Capturing the Effects of Explicit Waters in Implicit Electrostatics Modeling: Qualitative Justification of Gaussian-Based Dielectric Models in DelPhi

The oxidation of the [4Fe-4S] cluster of DNA primase alters the binding energies with DNA and RNA primers

DNA primase is an iron sulfur enzyme in DNA replication responsible for synthesizing short RNA primers that serve as starting points for DNA synthesis. The role of the [4Fe-4S] cluster is not well determined. Here, we calculate the redox potential of the [4Fe-4S] with and without DNA/RNA using continuum electrostatics. In addition, we identify the structural changes coupled to the oxidation/reduction. Our calculations show that the DNA/RNA primer lowers the redox potential by 110 and 50 mV for the [4Fe-4S]+ and [4Fe-4S]2+ states, respectively. The oxidation of the cluster is coupled to structural changes that significantly reduce the binding energies between the DNA and the nearby residues. The negative charges accumulated by the DNA and the RNA primers induce the oxidation of the [4Fe-4S] cluster. This in turn stimulates structural changes on the DNA-protein interface that significantly reduce the binding energies.

Uncovering water effects in protein-ligand recognition: importance in the second hydration shell and binding kinetics

Developing a ligand with high affinity for a specific protein target is essential for drug design, and water molecules are well known to play a key role in protein-drug recognition. However, predicting the role of particularly ordered water molecules in drug binding remains challenging. Furthermore, hydration free energy contributed from the water network, including the second shell of water molecules, is far from being well studied. In this research we focused on these aspects to accurately and efficiently evaluate water effects in protein-ligand binding affinity. We developed a new strategy using a free-energy calculation method, VM2. We successfully predicted the stable ordered water molecules in a number of protein systems: PDE 10a, HSP90, tryptophan synthase (TRPS), CDK2 and Factor Xa. In some of these, the second shell of water molecules appeared to be critical in protein-ligand binding. We also applied the strategy to largely improve binding free energy calculation using the MM/PBSA method. When applying MM/PBSA alone for two systems, CDK2 and Factor Xa, the computed binding free energy resulted in poor to moderate R² values with experimental data. However, including water free energy correction greatly improved the free energy calculation. Furthermore, our work helped to explain how xk263 is a 1000 times faster binder to HIVp than ritonavir, a potentially useful tool for investigating binding kinetics. Our studies reveal the importance of fully considering water effects in therapeutic developments in pharmaceutical and biotechnology industries and for fundamental research in protein-ligand recognition.

Identification of Potential ACE2-Derived Peptide Mimetics in SARS-CoV-2 Omicron Variant Therapeutics using Computational Approaches

The COVID-19 pandemic has become a global health challenge because of the emergence of distinct variants. Omicron, a new variant, is recognized as a variant of concern (VOC) by the World Health Organization (WHO) because of its higher mutations and accelerated human infection. The infection rate is strongly dependent on the binding rate of the receptor binding domain (RBD) against human angiotensin converting enzyme-2 (ACE2human) receptor. Inhibition of protein-protein (RBDs(SARS-CoV-2/omicron)-ACE2human) interaction has been already proven to inhibit viral infection. We have systematically designed ACE2human-derived peptides and peptide mimetics that have high binding affinity toward RBDomicron. Our peptide mutational analysis indicated the influence of canonical amino acids on the peptide binding process. Herein, efforts have been made to explore the atomistic details and events of RBDs(SARS-CoV-2/omicron)-ACE2human interactions by using molecular dynamics simulation. Our studies pave a path for developing therapeutic peptidomimetics against omicron.

Protein pKa Prediction by Tree-Based Machine Learning

Protonation states of ionizable protein residues modulate many essential biological processes. For correct modeling and understanding of these processes, it is crucial to accurately determine their pKa values. Here, we present four tree-based machine learning models for protein pKa prediction. The four models, Random Forest, Extra Trees, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), were trained on three experimental PDB and pKa datasets, two of which included a notable portion of internal residues. We observed similar performance among the four machine learning algorithms. The best model trained on the largest dataset performs 37% better than the widely used empirical pKa prediction tool PROPKA and 15% better than the published result from the pKa prediction method DelPhiPKa. The overall root-mean-square error (RMSE) for this model is 0.69, with surface and buried RMSE values being 0.56 and 0.78, respectively, considering six residue types (Asp, Glu, His, Lys, Cys, and Tyr), and 0.63 when considering Asp, Glu, His, and Lys only. We provide pKa predictions for proteins in human proteome from the AlphaFold Protein Structure Database and observed that 1% of Asp/Glu/Lys residues have highly shifted pKa values close to the physiological pH.

Protein-Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation

Here, we present a Gaussian-based method for estimation of protein-protein binding entropy to augment the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) method for computational prediction of binding free energy (ΔG). The method is termed f5-MM/PBSA/E, where "E" stands for entropy and f5 for five adjustable parameters. The enthalpy components of ΔG (molecular mechanics, polar and non-polar solvation energies) are computed from a single implicit solvent generalized Born (GB) energy minimized structure of a protein-protein complex, while the binding entropy is computed using independently GB energy minimized unbound and bound structures. It should be emphasized that the f5-MM/PBSA/E method does not use snapshots, just energy minimized structures, and is thus very fast and computationally efficient. The method is trained and benchmarked in 5-fold validation test over a data set consisting of 46 protein-protein binding cases with experimentally determined dissociation constant K _d values. This data set has been used for benchmarking in recently published protein-protein binding studies that apply conventional MM/PBSA and MM/PBSA with an enhanced sampling method. The f5-MM/PBSA/E tested on the same data set achieves similar or better performance than these computationally demanding approaches, making it an excellent choice for high throughput protein-protein binding affinity prediction studies.

Prediction of multiple dry-wet transition pathways with a mesoscale variational approach

Water fluctuates in a hydrophobic confinement, forming multiple dry and wet hydration states through evaporation and condensation. Transitions between such states are critical to both thermodynamics and kinetics of solute molecular processes, such as protein folding and protein-ligand binding and unbinding. To efficiently predict such dry-wet transition paths, we develop a hybrid approach that combines a variational implicit solvation model, a generalized string method for minimum free-energy paths, and the level-set numerical implementation. This approach is applied to three molecular systems: two hydrophobic plates, a carbon nanotube, and a synthetic host molecule Cucurbit[7]uril. Without an explicit description of individual water molecules, our mesoscale approach effectively captures multiple dry and wet hydration states, multiple dry-wet transition paths, such as those geometrically symmetric and asymmetric paths, and transition states, providing activation energy barriers between different states. Further analysis shows that energy barriers depend on mesoscopic lengths, such as the separation distance between the two plates and the cross section diameter of the nanotube, and that the electrostatic interactions strongly influence the dry-wet transitions. With the inclusion of solute atomic motion, general collective variables as reaction coordinates, and the finite-temperature string method, together with an improved treatment of continuum electrostatics, our approach can be further developed to sample an ensemble of transition paths, providing more accurate predictions of the transition kinetics.

Design, Simulation, and Development of a BioSensor for Viruses Detection Using FPGA

Objective: Impedance based biosensing provides a unique, highly sensitive electrical approach to biomolecule detection, cell growth, and other biological events. To date, an impedance change due to the cell growth has been considered as a solution to detect some changes in a cell’s behavior. The impedance change detection is normally measured via an impedance analyzer which is expensive and also cumbersome. Rapid and definitive diagnosis of viral infections is imperative in patient treatment process. Early detection followed by appropriate lifestyle and treatment may result to a longer, healthier life. Certain patients require continues monitoring that may require regular visits to hospitals which is not practical. Therefore, a continuous home healthcare device is needed to monitor and detect any change in a patient’s health condition. Methods & Results: In this research, a novel sensor and healthcare monitoring system is modeled, simulated, developed, and tested to detect viruses by detecting the change in the impedance due to antibodies and antigens binding. First, COMSOL simulation tool is used to develop a model to prove the concept. The model predicts increasing impedance during functionalization of electrodes with antibodies and after antigen binding steps. Second, to understand how nanoscale electrode size and spacing would affect biosensing assay (antibody-based affinity binding of a protein antigen), a model using COMSOL is developed. Third, Field Programmable Gate Arrays (FPGA) based signal processing system is developed as well to be connected to analog to digital converter (ADC) to acquire the current and voltage readings of the sensors over time. This healthcare monitoring system is used to continuously monitoring a patient’s condition and reports any changes in the impedance readings which represents virus detection or at least change in the cell’s behavior. Conclusions: The proposed sensor model is simulated, tested and verified via COMSOL and the FPGA prototype is tested and it verified the COMSOL model. This work reports that the proposed sensor can be used to detect viruses via detecting a change in the impedance.

On regularization of charge singularities in solving the Poisson-Boltzmann equation with a smooth solute-solvent boundary

Numerical treatment of singular charges is a grand challenge in solving the Poisson-Boltzmann (PB) equation for analyzing electrostatic interactions between the solute biomolecules and the surrounding solvent with ions. For diffuse interface PB models in which solute and solvent are separated by a smooth boundary, no effective algorithm for singular charges has been developed, because the fundamental solution with a space dependent dielectric function is intractable. In this work, a novel regularization formulation is proposed to capture the singularity analytically, which is the first of its kind for diffuse interface PB models. The success lies in a dual decomposition - besides decomposing the potential into Coulomb and reaction field components, the dielectric function is also split into a constant base plus space changing part. Using the constant dielectric base, the Coulomb potential is represented analytically via Green's functions. After removing the singularity, the reaction field potential satisfies a regularized PB equation with a smooth source. To validate the proposed regularization, a Gaussian convolution surface (GCS) is also introduced, which efficiently generates a diffuse interface for three-dimensional realistic biomolecules. The performance of the proposed regularization is examined by considering both analytical and GCS diffuse interfaces, and compared with the trilinear method. Moreover, the proposed GCS-regularization algorithm is validated by calculating electrostatic free energies for a set of proteins and by estimating salt affinities for seven protein complexes. The results are consistent with experimental data and estimates of sharp interface PB models.

BION-2: Predicting Positions of Non-Specifically Bound Ions on Protein Surface by a Gaussian-Based Treatment of Electrostatics

Ions play significant roles in biological processes—they may specifically bind to a protein site or bind non-specifically on its surface. Although the role of specifically bound ions ranges from actively providing structural compactness via coordination of charge–charge interactions to numerous enzymatic activities, non-specifically surface-bound ions are also crucial to maintaining a protein’s stability, responding to pH and ion concentration changes, and contributing to other biological processes. However, the experimental determination of the positions of non-specifically bound ions is not trivial, since they may have a low residential time and experience significant thermal fluctuation of their positions. Here, we report a new release of a computational method, the BION-2 method, that predicts the positions of non-specifically surface-bound ions. The BION-2 utilizes the Gaussian-based treatment of ions within the framework of the modified Poisson–Boltzmann equation, which does not require a sharp boundary between the protein and water phase. Thus, the predictions are done by the balance of the energy of interaction between the protein charges and the corresponding ions and the de-solvation penalty of the ions as they approach the protein. The BION-2 is tested against experimentally determined ion’s positions and it is demonstrated that it outperforms the old BION and other available tools.