Ion Transport, Selectivity, and Electronic Polarization in Fluoride Channels

Fluoride channels (Fluc) export toxic F- from the cytoplasm. Crystallography and mutagenesis have identified several conserved residues crucial for fluoride transport, but the transport mechanism at the molecular level has remained elusive. Herein we have applied constant-pH molecular dynamics and free energy sampling methods to investigate fluoride transfer through a Fluc protein from Escherichia coli. We find that fluoride is facile to transfer in its charged form, i.e., F-, by traversing through a non-bonded network. The extraordinary F- selectivity is gained by the hydrogen-bonding capability of the central binding site and the Coulombic filter at the channel entrance. The F- transfer rate calculated using an electronically polarizable force field is significantly more accurate compared to the experimental value than that calculated using a more standard additive force field, suggesting an essential role for electronic polarization in the F- - Fluc interactions.

Protein pKa prediction with machine learning

Protein pKa prediction is essential for the investigation of pH-associated relationship between protein structure and function. In this work, we introduce a deep learning based protein pKa predictor DeepKa, which is trained and validated with the pKa values derived from continuous constant pH molecular dynamics (CpHMD) simulations of 279 soluble proteins. Here the CpHMD implemented in the Amber molecular dynamics package has been employed (Huang, Harris, and Shen J. Chem. Inf. Model. 2018, 58, 1372-1383). Notably, to avoid discontinuities at the boundary, grid charges are proposed to represent protein electrostatics. We show that the prediction accuracy by DeepKa is close to that by CpHMD benchmarking simulations, validating DeepKa as an efficient protein pKa predictor. In addition, the training and validation sets created in this study can be applied to the development of machine learning based protein pKa predictors in future. Finally, the grid charge representation is general and applicable to other topics, such as the protein-ligand binding affinity prediction.

Unravelling Constant pH Molecular Dynamics in Oligopeptides with Explicit Solvation Model

An accurate description of the protonation state of amino acids is essential to correctly simulate the conformational space and the mechanisms of action of proteins or other biochemical systems. The pH and the electrochemical environments are decisive factors to define the effective pKa of amino acids and, therefore, the protonation state. However, they are poorly considered in Molecular Dynamics (MD) simulations. To deal with this problem, constant pH Molecular Dynamics (cpHMD) methods have been developed in recent decades, demonstrating a great ability to consider the effective pKa of amino acids within complex structures. Nonetheless, there are very few studies that assess the effect of these approaches in the conformational sampling. In a previous work of our research group, we detected strengths and weaknesses of the discrete cpHMD method implemented in AMBER when simulating capped tripeptides in implicit solvent. Now, we progressed this assessment by including explicit solvation in these peptides. To analyze more in depth the scope of the reported limitations, we also carried out simulations of oligopeptides with distinct positions of the titratable amino acids. Our study showed that the explicit solvation model does not improve the previously noted weaknesses and, furthermore, the separation of the titratable amino acids in oligopeptides can minimize them, thus providing guidelines to improve the conformational sampling in the cpHMD simulations.

Reactivities of the Front Pocket N-Cap Cysteines in Human Kinases

Discovery of targeted covalent inhibitors directed at nucleophilic cysteines is attracting enormous interest. The front pocket (FP) N-cap cysteine has been the most popular site of covalent modification in kinases. Curiously, a long-standing hypothesis associates the N-cap position with cysteine hyper-reactivity; however, traditional computational methods suggest that the FP N-cap cysteines in all human kinases are predominantly unreactive at physiological pH. Here we applied a newly developed GPU-accelerated continuous constant pH molecular dynamics (CpHMD) tool to test the N-cap hypothesis and elucidate the cysteine reactivities. Simulations showed that the N-cap cysteines in BTK/BMX/TEC/ITK/TXK, JAK3, and MKK7 sample the reactive thiolate form to varying degrees at physiological pH; however, those in BLK and EGFR/ERBB2/ERBB4 which contain an Asp at the N-cap+3 position adopt the unreactive thiol form. The latter argues in favor of the base-assisted thiol-Michael addition mechanisms as suggested by the quantum mechanical calculations and experimental structure-function studies of EGFR inhibitors. Analysis revealed that the reactive N-cap cysteines are stabilized by hydrogen bond as well as electrostatic interactions, and in their absence a N-cap cysteine is unreactive due to desolvation. To test a corollary of the N-cap hypothesis, we also examined the reactivities of the FP N-cap+2 cysteines in JNK1/JNK2/JNK3 and CASK. Additionally, our simulations predicted the reactive cysteine and lysine locations in all 15 kinases. Our findings offer a systematic understanding of cysteine reactivities in kinases and demonstrate the predictive power and physical insights CpHMD can provide to guide the rational design of targeted covalent inhibitors.

Titr-DMD – A Rapid, Coarse-Grained Quasi-All-Atom Constant pH Molecular Dynamics Framework

The pH dependence of enzyme fold stability and catalytic activity is a fundamentally dynamic, structural property which is difficult to study. Computational methods, particularly constant pH molecular dynamics (CpHMD), are the best situated tools for this. However, these often struggle with affordable sampling of sufficiently long timescales, accuracy of pKa prediction, and verification of the structures they generate. We introduce Titr-DMD, an affordable CpHMD method with a protonation state sampler that can be systematically improved, to circumvent these issues. We benchmark the method on a set of proteins with experimentally attested pKa and on the pH triggered conformational change in a staphylococcal nuclease mutant, a rare experimental study of such behavior. Our results show Titr-DMD to be an effective method to study pH coupled protein dynamics. <br>

Titr-DMD – A Rapid, Coarse-Grained Quasi-All-Atom Constant pH Molecular Dynamics Framework

The pH dependence of enzyme fold stability and catalytic activity is a fundamentally dynamic, structural property which is difficult to study. Computational methods, particularly constant pH molecular dynamics (CpHMD), are the best situated tools for this. However, these often struggle with affordable sampling of sufficiently long timescales, accuracy of pKa prediction, and verification of the structures they generate. We introduce Titr-DMD, an affordable CpHMD method with a protonation state sampler that can be systematically improved, to circumvent these issues. We benchmark the method on a set of proteins with experimentally attested pKa and on the pH triggered conformational change in a staphylococcal nuclease mutant, a rare experimental study of such behavior. Our results show Titr-DMD to be an effective method to study pH coupled protein dynamics. <br>

Anti-TNF Alpha Antibody Humira with pH-dependent Binding Characteristics: A constant-pH Molecular Dynamics, Gaussian Accelerated Molecular Dynamics, and In Vitro Study

Humira is a monoclonal antibody that binds to TNF alpha, inactivates TNF alpha receptors, and inhibits inflammation. Neonatal Fc receptors can mediate the transcytosis of Humira–TNF alpha complex structures and process them toward degradation pathways, which reduces the therapeutic effect of Humira. Allowing the Humira–TNF alpha complex structures to dissociate to Humira and soluble TNF alpha in the early endosome to enable Humira recycling is crucial. We used the cytoplasmic pH (7.4), the early endosomal pH (6.0), and pKa of histidine side chains (6.0–6.4) to mutate the residues of complementarity-determining regions with histidine. Our engineered Humira (W1-Humira) can bind to TNF alpha in plasma at neutral pH and dissociate from the TNF alpha in the endosome at acidic pH. We used the constant-pH molecular dynamics, Gaussian accelerated molecular dynamics, two-dimensional potential mean force profiles, and in vitro methods to investigate the characteristics of W1-Humira. Our results revealed that the proposed Humira can bind TNF alpha with pH-dependent affinity in vitro. The W1-Humira was weaker than wild-type Humira at neutral pH in vitro, and our prediction results were close to the in vitro results. Furthermore, our approach displayed a high accuracy in antibody pH-dependent binding characteristics prediction, which may facilitate antibody drug design. Advancements in computational methods and computing power may further aid in addressing the challenges in antibody drug design.

Titr-DMD – A Rapid Constant pH Molecular Dynamics Framework

The pH dependence of enzyme fold stability and catalytic activity is a fundamentally dynamic, structural property which is difficult to study. Computational methods, particularly constant pH molecular dynamics (CpHMD), are the best situated tools for this. However, these often struggle with affordable sampling of sufficiently long timescales, accuracy of pKa prediction, and verification of the structures they generate. We introduce Titr-DMD, an affordable CpHMD method with a protonation state sampler that can be systematically improved, to circumvent these issues. We benchmark the method on a set of proteins with experimentally attested pKa and on the pH triggered conformational change in a staphylococcal nuclease mutant, a rare experimental study of such behavior. Our results show Titr-DMD to be an effective method to study pH coupled protein dynamics. <br>