p Calculations of GPCRs: Understanding Protonation States in Receptor Activation.

The increase in the available G protein-coupled receptor (GPCR) structures has been pivotal in helping to understand their activation process. However, the role of protonation-conformation coupling in GPCR activation still needs to be clarified. We studied the protonation behavior of the highly conserved Asp residue in five different class A GPCRs (active and inactive conformations) using a linear response approximation (LRA) p calculation protocol.

PypKa server: online pKa predictions and biomolecular structure preparation with precomputed data from PDB and AlphaFold DB.

When preparing biomolecular structures for molecular dynamics simulations, pKa calculations are required to provide at least a representative protonation state at a given pH value. Neglecting this step and adopting the reference protonation states of the amino acid residues in water, often leads to wrong electrostatics and nonphysical simulations. Fortunately, several methods have been developed to prepare structures considering the protonation preference of residues in their specific environments (pKa values), and some are even available for online usage.

Extending the Stochastic Titration CpHMD to CHARMM36m.

The impact of pH on proteins is significant but often neglected in molecular dynamics simulations. Constant-pH Molecular Dynamics (CpHMD) is the state-of-the-art methodology to deal with these effects. However, it still lacks widespread adoption by the scientific community.

A Fast and Interpretable Deep Learning Approach for Accurate Electrostatics-Driven p Predictions in Proteins.

Existing computational methods for estimating p values in proteins rely on theoretical approximations and lengthy computations. In this work, we use a data set of 6 million theoretically determined p shifts to train deep learning models, which are shown to rival the physics-based predictors. These neural networks managed to infer the electrostatic contributions of different chemical groups and learned the importance of solvent exposure and close interactions, including hydrogen bonds.

Optimization of an Protocol Using Probe Permeabilities to Identify Membrane Pan-Assay Interference Compounds.

Membrane pan-assay interference compounds (PAINS) are a class of molecules that interact nonspecifically with lipid bilayers and alter their physicochemical properties. An early identification of these compounds avoids chasing false leads and the needless waste of time and resources in drug discovery campaigns. In this work, we optimized an protocol on the basis of umbrella sampling (US)/molecular dynamics (MD) simulations to discriminate between compounds with different membrane PAINS behavior.

Identification of Pan-Assay INterference compoundS (PAINS) Using an MD-Based Protocol.

Pan-assay interference compounds (PAINS) are promiscuous molecules with multiple behaviors that interfere with assay readouts. Membrane PAINS are a subset of these compounds that influence the function of membrane proteins by nonspecifically perturbing the lipid membranes that surround them. Here, we describe a computational protocol to identify potential membrane PAINS molecules by calculating the effect that a given compound has on the bilayer deformation propensity.

pK Calculations in Membrane Proteins from Molecular Dynamics Simulations.

The conformational changes of membrane proteins are crucial to their function and usually lead to fluctuations in the electrostatic environment of the protein surface. A very effective way to quantify these changes is by calculating the pK values of the protein's titratable residues, which can be regarded as electrostatic probes. To achieve this, we need to take advantage of the fast and reliable pK calculators developed for globular proteins and adapt them to include the explicit effects of membranes.

pKPDB: a protein data bank extension database of pKa and pI theoretical values.

Summary: pKa values of ionizable residues and isoelectric points of proteins provide valuable local and global insights about their structure and function. These properties can be estimated with reasonably good accuracy using Poisson-Boltzmann and Monte Carlo calculations at a considerable computational cost (from some minutes to several hours). pKPDB is a database of over 12 M theoretical pKa values calculated over 120k protein structures deposited in the Protein Data Bank.

PypKa: A Flexible Python Module for Poisson-Boltzmann-Based p Calculations.

The protonation of titratable residues has a significant impact on the structure and function of biomolecules, influencing many physicochemical and ADME properties. Thus, the importance of the estimation of protonation free energies (p values) is paramount in different scientific communities, including bioinformatics, structural biology, or medicinal chemistry. Here, we introduce PypKa, a flexible tool to predict Poisson-Boltzmann/Monte Carlo-based p values of titratable sites in proteins.

Bioactivities of (Gentianaceae) Decoctions: Antioxidant Activity, Enzyme Inhibition and Docking Studies.

is recommended for the treatment of gastrointestinal disorders and to reduce hypercholesterolemia in ethno-medicinal practice. To perform a top-down study that could give some insight into the molecular basis of these bioactivities, decoctions from leaves were prepared and the compounds were identified by liquid chromatography-high resolution tandem mass spectrometry (LC-MS/MS). Secoiridoids glycosides, like gentiopicroside and sweroside, and several xanthones, such as di-hydroxy-dimethoxyxanthone, were identified.

A pH Replica Exchange Scheme in the Stochastic Titration Constant-pH MD Method.

Solution pH is a physicochemical property that has a key role in cellular regulation, and its impact at the molecular level is often difficult to study by experimental methods. In this context, several theoretical methods were developed to study pH effects in macromolecules. The stochastic titration constant-pH molecular dynamics method (CpHMD) was developed by coupling molecular sampling methods, which are appropriate to study the conformational ensemble of biomolecules, with continuum electrostatics approaches, which properly describe pH-dependent protonation states.

The Impact of Using Single Atomistic Long-Range Cutoff Schemes with the GROMOS 54A7 Force Field.

With the recent increase in computing power, the molecular modeling community is now more focused on improving the accuracy and overall quality of biomolecular simulations. For the available simulation packages, force fields, and all other associated methods used, this relates to how well they describe the conformational space and thermodynamic properties of a biomolecular system. The parameter sets of GROMOS force fields have been parametrized and validated with the reaction field (RF) method using charge groups and a twin-range cutoff scheme (0.

Role of Counterions in Constant-pH Molecular Dynamics Simulations of PAMAM Dendrimers.

Electrostatic interactions play a pivotal role in the structure and mechanism of action of most biomolecules. There are several conceptually different methods to deal with electrostatics in molecular dynamics simulations. Ionic strength effects are usually introduced using such methodologies and can have a significant impact on the quality of the final conformation space obtained.

pK(a) Values of Titrable Amino Acids at the Water/Membrane Interface.

Peptides and proteins protonation equilibrium is strongly influenced by its surrounding media. Remarkably, until now, there have been no quantitative and systematic studies reporting the pK(a) shifts in the common titrable amino acids upon lipid membrane insertion. Here, we applied our recently developed CpHMD-L method to calculate the pK(a) values of titrable amino acid residues incorporated in Ala-based pentapeptides at the water/membrane interface.