A5060038712- Protein Structure and Dynamics
- Machine Learning in Materials Science
- Computational Drug Discovery Methods
- Enzyme Structure and Function
- Machine Learning and Algorithms
- Advanced Bandit Algorithms Research
- RNA and protein synthesis mechanisms
- Block Copolymer Self-Assembly
- Fire effects on concrete materials
- Fuel Cells and Related Materials
- Concrete and Cement Materials Research
- Spectroscopy and Quantum Chemical Studies
- Vibrio bacteria research studies
- Mass Spectrometry Techniques and Applications
- Antibiotic Resistance in Bacteria
- Advanced Electron Microscopy Techniques and Applications
- Masonry and Concrete Structural Analysis
- Genetics, Bioinformatics, and Biomedical Research
- Building materials and conservation
- Gaussian Processes and Bayesian Inference
Universitat Pompeu Fabra
2018-2023
Acellera (Spain)
2023
Barcelona Biomedical Research Park
2018-2023
Stanford University
2020
Max Planck Institute for Molecular Genetics
2020
Center for Theoretical Biological Physics
2020
Rice University
2020
Institució Catalana de Recerca i Estudis Avançats
2020
Atomistic or ab initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics relate them structure. A common approach go beyond the time- length-scales accessible with such computationally expensive is definition of coarse-grained models. Existing coarse-graining approaches define an effective interaction potential match defined properties high-resolution models experimental data. In this paper, we reformulate as a supervised machine learning problem. We use...
Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability such potentials can be improved leveraging data-driven models derived with machine learning approaches. Here, we present TorchMD, framework for molecular mixed classical All force computations including bond, angle, dihedral, Lennard-Jones, Coulomb interactions are expressed as PyTorch arrays operations. Moreover, TorchMD enables simulating neural...
Coarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible atomic resolution. However, a coarse model must be formulated such that conclusions we draw from it are consistent with would finer level detail. It has been proven force matching scheme defines thermodynamically coarse-grained an atomistic system in variational limit. Wang et al. [ACS Cent. Sci. 5, 755 (2019)] demonstrated existence limit use supervised machine...
Abstract A generalized understanding of protein dynamics is an unsolved scientific problem, the solution which critical to interpretation structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, build a unique dataset unbiased all-atom simulations approximately 9 ms for twelve different proteins with...
Sampling from the equilibrium distribution has always been a major problem in molecular simulations due to very high dimensionality of conformational space. Over several decades, many approaches have used overcome problem. In particular, we focus on unbiased simulation methods such as parallel and adaptive sampling. Here, recast sampling schemes basis multi-armed bandits develop novel algorithm under this framework, AdaptiveBandit. We test it multiple simplified potentials protein folding...
Abstract The exploration of intrinsically disordered proteins in isolation is a crucial step to understand their complex dynamical behavior. In particular, the emergence partially ordered states has not been explored depth. experimental characterization such remains elusive due transient nature. Molecular dynamics mitigates this limitation thanks its capability explore biologically relevant timescales while retaining atomistic resolution. Here, millisecond unbiased molecular simulations were...
The accurate prediction of protein-ligand binding affinities is crucial for drug discovery. Alchemical free energy calculations have become a popular tool this purpose. However, the accuracy and reliability these methods can vary depending on methodology. In study, we evaluate performance relative protocol based alchemical transfer method (ATM), novel approach coordinate transformation that swaps positions two ligands. results show ATM matches more complex perturbation (FEP) in terms Pearson...
The extreme dynamic behavior of intrinsically disordered proteins hinders the development drug-like compounds capable modulating them. There are several examples small molecules that specifically interact with peptides. However, their mechanisms action still not well understood. Here, we use extensive molecular dynamics simulations combined adaptive sampling algorithms to perform free ligand binding studies in context proteins. We tested this approach system composed by D2 sub-domain protein...
Class A β-lactamases are known for being able to rapidly gain broad spectrum catalytic efficiency against most β-lactamase inhibitor combinations as a result of elusively minor point mutations. The evolution in class occurs through optimisation their dynamic phenotypes at different timescales. At long-timescales, certain conformations more catalytically permissive than others while the short timescales, fine-grained free energy barriers can improve ligand processing by active site. Free...
Intrinsically disordered proteins participate in many biological processes by folding upon binding to other proteins. However, coupled and are not well understood from an atomistic point of view. One the main questions is whether occurs prior or after binding. Here we use a novel, unbiased, high-throughput adaptive sampling approach reconstruct between transactivation domain c-Myb KIX CREB-binding protein. The reconstructed long-term dynamical process highlights short stretch amino acids on...
Abstract The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost. development of a universal, computationally efficient coarse-grained (CG) model with similar prediction performance has been long-standing challenge. By combining recent deep learning methods large diverse training set simulations, we here develop bottom-up CG force field chemical transferability, which can be used for...
Atomistic or ab-initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics relate them structure. A common approach go beyond the time- length-scales accessible with such computationally expensive is definition of coarse-grained models. Existing coarse-graining approaches define an effective interaction potential match defined properties high-resolution models experimental data. In this paper, we reformulate as a supervised machine learning problem. We use...
A generalized understanding of protein dynamics is an unsolved scientific problem, the solution which critical to interpretation structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, build a unique dataset unbiased all-atom simulations approximately 9 ms for twelve different proteins with multiple...
The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost. development of a universal, computationally efficient coarse-grained (CG) model with similar prediction performance has been long-standing challenge. By combining recent deep learning methods large diverse training set simulations, we here develop bottom-up CG force field chemical transferability, which can be used for extrapolative on...
Intrinsically disordered proteins participate in many biological processes by folding upon binding with other proteins. However, coupled and are not well understood from an atomistic point of view. One the main questions is whether occurs prior to or after binding. Here we use a novel unbiased high-throughput adaptive sampling approach reconstruct between transactivation domain \mbox{c-Myb} KIX CREB-binding protein. The reconstructed long-term dynamical process highlights short stretch amino...
The accurate prediction of protein-ligand binding affinities is crucial for drug discovery. Alchemical free energy calculations have become a popular tool this purpose. However, the accuracy and reliability these methods can vary depending on methodology. In study, we evaluate performance relative protocol based alchemical transfer method (ATM), novel approach coordinate transformation that swaps positions two ligands. results show ATM matches more complex perturbation (FEP) in terms Pearson...
Sampling from the equilibrium distribution has always been a major problem in molecular simulations due to very high dimensionality of conformational space. Over several decades, many approaches have used overcome problem. In particular, we focus on unbiased simulation methods such as parallel and adaptive sampling. Here, recast sampling schemes basis multi-armed bandits develop novel algorithm under this framework, \UCB. We test it multiple simplified potentials protein folding scenario....