Diphenyl Urea Derivatives as Inhibitors of Transketolase: A Structure-Based Virtual Screening

Transketolase is an enzyme involved in a critical step of the non-oxidative branch of the pentose phosphate pathway whose inhibition could lead to new anticancer drugs. Here, we report new human transketolase inhibitors, based on the phenyl urea scaffold, found by applying structure-based virtual screening. These inhibitors are designed to cover a hot spot in the dimerization interface of the homodimer of the enzyme, providing for the first time compounds with a suggested novel binding mode not based on mimicking the thiamine pyrophosphate cofactor

Disrupting the protein-protein recognition in cancer pathways by molecular modeling.

Cancer is the second disease leading cause of death in industrialized countries. Although early detection and more efficient drugs are responsible of the reduction of mortality, several cancers still present difficult treatments and low survival rates. Conventional drugs only exhibit moderate therapeutic index between cancer and normal tissues but recent advances are focused to improve lesstoxic treatments. Hence, new drugs must target specific signaling pathways involved in cell growth and proliferation. Concerning this aim, two mechanism involved in cancer disease, named apoptosis (or programmed cell death) and pentose phosphate pathway, have been selected in this work to search new inhibitors to target crucial proteins of both cell routes.Overexpression of antiapoptotic genes has been correlated with tumor growth and resistance tochemotherapy, thus many efforts have been done to block the activity of XIAP and Survivin, central proteins acting in apoptosis and studied in the present work. Moreover, the two most active proteins detected in both the oxidative and nonoxidative branches of the pentose phosphate pathway, Glucose-6-Phosphate Dehydrogenase (G6PDH) and Transketolase (TKT), have been also selected in this thesis.Molecular Modeling methods, covering topics in protein and peptide recognition, molecular dynamics, pharmacophore generation, database searching, docking and scoring in virtual screening and binding free energy prediction, have been applied with success to discover new active molecules inhibitors of XIAP, Survivin, G6PDH and TKT proteins.TÍTULO: "Ruptura del reconocimiento proteína-proteína en rutas tumorales mediante modelizaciónmolecular".TEXTO:El cáncer es el segunda causa de muerte por enfermedad en los paises industrializados. A pesar de la existencia de métodos eficaces de detección precoz y tratamientos cada vez más efectivos responsables de la reducción de mortalidad, algunos tipos de tumores presentan todavía tratamientos difíciles y bajos índices de supervivencia. Los fármacos convencionales sólo exhiben un índice terapéutico moderado, entre células sanas y tumorales, por ello los avances recientes se centran en encontrar tratamientos menos tóxicos para esta enfermedad. Así pues, los fármacos del futuro deberán incidir en rutas biológicas específicas, involucrando el crecimiento celular y laproliferación descontrolada. Siguiendo este planteamiento, en este trabajo se han seleccionado dos mecanismos biológicos involucrados en el cáncer, llamados apoptosis (o muerte celular programada) y ruta de las pentosas fosfato, con el objetivo de encontrar nuevos inhibidores de las proteínas más sensibles de ambas rutas.La sobreexpresión de genes antiapoptóticos se ha correlacionado con el crecimiento tumoral y laresistencia a los tratamientos habituales. Así, se está trabajando en entender el funcionamiento de dos proteínas importantes de esta ruta, el XIAP y el Survivin, las cuales se han seleccionado en este trabajo, debido a que todavía no existen fármacos en el mercado que actúen sobre estas dos proteínas y debido a que su interés terapéutico se ha demostrado claramente.Por otro lado, en este trabajo también se han estudiado las dos proteínas más activas detectadas en la rama oxidativa y no oxidativa de la ruta de las pentosas fosfato, la Glucosa-6-Fosfato Deshidrogenasa y la Transketolasa.El objetivo principal ha consistido en aplicar métodos de la Modelización Molecular, que cubrentópicos recientes, como el reconocimiento de péptidos y proteínas, la búsqueda en bases de datos, el anclaje y evaluación del cribado virtual de compuestos y la predicción de energías libres de unión, para encontrar nuevos inhibidores de las proteínas XIAP, Survivin, Glucosa-6-Fosfato Deshidrogenasa y Transketolasa.</I

Proposed binding mode of compound T2 and its derivatives (T2A–T2F).

<p>Black dots represent hydrogen bonds.</p

Transketolase structure model.

<p>A) Homology model of human transketolase showing the antiparallel alpha helices involved in dimerization. B) Close view of the alpha helix D200-G210 showing the selected residues of the 5-point pharmacophore. HY: hydrophobic contact, HA: hydrogen acceptor, HD: hydrogen donor.</p

Structures of the reported diphenyl urea derivatives.

<p>In the upper part, compounds which showed good inhibitory activity. In the bottom part, compounds which showed poor activity. The pharmacophoric points are also shown; in red for hydrogen acceptor points, in blue for hydrogen donor points and in green for hydrophobic points.</p

Activities of the reported diphenyl urea derivatives.

<p>Activities of the reported diphenyl urea derivatives.</p

Comparison of transketolase model with crystal structure.

<p>A) Superimposition of the homology model of human transketolase (in pink) with the recently released crystal structure (in white). B) Close view of the residues used for pharmacophore definition, in the homology model (in pink and thin residues) and in the crystal structure (in white and thick residues).</p

Fractal dimension as a measure of surface roughness of G protein-coupled receptors: implications for structure and function

Protein surface roughness is a structural property associated with ligand-protein and protein-protein binding interfaces. In this work we apply for the first time the concept of surface roughness, expressed as the fractal dimension, to address structure and function of G protein-coupled receptors (GPCRs) which are an important group of drug targets. We calculate the exposure ratio and the fractal dimension for helix-forming residues of the β(2) adrenergic receptor (β(2)AR), a model system in GPCR studies, in different conformational states: in complex with agonist, antagonist and partial inverse agonists. We show that both exposure ratio and roughness exhibit periodicity which results from the helical structure of GPCRs. The pattern of roughness and exposure ratio of a protein patch depends on its environment: the residues most exposed to membrane are in general most rough whereas parts of receptors mediating interhelical contacts in a monomer or protein complex are much smoother. We also find that intracellular ends (TM3, TM5, TM6 and TM7) which are relevant for G protein binding and thus receptor signaling, are exposed but smooth. Mapping the values of residual fractal dimension onto receptor 3D structures makes it possible to conclude that the binding sites of orthosteric ligands as well as of cholesterol are characterized with significantly higher roughness than the average for the whole protein. In summary, our study suggests that identification of specific patterns of roughness could be a novel approach to spot possible binding sites which could serve as original drug targets for GPCRs modulation