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. We observed that membrane insertion leads to desolvation and a clear stabilization of the neutral forms, and we quantified the increases/decreases of the pK(a) values in the anionic/cationic residues along the membrane normal. This work highlights the importance of properly modeling the protonation equilibrium in peptides and proteins interacting with membranes using molecular dynamics simulations.

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 (pK a 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 pK a values of titratable sites in proteins. This application was benchmarked using a large data set of experimental values to show that our single structure-based method is fast and has a competitive performance. This is a free and open-source tool that provides a simple, reusable, and extensible Python API and CLI for pK a calculations with a valuable trade-off between fast and accurate predictions. PypKa allows pK a calculations in existing protocols with the addition of a few extra lines of code. PypKa supports CPU parallel computing on solvated proteins obtained from the PDB repository but also from MD simulations using three common naming schemes: GROMOS, AMBER, and CHARMM. The code and documentation to this open-source project is publicly available at .

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 (0.8/1.4 nm). However, the most recent versions of GROMACS (since v.2016) discontinued the support for charge groups. To take full advantage of the newer and faster versions of this software package with GROMOS 54A7 and RF, we need to evaluate the impact of using a single cutoff scheme (vs twin-range) and of using the Verlet list update method (which is atomistic) compared to the group-based cutoff scheme. Our results show that the GROMOS 54A7 force field seems consistent with a single cutoff, since the resulting conformation and protonation ensembles were indistinguishable. The GROMOS parametrization procedure was also reproduced using an atomistic cutoff scheme, and we have observed that the hydration free energy values of small amino acid side-chain analogues were similar to the ones obtained with the group-based protocol. We do observe a small impact of the atomistic cutoff scheme in the conformational space of the model systems studied (G1-PAMAM and DMPC). However, since the structural properties of these systems are well converged for the cutoff range used (1.4–2.0 nm), unlike with the group-based cutoff schemes, we are confident that the atomistic cutoff can be adopted with RF for MD and constant-pH MD biomolecular simulations using the GROMOS 54A7 force field.

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. However, in difficult cases, the protonation sampling can be too slow for the commonly accessible computational times. In this work, we combined a pH replica exchange scheme with this CpHMD method and explored several optimization strategies and possible limitations.

Existing computational methods to estimate pK a values in proteins rely on theoretical approximations and lengthy computations. In this work, we use a data set of 6 million theoretically determined pK a shifts to train deep learning models that are shown to rival the physics-based predictors. These neural networks managed to assign proper electrostatic charges to chemical groups, and learned the importance of solvent exposure and close interactions, including hydrogen bonds. Although trained only using theoretical data, our pKAI+ model displays the best accuracy on a test set of ∼750 experimental values. Inference times allow speedups of more than 1000 times faster than physics-based methods.By combining speed, accuracy and a reasonable understanding of the underlying physics, our models provide a game-changing solution for fast estimations of macroscopic pK a from ensembles of microscopic values as well as for many downstream applications such as molecular docking and constant-pH molecular dynamics simulations. MainMany biological processes are triggered by changes in the ionization state of key amino acid side-chains 1, 2 .Experimentally, the titration behavior of a molecule can be measured using potentiometry or by tracking free energy changes across a pH range. For individual sites, titration curves can be derived from infrared

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. We have previously shown that full system neutralization can lead to wrong lipidic phases in the 25% PA/PC bilayer (J. Chem. Theory Comput. 2014,10, 5483–5492). In this work, we investigate how two limit approaches to the ionic strength treatment (implicitly with GRF or using full system neutralization with either GRF or PME) can influence the conformational space of the second-generation PAMAM dendrimer. Constant-pH MD simulations were used to map PAMAM’s conformational space at its full pH range (from 2.5 to 12.5). Our simulations clearly captured the coupling between protonation and conformation in PAMAM. Interestingly, the dendrimer conformational distribution was almost independent of the ionic strength treatment methods, which is in contrast to what we have observed in charged lipid bilayers. Overall, our results confirm that both GRF with implicit ionic strength and a fully neutralized system with PME are valid approaches to model charged globular systems, using the GROMOS 54A7 force field.

Centaurium erythraea 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 C. erythraea 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. Following some of the bioactivities previously ascribed to C. erythraea, we have studied its antioxidant capacity and the ability to inhibit acetylcholinesterase (AChE) and 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR). Significant antioxidant activities were observed, following three assays: free radical 2,2-diphenyl-1-picrylhydrazyl (DPPH) reduction; lipoperoxidation; and NO radical scavenging capacity. The AChE and HMGR inhibitory activities for the decoction were also measured (56% at 500 μg/mL and 48% at 10 μg/mL, respectively). Molecular docking studies indicated that xanthones are better AChE inhibitors than gentiopicroside, while this compound exhibits a better shape complementarity with the HMGR active site than xanthones. To the extent of our knowledge, this is the first report on AChE and HMGR activities by C. erythraea decoctions, in a top-down analysis, complemented with in silico molecular docking, which aims to understand, at the molecular level, some of the biological effects ascribed to infusions from this plant.

Summary p Ka 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 p K a values calculated over 120k protein structures deposited in the Protein Data Bank. By providing precomputed p K a and pI values, users can retrieve results instantaneously for their protein(s) of interest while also saving countless hours and resources that would be spent on repeated calculations. Furthermore, there is an ever-growing imbalance between experimental p K a and pI values and the number of resolved structures. This database will complement the experimental and computational data already available and can also provide crucial information regarding buried residues that are underrepresented in experimental measurements. Availability and implementation Gzipped csv files containing p Ka and isoelectric point values can be downloaded from https://pypka.org/pKPDB. To query a single PDB code please use the PypKa free server at https://pypka.org. The pKPDB source code can be found at https://github.com/mms-fcul/pKPDB. Supplementary information Supplementary data are available at Bioinformatics online.