The Research


The study of biologically relevant macromolecules at the molecular level has been challenging for quite some time for researchers. In recent years, molecular modeling and simulation is the field of research that probably has more enthusiastically contributed with atomistic information.

In computational studies, the pH effects on biologically relevant molecules have been addressed with several limitations. Biological membranes are inherently complex and some important aspects, like protonation equilibrium, have also been unsatisfactorily modeled. The complexity of the membrane/water interface in biological membranes can even increase many-fold due to the presence of a myriad of different lipid molecules in their composition (e.g. anionic lipids). The convoluted contribution of the complex electrostatic interactions has rendered the problem of peptide, protein, drug, and/or lipid protonation rather inaccessible, consequently deterring their study. However, the gradual raise of computational power, and the appearance of new and more refined force-fields, have made it possible to model the water/membrane region with increased realism.

The so-called constant-pH MD (CpHMD) methods aimed at solving the problem of including the pH effects in MD simulations by allowing protonable groups to periodically change their state during the simulation, thereby capturing the coupling between conformation and protonation. In our group, we are actively developing new CpHMD strategies that enable us to study the dynamic properties of solutes (peptides, proteins, or drugs) and lipid bilayers under different conditions of pH, ionic strength, redox potential and solvent composition.

Coupling of CpHMD methods with AI and enhanced sampling techniques

We are developing new extensions to the stochastic titration method to improve its computational efficiency. We have plans to run the MD simulations in GPU code and predict the protonation states using an Artificial Intelligence (AI) predictor. Additionally, we are also using Replica-Exchange (RE), Umbrella Sampling (US), REUS, and Metadynamics schemes to help circumvent several sampling limitations. These are associated with protonation events coupled with high energy barriers in several phenomena like membrane insertions or drug binding into protein pockets. These new extensions to the CpHMD framework will improve significantly the conformation/protonation sampling in our simulations and allow us to tackle larger and even more exciting systems.

Extending CpHMD to include/support the three larger
FF families: GROMOS, CHARMM, and AMBER

The stochastic titration CpHMD, which is the base for all our implementations, only supported the GROMOS force field family (G54A7 is the latest version). We are now working on new extensions to the code to include both CHARMM36m and AMBER14SB. This will allow the study of pH effects in many systems for which these force fields are particularly suited, i.e., proteins, membrane proteins, lipid bilayers, and nucleic acids.

Improve the computational model describing
proteins' conformational space

We are now also using, and continuously improving, the CpHMD techniques to capture the pH and redox potential effects on proteins' structure, stability, and function. Such methodologies provide a significant increase in realism, allowing to capture complex proteins at work since their conformations react to changes in pH and/or redox potential. Many of our projects involve ongoing collaborations with experimentalists that ask for the best and most reliable models describing their systems.

Including pH effects in membrane protein systems

Membrane proteins are embedded in the lipid bilayers of cell membranes and function naturally in tandem with the membrane's biophysical properties. A detailed description of these systems at the atomic level has to take into consideration all important factors that affect in some way the membrane behavior and stability. pH is recognizably one of these factors even though it is usually forgotten due to the high complexity in terms of modeling. We are now extended our CpHMD framework to study several of these proteins, which include: ADP/ATP carrier, P-glycoprotein, GPCRs, ASICs, Aquaporins, Cyt. c Oxidase, etc. These complex models significantly increase the realism of our computational biophysics simulations and open new opportunities to study phenomena involving these systems with an unprecedented level of detail.

New strategies to include pH effects
at the water/membrane interface

Several drugs, peptides and proteins are able to insert into a biological membrane, even containing pH-sensitive groups in their structure/amino acid sequence that are usually charged at physiological pH. These molecules are able to shift their pKa values in favour of their neutral forms when interacting with lipid bilayers. The special environment created at the membrane/water interface makes it difficult to predict which protonation species is the most abundant at a given moment. We have devised different strategies to follow the proton binding affinity of titratable groups along the membrane normal and at different membrane-insertion depths.

The pH-dependent membrane stability of pHLIP
peptide studied using CpHMD simulations

The pH (Low) Insertion Peptide (pHLIP) is a 36 amino acid peptide derived from bacteriorhodopsin that targets tissues with acidic pH. It simultaneously targets tumors, carries the cargo, and translocate it across the plasma membrane at low pH values. At neutral pH, pHLIP is soluble as a monomer in water and associates with lipid bilayer surfaces largely as an unstructured peptide. Under acidic conditions, pHLIP inserts across a lipid bilayer with an apparent pK of 6, forming a transmembrane helix. The pH-dependent insertion process is coupled to the protonation of several Asp residues located in the transmembrane region of the peptide. We are studying the pH-dependent mechanism of action of pHLIP, taking advantage of the recent advancements in the constant-pH MD method for lipid bilayers.

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