Ionic Solution: What Goes Right and Wrong with Continuum Solvation Modeling

Solvent-mediated electrostatic interactions were well recognized to be important in the structure and function of molecular systems. Ionic interaction is an important component in electrostatic interactions, especially in highly charged molecules, such as nucleic acids. Here, we focus on the quality of the widely used Poisson–Boltzmann surface area (PBSA) continuum models in modeling ionic interactions by comparing with both explicit solvent simulations and the experiment. In this work, the molality-dependent chemical potentials for sodium chloride (NaCl) electrolyte were first simulated in the SPC/E explicit solvent. Our high-quality simulation agrees well with both the previous study and the experiment. Given the free-energy simulations in SPC/E as the benchmark, we used the same sets of snapshots collected in the SPC/E solvent model for PBSA free-energy calculations in the hope to achieve the maximum consistency between the two solvent models. Our comparative analysis shows that the molality-dependent chemical potentials of NaCl were reproduced well with both linear PB and nonlinear PB methods, although nonlinear PB agrees better with SPC/E and the experiment. Our free-energy simulations also show that the presence of salt increases the hydrophobic effect in a nonlinear fashion, in qualitative agreement with previous theoretical studies of Onsager and Samaras. However, the lack of molality-dependency in the nonelectrostatics continuum models dramatically reduces the overall quality of PBSA methods in modeling salt-dependent energetics. These analyses point to further improvements needed for more robust modeling of solvent-mediated interactions by the continuum solvation frameworks

The IDP-Specific Force Field <i>ff14IDPSFF</i> Improves the Conformer Sampling of Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) or intrinsically disordered regions do not have a fixed tertiary structure but play key roles in signal regulation, molecule recognition, and drug targeting. However, it is difficult to study the structure and function of IDPs by traditional experimental methods because of their diverse conformations. Limitations of current generic protein force fields and solvent models were reported in the previous simulations of IDPs. We have also explored overcoming these limitations by developing the <i>ff99IDPs</i> and <i>ff14IDPs</i> force fields to correct the dihedral distribution for eight disorder-promoting residues often observed in IDPs and found encouraging improvements. Here we extend our correction of backbone dihedral terms to all 20 naturally occurring amino acids in the IDP-specific force field <i>ff14IDPSFF</i> to further improve the quality of the modeling of IDPs. Extensive tests of seven IDPs and 14 unstructured short peptides show that the simulated Cα chemical shifts obtained with the <i>ff14IDPSFF</i> force field are in quantitative agreement with those from NMR experiments and are more accurate than those obtained with the base generic force field and also our previous <i>ff14IDPs</i> that only corrects the eight disorder-promoting amino acids. The influence of the solvent model was also investigated and found to be less important. Finally, our explicit-solvent MD simulations further show that <i>ff14IDPSFF</i> can still be used to model structural and dynamical properties of two tested folded proteins, with a slightly better agreement in the loop regions for both structural and dynamical properties. These findings confirm that the newly developed IDP-specific force field <i>ff14IDPSFF</i> can improve the conformer sampling of intrinsically disordered proteins

Development of a High-Throughput, <i>In Vivo</i> Selection Platform for NADPH-Dependent Reactions Based on Redox Balance Principles

Bacteria undergoing anaerobic fermentation must maintain redox balance. <i>In vivo</i> metabolic evolution schemes based on this principle have been limited to targeting NADH-dependent reactions. Here, we developed a facile, specific, and high-throughput growth-based selection platform for NADPH-consuming reactions <i>in vivo</i>, based on an engineered NADPH-producing glycolytic pathway in <i>Escherichia coli</i>. We used the selection system in the directed evolution of a NADH-dependent d-lactate dehydrogenase from <i>Lactobacillus delbrueckii</i> toward utilization of NADPH. Through one round of selection, we obtained multiple enzyme variants with superior NADPH-dependent activities and protein expression levels; these mutants may serve as important tools in biomanufacturing d-lactate as a renewable polymer building block. Importantly, sequence analysis and computational protein modeling revealed that diverging evolutionary paths during the selection resulted in two distinct cofactor binding modes, which suggests that the high throughput of our selection system allowed deep searching of protein sequence space to discover diverse candidates <i>en masse</i>

Reducing Grid Dependence in Finite-Difference Poisson–Boltzmann Calculations

Grid dependence in numerical reaction field energies and solvation forces is a well-known limitation in the finite-difference Poisson–Boltzmann methods. In this study, we have investigated several numerical strategies to overcome the limitation. Specifically, we have included trimeric solvent accessible arc dots during analytical molecular surface generation to improve the convergence of numerical reaction field energies and solvation forces. We have also utilized the level set function to trace the molecular surface implicitly to simplify the numerical mapping of the grid-independent molecular surface. We have further explored combining the weighted harmonic averaging of boundary dielectrics with a charge-based approach to improve the convergence and stability of numerical reaction field energies and solvation forces. Our test data show that the convergence and stability in both numerical energies and forces can be improved significantly when the combined strategy is applied to either the Poisson equation or the full Poisson–Boltzmann equation

Computational Analysis for the Rational Design of Anti-Amyloid Beta (Aβ) Antibodies

Alzheimer’s disease (AD) is a neurodegenerative disorder that lacks effective treatment options. Anti-amyloid beta (Aβ) antibodies are the leading drug candidates to treat AD, but the results of clinical trials have been disappointing. Introducing rational mutations into anti-Aβ antibodies to increase their effectiveness is a way forward, but the path to take is unclear. In this study, we demonstrate the use of computational fragment-based docking and MMPBSA binding free energy calculations in the analysis of anti-Aβ antibodies for rational drug design efforts. Our fragment-based docking method successfully predicts the emergence of the common EFRH epitope. MD simulations coupled with MMPBSA binding free energy calculations are used to analyze scenarios described in prior studies, and we computationally introduce rational mutations into PFA1 to predict mutations that can improve its binding affinity toward the pE3-Aβ_3–8 form of Aβ. Two out of our four proposed mutations are predicted to stabilize binding. Our study demonstrates that a computational approach may lead to an improved drug candidate for AD in the future

Specific Recognition Mechanism between RNA and the KH3 Domain of Nova‑2 Protein

The KH3 domain of Nova-2 protein can precisely recognize the sequence-specific target RNA of human glycine receptor α2. However, the recognition mechanism between the protein and its target RNA is still hotly debated. In this study, molecular dynamic simulations in explicit solvent were utilized to understand the recognition mechanism. The structural analysis and the Kolmogorov–Smirnov <i>P</i>-test statistics reveal that the KH3 domain might obey a conformational selection mechanism upon RNA binding. However, the induced fit mechanism could not be completely ruled out. Unfolding kinetics indicates that the folding of RNA and KH3 happens first and then the binding between RNA and KH3 follows. Principle component analysis shows that the invariant Gly-Lys-Gly-Gly loop moves toward to the RNA molecule but the C-terminal domain moves away from the RNA molecule upon binding. These specific dominant motions were hypothesized to stabilize the complex structure. The hydrophobic and hydrogen bonding interactions were found to be the driving forces for the specific recognition, in contrast to the dominant electrostatic interactions for nonspecific recognition

Test and Evaluation of <i>ff99IDPs</i> Force Field for Intrinsically Disordered Proteins

Over 40% of eukaryotic proteomic sequences have been predicted to be intrinsically disordered proteins (IDPs) or intrinsically disordered regions (IDRs) and confirmed to be associated with many diseases. However, widely used force fields cannot well reproduce the conformers of IDPs. Previously the <i>ff99IDPs</i> force field was released to simulate IDPs with CMAP energy corrections for the eight disorder-promoting residues. In order to further confirm the performance of <i>ff99IDPs</i>, three representative IDP systems (arginine-rich HIV-1 Rev, aspartic proteinase inhibitor IA₃, and α-synuclein) were used to test and evaluate the simulation results. The results show that for free disordered proteins, the chemical shifts from the <i>ff99IDPs</i> simulations are in quantitative agreement with those from reported NMR measurements and better than those from <i>ff99SBildn</i>. Thus, <i>ff99IDPs</i> can sample more clusters of disordered conformers than <i>ff99SBildn</i>. For structural proteins, both <i>ff99IDPs</i> and <i>ff99SBildn</i> can well reproduce the conformations. In general, <i>ff99IDPs</i> can successfully be used to simulate the conformations of IDPs and IDRs in both bound and free states. However, relative errors could still be found at the boundaries of ordered residues scattered in long disorder-promoting sequences. Therefore, polarizable force fields might be one of the possible ways to further improve the performance on IDPs

Allosteric Autoinhibition Pathway in Transcription Factor ERG: Dynamics Network and Mutant Experimental Evaluations

Allosteric autoinhibition exists in many transcription factors. The ERG proteins exhibit autoinhibition on DNA binding by the C-terminal and N-terminal inhibitory domains (CID and NID). However, the autoinhibition mechanism and allosteric pathway of ERG are unknown. In this study we intend to elucidate the residue-level allosteric mechanism and pathway via a combined approach of computational and experimental analyses. Specifically computational residue-level fluctuation correlation data was analyzed to reveal detailed dynamics signatures in the allosteric autoinhibition process. A hypothesis of “NID/CID binding induced allostery” is proposed to link similar structures and different protein functions, which is subsequently validated by perturbation and mutation analyses in both computation and experiment. Two possible allosteric autoinhibition pathways of L286-L382-A379-G377-I360-Y355-R353 and L286-L382-A379-G377-I360-Y355- A351-K347-R350 were identified computationally and were confirmed by the computational and experimental mutations. Specifically we identified two mutation sites on the allosteric inhibition pathways, L286P/Q383P (NID/CID binding site) and I360G (pathway junction), which completely restore the wild type DNA binding affinity. These results suggest that the putative protein structure–function relationship may be augmented with a general relationship of protein “structure/fluctuation–correlation/function” for more thorough analyses of protein functions

microRNA up-regulation translation mechanism.

<p>microRNA up-regulation translation mechanism.</p

Summary of simulation conditions.

<p>Summary of simulation conditions.</p