Stefan Henrich

Computational approaches to identifying and characterizing protein binding sites for ligand design

Given the three‐dimensional structure of a protein, how can one find the sites where other molecules might bind to it? Do these sites have the properties necessary for high affinity binding? Is this protein a suitable target for drug design? Here, we discuss recent developments in computational methods to address these and related questions. Geometric methods to identify pockets on protein surfaces have been developed over many years but, with new algorithms, their performance is still improving. Simulation methods show promise in accounting for protein conformational variability to identify transient pockets but lack the ease of use of many of the (rigid) shape‐based tools. Sequence and structure comparison approaches are benefiting from the constantly increasing size of sequence and structure databases. Energetic methods can aid identification and characterization of binding pockets, and have undergone recent improvements in the treatment of solvation and hydrophobicity. The “druggability” of a binding site is still difficult to predict with an automated procedure. The methodologies available for this purpose range from simple shape and hydrophobicity scores to computationally demanding free energy simulations. Copyright

Protein–protein interface-binding peptides inhibit the cancer therapy target human thymidylate synthase

Human thymidylate synthase is a homodimeric enzyme that plays a key role in DNA synthesis and is a target for several clinically important anticancer drugs that bind to its active site. We have designed peptides to specifically target its dimer interface. Here we show through X-ray diffraction, spectroscopic, kinetic, and calorimetric evidence that the peptides do indeed bind at the interface of the dimeric protein and stabilize its di-inactive form. The “LR” peptide binds at a previously unknown binding site and shows a previously undescribed mechanism for the allosteric inhibition of a homodimeric enzyme. It inhibits the intracellular enzyme in ovarian cancer cells and reduces cellular growth at low micromolar concentrations in both cisplatin-sensitive and -resistant cells without causing protein overexpression. This peptide demonstrates the potential of allosteric inhibition of hTS for overcoming platinum drug resistance in ovarian cancer.

Virtual screening identification of nonfolate compounds, including a CNS drug, as antiparasitic agents inhibiting pteridine reductase.

Folate analogue inhibitors of Leishmania major pteridine reductase (PTR1) are potential antiparasitic drug candidates for combined therapy with dihydrofolate reductase (DHFR) inhibitors. To identify new molecules with specificity for PTR1, we carried out a virtual screening of the Available Chemicals Directory (ACD) database to select compounds that could interact with L. major PTR1 but not with human DHFR. Through two rounds of drug discovery, we successfully identified eighteen drug-like molecules with low micromolar affinities and high in vitro specificity profiles. Their efficacy against Leishmania species was studied in cultured cells of the promastigote stage, using the compounds both alone and in combination with 1 (pyrimethamine; 5-(4-chlorophenyl)-6-ethylpyrimidine-2,4-diamine). Six compounds showed efficacy only in combination. In toxicity tests against human fibroblasts, several compounds showed low toxicity. One compound, 5c (riluzole; 6-(trifluoromethoxy)-1,3-benzothiazol-2-ylamine), a known drug approved for CNS pathologies, was active in combination and is suitable for early preclinical evaluation of its potential for label extension as a PTR1 inhibitor and antiparasitic drug candidate.

TRAPP: A Tool for Analysis of Transient Binding Pockets in Proteins

We present TRAPP (TRAnsient Pockets in Proteins), a new automated software platform for tracking, analysis, and visualization of binding pocket variations along a protein motion trajectory or within an ensemble of protein structures that may encompass conformational changes ranging from local side chain fluctuations to global backbone motions. TRAPP performs accurate grid-based calculations of the shape and physicochemical characteristics of a binding pocket for each structure and detects the conserved and transient regions of the pocket in an ensemble of protein conformations. It also provides tools for tracing the opening of a particular subpocket and residues that contribute to the binding site. TRAPP thus enables an assessment of the druggability of a disease-related target protein taking its flexibility into account.

Comparative binding energy analysis for binding affinity and target selectivity prediction

A major challenge in drug design is to obtain compounds that bind selectively to their target receptors and do not cause side‐effects by binding to other similar receptors. Here, we investigate strategies for applying COMBINE (COMparative BINding Energy) analysis, in conjunction with PIPSA (Protein Interaction Property Similarity Analysis) and ligand docking methods, to address this problem. We evaluate these approaches by application to diverse sets of inhibitors of three structurally related serine proteases of medical relevance: thrombin, trypsin, and urokinase‐type plasminogen activator (uPA). We generated target‐specific scoring functions (COMBINE models) for the three targets using training sets of ligands with known inhibition constants and structures of their receptor‐ligand complexes. These COMBINE models were compared with the PIPSA results and experimental data on receptor selectivity. These scoring functions highlight the ligand‐receptor interactions that are particularly important for binding specificity for the different targets. To predict target selectivity in virtual screening, compounds were docked into the three protein binding sites using the program GOLD and the docking solutions were re‐ranked with the target‐specific scoring functions and computed electrostatic binding free energies. Limits in the accuracy of some of the docking solutions and difficulties in scoring them adversely affected the predictive ability of the target specific scoring functions. Nevertheless, the target‐specific scoring functions enabled the selectivity of ligands to thrombin versus trypsin and uPA to be predicted. Proteins 2010.

Profiling of Flavonol Derivatives for the Development of Antitrypanosomatidic Drugs

Flavonoids represent a potential source of new antitrypanosomatidic leads. Starting from a library of natural products, we combined target-based screening on pteridine reductase 1 with phenotypic screening on Trypanosoma brucei for hit identification. Flavonols were identified as hits, and a library of 16 derivatives was synthesized. Twelve compounds showed EC50 values against T. brucei below 10 μM. Four X-ray crystal structures and docking studies explained the observed structure-activity relationships. Compound 2 (3,6-dihydroxy-2-(3-hydroxyphenyl)-4H-chromen-4-one) was selected for pharmacokinetic studies. Encapsulation of compound 2 in PLGA nanoparticles or cyclodextrins resulted in lower in vitro toxicity when compared to the free compound. Combination studies with methotrexate revealed that compound 13 (3-hydroxy-6-methoxy-2-(4-methoxyphenyl)-4H-chromen-4-one) has the highest synergistic effect at concentration of 1.3 μM, 11.7-fold dose reduction index and no toxicity toward host cells. Our results provide the basis for further chemical modifications aimed at identifying novel antitrypanosomatidic agents showing higher potency toward PTR1 and increased metabolic stability.

On the use of PIPSA to guide target-selective drug design.

In structure-based drug design, it is important to design compounds to bind specifically and selectively to their macromolecular target receptor(s). Binding to other macromolecules that are similar to the target may result in adverse side effects and should be avoided. The decision as to which macromolecular target and which region of the target a drug should bind to should therefore include consideration of the binding properties of related macromolecules. Herein, we show how PIPSA (protein interaction property similarity analysis) can aid in surveying the interaction properties of structurally-related macromolecules before embarking on detailed design towards a chosen target site. This is illustrated by application of PIPSA to dihydrofolate reductase (DHFR; EC 1.5.1.3). DHFR is an essential and conserved enzyme in many species. It takes part in folate metabolism and is important for thymidine synthesis. It binds the cofactor NADPH and converts dihydrofolate (DHF) into tetrahydrofolate (THF). As a result of its central role, DHFR is an important drug target and inhibitors, such as methotrexate (MTX), trimethoprim, and pyrimethamine, are used against cancer, bacterial, and parasitic diseases, respectively. A challenge in the application of DHFR inhibitors as antibiotics is the occurrence of side effects. These may arise from the binding of the compounds to human DHFR. On the other hand, it may be advantageous for antibiotics to have a broad spectrum of activity and to bind to several microbial DHFRs. By way of example, we consider herein the selective targeting of compounds to Candida albicans DHFR. Such compounds would be particularly useful to treat common opportunistic infections in immunocompromised patients. Although known clinical drugs against DHFR show weak activity against C. albicans, potent, selective inhibitors of C. albicans DHFR have been reported. Their selectivity for C. albicans versus human DHFR has been ascribed in part to differences in protein–ligand hydrogen bonding. Such differences can be detected by analysis of the protein electrostatic potentials. PIPSA permits quantification of the similarity in the interaction properties of homologous proteins and has been applied to a variety of protein types. PIPSA is available as standalone software but has recently been made available online in the SYCAMORE webserver. In the latter case, it is combined in a workflow with automated protein homology model building and electrostatic potential calculation. Herein, we demonstrate use of the online PIPSA workflow for DHFRs from different species. In the first step of PIPSA, one crystal structure of DHFR (human DHFR, Swiss-Prot identifier P00374) was chosen as a template for homology modeling (Figure 1). All related DHFR

Organism-Adapted Specificity of the Allosteric Regulation of Pyruvate Kinase in Lactic Acid Bacteria

Pyruvate kinase (PYK) is a critical allosterically regulated enzyme that links glycolysis, the primary energy metabolism, to cellular metabolism. Lactic acid bacteria rely almost exclusively on glycolysis for their energy production under anaerobic conditions, which reinforces the key role of PYK in their metabolism. These organisms are closely related, but have adapted to a huge variety of native environments. They include food-fermenting organisms, important symbionts in the human gut, and antibiotic-resistant pathogens. In contrast to the rather conserved inhibition of PYK by inorganic phosphate, the activation of PYK shows high variability in the type of activating compound between different lactic acid bacteria. System-wide comparative studies of the metabolism of lactic acid bacteria are required to understand the reasons for the diversity of these closely related microorganisms. These require knowledge of the identities of the enzyme modifiers. Here, we predict potential allosteric activators of PYKs from three lactic acid bacteria which are adapted to different native environments. We used protein structure-based molecular modeling and enzyme kinetic modeling to predict and validate potential activators of PYK. Specifically, we compared the electrostatic potential and the binding of phosphate moieties at the allosteric binding sites, and predicted potential allosteric activators by docking. We then made a kinetic model of Lactococcus lactis PYK to relate the activator predictions to the intracellular sugar-phosphate conditions in lactic acid bacteria. This strategy enabled us to predict fructose 1,6-bisphosphate as the sole activator of the Enterococcus faecalis PYK, and to predict that the PYKs from Streptococcus pyogenes and Lactobacillus plantarum show weaker specificity for their allosteric activators, while still having fructose 1,6-bisphosphate play the main activator role in vivo. These differences in the specificity of allosteric activation may reflect adaptation to different environments with different concentrations of activating compounds. The combined computational approach employed can readily be applied to other enzymes.

Using 3D protein structures to derive 3D-QSARs.

The three-dimensional structures of proteins are being solved apace, yet this information is often underused in quantitative structure-activity relationship (QSAR) studies. Here, we describe and compare methods for exploiting protein structures to derive 3D-QSARs. These methods can facilitate molecular design and lead optimization and should increasingly become a standard component of the drug designers repertoire.:

LigDig: a web server for querying ligand-protein interactions

Summary: LigDig is a web server designed to answer questions that previously required several independent queries to diverse data sources. It also performs basic manipulations and analyses of the structures of protein–ligand complexes. The LigDig webserver is modular in design and consists of seven tools, which can be used separately, or via linking the output from one tool to the next, in order to answer more complex questions. Currently, the tools allow a user to: (i) perform a free-text compound search, (ii) search for suitable ligands, particularly inhibitors, of a protein and query their interaction network, (iii) search for the likely function of a ligand, (iv) perform a batch search for compound identifiers, (v) find structures of protein–ligand complexes, (vi) compare three-dimensional structures of ligand binding sites and (vii) prepare coordinate files of protein–ligand complexes for further calculations. Availability and implementation: LigDig makes use of freely available databases, including ChEMBL, PubChem and SABIO-RK, and software programs, including cytoscape.js, PDB2PQR, ProBiS and Fconv. LigDig can be used by non-experts in bio- and chemoinformatics. LigDig is available at: http://mcm.h-its.org/ligdig. Contact: jonathan.fuller@h-its.org, rebecca.wade@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.