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Felipe Silva Carvalho

Publications and source records attributed to Felipe Silva Carvalho.

3 recordsLinked to original sources

Solv-eze: Automated Placement of Explicit Water Molecules Using 3D-RISM

Molecular dynamics (MD) simulations are widely used to study biological systems, where water molecules often play a critical role in protein-ligand interactions. In conventional MD preparation protocols, water molecules are typically added from a pre-equilibrated solvent box and removed using conservative steric cutoffs, an approach that can eliminate important interfacial waters that are often not recovered during equilibration due to kinetic barriers limiting exchange with bulk solvent. In this work, we present an automated and computationally efficient method for placing water molecules around biomolecular solutes using three-dimensional reference interaction site model (3D-RISM) solvent density distributions. By identifying regions of high solvent probability, the method generates physically meaningful initial hydration structures without requiring extended sampling or specialized techniques such as grand canonical Monte Carlo (MC) or hybrid MC/MD approaches, and will be released as an update to AmberTools 26, enabling seamless integration into standard MD preparation pipelines. We validated the approach on a diverse set of protein-ligand complexes with crystallographically resolved bridging waters, showing that the method reproduced over 80% of experimentally observed bridging waters and 85% of buried waters not accessible to the bulk. Subsequent energy minimization of both crystallographic and predicted waters further improved agreement. Overall, this method enables more accurate and practical initialization of interfacial hydration, improving the reliability of MD simulations with modest computational cost relative to routine system preparation.

physics.chem-ph

Development of an Optimized Parameter Set for Monovalent Ions in the Reference Interaction Site Model of Solvation

Accurate modeling of aqueous monovalent ions is essential for understanding the function of biomolecules, such as nucleic acid stability and binding of charged drugs to protein targets. The 1D and 3D reference interaction site models (1D- and 3D-RISM) of molecular solvation, as implemented in the AmberTools molecular modeling suite, are well suited for modeling mixtures of ionic species around biomolecules across a wide range of concentrations. However, the available ion model parameters were optimized for molecular dynamics simulations, not for the RISM framework, which includes a closure approximation. To address this, we optimized the Lennard-Jones 12-6 model for monovalent ions for 1D-RISM with the partial series expansion of order 3 closure by fitting to experimental values of ion-oxygen distance (IOD), hydration free energy (HFE), partial molar volume (PMV) and mean activity coefficient. The new parameter set demonstrated significant improvement in HFE, IOD, and mean activity coefficients, whereas no overall change was observed for the PMV. A second optimization step was necessary to account for the cation-anion interactions that affect the mean activity coefficients. The new parameters were validated at finite salt concentrations against experimental data for 16 ion pairs and showed improved accuracy for 14 of them, while the results for CsI and CsF were the second best. 1D-RISM results obtained with the new NaCl parameters were used to calculate the preferential interaction parameter of the ions around the 24L B-DNA using 3D-RISM. The new parameters demonstrated better agreement with experiment at physiological and higher concentrations. At lower concentrations, the results primarily depended on the closure with little effect from the ion parameters. Overall, the ion parameters specifically developed for RISM show improved accuracy at infinite dilution and finite concentrations.

physics.chem-ph

The Gâteaux-Hopfield Neural Network method

In the present work a new set of differential equations for the Hopfield Neural Network (HNN) method were established by means of the Linear Extended Gateaux Derivative (LEGD). This new approach will be referred to as Gâteaux-Hopfiel Neural Network (GHNN). A first order Fredholm integral problem was used to test this new method and it was found to converge 22 times faster to the exact solutions for α > 1 if compared with the HNN integer order differential equations. Also a limit to the learning time is observed by analysing the results for different values of α. The robustness and advantages of this new method will be pointed out.

cs.LG