Structural study of the therapeutic potential of protein-ligand interactions

Abstract

Most of the cellular functions are driven by small-molecules that selectively bind to their protein targets. Is such their importance, that the pharmacological intervention of proteins by small molecule drugs is frequently used to treat multiple conditions. Herein I present a thesis that leverages a threedimensional study of small molecule protein interactions to improve their therapeutic relevance. More specifically, it introduces nAnnolyze, a method for predicting structurally detailed protein-ligand interactions at proteome scale. The method exemplified its applicability by predicting the human targets of all small molecule FDA-approved drugs. A second application of nAnnolyze in Mycobacterium tuberculosis identified the bacterial targets for two sets of compounds with known antitubercular activity. Finally, the thesis describes a computational model that predicts cancer associated mutations with the highest chances to confer resistance to a targeted cancer therapy. Additionally, for those mutations identified as responsible of resistance, the model also suggested alternative non-resistant treatments.La mayor´ıa de las funciones celulares est´an dirigidas por peque˜nas mol´eculas que selectivamente se unen a sus prote´ınas diana. Es tal su importancia que la intervenci´on farmacol´ogica de prote´ınas mediante peque˜nas mol´eculas es frecuentemente usada para tratar m´ultiples enfermedades. A continuaci´on presento a una tesis que utiliza un estudio tridimensional de las interacciones entre peque˜nas mol´eculas y prote´ınas para mejorar su relevancia terap´eutica. Espec´ıficamente, presento nAnnolyze, un m´etodo que predice interacciones prote´ına-ligando estructuralmente detalladas y a nivel de proteoma. El m´etodo ejemplifica su aplicabilidad a trav´es de la predicci´on de dianas terap´euticas humanas para todas las peque˜nas mol´eculas usadas como f´armacos aprobados por la FDA. Una segunda aplicaci´on de nAnnolyze en Mycobacterium tuberculosis identific´o las prote´ınas diana para dos conjuntos de compuestos con actividad contra dicha bacteria. Finalmente, la tesis describe un modelo computacional que predice mutaciones asociadas a c´ancer con alta probabilidad de conferir resistencia a una terapia dirigida. Adem´as, para aquellas mutaciones identificadas como responsables de producir resistencia, el modelo tambi´en sugiere terapias alternativas predichas como no resistentes. IIIPrograma de doctorat en Biomedicina