| 07-03-2023 |
Simulaciones de dinámica molecular de la interacción enzima-sustrato de resveratol O-metiltransferasa
La enzima resveratrol O-metiltransferasa de Vitis vinifera (VvROMT) es una enzima de interés industrial que sintetiza productos benéficos para la salud humana y de elevado valor comercial. En nuestro laboratorio mejoramos con éxito la eficiencia enzimática y selectividad de esta enzima. Sin embargo, nuestros actuales modelos moleculares estructurales de la enzima no son capaces de explicar de manera satisfactoria los resultados experimentales. Objetivo de este proyecto es realizar simulaciones de dinámica molecular de variantes de VvROMT en complejo con diversos sustratos para generar una modelo robusto de las relaciones bioactividad-estructura.
Prerequisitos:
no tiene.
Tiene un método de evaluación Nota 1-7, con 10 créditos y tiene 1/1 vacantes disponibles |
Mentor(es): Ver en la plataforma |
| 07-03-2023 |
SimSpread-Ensemble - Development of an ensemble network-based method for drug discovery
SimSpread is a novel computational method to predict protein–ligand interaction that combines network-based inference with chemical similarity, useful for predicting drug targets, virtual screening, and drug repositioning. This project proposal intents to improve several limitations of SimSpread. Hypothesis: The combination of predictions obtained from SimSpread that use different similarity cutoffs into a single score using a machine learning (ML) model will increase predictive performance, eliminate empty predictions and eliminate the need to optimize similarity cutoff parameter, resulting in a more straightfoward and user-friendly model to predict drug-target interactions. Tasks: • Propose an ensemble predictive model. • Implement a hyperparameter optimization. • Compare the predictive performance. Candidates should have good programming skills.
Keywords:
machine learning
bioinformatica
red
red social
aprendizaje maquina
Proteínas
Diseño de fármacos
Prerequisitos:
no tiene.
Tiene un método de evaluación Nota 1-7, con 20 créditos y tiene 1/1 vacantes disponibles |
Mentor(es): Ver en la plataforma |
| 07-03-2023 |
Prerequisites:
None.
Evaluation method: Nota 1-7, with 1/1 available vacants |
Mentor(s): Open in the plataform |
| 07-03-2023 |
Keywords:
machine learning
bioinformatica
red
red social
aprendizaje maquina
Proteínas
Diseño de fármacos
Prerequisites:
None.
Evaluation method: Nota 1-7, with 1/1 available vacants |
Mentor(s): Open in the plataform |
| 06-12-2021 |
Development and use of the GRAPE computational engineering strategy in Antarctic PET hydrolases
In recent decades, numerous enzymes from bacteria that degrade PET plastics have been described, including enzymes described by our laboratory from Antarctic organisms that are catalytically active at room temperature. However, the stability of these enzymes is insufficient for their biotechnological use, thus experimental and computational enzyme engineering strategies have been developed to optimize these parameters. In this research, we seek to develop and use the GRAPE computational engineering strategy, which produces a library of mutations for preliminary experimental characterization and subsequent clustering in a final designed enzyme, to improve the stability of Antarctic PET hydrolases.
Prerequisites:
None.
Evaluation method: Nota 1-7, with 0/1 available vacants |
Mentor(s): Open in the plataform |
| 19-07-2021 |
Generation of multi-colour 3d printable models of bacterial pumps for antibiotic resistance
This is a remote research project to generate good 3d printable models from protein structures in biological databases of the cell-membrane pumps that cause antibiotic resistances in bacteria. In addition to improving the 3d printability of the protein complexes, the quaternary structure of the proteins should also be used to segment the model into up to 4 materials (different colours for 3d prints), which help to understand the function of the pumps. Basic experience in 3d printing and interest to find solutions to problems independently is a requirement.
Prerequisites:
None.
Evaluation method: Nota 1-7, with 0/1 available vacants |
Mentor(s): Open in the plataform |