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This study focuses on the stereoselective syntheses of 1-deoxysphingosine analogues as potential inhibitors of sphingosine kinase (SphK), particularly targeting its isoforms SphK1 and SphK2, which are implicated in cancer progression and therapy resistance. The research builds on previous work by designing a series of analogues featuring systematic structural modifications like the incorporation of a triazole ring, varying degrees of fluorination, and different head groups ( e.g. , guanidino, N -methylamino, and N , N -dimethylamino). These modifications aimed to enhance polar and hydrophobic interactions especially with SphK2. The synthesized compounds were evaluated for their inhibitory activity, revealing that certain derivatives, particularly those with guanidino groups and heptafluoropropyl fragments at the lipidic tail, exhibited significant potency and selectivity towards SphK2. Docking studies supported these findings by showing favorable binding interactions within the SphK2 active site, which were less pronounced in SphK1, correlating with the observed selectivity. This work contributes to the development of novel 1-deoxysphingosine analogues targeting SphK inhibition, as well as to the knowledge of the differential topology of the active sites in SphK1 and SphK2.

In search of new opportunities to develop Trypanosoma brucei phosphodiesterase B1 (TbrPDEB1) inhibitors that have selectivity over the off-target human PDE4 (hPDE4), different stages of a fragment-growing campaign were studied using a variety of biochemical, structural, thermodynamic, and kinetic binding assays. Remarkable differences in binding kinetics were identified and this kinetic selectivity was explored with computational methods, including molecular dynamics and interaction fingerprint analyses. These studies indicate that a key hydrogen bond between Gln Q.50 and the inhibitors is exposed to a water channel in TbrPDEB1, leading to fast unbinding. This water channel is not present in hPDE4, leading to inhibitors with a longer residence time. The computer-aided drug design protocols were applied to a recently disclosed TbrPDEB1 inhibitor with a different scaffold and our results confirm that shielding this key hydrogen bond through disruption of the water channel represents a viable design strategy to develop more selective inhibitors of TbrPDEB1. Our work shows how computational protocols can be used to understand the contribution of solvent dynamics to inhibitor binding, and our results can be applied in the design of selective inhibitors for homologous PDEs found in related parasites., (© 2024 The Authors. ChemMedChem published by Wiley-VCH GmbH.)

Allosteric regulators acting as pharmacological chaperones hold promise for innovative therapeutics since they target noncatalytic sites and stabilize the folded protein without competing with the natural substrate, resulting in a net gain of function. Exogenous allosteric regulators are typically more selective than active site inhibitors and can be more potent than competitive inhibitors when the natural substrate levels are high. To identify novel structure-targeted allosteric regulators (STARs) that bind to and stabilize the mitochondrial enzyme glutaryl-CoA dehydrogenase (GCDH), the computational site-directed enzyme enhancement therapy (SEE-Tx) technology was applied. SEE-Tx is an innovative drug discovery platform with the potential to identify drugs for treating protein misfolding disorders, such as glutaric acidemia type 1 (GA1) disease. Putative allosteric regulators were discovered using structure- and ligand-based virtual screening methods and validated using orthogonal biophysical and biochemical assays. The computational approach presented here could be used to discover allosteric regulators of other protein misfolding disorders.

Druggable pockets are protein regions that have the ability to bind organic small molecules, and their characterization is essential in target-based drug discovery. However, deriving pocket descriptors is challenging and existing strategies are often limited in applicability. We introduce PocketVec, an approach to generate pocket descriptors via inverse virtual screening of lead-like molecules. PocketVec performs comparably to leading methodologies while addressing key limitations. Additionally, we systematically search for druggable pockets in the human proteome, using experimentally determined structures and AlphaFold2 models, identifying over 32,000 binding sites across 20,000 protein domains. We then generate PocketVec descriptors for each site and conduct an extensive similarity search, exploring over 1.2 billion pairwise comparisons. Our results reveal druggable pocket similarities not detected by structure- or sequence-based methods, uncovering clusters of similar pockets in proteins lacking crystallized inhibitors and opening the door to strategies for prioritizing chemical probe development to explore the druggable space., (© 2024. The Author(s).)

Mucopolysaccharidosis type I (MPS I) is an inherited lysosomal disease caused by lowered activity of the enzyme alpha-L-iduronidase (IDUA). Current therapeutic options show limited efficacy and do not treat some important aspects of the disease. Therefore, it may be advantageous to identify strategies that could improve the efficacy of existing treatments. Pharmacological chaperones are small molecules that protect proteins from degradation, and their use in combination with enzyme replacement therapy (ERT) has been proposed as an alternative therapeutic strategy. Using the SEE-Tx® proprietary computational drug discovery platform, a new allosteric ligand binding cavity in IDUA was identified distal from the active site. Virtual high-throughput screening of approximately 5 million compounds using the SEE-Tx® docking platform identified a subset of small molecules that bound to the druggable cavity and functioned as novel allosteric chaperones of IDUA. Experimental validation by differential scanning fluorimetry showed an overall hit rate of 11.4%. Biophysical studies showed that one exemplary hit molecule GT-01803 bound to (Kd = 22 μM) and stabilized recombinant human IDUA (rhIDUA) in a dose-dependent manner. Co-administration of rhIDUA and GT-01803 increased IDUA activity in patient-derived fibroblasts. Preliminary in vivo studies have shown that GT-01803 improved the pharmacokinetic (PK) profile of rhIDUA, increasing plasma levels in a dose-dependent manner. Furthermore, GT-01803 also increased IDUA enzymatic activity in bone marrow tissue, which benefits least from standard ERT. Oral bioavailability of GT-01803 was found to be good (50%). Overall, the discovery and validation of a novel allosteric chaperone for rhIDUA presents a promising strategy to enhance the efficacy of existing treatments for MPS I. The compound's ability to increase rhIDUA activity in patient-derived fibroblasts and its good oral bioavailability underscore its potential as a potent adjunct to ERT, particularly for addressing aspects of the disease less responsive to standard treatment., Competing Interests: Elena Cubero, Ana Ruano, Aida Delgado, Xavier Barril, Ana Trapero, Manolo Bellotto, Beatriz Calvo-Flores Guzmán, Natalia Pérez-Carmona and Ana Maria Garcia-Collazo are employees of Gain Therapeutics Sucursal en España or GT Gain Therapeutics SA. Sara Morales, Roberto Maj, past employees, and Lorenzo Leoni, scientific adviser, declare no conflicts of interest., (Copyright: © 2024 Cubero et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Dihydroceramide desaturase 1 (Des1) catalyzes the formation of a CC double bond in dihydroceramide to furnish ceramide. Inhibition of Des1 is related to cell cycle arrest and programmed cell death. The lack of the Des1 crystalline structure, as well as that of a close homologue, hampers the detailed understanding of its inhibition mechanism and difficults the design of new inhibitors, thus making Des1 a strategic target. Based on previous structure-activity studies, different ceramides containing rigid scaffolds were designed. The synthesis and evaluation of these compounds as Des1 inhibitors allowed the identification of PR280 as a better Des 1 inhibitor in vitro (IC 50  = 700 nM) than GT11 and XM462, the current reference inhibitors. This cyclopropenone ceramide was obtained in a 6-step synthesis with a 24 % overall yield. The highly confident 3D structure of Des1, recently predicted by AlphaFold2, served as the basis for conducting docking studies of known Des1 inhibitors and the ceramide derivatives synthesized by us in this study. For this purpose, a complete holoprotein structure was previously constructed. This study has allowed a better knowledge of key ligand-enzyme interactions for Des1 inhibitory activity. Furthermore, it sheds some light on the inhibition mechanism of GT11., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Site-directed Enzyme Enhancement Therapy (SEE-Tx®) technology is a disease-agnostic drug discovery tool that can be applied to any protein target of interest with a known three-dimensional structure. We used this proprietary technology to identify and characterize the therapeutic potential of structurally targeted allosteric regulators (STARs) of the lysosomal hydrolase β-galactosidase (β-Gal), which is deficient due to gene mutations in galactosidase beta 1 (GLB1)-related lysosomal storage disorders (LSDs). The biochemical HaloTag cleavage assay was used to monitor the delivery of wildtype (WT) β-Gal and four disease-related β-Gal variants (p.Ile51Thr, p.Arg59His, p.Arg201Cys and p.Trp273Leu) in the presence and absence of two identified STAR compounds. In addition, the ability of STARs to reduce toxic substrate was assessed in a canine fibroblast cell model. In contrast to the competitive pharmacological chaperone N-nonyl-deoxygalactonojirimycin (NN-DGJ), the two identified STAR compounds stabilized and substantially enhanced the lysosomal transport of wildtype enzyme and disease-causing β-Gal variants. In addition, the two STAR compounds reduced the intracellular accumulation of exogenous GM1 ganglioside, an effect not observed with the competitive chaperone NN-DGJ. This proof-of-concept study demonstrates that the SEE-Tx® platform is a rapid and cost-effective drug discovery tool for identifying STARs for the treatment of LSDs. In addition, the HaloTag assay developed in our lab has proved valuable in investigating the effect of STARs in promoting enzyme transport and lysosomal delivery. Automatization and upscaling of this assay would be beneficial for screening STARs as part of the drug discovery process., Competing Interests: MPV, AR, AD, SM, XB, MB, EC, AMG, and NPC are employed by Gain Therapeutics, Sucursal en España or Lugano Switzerland, and their research and authorship of this article were completed within the scope of their employment. MM works as a consultant for Gain, except in this project where he participated as a member of the European consortium created for the Eurostars grant., (Copyright: © 2023 Rudinskiy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

A multi-responsive receptor consisting of two (acridinium-Zn(II) porphyrin) conjugates has been designed. The binding constant between this receptor and a ditopic guest has been modulated (i) upon addition of nucleophiles converting acridinium moieties into the non-aromatic acridane derivatives and (ii) upon oxidation of the porphyrin units. A total of eight states has been probed for this receptor resulting from the cascade of the recognition and responsive events. Moreover, the acridinium/acridane conversion leads to a significant change of the photophysical properties, switching from electron to energy transfer processes. Interestingly, for the bis(acridinium-Zn(II) porphyrin) receptor, charge-transfer luminescence in the near-infrared has been observed.

Targeted protein degradation is a promising therapeutic strategy, spearheaded by the anti-myeloma drugs lenalidomide and pomalidomide. These drugs stabilize very efficiently the complex between the E3 ligase Cereblon (CRBN) and several non-native client proteins (neo-substrates), including the transcription factors Ikaros and Aiolos and the enzyme Caseine Kinase 1α (CK1α,), resulting in their degradation. Although the structures for these complexes have been determined, there are no evident interactions that can account for the high efficiency of formation of the ternary complex. We show that lenalidomide's stabilization of the CRBN-CK1α complex is largely due to hydrophobic shielding of intermolecular hydrogen bonds. We also find a quantitative relationship between hydrogen bond robustness and binding affinities of the ternary complexes. These results pave the way to further understand cooperativity effects in drug-induced protein-protein complexes and could help in the design of improved molecular glues and more efficient protein degraders.

Computer-aided drug discovery methods play a major role in the development of therapeutically important small molecules, but their performance needs to be improved. Molecular dynamics simulations in mixed solvents are useful in understanding protein-ligand recognition and improving molecular docking predictions. In this work, we used ethanol as a cosolvent to find relevant interactions for ligands toward protein kinase G, an essential protein of Mycobacterium tuberculosis ( Mtb ). We validated the hot spots by screening a database of fragment-like compounds and another one of known kinase inhibitors. Next, we performed a pharmacophore-guided docking simulation and found three low micromolar inhibitors, including one with a novel chemical scaffold that we expanded to four derivative compounds. Binding affinities were characterized by intrinsic fluorescence quenching assays, isothermal titration calorimetry, and the analysis of melting curves. The predicted binding mode was confirmed by X-ray crystallography. Finally, the compounds significantly inhibited the viability of Mtb in infected THP-1 macrophages.

The prediction of how a ligand binds to its target is an essential step for Structure-Based Drug Design (SBDD) methods. Molecular docking is a standard tool to predict the binding mode of a ligand to its macromolecular receptor and to quantify their mutual complementarity, with multiple applications in drug design. However, docking programs do not always find correct solutions, either because they are not sampled or due to inaccuracies in the scoring functions. Quantifying the docking performance in real scenarios is essential to understanding their limitations, managing expectations and guiding future developments. Here, we present a fully automated pipeline for pose prediction validated by participating in the Continuous Evaluation of Ligand Pose Prediction (CELPP) Challenge. Acknowledging the intrinsic limitations of the docking method, we devised a strategy to automatically mine and exploit pre-existing data, defining-whenever possible-empirical restraints to guide the docking process. We prove that the pipeline is able to generate predictions for most of the proposed targets as well as obtain poses with low RMSD values when compared to the crystal structure. All things considered, our pipeline highlights some major challenges in the automatic prediction of protein-ligand complexes, which will be addressed in future versions of the pipeline.

With the uncontrolled growth of multidrug-resistant bacteria, there is an urgent need to search for new therapeutic targets, to develop drugs with novel modes of bactericidal action. FoF1-ATP synthase plays a crucial role in bacterial bioenergetic processes, and it has emerged as an attractive antimicrobial target, validated by the pharmaceutical approval of an inhibitor to treat multidrug-resistant tuberculosis. In this work, we aimed to design, through two types of in silico strategies, new allosteric inhibitors of the ATP synthase, by targeting the catalytic β subunit, a centerpiece in communication between rotor subunits and catalytic sites, to drive the rotary mechanism. As a model system, we used the F1 sector of Escherichia coli, a bacterium included in the priority list of multidrug-resistant pathogens. Drug-like molecules and an IF1-derived peptide, designed through molecular dynamics simulations and sequence mining approaches, respectively, exhibited in vitro micromolar inhibitor potency against F1. An analysis of bacterial and Mammalia sequences of the key structural helix-turn-turn motif of the C-terminal domain of the β subunit revealed highly and moderately conserved positions that could be exploited for the development of new species-specific allosteric inhibitors. To our knowledge, these inhibitors are the first binders computationally designed against the catalytic subunit of FOF1-ATP synthase.

Sphingosine kinase (SphK), which catalyzes the transfer of phosphate from ATP to sphingosine (Sph) generating sphingosine-1-phosphate (S1P) has emerged as therapeutic target since the discovery of connections of S1P with cancer progress. So far, most effort has focused on the development of inhibitors of SphK1, and selective inhibitors of SphK2 have been much less explored. Here, we describe the syntheses of new sphingosine derivatives bearing a tetrasubstituted carbon atom at C-2, dimethylhydrazino or azo moieties in the polar head, and alkane, alkene or alkyne moieties as linkers between the polar ahead and the fatty tail. In vitro inhibitory assays based on a time resolved fluorescence energy transfer (TR-FRET) have revealed the hydrazino and alkynyl moieties as the best combination for the design of selective SphK2 inhibitors (19a and 19b). Docking studies showed that compounds 19a-b have the optimal binding to SphK2 through the exploitation of polar but also hydrophobic interactions of their head group with the head of the enzyme binding pocket, while also producing full contact of the fatty tail with the hydrophobic pocket of the enzyme. By contrast, this elongation causes loss of contact surface with the shorter hydrophobic toe of the SphK1 isoform, thus accounting for the SphK2-biased selectivity of these compounds. Cell viability assays of the most promising candidates 19a-b have shown that 19a is not cytotoxic to human endothelial cells at 30 μM., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Mixed solvents MD (MDmix) simulations have proved to be a useful and increasingly accepted technique with several applications in structure-based drug discovery. One of the assumptions behind the methodology is the transferability of free energy values from the simulated cosolvent molecules to larger drug-like molecules. However, the binding free energy maps (ΔGbind) calculated for the different moieties of the cosolvent molecules (e.g. a hydroxyl map for the ethanol) are largely influenced by the rest of the solvent molecule and do not reflect the intrinsic affinity of the moiety in question. As such, they are hardly transferable to different molecules., Method: To achieve transferable energies, we present here a method for decomposing the molecular binding free energy into accurate atomic contributions., Result: We demonstrate with two qualitative visual examples how the corrected energy maps better match known binding hotspots and how they can reveal hidden hotspots with actual drug design potential., Conclusion: Atomic decomposition of binding free energies derived from MDmix simulations provides transferable and quantitative binding free energy maps., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Fragment-based drug discovery (FBDD) is a very effective hit identification method. However, the evolution of fragment hits into suitable leads remains challenging and largely artisanal. Fragment evolution is often scaffold-centric, meaning that its outcome depends crucially on the chemical structure of the starting fragment. Considering that fragment screening libraries cover only a small proportion of the corresponding chemical space, hits should be seen as probes highlighting privileged areas of the chemical space rather than actual starting points. We have developed an automated computational pipeline to mine the chemical space around any specific fragment hit, rapidly finding analogues that share a common interaction motif but are structurally novel and diverse. On a prospective application on the bromodomain-containing protein 4 (BRD4), starting from a known fragment, the platform yields active molecules with nonobvious scaffold changes. The procedure is fast and inexpensive and has the potential to uncover many hidden opportunities in FBDD.

Fragment-based drug discovery (FBDD) emerged as a disruptive technology and became established during the last two decades. Its rationality and low entry costs make it appealing, and the numerous examples of approved drugs discovered through FBDD validate the approach. However, FBDD still faces numerous challenges. Perhaps the most important one is the transformation of the initial fragment hits into viable leads. Fragment-to-lead (F2L) optimization is resource-intensive and is therefore limited in the possibilities that can be actively pursued. In silico strategies play an important role in F2L, as they can perform a deeper exploration of chemical space, prioritize molecules with high probabilities of being active and generate non-obvious ideas. Here we provide a critical overview of current in silico strategies in F2L optimization and highlight their remarkable impact. While very effective, most solutions are target- or fragment- specific. We propose that fully integrated in silico strategies, capable of automatically and systematically exploring the fast-growing available chemical space can have a significant impact on accelerating the release of fragment originated drugs., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Motivation: Machine-learning scoring functions (SFs) have been found to outperform standard SFs for binding affinity prediction of protein-ligand complexes. A plethora of reports focus on the implementation of increasingly complex algorithms, while the chemical description of the system has not been fully exploited., Results: Herein, we introduce Extended Connectivity Interaction Features (ECIF) to describe protein-ligand complexes and build machine-learning SFs with improved predictions of binding affinity. ECIF are a set of protein-ligand atom-type pair counts that take into account each atom's connectivity to describe it and thus define the pair types. ECIF were used to build different machine-learning models to predict protein-ligand affinities (pKd/pKi). The models were evaluated in terms of 'scoring power' on the Comparative Assessment of Scoring Functions 2016. The best models built on ECIF achieved Pearson correlation coefficients of 0.857 when used on its own, and 0.866 when used in combination with ligand descriptors, demonstrating ECIF descriptive power., Availability and Implementation: Data and code to reproduce all the results are freely available at https://github.com/DIFACQUIM/ECIF., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

SMYD3 is a multifunctional epigenetic enzyme with lysine methyltransferase activity and various interaction partners. It is implicated in the pathophysiology of cancers but with an unclear mechanism. To discover tool compounds for clarifying its biochemistry and potential as a therapeutic target, a set of drug-like compounds was screened in a biosensor-based competition assay. Diperodon was identified as an allosteric ligand; its R and S enantiomers were isolated, and their affinities to SMYD3 were determined (K D =42 and 84 μM, respectively). Co-crystallization revealed that both enantiomers bind to a previously unidentified allosteric site in the C-terminal protein binding domain, consistent with its weak inhibitory effect. No competition between diperodon and HSP90 (a known SMYD3 interaction partner) was observed although SMYD3-HSP90 binding was confirmed (K D =13 μM). Diperodon clearly represents a novel starting point for the design of tool compounds interacting with a druggable allosteric site, suitable for the exploration of noncatalytic SMYD3 functions and therapeutics with new mechanisms of action., (© 2021 The Authors. ChemBioChem published by Wiley-VCH GmbH.)

The design of new host-guest complexes represents a fundamental challenge in supramolecular chemistry. At the same time, it opens new opportunities in material sciences or biotechnological applications. A computational tool capable of automatically predicting the binding free energy of any host-guest complex would be a great aid in the design of new host systems, or to identify new guest molecules for a given host. We aim to build such a platform and have used the SAMPL7 challenge to test several methods and design a specific computational pipeline. Predictions will be based on machine learning (when previous knowledge is available) or a physics-based method (otherwise). The formerly delivered predictions with an RMSE of 1.67 kcal/mol but will require further work to identify when a specific system is outside of the scope of the model. The latter is combines the semiempirical GFN2B functional, with docking, molecular mechanics, and molecular dynamics. Correct predictions (RMSE of 1.45 kcal/mol) are contingent on the identification of the correct binding mode, which can be very challenging for host-guest systems with a large number of degrees of freedom. Participation in the blind SAMPL7 challenge provided fundamental direction to the project. More advanced versions of the pipeline will be tested against future SAMPL challenges.

The prediction of a ligand's binding mode into its macromolecular target is essential in structure-based drug discovery. Even though tremendous effort has been made to address this problem, most of the developed tools work similarly, trying to predict the binding free energy associated with each particular binding mode. In this study, we decided to abandon this criterion, following structural stability instead. This view, implemented in a novel computational workflow, quantifies the steepness of the local energy minimum associated with each potential binding mode. Surprisingly, the protocol outperforms docking scoring functions in case of fragments (ligands with MW < 300 Da) and is as good as docking for drug-like molecules. It also identifies substructures that act as structural anchors, predicting their binding mode with particular accuracy. The results open a new physical perspective for binding mode prediction, which can be combined with existing thermodynamic-based approaches.

One of the key motifs of type I kinase inhibitors is their interactions with the hinge region of ATP binding sites. These interactions contribute significantly to the potency of the inhibitors; however, only a tiny fraction of the available chemical space has been explored with kinase inhibitors reported in the last twenty years. This paper describes a workflow utilizing docking with rDock and dynamic undocking (DUck) for the virtual screening of fragment libraries in order to identify fragments that bind to the kinase hinge region. We have identified 8-amino-2 H -isoquinolin-1-one ( MR1 ), a novel and potent hinge binding fragment, which was experimentally tested on a diverse set of kinases, and is hereby suggested for future fragment growing or merging efforts against various kinases, particularly MELK. Direct binding of MR1 to MELK was confirmed by STD-NMR, and its binding to the ATP-pocket was confirmed by a new competitive binding assay based on microscale thermophoresis., (This journal is © The Royal Society of Chemistry 2020.)

Keratin 1 (KRT1) is overexpressed in squamous carcinomas and associated with aggressive pathologies in breast cancer. Herein we report the design and preparation of the first Trp-based red fluorogenic amino acid, which is synthetically accessible in a few steps and displays excellent photophysical properties, and its application in a minimally-disruptive labelling strategy to prepare a new fluorogenic cyclopeptide for imaging of KRT1+ cells in whole intact tumour tissues., Competing Interests: The authors declare no conflicts of interest., (This journal is © The Royal Society of Chemistry.)

Virtual screening of large compound databases, looking for potential ligands of a target protein, is a major tool in computer-aided drug discovery. Throughout the years, different techniques such as similarity searching, pharmacophore matching, or molecular docking have been applied with the aim of finding hit compounds showing appreciable affinity. Molecular dynamics simulations in mixed solvents have been shown to identify hot spots relevant for protein-drug interaction, and implementations based on this knowledge were developed to improve pharmacophore matching of small molecules, binding free-energy estimations, and docking performance in terms of pose prediction. Here, we proved in a retrospective manner that cosolvent-derived pharmacophores from molecular dynamics (solvent sites) improve the performance of docking-based virtual screening campaigns. We applied a biased docking scheme based on solvent sites to nine relevant target proteins that have a set of known ligands or actives and compounds that are, presumably, nonbinders (decoys). Our results show improvement in virtual screening performance compared to traditional docking programs both at a global level, with up to 35% increase in areas under the receiver operating characteristic curve, and in early stages, with up to a 7-fold increase in enrichment factors at 1%. However, the improvement in pose prediction of actives was less profound. The presented application makes use of the AutoDock Bias method and is the only cosolvent-derived pharmacophore technique that employs its knowledge both in the ligand conformational search algorithm and the final affinity scoring for virtual screening purposes.

Thanks to recent guidelines, the design of safe and effective covalent drugs has gained significant interest. Other than targeting non-conserved nucleophilic residues, optimizing the noncovalent binding framework is important to improve potency and selectivity of covalent binders toward the desired target. Significant efforts have been made in extending the computational toolkits to include a covalent mechanism of protein targeting, like in the development of covalent docking methods for binding mode prediction. To highlight the value of the noncovalent complex in the covalent binding process, here we describe a new protocol using tethered and constrained docking in combination with Dynamic Undocking (DUck) as a tool to privilege strong protein binders for the identification of novel covalent inhibitors. At the end of the protocol, dedicated covalent docking methods were used to rank and select the virtual hits based on the predicted binding mode. By validating the method on JAK3 and KRas, we demonstrate how this fast iterative protocol can be applied to explore a wide chemical space and identify potent targeted covalent inhibitors., (© 2019 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.)

Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognition and to improve the predictive capability of well-established methods such as molecular docking. The application of MD simulations to the study of the association of proteins with drug-like compounds is currently only possible for specific cases, as it remains computationally very expensive and labor intensive. MDmix simulations on the other hand, can be used systematically to address some of the common tasks in SBDD. With the advent of new tools and faster computers we expect to see an increase in the application of mixed solvent MD simulations to a plethora of protein targets to identify new drug candidates.

There have been substantial advances in the application of molecular modelling and simulation to drug discovery in recent years, as massive increases in computer power are coupled with continued development in the underlying methods and understanding of how to apply them. Here, we survey recent advances in one particular area-predicting how a known ligand binds to a particular protein. We focus on the four contributing classes of calculation: predicting where a binding site is on a protein; characterizing where chemical functional groups will bind to that site; molecular docking to generate a binding mode for a ligand and dynamics simulations to refine that pose and allow for protein conformation change. Examples of successful application are provided for each class., (Copyright © 2018. Published by Elsevier Ltd.)

Computer-aided methods have been broadly used in pharmaceutical research to identify potential ligands and design effective therapeutics. Most of the approaches rely on the binding affinity prediction and approximate thermodynamic properties of the system. Our alternative approach focuses on structural stability, provided by native protein-ligand interactions, in particular hydrogen bonds. Based on this idea, we designed new fast computational method, called dynamic undocking (DUck), that evaluates stability by calculating the work necessary to break the most important native contact in a ligand-receptor complex. This property is effective in distinguishing true ligands from decoys and is orthogonal to currently existing docking methods, thus making it exceptionally useful in virtual screening. Here, we present a protocol suitable for DUck's application in drug design strategy, as well as notes that will help to solve common problems addressed by users.

Virtual screening is a powerful methodology to search for new small molecule inhibitors against a desired molecular target. Usually, it involves evaluating thousands of compounds (derived from large databases) in order to select a set of potential binders that will be tested in the wet-lab. The number of tested compounds is directly proportional to the cost, and thus, the best possible set of ligands is the one with the highest number of true binders, for the smallest possible compound set size. Therefore, methods that are able to trim down large universal data sets enriching them in potential binders are highly appreciated. Here we present LigQ, a free webserver that is able to (i) determine best structure and ligand binding pocket for a desired protein, (ii) find known binders, as well as potential ligands known to bind to similar protein domains, (iii) most importantly, select a small set of commercial compounds enriched in potential binders, and (iv) prepare them for virtual screening. LigQ was tested with several proteins, showing an impressive capacity to retrieve true ligands from large data sets, achieving enrichment factors of over 10%. LigQ is available at http://ligq.qb.fcen.uba.ar/ .

The knowledge of the free energy of binding of small molecules to a macromolecular target is crucial in drug design as is the ability to predict the functional consequences of binding. We highlight how a molecular dynamics (MD)-based approach can be used to predict the free energy of small molecules, and to provide priorities for the synthesis and the validation via in vitro tests. Here, we study the dynamics and energetics of the nuclear receptor REV-ERBα with its co-repressor NCoR and 35 novel agonists. Our in silico approach combines molecular docking, molecular dynamics (MD), solvent-accessible surface area (SASA) and molecular mechanics poisson boltzmann surface area (MMPBSA) calculations. While docking yielded initial hints on the binding modes, their stability was assessed by MD. The SASA calculations revealed that the presence of the ligand led to a higher exposure of hydrophobic REV-ERB residues for NCoR recruitment. MMPBSA was very successful in ranking ligands by potency in a retrospective and prospective manner. Particularly, the prospective MMPBSA ranking-based validations for four compounds, three predicted to be active and one weakly active, were confirmed experimentally.

In the era of systems biology, multi-target pharmacological strategies hold promise for tackling disease-related networks. In this regard, drug promiscuity may be leveraged to interfere with multiple receptors: the so-called polypharmacology of drugs can be anticipated by analyzing the similarity of binding sites across the proteome. Here, we perform a pairwise comparison of 90,000 putative binding pockets detected in 3,700 proteins, and find that 23,000 pairs of proteins have at least one similar cavity that could, in principle, accommodate similar ligands. By inspecting these pairs, we demonstrate how the detection of similar binding sites expands the space of opportunities for the rational design of drug polypharmacology. Finally, we illustrate how to leverage these opportunities in protein-protein interaction networks related to several therapeutic classes and tumor types, and in a genome-scale metabolic model of leukemia.

The family of concentrative Na + /nucleoside cotransporters in humans is constituted by three subtypes, namely, hCNT1, hCNT2, and hCNT3. Besides their different nucleoside selectivity, hCNT1 and hCNT2 have a Na + /nucleoside stoichiometry of 1:1, while for hCNT3 it is 2:1. This distinct stoichiometry of subtype 3 might hint the existence of a secondary sodium-binding site that is not present in the other two subtypes, but to date their three-dimensional structures remain unknown and the residues implicated in Na + binding are unclear. In this work, we have identified and characterized the Na + binding sites of hCNT3 by combining molecular modeling and mutagenesis studies. A model of the transporter was obtained by homology modeling, and key residues of two sodium-binding sites were identified and verified with a mutagenesis strategy. The structural model explains the altered sodium-binding properties of the hCNT3C602R polymorphic variant and supports previously generated data identifying the determinant residues of nucleoside selectivity, paving the way to understand how drugs can target this plasma membrane transporter.

One of the most important biological processes at the molecular level is the formation of protein-ligand complexes. Therefore, determining their structure and underlying key interactions is of paramount relevance and has direct applications in drug development. Because of its low cost relative to its experimental sibling, molecular dynamics (MD) simulations in the presence of different solvent probes mimicking specific types of interactions have been increasingly used to analyze protein binding sites and reveal protein-ligand interaction hot spots. However, a systematic comparison of different probes and their real predictive power from a quantitative and thermodynamic point of view is still missing. In the present work, we have performed MD simulations of 18 different proteins in pure water as well as water mixtures of ethanol, acetamide, acetonitrile and methylammonium acetate, leading to a total of 5.4 μs simulation time. For each system, we determined the corresponding solvent sites, defined as space regions adjacent to the protein surface where the probability of finding a probe atom is higher than that in the bulk solvent. Finally, we compared the identified solvent sites with 121 different protein-ligand complexes and used them to perform molecular docking and ligand binding free energy estimates. Our results show that combining solely water and ethanol sites allows sampling over 70% of all possible protein-ligand interactions, especially those that coincide with ligand-based pharmacophoric points. Most important, we also show how the solvent sites can be used to significantly improve ligand docking in terms of both accuracy and precision, and that accurate predictions of ligand binding free energies, along with relative ranking of ligand affinity, can be performed.

There is a pressing need for new technologies that improve the efficacy and efficiency of drug discovery. Structure-based methods have contributed towards this goal but they focus on predicting the binding affinity of protein-ligand complexes, which is notoriously difficult. We adopt an alternative approach that evaluates structural, rather than thermodynamic, stability. As bioactive molecules present a static binding mode, we devised dynamic undocking (DUck), a fast computational method to calculate the work necessary to reach a quasi-bound state at which the ligand has just broken the most important native contact with the receptor. This non-equilibrium property is surprisingly effective in virtual screening because true ligands form more-resilient interactions than decoys. Notably, DUck is orthogonal to docking and other 'thermodynamic' methods. We demonstrate the potential of the docking-undocking combination in a fragment screening against the molecular chaperone and oncology target Hsp90, for which we obtain novel chemotypes and a hit rate that approaches 40%.

Novel methods for drug discovery are constantly under development and independent exercises to test and validate them for different goals are extremely useful. The drug discovery data resource (D3R) Grand Challenge 2015 offers an excellent opportunity as an external assessment and validation experiment for Computer-Aided Drug Discovery methods. The challenge comprises two protein targets and prediction tests: binding mode and ligand ranking. We have faced both of them with the same strategy: pharmacophore-guided docking followed by dynamic undocking (a new method tested experimentally here) and, where possible, critical assessment of the results based on pre-existing information. In spite of using methods that are qualitative in nature, our results for binding mode and ligand ranking were amongst the best on Hsp90. Results for MAP4K4 were less positive and we track the different performance across systems to the level of previous knowledge about accessible conformational states. We conclude that docking is quite effective if supplemented by dynamic undocking and empirical information (e.g. binding hot spots, productive protein conformations). This setup is well suited for virtual screening, a frequent application that was not explicitly tested in this edition of the D3R Grand Challenge 2015. Protein flexibility remains as the main cause for hard failures.

The core of F1-ATPase consists of three catalytic (β) and three noncatalytic (α) subunits, forming a hexameric ring in alternating positions. A wealth of experimental and theoretical data has provided a detailed picture of the complex role played by catalytic subunits. Although major conformational changes have only been seen in β-subunits, it is clear that α-subunits have to respond to these changes in order to be able to transmit information during the rotary mechanism. However, the conformational behavior of α-subunits has not been explored in detail. Here, we have combined unbiased molecular dynamics (MD) simulations and calorimetrically measured thermodynamic signatures to investigate the conformational flexibility of isolated α-subunits, as a step toward deepening our understanding of its function inside the α3β3 ring. The simulations indicate that the open-to-closed conformational transition of the α-subunit is essentially barrierless, which is ideal to accompany and transmit the movement of the catalytic subunits. Calorimetric measurements of the recombinant α-subunit from Geobacillus kaustophilus indicate that the isolated subunit undergoes no significant conformational changes upon nucleotide binding. Simulations confirm that the nucleotide-free and nucleotide-bound subunits show average conformations similar to that observed in the F1 crystal structure, but they reveal an increased conformational flexibility of the isolated α-subunit upon MgATP binding, which might explain the evolutionary conserved capacity of α-subunits to recognize nucleotides with considerable strength. Furthermore, we elucidate the different dependencies that α- and β-subunits show on Mg(II) for recognizing ATP., (Copyright © 2016 Elsevier B.V. All rights reserved.)

The modulation of protein-protein interactions (PPIs) is emerging as a highly promising tool to fight diseases. However, whereas an increasing number of compounds are able to disrupt peptide-mediated PPIs efficiently, the inhibition of domain-domain PPIs appears to be much more challenging. Herein, we report our results related to the interaction between vascular endothelial growth factor (VEGF) and its receptor (VEGFR). The VEGF-VEGFR interaction is a typical domain-domain PPI that is highly relevant for the treatment of cancer and some retinopathies. Our final goal was to identify ligands able to bind VEGF at the region used by the growth factor to interact with its receptor. We undertook an extensive study, combining a variety of experimental approaches, including NMR-spectroscopy-based screening of small organic fragments, peptide libraries, and medicinal plant extracts. The key feature of the successful ligands that emerged from this study was their capacity to expose hydrophobic functional groups able to interact with the hydrophobic hot spots at the interacting VEGF surface patch., (© 2015 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.)

As previously reported, P. aeruginosa genes PA2077 and PA2078 code for 10S-DOX (10S-Dioxygenase) and 7,10-DS (7,10-Diol Synthase) enzymes involved in long-chain fatty acid oxygenation through the recently described oleate-diol synthase pathway. Analysis of the amino acid sequence of both enzymes revealed the presence of two heme-binding motifs (CXXCH) on each protein. Phylogenetic analysis showed the relation of both proteins to bacterial di-heme cytochrome c peroxidases (Ccps), similar to Xanthomonas sp. 35Y rubber oxidase RoxA. Structural homology modelling of PA2077 and PA2078 was achieved using RoxA (pdb 4b2n) as a template. From the 3D model obtained, presence of significant amino acid variations in the predicted heme-environment was found. Moreover, the presence of palindromic repeats located in enzyme-coding regions, acting as protein evolution elements, is reported here for the first time in P. aeruginosa genome. These observations and the constructed phylogenetic tree of the two proteins, allow the proposal of an evolutionary pathway for P. aeruginosa oleate-diol synthase operon. Taking together the in silico and in vivo results obtained we conclude that enzymes PA2077 and PA2078 are the first described members of a new subfamily of bacterial peroxidases, designated as Fatty acid-di-heme Cytochrome c peroxidases (FadCcp).