in its infancy. Thus, computer simulations, particularly molecular dynamics (MD), are compelled to play a prominent role, allowing a deeper comprehension of the binding process and its causes and thus a more informed compound selection, making more significant the computational contribution to drug discovery (Carlson, H. A. Curr. Opin. Chem. Biol. 2002, 6, 447-452). Unfortunately, MD-based approaches cannot yet describe complex events without incurring prohibitive time and computational costs. Here, we present a new method for fully and dynamically simulating drug-target-complex formations, tested against a real world and pharmaceutically relevant benchmark set. The method, based on an adaptive, electrostatics-inspired bias, envisions a campaign of trivially parallel short MD simulations and a strategy to identify a near native binding pose from the sampled configurations. At an affordable computational cost, this method provided predictions of good accuracy also when the starting protein conformation was different from that of the crystal complex, a known hurdle for traditional molecular docking (Lexa, K. W.; Carlson, H. A. Q. Rev. Biophys. 2012, 45, 301-343). Moreover, along the observed binding routes, it identified some key features also found by much more computationally expensive plain-MD simulations. Overall, this methodology represents significant progress in the description of binding phenomena.