Expression of Concern for "Deficiencies in Molecular Dynamics Simulation-Based Prediction of Protein-DNA Binding Free Energy Landscapes"

Multisite λ-Dynamics for Protein-DNA Binding Affinity Prediction

Transcription factors (TFs) regulate gene expression by binding to specific DNA sequences, playing critical roles in cellular processes and disease pathways. Computational methods, particularly λ-Dynamics, offer a promising approach for predicting TF relative binding affinities. This study evaluates the effectiveness of different λ-Dynamics perturbation schemes in determining binding free energy changes (ΔΔGb) of the WRKY transcription factor upon mutating its W-box binding site (GGTCAA) to a nonspecific sequence (GATAAA). Among the schemes tested, the single λ per base pair protocol demonstrated the fastest convergence and highest precision. Extending this protocol to additional mutants (GGTCCG and GGACAA) yielded ΔΔGb values that successfully ranked binding affinities, showcasing its strong potential for high-throughput screening of DNA binding sites.

Deciphering Sequence-Specific DNA Binding by H-NS Using Molecular Simulation

H-NS is a DNA organizing protein that occurs in Gram-negative bacteria. It can form long filaments between two DNA duplexes by first binding to a high-affinity AT-rich nucleotide sequence and extending from there. Using molecular dynamics simulations and steered molecular dynamics, we are able to determine the free energy of formation and dissociation of a protein-DNA complex comprising an H-NS DNA-binding domain and a specific nucleotide sequence. The molecular dynamics simulations allow detailed characterization of the interactions between the protein and a specific nucleotide sequence. To quantify the strength of the interaction, we employ an additional potential based on protein-DNA contacts to speed up dissociation of the protein-DNA complex. The work required for the dissociation results in an estimate of the free energy of dissociation/complex formation. Our protocol can provide quantitative prediction of protein-DNA complex stability, while also providing high-resolution insights into recognition mechanisms. In this chapter, we have used this approach to quantify the sequence specificity of H-NS DNA-binding domains to various nucleotide sequences, thus elucidating the mechanism with which H-NS can specifically bind to AT-rich DNA.

Decoding dissociation of sequence-specific protein-DNA complexes with non-equilibrium simulations

Sequence-specific protein-DNA interactions are crucial in processes such as DNA organization, gene regulation and DNA replication. Obtaining detailed insights into the recognition mechanisms of protein-DNA complexes through experiments is hampered by a lack of resolution in both space and time. Here, we present a molecular simulation approach to quantify the sequence specificity of protein-DNA complexes, that yields results fast, and is generally applicable to any protein-DNA complex. The approach is based on molecular dynamics simulations in combination with a sophisticated steering potential and results in an estimate of the free energy difference of dissociation. We provide predictions of the nucleotide specific binding affinity of the minor groove binding Histone-like Nucleoid Structuring (H-NS) protein, that are in agreement with experimental data. Furthermore, our approach offers mechanistic insight into the process of dissociation. Applying our approach to the major groove binding ETS domain in complex with three different nucleotide sequences identified the high affinity consensus sequence, quantitatively in agreement with experiments. Our protocol facilitates quantitative prediction of protein-DNA complex stability, while also providing high resolution insights into recognition mechanisms. As such, our simulation approach has the potential to yield detailed and quantitative insights into biological processes involving sequence-specific protein-DNA interactions.

Engineering of Ocriplasmin Variants by Bioinformatics Methods for the Reduction of Proteolytic and Autolytic Activities

BACKGROUND

Ocriplasmin has been developed for the induction of posterior vitreous detachment in patients with vitreomacular adhesion. At physiological pH, ocriplasmin is susceptible to autolytic and proteolytic degradation, limiting its activity duration. These undesirable properties of ocriplasmin can be reduced by site-directed mutagenesis, so that its enzymatic activities can be augmented. This study aimed to design ocriplasmin variants with improved biological/physicochemical characteristics via bioinformatics tools.

METHODS

This study was performed in Tabriz University of Medical Sciences, Tabriz, Iran, 2019. Through site-directed mutagenesis, three ocriplasmin variants were designed. Structural analysis was performed on the wild-type variant and the mutant variants using the Protein Interactions Calculator (PIC) server. The interactions between the S-2403 substrate and the ocriplasmin variants were studied by molecular docking simulations, and binding capability was evaluated by the calculation of free binding energy. The conformational features of protein-substrate complex systems for all the variants were evaluated using molecular dynamic simulations at 100 nanoseconds.

RESULTS

The structural analysis of ocriplasmin revealed that the substitution of threonine for alanine 59 significantly reduced proteolytic activity, while the substitution of glutamic acid for lysine 156 influenced autolytic function. The molecular docking simulation results indicated the appropriate binding of the substrate to the ocriplasmin variants with high-to-low affinities. The binding affinity of the wild-type variant for the substrate was higher than that between the mutant variants and the substrate. Simulation analyses, consisting of the root-mean-square deviation, the root-mean-square fluctuation, and the center-of-mass average distance showed a higher affinity of the substrate for the wild type than for the mutant variants.

CONCLUSION

The mutational analysis of ocriplasmin revealed that A59T and K156E mutagenesis could be used for the development of a new variant with higher therapeutic efficacy.

Improved Alchemical Free Energy Calculations with Optimized Smoothstep Softcore Potentials

Progress in the development of GPU-accelerated free energy simulation software has enabled practical applications on complex biological systems and fueled efforts to develop more accurate and robust predictive methods. In particular, this work re-examines concerted (a.k.a., one-step or unified) alchemical transformations commonly used in the prediction of hydration and relative binding free energies (RBFEs). We first classify several known challenges in these calculations into three categories: endpoint catastrophes, particle collapse, and large gradient-jumps. While endpoint catastrophes have long been addressed using softcore potentials, the remaining two problems occur much more sporadically and can result in either numerical instability (i.e., complete failure of a simulation) or inconsistent estimation (i.e., stochastic convergence to an incorrect result). The particle collapse problem stems from an imbalance in short-range electrostatic and repulsive interactions and can, in principle, be solved by appropriately balancing the respective softcore parameters. However, the large gradient-jump problem itself arises from the sensitivity of the free energy to large values of the softcore parameters, as might be used in trying to solve the particle collapse issue. Often, no satisfactory compromise exists with the existing softcore potential form. As a framework for solving these problems, we developed a new family of smoothstep softcore (SSC) potentials motivated by an analysis of the derivatives along the alchemical path. The smoothstep polynomials generalize the monomial functions that are used in most implementations and provide an additional path-dependent smoothing parameter. The effectiveness of this approach is demonstrated on simple yet pathological cases that illustrate the three problems outlined. With appropriate parameter selection, we find that a second-order SSC(2) potential does at least as well as the conventional approach and provides vast improvement in terms of consistency across all cases. Last, we compare the concerted SSC(2) approach against the gold-standard stepwise (a.k.a., decoupled or multistep) scheme over a large set of RBFE calculations as might be encountered in drug discovery.