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

Optimiertes Design kombinatorischer Verbindungsbibliotheken durch Genetische Algorithmen und deren Bewertung anhand wissensbasierter Protein-Ligand Bindungsprofile

In dieser Arbeit sind die zwei neuen Computer-Methoden DrugScore Fingerprint (DrugScoreFP) und GARLig in ihrer Theorie und Funktionsweise vorgestellt und validiert worden. DrugScoreFP ist ein neuartiger Ansatz zur Bewertung von computergenerierten Bindemodi potentieller Liganden für eine bestimmte Zielstruktur. Das Programm basiert auf der etablierten Bewertungsfunktion DrugScoreCSD und unterscheidet sich darin, dass anhand bereits bekannter Kristallstrukturen für den zu untersuchenden Rezeptor ein Referenzvektor generiert wird, der zu jedem Bindetaschenatom Potentialwerte für alle möglichen Interaktionen enthält. Für jeden neuen, computergenerierten Bindungsmodus eines Liganden lässt sich ein entsprechender Vektor generieren. Dessen Distanz zum Referenzvektor ist ein Maß dafür, wie ähnlich generierte Bindungsmodi zu bereits bekannten sind. Eine experimentelle Validierung der durch DrugScoreFP als ähnlich vorhergesagten Liganden ergab für die in unserem Arbeitskreis untersuchten Proteinstrukturen Trypsin, Thermolysin und tRNA-Guanin Transglykosylase (TGT) sechs Inhibitoren fragmentärer Größe und eine Thermolysin Kristallstruktur in Komplex mit einem der gefundenen Fragmente. Das in dieser Arbeit entwickelte Programm GARLig ist eine auf einem Genetischen Algorithmus basierende Methode, um chemische Seitenkettenmodifikationen niedermolekularer Verbindungen hinsichtlich eines untersuchten Rezeptors effizient durchzuführen. Zielsetzung ist hier die Zusammenstellung einer Verbindungsbibliothek, welche eine benutzerdefiniert große Untermenge aller möglichen chemischen Modifikationen Ligand-ähnlicher Grundgerüste darstellt. Als zentrales Qualitätskriterium einzelner Vertreter der Verbindungsbibliothek dienen durch Docking erzeugte Ligand-Geometrien und deren Bewertungen durch Protein-Ligand-Bewertungsfunktionen. In mehreren Validierungsszenarien an den Proteinen Trypsin, Thrombin, Faktor Xa, Plasmin und Cathepsin D konnte gezeigt werden, dass eine effiziente Zusammenstellung Rezeptor-spezifischer Substrat- oder Ligand-Bibliotheken lediglich eine Durchsuchung von weniger als 8% der vorgegebenen Suchräume erfordert und GARLig dennoch im Stande ist, bekannte Inhibitoren in der Zielbibliothek anzureichern

Analysis, design and "in silico" evaluation of e-selectin antagonists

E-selectin, is member of a family of cell-adhesion proteins, which plays a crucial role in many physiological processes and diseases [1], and in particular, in the early phases of the inflammatory response. Its role is to promote the tethering and the rolling of leukocytes along the endothelial surface [2]. These steps are then followed by integrin-mediated firm adhesion and final transendothelial migration. Therefore, control of the leukocyte-endothelial cell adhesion process may be useful in cases, where excessive recruitment of leukocytes can contribute to acute or chronic diseases such as stroke, reperfusion injury, psoriasis or rheumatoid arthritis [3]. In this work, efforts to develop in silico-based protocols to study the interaction between E-selectin and its ligands, are presented. Hence, different protocols had to be developed and validated. In particular, a new procedure for the analysis of the conformational preferences of E-selectin antagonists was established and the results compared to those obtained with the MC(JBW)/SD approach, which had already demonstrated its validity in the past [161,168]. Thus, the comparison between the two protocols permitted to recognize a different conformational preference of the two methods for the orientation of the sialic acid moiety of sLex (3) (torsions Φ3 and Ψ3, Figure A), which reflects the contrasting opinions existing for the conformation adopted by sLex (3) in solution [150–168]. A more detailed analysis revealed that probably both approaches deliver only a partially correct view and that in reality, in solution, sLex (3) exists as a mixture of low energy conformers and not as supposed to date [150–154,161–163] as a population of a single conformer. In addition, a docking routine was established and the impact of different partialcharge methods and of explicit solvation on the binding mode studied. MD simulations enabled to gain an insight into the dynamical character of the protein-ligand interactions. In particular, the observations done in an atomic-force microscopy study [350], describing the interactions between the carboxylic group of sLex and Arg97, and between the 3– and 4–hydroxyls of fucose and the calcium ion, as the two main energy barriers for the dissociation process of the protein-ligand complex, found confirmation in our MD-investigations. Thus, these two contacts always lasted longer than any other in the MD simulation. QSAR-models with Quasar [270–272,351] and Raptor [315,316,335] were successfully derived and will permit a semi-quantitative in silico estimation of the binding affinity for the ligands that will be designed in the future. Finally, the developed protocols and models were applied for the development of new E-selectin antagonists. Unfortunately, to date, only few biological data is available to evaluate our design strategies. However, the impact of the ligand’s pre-organization on the binding affinity could be established at least for the Lexcore of sLex (3). Hence, the importance of the exo-anomeric effect, of the steric compression, and of the hydrophobic interaction between the methyl group of fucose and the β-face of galactose was clearly demonstrated

Analysis, design and "in silico" evaluation of e-selectin antagonists

E-selectin, is member of a family of cell-adhesion proteins, which plays a crucial role in many physiological processes and diseases [1], and in particular, in the early phases of the inflammatory response. Its role is to promote the tethering and the rolling of leukocytes along the endothelial surface [2]. These steps are then followed by integrin-mediated firm adhesion and final transendothelial migration. Therefore, control of the leukocyte-endothelial cell adhesion process may be useful in cases, where excessive recruitment of leukocytes can contribute to acute or chronic diseases such as stroke, reperfusion injury, psoriasis or rheumatoid arthritis [3]. In this work, efforts to develop in silico-based protocols to study the interaction between E-selectin and its ligands, are presented. Hence, different protocols had to be developed and validated. In particular, a new procedure for the analysis of the conformational preferences of E-selectin antagonists was established and the results compared to those obtained with the MC(JBW)/SD approach, which had already demonstrated its validity in the past [161,168]. Thus, the comparison between the two protocols permitted to recognize a different conformational preference of the two methods for the orientation of the sialic acid moiety of sLex (3) (torsions Φ3 and Ψ3, Figure A), which reflects the contrasting opinions existing for the conformation adopted by sLex (3) in solution [150–168]. A more detailed analysis revealed that probably both approaches deliver only a partially correct view and that in reality, in solution, sLex (3) exists as a mixture of low energy conformers and not as supposed to date [150–154,161–163] as a population of a single conformer. In addition, a docking routine was established and the impact of different partialcharge methods and of explicit solvation on the binding mode studied. MD simulations enabled to gain an insight into the dynamical character of the protein-ligand interactions. In particular, the observations done in an atomic-force microscopy study [350], describing the interactions between the carboxylic group of sLex and Arg97, and between the 3– and 4–hydroxyls of fucose and the calcium ion, as the two main energy barriers for the dissociation process of the protein-ligand complex, found confirmation in our MD-investigations. Thus, these two contacts always lasted longer than any other in the MD simulation. QSAR-models with Quasar [270–272,351] and Raptor [315,316,335] were successfully derived and will permit a semi-quantitative in silico estimation of the binding affinity for the ligands that will be designed in the future. Finally, the developed protocols and models were applied for the development of new E-selectin antagonists. Unfortunately, to date, only few biological data is available to evaluate our design strategies. However, the impact of the ligand’s pre-organization on the binding affinity could be established at least for the Lexcore of sLex (3). Hence, the importance of the exo-anomeric effect, of the steric compression, and of the hydrophobic interaction between the methyl group of fucose and the β-face of galactose was clearly demonstrated

Insights into the Development of Chemotherapeutics Targeting PFKFB Enzymes

The PFKFB enzymes control the primary checkpoint in the glycolytic pathway and are implicated in a multitude of diseases: from cancer, to schizophrenia, to diabetes, and heart disease. The inducible isoform, PFKFB3, is known to be associated with the upregulation of glycolysis in many cancers. The first study within this work investigates the potential for using tier-based approaches of virtual screening to target small molecule kinases, with PFKFB3 serving as a case study. For this investigation, bioactive compounds for PFKFB3 were identified from a compound library of 1364 compounds via high-throughput screening, with bioactive compounds being further characterized as either competitive or non-competitive for F6P. Using the F6P-competitive compounds, several structure based docking programs were assessed individually and in conjunction with a pharmacophore screening. The results showed that the tiered virtual screening approach, using pharmacophore screening in addition to structure-based docking, improved enrichments rates in 80% of cases, reduced CPU costs up to 7-fold, and lessened variability among different structure-based docking methods. The second study investigates the structural and kinetic characteristics of citrate inhibition on the heart PFKFB isoenzyme, PFKFB2. High levels of citrate, an intermediate of the TCA cycle, signify an abundance of biosynthetic precursors and that additional glucose need not be degraded for this purpose. Previous studies have noted that citrate acts as an important negative feed-back mechanism to limit glycolytic activity by inhibiting PFKFB enzymes, yet the structural and mechanistic details of citrate’s inhibition had not been determined. To study the molecular basis for citrate inhibition, the three-dimensional structures of the human and bovine PFKFB2 orthologues were solved, each in complex with citrate. For both cases, citrate primarily occupied the binding site of Fructose-6-phosphate (F6P), competitively blocking F6P from binding. Additionally, a carboxy arm of citrate extended into the γ-phosphate binding site of ATP, sterically and electrostatically blocking the catalytic binding mode for ATP. In the human orthologue, which utilized AMPPNP as an ATP analogue, conformational changes were observed in the 2-kinase domain as well as the binding mode for AMPPNP. This study gives new insights as to how the citrate-mediate negative feedback loop influences glycolytic flux through PFKFB enzymes

Peptide and Protein Interaction Prediction and Intervention with Computational Methods

Proteins are the most fascinating multifaceted biomacromolecules in living systems and play various important roles such as structural, sensory, catalytic, and regulatory function. Protein and peptide interactions have emerged as an important and challenging topic inbiochemistry and medicinal chemistry. Computational methods as promising tools have been utilized to predict protein and peptide interactions in order to intervene in the biochemical processes and facilitate pharmaceutical peptide design and clarify the complications. This review will introduce the computational methods which are applicable in protein and peptide interaction prediction and summarizes the most successful examples of computational methods described in the literature.HIGHLIGHTS•Highlights the importance of peptides and proteins interactions.•Summarizes the computational methods which are applicable in peptide and protein interaction prediction.•Highlights the applications of computational methods in peptides and proteins interactions

Software for molecular docking: a review

Publshed ArticleMolecular docking methodology explores the behavior of small molecules in the binding site of a target protein. As more protein structures are determined experimentally using X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy, molecular docking is increasingly used as a tool in drug discovery. Docking against homologymodeled targets also becomes possible for proteins whose structures are not known. With the docking strategies, the druggability of the compounds and their specificity against a particular target can be calculated for further lead optimization processes. Molecular docking programs perform a search algorithm in which the conformation of the ligand is evaluated recursively until the convergence to the minimum energy is reached. Finally, an affinity scoring function, ΔG [U total in kcal/mol], is employed to rank the candidate poses as the sum of the electrostatic and van der Waals energies. The driving forces for these specific interactions in biological systems aim toward complementarities between the shape and electrostatics of the binding site surfaces and the ligand or substrate