Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback?

A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated ‘bioactive’ 3D ligand conformation is constructed as a ‘sophisticated guess’ (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis—sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its ‘dialects’ have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the ‘mainstream’ algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes

Investigating the Molecular Basis of N-Substituted 1-Hydroxy-4-Sulfamoyl-2-Naphthoate Compounds Binding to Mcl1

Myeloid cell leukemia-1 (Mcl1) is an anti-apoptotic protein that has gained considerable attention due to its overexpression activity prevents cell death. Therefore, a potential inhibitor that specifically targets Mcl1 with higher binding affinity is necessary. Recently, a series of N-substituted 1-hydroxy-4-sulfamoyl-2-naphthoate compounds was reported that targets Mcl1, but its binding mechanism remains unexplored. Here, we attempted to explore the molecular mechanism of binding to Mcl1 using advanced computational approaches: pharmacophore-based 3D-QSAR, docking, and MD simulation. The selected pharmacophoreNNRRRyielded a statistically significant 3D-QSAR model containing high confidence scores (R-2 = 0.9209, Q(2) = 0.8459, and RMSE = 0.3473). The contour mapscomprising hydrogen bond donor, hydrophobic, negative ionic and electron withdrawal effectsfrom our 3D-QSAR model identified the favorable regions crucial for maximum activity. Furthermore, the external validation of the selected model using enrichment and decoys analysis reveals a high predictive power. Also, the screening capacity of the selected model had scores of 0.94, 0.90, and 8.26 from ROC, AUC, and RIE analysis, respectively. The molecular docking of the highly active compoundC40; 4-(N-benzyl-N-(4-(4-chloro-3,5-dimethylphenoxy) phenyl) sulfamoyl)-1-hydroxy-2-naphthoatepredicted the low-energy conformational pose, and the MD simulation revealed crucial details responsible for the molecular mechanism of binding with Mcl1

N1-(3-(Trifluoromethyl)Phenyl) Isophthalamide Derivatives as Promising Inhibitors of Vascular Endothelial Growth Factor Receptor: Pharmacophore-Based Design, Docking, and MM-PBSA/MM-GBSA Binding Energy Estimation

Targeting protein kinases is a common approach for cancer treatment. In this study, a series of novel terephthalic and isophthalic derivatives were constructed as potential type 2 protein kinase inhibitors adapting pharmacophore features of approved anticancer drugs of this class. Inhibitory activity of designed structures was studied in silico against various cancer-related protein kinases and compared with that of known inhibitors. Obtained docking scores, MM-PBSA/MM-GBSA binding energy, and RF-Score-VS affinities suggest that N1-(3-(trifluoromethyl) phenyl) isophthalamide could be considered as promising scaffold for the development of novel protein kinase inhibitors which are able to target the inactive conformation of vascular endothelial growth factor receptor

Quantifying the Role of Water in Ligand-Protein Binding Processes

The aim of this thesis is to quantify the contributions of water thermodynamics to the binding free energy in protein-ligand complexes. Various computational tools were directly applied, implemented, benchmarked and discussed. An own implementation of the IFST formulation was developed to facilitate easy integration in workflows that are based on Schrödinger software. By applying the tool to a well-defined test set of congeneric ligand pairs, the potential of IFST for quantitative predictions in lead-optimization was assessed. Furthermore, FEP calculations were applied to an extended test set to validate if these simulations can accurately account for solvent displacement in ligand modifications. As a fast tool that has applications in virtual screening problems, we finally developed and validated a new scoring function that incorporates terms for protein and ligand desolvation. This resulted in total in three distinct studies, that all elucidated different aspects of water thermodynamics in CADD. These three studies are presented in the next section. In the conclusion, the results and implications of these studies are discussed jointly, as well with possible future developments. An additional study was focused on virtual screening and toxicity prediction at the androgen receptor, where distinguishing agonists and antagonists poses difficulties. We proposed and validated an approach based on MD simulations and ensemble docking to improve predictions of androgen agonists and antagonists

Bcr-Abl Allosteric Inhibitors: Where We Are and Where We Are Going to

The fusion oncoprotein Bcr-Abl is an aberrant tyrosine kinase responsible for chronic myeloid leukemia and acute lymphoblastic leukemia. The auto-inhibition regulatory module observed in the progenitor kinase c-Abl is lost in the aberrant Bcr-Abl, because of the lack of the N-myristoylated cap able to bind the myristoyl binding pocket also conserved in the Bcr-Abl kinase domain. A way to overcome the occurrence of resistance phenomena frequently observed for Bcr-Abl orthosteric drugs is the rational design of allosteric ligands approaching the so-called myristoyl binding pocket. The discovery of these allosteric inhibitors although very difficult and extremely challenging, represents a valuable option to minimize drug resistance, mostly due to the occurrence of mutations more frequently affecting orthosteric pockets, and to enhance target selectivity with lower off-target effects. In this perspective, we will elucidate at a molecular level the structural bases behind the Bcr-Abl allosteric control and will show how artificial intelligence can be effective to drive the automated de novo design towards off-patent regions of the chemical space

Sensitizing triple negative breast cancer to approved therapies: Design, synthesis and biological activity of MNK inhibitors

La desregulació de la síntesi de proteïnes és comuna en càncer. Un factor clau en el control de la traducció de proteïnes és el factor d'inici de la traducció 4E (eIF4E) que es troba regulat per les cinases MNK1/2 (MAP kinase interacting kinases 1 i 2) mitjançant fosforilació. En els últims anys, l’eIF4E s'ha descrit com un factor de pronòstic independent associat amb la progressió maligna i el desenvolupament de resistència. A més, l’eIF4E es troba sobreexpressat en càncer d'ovari, mama, pulmó, bufeta i pròstata. La fosforilació de l'eIF4E és necessària per a la transformació tumoral però és prescindible per al desenvolupament normal. Per tant, la inhibició farmacològica de les MNKs pot proporcionar una estratègia no tòxica i eficaç per al tractament del càncer, especialment en combinació amb els tractaments aprovats. En aquest projecte, es proposen els sistemes pirazolo[3,4-b]piridínics com a possibles candidats a inhibidors de MNK degut a la seva similitud amb inhibidors coneguts. S'han estudiat les possibilitats sintètiques que ofereixen aquestes estructures, definint metodologies generals per introduir substitucions selectives i controlades en 6 punts diferents de la molècula. A més, s'han descrit els mecanismes de reacció. S'han estudiat 5 famílies de compostos basades en les pirazolo[3,4-b]piridines i una de les famílies ha mostrat una activitat interessant en els assajos preliminars. S'han identificat tres compostos (EB1-3), amb valors micromolars de IC50 (0.7-4 μM), que inhibeixen de forma completa i selectiva les MNKs (entre 2.5 i 5 μM) i sense presentar citotoxicitat en cèl·lules de càncer de mama triple negatiu (MDA-MB-231). A més, el co tractament amb EB1 augmenta la sensibilitat de les cèl·lules MDA-MB-231 a la doxorubicina, millorant la seva eficàcia d'inhibir el creixement cel·lular. S'ha fet servir una estratègia de disseny basada en estructura per estudiar el mecanisme d'interacció dels diferents candidats. S'han creat models de les formes actives i inactives de MNK1 que s'han fet servir per predir la manera d'unió dels candidats. EB1 s'ha definit com un inhibidor de tipus II que s'uneix selectivament la forma inactiva de MNK1 i interacciona amb el motiu DFD (Asp-Phe-Asp), característic de les MNKs.La desregulación de la síntesis de proteínas es común en cáncer. Un factor clave en el control de la traducción de proteínas es el factor de inicio de la traducción 4E (eIF4E) cuya función está modulada por las quinasas MNK1/2 (MAP kinase interacting kinases 1 y 2) mediante fosforilación. En los últimos años, el eIF4E se ha descrito como un factor de pronóstico independiente asociado con la progresión maligna y el desarrollo de resistencia. Además, el eIF4E se encuentra sobreexpresado en cáncer de ovario, mama, pulmón, vejiga y próstata. La fosforilación del eIF4E es necesaria para la transformación tumoral pero es prescindible para el desarrollo normal. Por lo tanto, la inhibición farmacológica de las MNKs puede proporcionar una estrategia no tóxica y eficaz para el tratamiento del cáncer, especialmente en combinación con los tratamientos aprobados. En este proyecto, se proponen los sistemas pirazolo[3,4-b]piridínicos como posibles candidatos a inhibidores de MNK debido a su similitud con inhibidores conocidos. Se han estudiado las posibilidades sintéticas que ofrecen estas estructuras, definiendo metodologías generales para introducir sustituciones selectivas y controladas en 6 puntos diferentes de la molécula. Además, se han descrito los mecanismos de reacción. Se han estudiado 5 familias de compuestos basados en los compuestos pirazolo[3,4-b]piridínicos y una de las familias ha mostrado una actividad interesante en los ensayos preliminares. Se han identificado tres compuestos (EB1-3), con valores micromolares de IC50 (0.7 a 4 μM), que inhiben de forma completa y selectiva las MNKs (entre 2.5 y 5 μM) y sin presentar citotoxicidad en células de cáncer de mama triple negativo (MDA-MB-231). Además, el co-tratamiento con EB1 aumenta la sensibilidad de las células MDA-MB-231 a la doxorrubicina, mejorando su eficacia de inhibir el crecimiento celular. Se ha usado una estrategia de diseño basada en estructura para estudiar el mecanismo de interacción de los diferentes candidatos. Se han creado modelos de las formas activas e inactivas de MNK1 que se han usado para predecir el modo de unión de los hits. EB1 se ha definido como un inhibidor de tipo II que se une selectivamente la forma inactiva de MNK1 e interacciona con el motivo DFD (Asp-Phe-Asp), característico de las MNKs.Deregulation of protein synthesis is a common event in cancer. A key player in translational control is eIF4E whose function is modulated by the MAP kinase interacting kinases 1 and 2 (MNK1/2) through phosphorylation of a conserved serine (Ser209). In the recent years, eIF4E has been described as an independent prognostic factor associated with malignant progression and development of resistance. Moreover, eIF4E is found to be overexpressed in ovarian, breast, lung, colon, bladder and prostate cancer. eIF4E phosphorylation is necessary for oncogenic transformation while dispensable for normal development. Hence, pharmacologic MNK inhibitors may provide a non-toxic and effective anti-cancer strategy, especially in combination with approved treatments. In this project, the pyrazolo[3,4-b]pyridinic systems have been proposed as potential candidates as MNK inhibitors due to their similarity with known effective inhibitors. During this project, the synthetic possibilities offered by these scaffolds have been deeply studied defining general methodologies to achieve selective and controlled substitutions in 6 different points of the central core. Moreover, the reaction mechanisms have been described. Up to 5 families of compounds based on the pyrazolo[3,4-b]pyridine scaffold were studied and one of the families showed interesting activity on the preliminary assays. Three compounds (EB1-3), with IC50 values in the low μM range (0.7 to 4 μM), showed a complete and selective inhibition of MNKs (between 2.5 and 5 μM) and no significant cell toxicity in the triple negative breast cancer cell line MDA-MB-231. Moreover, co-treatment with EB1 clearly increased the sensitivity of MDA-MB 231 cells to doxorubicin improving the efficacy of the drug in inhibiting cell growth. A structure-based drug design strategy was applied to understand the mechanism of interaction of the different candidates. Models of the active/inactive forms of MNK1 were created and used to predict the binding mode of the hits. EB1 seems to be a type II inhibitor which selectively binds to the inactive form of MNK1 and interacts with the DFD (Asp-Phe-Asp) motif, a unique feature of MNKs

Efficient Methods for Exploring Chemical Space in Computational Drug Discovery

In this work novel computational methods will be developed to efficiently explore chemical space in the search for compounds with desirable properties. To improve the efficiency of exploration two methods will be used: reducing the cost of evaluating a point in chemical space, or reducing the number of points which require evaluating to find the desired compound. The first chapter of this work will introduce the topics relevant to this work, place them in the wider context of drug design and outline the theory used to generate the results presented in subsequent chapters. The first result of this thesis, discussed in chapter 2, is for the application of free energy methods to the problem of computational fluorine scanning. The application made in this work will allow for all fluorinated analogues of a compound to be tested five times faster than existing computational methods and with comparable predictive accuracy. In chapters 3 and 4 we will consider the application of numerical methods to ligand-protein binding problems in order to optimize the charge/steric parameters of the ligand and maximize binding affinity of these ligands to a given protein target. In these two optimization-based chapters we will use free energy methods to calculate gradients of the binding free energy with respect to the parameters which describe the ligand, thus allowing optimal sets of parameters to be found efficiently. In chapter 3 we search for optimized sets of charge parameters from which design ideas can be generated and tested; 73% of the design ideas were found to beneficially improve binding affinity. In chapter 4 we find optimized sets of steric parameters from which beneficial growth vectors for methyl groups can be predicted. These predictions correlate with existing free energy methods with a Spearman's rank order correlation of 0.59. The advantage of the optimization methods presented in these chapters are: 1) the methods can generate ideas for mutations which improve ligand binding free energy and 2) these methods require less computational time to explore the same volume of chemical space than existing free energy methods. Finally, chapter 5 will discuss a collaborative open source work to find new malaria therapeutics. Ligand based machine learning methods will be applied to generate and evaluate the potency of hundreds of thousands of compounds in a manner far faster than is possible with free energy methods. Based on the computational predictions, compounds are selected and evaluated experimentally with one compound tested and verified to be active with a pIC50 of 6.2 in good agreement with the computational prediction of 6.42 +- 0.75.EPSRC Centre for Doctoral Training in Computational Methods for Materials Science, grant number EP/L015552/1