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. In this work, we present the PypKa server, which allows users to run physics-based, as well as ML-accelerated methods suitable for larger systems, to obtain pKa values, isoelectric points, titration curves, and structures with representative pH-dependent protonation states compatible with commonly used force fields (AMBER, CHARMM, GROMOS). The user may upload a custom structure or submit an identifier code from PBD or UniProtKB. The results for over 200k structures taken from the Protein Data Bank and the AlphaFold DB have been precomputed, and their data can be retrieved without extra calculations. All this information can also be obtained from an application programming interface (API) facilitating its usage and integration into existing pipelines as well as other web services. The web server is available at pypka.org.

pKa 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 Asp2.50 residue in five different class A GPCRs (active and inactive conformations) using a linear response approximation (LRA) pKa calculation protocol. We observed consistent differences (1.3 pK units) for the macroscopic pKa values between the inactive and active states of the A2AR and B2AR receptors, indicating the protonation of Asp2.50 during GPCR activation. This process seems to be specific and not conserved, as no differences were observed in the pKa values of the remaining receptors (CB1R, NT1R, and GHSR).

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. By providing precomputed pKa 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 pKa 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 under-represented 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.

Optimization of an in Silico 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 in silico protocol on the basis of umbrella sampling (US)/molecular dynamics (MD) simulations to discriminate between compounds with different membrane PAINS behavior. We showed that the method is quite sensitive to membrane thickness fluctuations, which was mitigated by changing the US reference position to the phosphate atoms of the closest interacting monolayer. The computational efficiency was improved further by decreasing the number of umbrellas and adjusting their strength and position in our US scheme. The inhomogeneous solubility-diffusion model (ISDM) used to calculate the membrane permeability coefficients confirmed that resveratrol and curcumin have distinct membrane PAINS characteristics and indicated a misclassification of nothofagin in a previous work. Overall, we have presented here a promising in silico protocol that can be adopted as a future reference method to identify membrane PAINS.

PypKa: A Flexible Python Module for Poisson-Boltzmann-Based pKa 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 (pKa 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 pKa 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 pKa calculations with a valuable trade-off between fast and accurate predictions. PypKa allows pKa 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 https://github.com/mms-fcul/PypKa.

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

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.

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

Existing computational methods for estimating pKa values in proteins rely on theoretical approximations and lengthy computations. In this work, we use a data set of 6 million theoretically determined pKa 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. Although trained only using theoretical data, our pKAI+ model displayed the best accuracy in a test set of ∼750 experimental values. Inference times allow speedups of more than 1000× compared to 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 pKa values from ensembles of microscopic values as well as for many downstream applications such as molecular docking and constant-pH molecular dynamics simulations.

Antibody-Antigen Binding Interface Analysis in the Big Data Era.

Antibodies have become the Swiss Army tool for molecular biology and nanotechnology. Their outstanding ability to specifically recognise molecular antigens allows their use in many different applications from medicine to the industry. Moreover, the improvement of conventional structural biology techniques (e.g., X-ray, NMR) as well as the emergence of new ones (e.g., Cryo-EM), have permitted in the last years a notable increase of resolved antibody-antigen structures. This offers a unique opportunity to perform an exhaustive structural analysis of antibody-antigen interfaces by employing the large amount of data available nowadays. To leverage this factor, different geometric as well as chemical descriptors were evaluated to perform a comprehensive characterization.

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. The stochastic titration CpHMD is one of such methods that, until now, only supported the GROMOS force field family. Here, we extend this method's implementation to include the CHARMM36m force field available in the GROMACS software package. We test this new implementation with a diverse group of proteins, namely, lysozyme, Staphylococcal nuclease, and human and E. coli thioredoxins. All proteins were conformationally stable in the simulations, even at extreme pH values. The RMSE values (pKa prediction vs experimental) obtained were very encouraging, in particular for lysozyme and human thioredoxin. We have also identified a few residues that challenged the CpHMD simulations, highlighting scenarios where the method still needs improvement independently of the force field. The CHARMM36m all-atom implementation was more computationally efficient when compared with the GROMOS 54A7, taking advantage of a shorter nonbonded interaction cutoff and a less frequent neighboring list update. The new extension will allow the study of pH effects in many systems for which this force field is particularly suited, i.e., proteins, membrane proteins, lipid bilayers, and nucleic acids.

pK a 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 a 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 a calculators developed for globular proteins and adapt them to include the explicit effects of membranes. Here, we provide a detailed linear response approximation protocol that uses our own software (PypKa) to calculate reliable pK a values from short MD simulations of membrane proteins.

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.

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. 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.

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. 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.

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 (0.8/1.4 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.

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. 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.