In silico modeling of protein-ligand binding

The affinity of a drug to its target protein is one of the key properties of a drug. Although there are experimental methods to measure the binding affinity, they are expensive and relatively slow. Hence, accurately predicting this property with software tools would be very beneficial to drug discovery. In this thesis, several applications have been developed to model and predict the binding mode of a ligand to a protein, to evaluate the feasibility of that prediction and to perform model interpretability in deep neural networks trained on protein-ligand complexes.La afinidad de un fármaco a su proteína diana es una de las propiedades clave de un fármaco. Actualmente, existen métodos experimentales para medir la afinidad, pero son muy costosos y relativamente lentos. Así, predecir esta propiedad con precisión empleando herramientas de software sería muy beneficioso para el descubrimiento de fármacos. En esta tesis se han desarrollado aplicaciones de software para modelar y predecir el modo de unión de ligando a proteína, para evaluar cómo de factible es tal predicción y para interpretar redes neuronales profundas entrenadas en complejos proteína-ligando

PlayMolecule glimpse: understanding protein-ligand property predictions with interpretable neural networks

Deep learning has been successfully applied to structure-based protein-ligand affinity prediction, yet the black box nature of these models raises some questions. In a previous study, we presented KDEEP, a convolutional neural network that predicted the binding affinity of a given protein-ligand complex while reaching state-of-the-art performance. However, it was unclear what this model was learning. In this work, we present a new application to visualize the contribution of each input atom to the prediction made by the convolutional neural network, aiding in the interpretability of such predictions. The results suggest that KDEEP is able to learn meaningful chemistry signals from the data, but it has also exposed the inaccuracies of the current model, serving as a guideline for further optimization of our prediction tools.The authors thank Acellera Ltd. for funding. G.D.F. acknowledges support from PID2020-116564GB-I00/MICIN/AEI/10.13039/501100011033 Ministerio de Ciencia e Innovación. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 823712 (CompBioMed2) and from the Industrial Doctorates Plan of the Secretariat of Universities and Research of the Department of Economy and Knowledge of the Generalitat of Catalonia

NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanics

Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations. However, their application is constrained by the significant computational cost arising from the vast number of parameters compared with traditional molecular mechanics. To tackle this issue, we introduce an optimized implementation of the hybrid method (NNP/MM), which combines a neural network potential (NNP) and molecular mechanics (MM). This approach models a portion of the system, such as a small molecule, using NNP while employing MM for the remaining system to boost efficiency. By conducting molecular dynamics (MD) simulations on various protein-ligand complexes and metadynamics (MTD) simulations on a ligand, we showcase the capabilities of our implementation of NNP/MM. It has enabled us to increase the simulation speed by ∼5 times and achieve a combined sampling of 1 μs for each complex, marking the longest simulations ever reported for this class of simulations.The authors thank the volunteers of GPUGRID.net for donating computing time. This project has received funding from the Torres-Quevedo Programme from the Spanish National Agency for Research (No. PTQ-17-09078/AEI/10.13039/501100011033) (R.G.); the European Union’s Horizon 2020 research and innovation programme, under Grant Agreement No. 823712 (R.G., A.V.-R., R.F.); the Industrial Doctorates Plan of the Secretariat of Universities and Research of the Department of Economy and Knowledge of the Generalitat of Catalonia (A.V.-R.); the Chan Zuckerberg Initiative DAF (Grant No. 2020-219414), an advised fund of Silicon Valley Community Foundation (S.D., P.E.); and the project PID2020-116564GB-I00 has been funded by MCIN/AEI/10.13039/501100011033. Research reported in this publication was supported by the National Institute of General Medical Sciences (NIGMS) of the National Institutes of Health, under Award No. GM140090 (P.E., T.E.M., J.D.C., G.D.F.). This research was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA008748 (J.D.C.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health

Strigolactones: new players in the nitrogen‐phosphorus signalling interplay

Nitrogen (N) and phosphorus (P) are among the most important macronutrients for plant growth and development, and the most widely used as fertilizers. Understanding how plants sense and respond to N and P deficiency is essential to optimize and reduce the use of chemical fertilizers. Strigolactones (SLs) are phytohormones acting as modulators and sensors of plant responses to P deficiency. In the present work, we assess the potential role of SLs in N starvation and in the N-P signalling interplay. Physiological, transcriptional and metabolic responses were analysed in wild-type and SL-deficient tomato plants grown under different P and N regimes, and in plants treated with a short-term pulse of the synthetic SL analogue 2′-epi-GR24. The results evidence that plants prioritize N over P status by affecting SL biosynthesis. We also show that SLs modulate the expression of key regulatory genes of phosphate and nitrate signalling pathways, including the N-P integrators PHO2 and NIGT1/HHO. The results support a key role for SLs as sensors during early plant responses to both N and phosphate starvation and mediating the N-P signalling interplay, indicating that SLs are involved in more physiological processes than so far proposedFil: Marro, Nicolás Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; Argentina. Czech Academy of Sciences. Institute of Botany; República ChecaFil: Lidoy, Javier. Estación Experimental del Zaidín. Department of Soil Microbiology and Symbiotic Systems; EspañaFil: Chico, María Ángeles. Estación Experimental del Zaidín. Department of Soil Microbiology and Symbiotic Systems; EspañaFil: Rial, Carlos. Universidad de Cádiz; EspañaFil: García, Juan. Estación Experimental del Zaidín. Department of Soil Microbiology and Symbiotic Systems; EspañaFil: Varela, Rosa M.. Universidad de Cádiz; EspañaFil: Macías, Francisco A.. Universidad de Cádiz; EspañaFil: Pozo, María J.. Estación Experimental del Zaidín. Department of Soil Microbiology and Symbiotic Systems; EspañaFil: Janoušková, Martina. Institute of Botany of the Czech Academy of Sciences; República ChecaFil: López-Ráez, Juan A.. Estación Experimental del Zaidín. Department of Soil Microbiology and Symbiotic Systems; Españ