Conformational sampling and polarization of Asp26 in pKa calculations of thioredoxin

Enhanced and Efficient Predictions of Dynamic Ionization through Constant-pH Adiabatic Free Energy Dynamics

Dynamic or structurally induced ionization is a critical aspect of many physical, chemical, and biological processes. Molecular dynamics (MD) based simulation approaches, specifically constant pH MD methods, have been developed to simulate ionization states of molecules or proteins under experimentally or physiologically relevant conditions. While such approaches are now widely utilized to predict ionization sites of macromolecules or to study physical or biological phenomena, they are often computationally expensive and require long simulation times to converge. In this article, using the principles of adiabatic free energy dynamics, we introduce an efficient technique for performing constant pH MD simulations within the framework of the adiabatic free energy dynamics (AFED) approach. We call the new approach pH-AFED. We show that pH-AFED provides highly accurate predictions of protein residue pKa values, with a MUE of 0.5 pKa units when coupled with driven adiabatic free energy dynamics (d-AFED), while reducing the required simulation times by more than an order of magnitude. In addition, pH-AFED can be easily integrated into most constant pH MD codes or implementations and flexibly adapted to work in conjunction with enhanced sampling algorithms that target collective variables. We demonstrate that our approaches, with both pH-AFED standalone as well as pH-AFED combined with collective variable based enhanced sampling, provide promising predictive accuracy, with a MUE of 0.6 and 0.5 pKa units respectively, on a diverse range of proteins and enzymes, ranging up to 186 residues and 21 titratable sites. Lastly, we demonstrate how this approach can be utilized to understand the in vivo performance engineered antibodies for immunotherapy.

A Comprehensive Study of Factors Affecting the Prediction of the pKa Shift of Asp26 in Thioredoxin Protein

The stable protonation state of ionizable amino acids in a protein can be predicted by computing the pKa shift of that residue within the protein environment. Thermodynamic Integration (TI) is an ideal molecular dynamics-based approach for predicting the pKa shift of ionizable protein residues. Here, we probe TI-based simulation protocols for their ability to accurately predict the pKa shift of Asp26 in thioredoxin. While implicit solvent models can predict the pKa shift accurately, explicit solvent models result in substantial errors. To understand the underlying reason for this surprising discrepancy, we investigate the role of various factors such as solvent models, conformational sampling, background charges, and polarization.

The tale of HORTON: Lessons learned in a decade of scientific software development

HORTON is a free and open-source electronic-structure package written primarily in Python 3 with some underlying C++ components. While HORTON's development has been mainly directed by the research interests of its leading contributing groups, it is designed to be easily modified, extended, and used by other developers of quantum chemistry methods or post-processing techniques. Most importantly, HORTON adheres to modern principles of software development, including modularity, readability, flexibility, comprehensive documentation, automatic testing, version control, and quality-assurance protocols. This article explains how the principles and structure of HORTON have evolved since we started developing it more than a decade ago. We review the features and functionality of the latest HORTON release (version 2.3) and discuss how HORTON is evolving to support electronic structure theory research for the next decade.

Molecular Dockings and Molecular Dynamics Simulations Reveal the Potency of Different Inhibitors against Xanthine Oxidase

Xanthine oxidase (XO), which can catalyze the formation of xanthine or hypoxanthine to uric acid, is the most important target of gout. To explore the conformational changes for inhibitor binding, molecular dockings and molecular dynamics simulations were performed. Docking results indicated that three inhibitors had similar pose binding to XO. Molecular dynamics simulations showed that the binding of three inhibitors influenced the secondary structure changes in XO. After binding to the inhibitor, the peptide Phe798-Leu814 formed different degrees of unhelix, while for the peptide Glu1065-Ser1075, only a partial helix region was formed when allopurinol was bound. Through the protein structure analysis in the simulation process, we found that the distance between the active residues Arg880 and Thr1010 was reduced and the distance between Glu802 and Thr1010 was increased after the addition of inhibitors. The above simulation results showed the similarities and differences of the interaction between the three inhibitors binding to the protein. MM-PBSA calculations suggested that, among three inhibitors, allopurinol had the best binding effect with XO followed by daidzin and puerarin. This finding was consistent with previous experimental data. Our results can provide some useful clues for further gout treatment research.

Quantum-Chemical ab initio Study of Side Chain pKa of Linear and Cyclic Lysine Dipeptides

Cyclic dipeptides show interesting biological activities such as antitumor, antiviral and so on. In biological systems, the bioavailability of drugs is determined by several parameters such as pKa values. In this study, we used DFT and thermodynamics cycle to determine pKa value of side chain of lysine in linear and cyclic dipeptides. All considered dipeptides were optimized using B3LYP and RMSD tool was used to compare the optimized structures. The calculated pKa values were compared with the available experimental data. Our results show that pKa of side chain of lysine increases for cyclic dipeptides compared to the linear ones. To justify the reason of increasing of pKa of cyclic dipeptides, we used NBO and AIM analyses. The analyses showed that a hydrogen bond in cyclic lysine dipeptides is responsible for increasing of pKa.