Alchemical Free Energy Calculations for Nucleotide Mutations in Protein–DNA Complexes

Correct Nucleotide Selection Is Confined at the Binding Site of Polymerase Enzymes

DNA polymerases (Pols) add incoming nucleotides (deoxyribonucleoside triphosphate (dNTPs)) to growing DNA strands, a crucial step for DNA synthesis. The insertion of correct (vs incorrect) nucleotides relates to Pols' fidelity, which defines Pols' ability to faithfully replicate DNA strands in a template-dependent manner. We and others have demonstrated that reactant alignment and correct base pairing at the Pols catalytic site are crucial structural features to fidelity. Here, we first used equilibrium molecular simulations to demonstrate that the local dynamics at the protein-DNA interface in the proximity of the catalytic site is different when correct vs incorrect dNTPs are bound to polymerase β (Pol β). Formation and dynamic stability of specific interatomic interactions around the incoming nucleotide influence the overall binding site architecture. This explains why certain Pols' mutants can affect the local catalytic environment and influence the selection of correct vs incorrect nucleotides. In particular, this is here demonstrated by analyzing the interaction network formed by the residue R283, whose mutant R283A has an experimentally measured lower capacity of differentiating correct (G:dCTP) vs incorrect (G:dATP) base pairing in Pol β. We also used alchemical free-energy calculations to quantify the G:dCTP →G:dATP transformation in Pol β wild-type and mutant R283A. These results correlate well with the experimental trend, thus corroborating our mechanistic insights. Sequence and structural comparisons with other Pols from the same family suggest that these findings may also be valid in similar enzymes.

Virtual screening and molecular growth guide the design of inhibitors for the influenza virus drug-resistant mutant M2-V27A/S31N

The influenza A virus matrix protein 2 (AM2) protein is a proton-gated, proton-selective ion channel essential for influenza replication that has been identified as an antiviral target. The drug-resistance of the M2-V27A/S31N strain, which has been growing more prevalent in recent years and has the potential to spread globally, prevents current amantadine inhibitors from having the desired impact. In this study, we compiled the most common influenza A virus strains from 2001-2020 from the U.S. National Center for Biotechnology Information database and hypothesized that M2-V27A/S31N would become a common strain. The lead compound ZINC299830590 was screened for M2-V27A/S31N in the ZINC15 database using a pharmacophore model and molecular descriptors. This lead compound was then optimized by molecular growth, which allowed us to identify important amino acid residues and create interactions with them to produce compound 4. Molecular dynamics simulation showed that the complex of compound 4 and M2-V27A/S31N had certain degrees of stability and flexibility. The binding free energy of compound 4 was calculated using the MM/PB(GB)SA method and totaled -106.525 kcal/mol. Finally, physicochemical and pharmacokinetic profiles were predicted using the Absorption, Distribution, Metabolism, Excretion, and Toxicity model, which indicated the good bioavailability of compound 4. These results provide the basis for further in vivo and in vitro studies to demonstrate that compound 4 is a promising drug candidate against M2-V27A/S31N.Communicated by Ramaswamy H. Sarma.

In silico λ-dynamics predicts protein binding specificities to modified RNAs

RNA modifications shape gene expression through a smorgasbord of chemical changes to canonical RNA bases. Although numbering in the hundreds, only a few RNA modifications are well characterized, in part due to the absence of methods to identify modification sites. Antibodies remain a common tool to identify modified RNA and infer modification sites through straightforward applications. However, specificity issues can result in off-target binding and confound conclusions. This work utilizes in silico λ-dynamics to efficiently estimate binding free energy differences of modification-targeting antibodies between a variety of naturally occurring RNA modifications. Crystal structures of inosine and N6-methyladenosine (m6A) targeting antibodies bound to their modified ribonucleosides were determined and served as structural starting points. λ-Dynamics was utilized to predict RNA modifications that permit or inhibit binding to these antibodies. In vitro RNA-antibody binding assays supported the accuracy of these in silico results. High agreement between experimental and computed binding propensities demonstrated that λ-dynamics can serve as a predictive screen for antibody specificity against libraries of RNA modifications. More importantly, this strategy is an innovative way to elucidate how hundreds of known RNA modifications interact with biological molecules without the limitations imposed by in vitro or in vivo methodologies.

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.

Web tools support predicting protein-nucleic acid complexes stability with affinity changes

Numerous biological processes, such as transcription, replication, and translation, rely on protein-nucleic acid interactions (PNIs). Demonstrating the binding stability of protein-nucleic acid complexes is vital to deciphering the code for PNIs. Numerous web-based tools have been developed to attach importance to protein-nucleic acid stability, facilitating the prediction of PNIs characteristics rapidly. However, the data and tools are dispersed and lack comprehensive integration to understand the stability of PNIs better. In this review, we first summarize existing databases for evaluating the stability of protein-nucleic acid binding. Then, we compare and evaluate the pros and cons of web tools for forecasting the interaction energies of protein-nucleic acid complexes. Finally, we discuss the application of combining models and capabilities of PNIs. We may hope these web-based tools will facilitate the discovery of recognition mechanisms for protein-nucleic acid binding stability. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications.

Quantitative investigation of the effects of DNA modifications and protein mutations on MeCP2-MBD-DNA interactions

DNA methylation as an important epigenetic marker, has gained attention for the significance of three oxidative modifications (hydroxymethyl-C (hmC), formyl-C (fC), and carboxyl-C (caC)). Mutations occurring in the methyl-CpG-binding domain (MBD) of MeCP2 result in Rett. However, uncertainties persist regarding DNA modification and MBD mutation-induced interaction changes. Here, molecular dynamics simulations were used to investigate the underlying mechanisms behind changes due to different modifications of DNA and MBD mutations. Alanine scanning combined with the interaction entropy method was employed to accurately evaluate the binding free energy. The results show that MBD has the strongest binding ability for mCDNA, followed by caC, hmC, and fCDNA, with the weakest binding ability observed for CDNA. Further analysis revealed that mC modification induces DNA bending, causing residues R91 and R162 closer to the DNA. This proximity enhances van der Waals and electrostatic interactions. Conversely, the caC/hmC and fC modifications lead to two loop regions (near K112 and K130) closer to DNA, respectively. Furthermore, DNA modifications promote the formation of stable hydrogen bond networks, however mutations in the MBD significantly reduce the binding free energy. This study provides detailed insight into the effects of DNA modifications and MBD mutations on binding ability. It emphasizes the necessity for research and development of targeted Rett compounds that induce conformational compatibility between MBD and DNA, enhancing the stability and strength of their interactions.

Mutagenesis and structural studies reveal the basis for the specific binding of SARS-CoV-2 SL3 RNA element with human TIA1 protein

Viral RNA-host protein interactions are indispensable during RNA virus transcription and replication, but their detailed structural and dynamical features remain largely elusive. Here, we characterize the binding interface for the SARS-CoV-2 stem-loop 3 (SL3) cis-acting element to human TIA1 protein with a combined theoretical and experimental approaches. The highly structured SARS-CoV-2 SL3 has a high binding affinity to TIA1 protein, in which the aromatic stacking, hydrogen bonds, and hydrophobic interactions collectively direct this specific binding. Further mutagenesis studies validate our proposed 3D binding model and reveal two SL3 variants have enhanced binding affinities to TIA1. And disruptions of the identified RNA-protein interactions with designed antisense oligonucleotides dramatically reduce SARS-CoV-2 infection in cells. Finally, TIA1 protein could interact with conserved SL3 RNA elements within other betacoronavirus lineages. These findings open an avenue to explore the viral RNA-host protein interactions and provide a pioneering structural basis for RNA-targeting antiviral drug design.

Structural predictions of protein-DNA binding: MELD-DNA

Structural, regulatory and enzymatic proteins interact with DNA to maintain a healthy and functional genome. Yet, our structural understanding of how proteins interact with DNA is limited. We present MELD-DNA, a novel computational approach to predict the structures of protein-DNA complexes. The method combines molecular dynamics simulations with general knowledge or experimental information through Bayesian inference. The physical model is sensitive to sequence-dependent properties and conformational changes required for binding, while information accelerates sampling of bound conformations. MELD-DNA can: (i) sample multiple binding modes; (ii) identify the preferred binding mode from the ensembles; and (iii) provide qualitative binding preferences between DNA sequences. We first assess performance on a dataset of 15 protein-DNA complexes and compare it with state-of-the-art methodologies. Furthermore, for three selected complexes, we show sequence dependence effects of binding in MELD predictions. We expect that the results presented herein, together with the freely available software, will impact structural biology (by complementing DNA structural databases) and molecular recognition (by bringing new insights into aspects governing protein-DNA interactions).

Predicting binding affinity changes from long-distance mutations using molecular dynamics simulations and Rosetta

Computationally modeling how mutations affect protein-protein binding not only helps uncover the biophysics of protein interfaces, but also enables the redesign and optimization of protein interactions. Traditional high-throughput methods for estimating binding free energy changes are currently limited to mutations directly at the interface due to difficulties in accurately modeling how long-distance mutations propagate their effects through the protein structure. However, the modeling and design of such mutations is of substantial interest as it allows for greater control and flexibility in protein design applications. We have developed a method that combines high-throughput Rosetta-based side-chain optimization with conformational sampling using classical molecular dynamics simulations, finding significant improvements in our ability to accurately predict long-distance mutational perturbations to protein binding. Our approach uses an analytical framework grounded in alchemical free energy calculations while enabling exploration of a vastly larger sequence space. When comparing to experimental data, we find that our method can predict internal long-distance mutational perturbations with a level of accuracy similar to that of traditional methods in predicting the effects of mutations at the protein-protein interface. This work represents a new and generalizable approach to optimize protein free energy landscapes for desired biological functions.

Prioritizing Small Sets of Molecules for Synthesis through in-silico Tools: A Comparison of Common Ranking Methods

Prioritizing molecules for synthesis is a key role of computational methods within medicinal chemistry. Multiple tools exist for ranking molecules, from the cheap and popular molecular docking methods to more computationally expensive molecular-dynamics (MD)-based methods. It is often questioned whether the accuracy of the more rigorous methods justifies the higher computational cost and associated calculation time. Here, we compared the performance on ranking the binding of small molecules for seven scoring functions from five docking programs, one end-point method (MM/GBSA), and two MD-based free energy methods (PMX, FEP+). We investigated 16 pharmaceutically relevant targets with a total of 423 known binders. The performance of docking methods for ligand ranking was strongly system dependent. We observed that MD-based methods predominantly outperformed docking algorithms and MM/GBSA calculations. Based on our results, we recommend the application of MD-based free energy methods for prioritization of molecules for synthesis in lead optimization, whenever feasible.

Effects of External Perturbations on Protein Systems: A Microscopic View

Protein folding can be viewed as the origami engineering of biology resulting from the long process of evolution. Even decades after its recognition, research efforts worldwide focus on demystifying molecular factors that underlie protein structure-function relationships; this is particularly relevant in the era of proteopathic disease. A complex co-occurrence of different physicochemical factors such as temperature, pressure, solvent, cosolvent, macromolecular crowding, confinement, and mutations that represent realistic biological environments are known to modulate the folding process and protein stability in unique ways. In the current review, we have contextually summarized the substantial efforts in unveiling individual effects of these perturbative factors, with major attention toward bottom-up approaches. Moreover, we briefly present some of the biotechnological applications of the insights derived from these studies over various applications including pharmaceuticals, biofuels, cryopreservation, and novel materials. Finally, we conclude by summarizing the challenges in studying the combined effects of multifactorial perturbations in protein folding and refer to complementary advances in experiment and computational techniques that lend insights to the emergent challenges.

Engineering and Design of Programmable Genome Editors

Programmable genome editors are enzymes that can be targeted to a specific location in the genome for making site-specific alterations or deletions. The engineering, design, and development of sequence-specific editors has resulted in a dramatic increase in the precision of editing for nucleotide sequences. These editors can target specific locations in a genome, in vivo. The genome editors are being deployed for the development of genetically modified organisms for agriculture and industry, and for gene therapy of inherited human genetic disorders, cancer, and immunotherapy. Experimental and computational studies of structure, binding, activity, dynamics, and folding, reviewed here, have provided valuable insights that have the potential for increasing the functional efficiency of these gene/genome editors. Biochemical and biophysical studies of the specificities of natural and engineered genome editors reveal that increased binding affinity can be detrimental because of the increase of off-target effects and that the engineering and design of genome editors with higher specificity may require modulation and control of the conformational dynamics.

Protein-Nucleic Acid Interactions for RNA Polymerase II Elongation Factors by Molecular Dynamics Simulations

RNA polymerase II (Pol II) forms a complex with elongation factors to proceed to the elongation stage of the transcription process. In this work, we studied the elongation factor SPT5 and explored the protein-nucleic acid interactions for the isolated systems of KOW1 and KOW4 domains of SPT5 with DNA and RNA, respectively. We performed molecular dynamics (MD) simulations using three commonly used force fields that are CHARMM c36m, AMBER ff14sb, and ff19sb. Simulations showed strong protein-nucleic acid interactions and low electrostatic binding free energies for all force fields used. RNA was found to be highly dynamic with all force fields, while DNA had relatively more stable conformations with the AMBER force fields compared to that with CHARMM. Furthermore, we performed MD simulations of the complete elongation complex using CHARMM c36m and AMBER ff19sb force fields to compare the dynamics and interactions with the isolated systems. Similarly, strong KOW1 and DNA interactions were observed in the complete elongation complex simulations and DNA was further stabilized by a network of interactions involving SPT5-KOW1, SPT4, and rpb2 of Pol II. Overall, our study showed that the differences between CHARMM and AMBER force fields strongly affect the dynamics of the nucleic acids. CHARMM provides highly flexible DNA, while AMBER largely stabilizes the DNA structure. Although the presence of the entire interaction network stabilized the DNA and decreased the differences in the results from the two force fields, the discrepancies of the force fields for smaller systems may reflect their problems in generating accurate dynamics of nucleic acids.

Structure- and Interaction-Based Design of Anti-SARS-CoV-2 Aptamers

Aptamer selection against novel infections is a complicated and time-consuming approach. Synergy can be achieved by using computational methods together with experimental procedures. This study aims to develop a reliable methodology for a rational aptamer in silico et vitro design. The new approach combines multiple steps: (1) Molecular design, based on screening in a DNA aptamer library and directed mutagenesis to fit the protein tertiary structure; (2) 3D molecular modeling of the target; (3) Molecular docking of an aptamer with the protein; (4) Molecular dynamics (MD) simulations of the complexes; (5) Quantum-mechanical (QM) evaluation of the interactions between aptamer and target with further analysis; (6) Experimental verification at each cycle for structure and binding affinity by using small-angle X-ray scattering, cytometry, and fluorescence polarization. By using a new iterative design procedure, structure- and interaction-based drug design (SIBDD), a highly specific aptamer to the receptor-binding domain of the SARS-CoV-2 spike protein, was developed and validated. The SIBDD approach enhances speed of the high-affinity aptamers development from scratch, using a target protein structure. The method could be used to improve existing aptamers for stronger binding. This approach brings to an advanced level the development of novel affinity probes, functional nucleic acids. It offers a blueprint for the straightforward design of targeting molecules for new pathogen agents and emerging variants.

A general theoretical framework to design base editors with reduced bystander effects

Base editors (BEs) hold great potential for medical applications of gene therapy. However, high precision base editing requires BEs that can discriminate between the target base and multiple bystander bases within a narrow active window (4 - 10 nucleotides). Here, to assist in the design of these optimized editors, we propose a discrete-state stochastic approach to build an analytical model that explicitly evaluates the probabilities of editing the target base and bystanders. Combined with all-atom molecular dynamic simulations, our model reproduces the experimental data of A3A-BE3 and its variants for targeting the "TC" motif and bystander editing. Analyzing this approach, we propose several general principles that can guide the design of BEs with a reduced bystander effect. These principles are then applied to design a series of point mutations at T218 position of A3G-BEs to further reduce its bystander editing. We verify experimentally that the new mutations provide different levels of stringency on reducing the bystander editing at different genomic loci, which is consistent with our theoretical model. Thus, our study provides a computational-aided platform to assist in the scientifically-based design of BEs with reduced bystander effects.

Free energy calculations of ALS-causing SOD1 mutants reveal common perturbations to stability and dynamics along the maturation pathway

With over 150 heritable mutations identified as disease-causative, superoxide dismutase 1 (SOD1) has been a main target of amyotrophic lateral sclerosis (ALS) research and therapeutic efforts. However, recent evidence has suggested that neither loss of function nor protein aggregation is responsible for promoting neurotoxicity. Furthermore, there is no clear pattern to the nature or the location of these mutations that could suggest a molecular mechanism behind SOD1-linked ALS. Here, we utilize reliable and accurate computational techniques to predict the perturbations of 10 such mutations to the free energy changes of SOD1 as it matures from apo monomer to metallated dimer. We find that the free energy perturbations caused by these mutations strongly depend on maturational progress, indicating the need for state-specific therapeutic targeting. We also find that many mutations exhibit similar patterns of perturbation to native and non-native maturation, indicating strong thermodynamic coupling between the dynamics at various sites of maturation within SOD1. These results suggest the presence of an allosteric network in SOD1 which is vulnerable to disruption by these mutations. Analysis of these perturbations may contribute to uncovering a unifying molecular mechanism which explains SOD1-linked ALS and help to guide future therapeutic efforts.

Accurate absolute free energies for ligand-protein binding based on non-equilibrium approaches

The accurate calculation of the binding free energy for arbitrary ligand-protein pairs is a considerable challenge in computer-aided drug discovery. Recently, it has been demonstrated that current state-of-the-art molecular dynamics (MD) based methods are capable of making highly accurate predictions. Conventional MD-based approaches rely on the first principles of statistical mechanics and assume equilibrium sampling of the phase space. In the current work we demonstrate that accurate absolute binding free energies (ABFE) can also be obtained via theoretically rigorous non-equilibrium approaches. Our investigation of ligands binding to bromodomains and T4 lysozyme reveals that both equilibrium and non-equilibrium approaches converge to the same results. The non-equilibrium approach achieves the same level of accuracy and convergence as an equilibrium free energy perturbation (FEP) method enhanced by Hamiltonian replica exchange. We also compare uni- and bi-directional non-equilibrium approaches and demonstrate that considering the work distributions from both forward and reverse directions provides substantial accuracy gains. In summary, non-equilibrium ABFE calculations are shown to yield reliable and well-converged estimates of protein-ligand binding affinity.

Inhibition mechanism and hot-spot prediction of nine potential drugs for SARS-CoV-2 Mpro by large-scale molecular dynamic simulations combined with accurate binding free energy calculations

Coronavirus disease 2019 (COVID-19), which is caused by a new coronavirus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is spreading around the world. However, a universally effective treatment regimen has not been developed to date. The main protease (M^pro), a key enzyme of SARS-CoV-2, plays a crucial role in the replication and transcription of this virus in cells and has become the ideal target for rational antiviral drug design. In this study, we performed molecular dynamics simulations three times for these complexes of M^pro (monomeric and dimeric) and nine potential drugs that have a certain effect on the treatment of COVID-19 to explore their binding mechanism. In addition, a total of 12 methods for calculating binding free energy were employed to determine the optimal drug. Ritonavir, Arbidol, and Chloroquine consistently showed an outstanding binding ability to monomeric M^pro under various methods. Ritonavir, Arbidol, and Saquinavir presented the best performance when binding to a dimer, which was independent of the protonated state of Hie41 (protonated at N_ε) and Hid41 (protonated at N_δ), and these findings suggest that Chloroquine may not effectively inhibit the activity of dimeric M^proin vivo. Furthermore, three common hot-spot residues of Met165, Hie41, and Gln189 of monomeric M^pro systems dominated the binding of Ritonavir, Arbidol, and Chloroquine. In dimeric M^pro, Gln189, Met165, and Met49 contributed significantly to binding with Ritonavir, Arbidol, and Saquinavir; therefore, Gln189 and Met165 might serve as the focus in the discovery and development of anti-COVID-19 drugs. In addition, the van der Waals interaction played a significant role in the binding process, and the benzene ring of the drugs showed an apparent inhibitory effect on the normal function of M^pro. The binding cavity had great flexibility to accommodate these different drugs. The results would be notably helpful for enabling a detailed understanding of the binding mechanisms for these important drug-M^pro interactions and provide valuable guidance for the design of potent inhibitors.

DNA-binding mechanisms of human and mouse cGAS: a comparative MD and MM/GBSA study

Cyclic GMP-AMP synthase (cGAS) can detect the presence of cytoplasmic DNA and activate the innate immune system via the cGAS-STING pathway. Although several structures of cGAS-DNA complexes were resolved recently, the molecular mechanism of cGAS in its recognition of DNA has not yet been fully understood. In order to reveal the subtle differences between human and mouse cGAS in terms of their DNA-binding mechanisms, four systems, both human and mouse cGAS in complex with two different DNA sequences of equal length, were studied by molecular dynamics simulations and molecular mechanics/generalized Born surface area analysis. Several residues, including ARG176/ARG161, ARG195/ARG180, ASN210/ASN196, LYS384/LYS372, CYM397/CYM385, LYS403/LYS391, LYS407/LYS395, and LYS411/LYS399, were identified to be the common key residues in the recognition of DNA for cGAS in both humans and mice. In addition, four residue pairs LYS173/ARG158, ASP177/LYS162, CYS199/LYS184, and GLU398/SER387 were suggested to be the major residues that make human cGAS and mouse cGAS different in terms of their binding to DNA. Besides the well-known zinc-thumb domain, two residues at the kink of the spine helix were also proposed for the first time to be the major binding motifs in cGAS-DNA interaction.

Non-equilibrium approach for binding free energies in cyclodextrins in SAMPL7: force fields and software

In the current work we report on our participation in the SAMPL7 challenge calculating absolute free energies of the host–guest systems, where 2 guest molecules were probed against 9 hosts-cyclodextrin and its derivatives. Our submission was based on the non-equilibrium free energy calculation protocol utilizing an averaged consensus result from two force fields (GAFF and CGenFF). The submitted prediction achieved accuracy of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${1.38}\,\hbox {kcal}/\hbox {mol}$$\end{document}1.38kcal/mol in terms of the unsigned error averaged over the whole dataset. Subsequently, we further report on the underlying reasons for discrepancies between our calculations and another submission to the SAMPL7 challenge which employed a similar methodology, but disparate ligand and water force fields. As a result we have uncovered a number of issues in the dihedral parameter definition of the GAFF 2 force field. In addition, we identified particular cases in the molecular topologies where different software packages had a different interpretation of the same force field. This latter observation might be of particular relevance for systematic comparisons of molecular simulation software packages. The aforementioned factors have an influence on the final free energy estimates and need to be considered when performing alchemical calculations.

Molecular dynamics simulations of DNA-DNA and DNA-protein interactions

The all-atom molecular dynamics method can characterize the molecular-level interactions in DNA and DNA-protein systems with unprecedented resolution. Recent advances in computational technologies have allowed the method to reveal the unbiased behavior of such systems at the microseconds time scale, whereas enhanced sampling approaches have matured enough to characterize the interaction free energy with quantitative precision. Here, we describe recent progress toward increasing the realism of such simulations by refining the accuracy of the molecular dynamics force field, and we highlight recent application of the method to systems of outstanding biological interest.

Repulsive Soft-Core Potentials for Efficient Alchemical Free Energy Calculations

In alchemical free energy (FE) simulations, annihilation and creation of atoms are generally achieved with the soft-core potential that shifts the interparticle separations. While this soft-core potential eliminates the numerical instability occurring near the two end states of the transformation, it makes the hybrid Hamiltonian vary nonlinearly with respect to the parameter λ, which interpolates between the Hamiltonians representing the two end states. This complicates FE estimation by Bennett acceptance ratio (BAR), free energy perturbation (FEP), and thermodynamic integration (TI) methods, thus reducing their calculation efficiency. In this work, we develop a new type of repulsive soft-core potential, called Gaussian soft-core (GSC) potential, with two parameters controlling its maximum and width. The main advantage of this potential is the linearity of the hybrid Hamiltonian with respect to λ, thus permitting the direct application of BAR, FEP, TI, and other variant FE methods. The accuracy and efficiency of the GSC potential are demonstrated by comparing the free energies of annihilation determined for 13 small molecules and an alchemical mutation of a protein side chain. In addition, in combination with a TI integrand (∂H/∂λ) estimation strategy, we show that GSC can considerably reduce the number of λ simulations compared to the commonly used separation-shifted soft-core potential.

An accurate free energy estimator: based on MM/PBSA combined with interaction entropy for protein-ligand binding affinity

The molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) method is constantly used to calculate the binding free energy of protein-ligand complexes, and has been shown to effectively balance computational cost against accuracy. The relative binding affinities obtained by the MM/PBSA approach are acceptable, while it usually overestimates the absolute binding free energy. This paper proposes four free energy estimators based on the MM/PBSA for enthalpy change combined with interaction entropy (IE) for entropy change using different weights for individual energy terms. The ΔG_{PBSA_IE} method is determined to be an optimal estimator based on its performance in terms of the correlation between experimental and theoretical values and error estimations. This approach is optimized using high-quality experimental values from a training set containing 84 protein-ligand systems, and the coefficients for the sum of electrostatic energy and polar solvation free energy, van der Waals (vdW) energy, non-polar solvation energy and entropy change are obtained by multivariate linear fitting to the corresponding experimental values. A comparison between the traditional MM/PBSA method and this method shows that the correlation coefficient is improved from 0.46 to 0.72 and the slope of the regression line increases from 0.10 to 1.00. More importantly, the mean absolute error (MAE) is significantly reduced from 22.52 to 1.59 kcal mol^-1. Furthermore, the numerical stability of this method is validated on a test set with a similar correlation coefficient, slope and MAE to those of the training set. Based on the above advantages, the ΔG_{PBSA_IE} method can be a powerful tool for a reliable and accurate estimation of binding free energy and plays a significant role in a detailed energetic investigation of protein-ligand interaction.

Identifying conformational changes of aptamer binding to theophylline: A combined biolayer interferometry, surface-enhanced Raman spectroscopy, and molecular dynamics study

Theophylline is a potent bronchodilator for the treatment of asthma, bronchitis, and emphysema. Its narrow therapeutic window (20-100 μM) demands that the blood concentration of theophylline be monitored carefully, which can be achieved by aptamer capture. Thus, an understanding of what occurs when aptamers bind to theophylline is critical for identifying a high-affinity and high-specificity aptamer, which improve the sensitivity and selectivity of theophylline detection. Consequently, there is an urgent need to develop a simple, convenient, and nondestructive method to monitor conformational changes during the binding process. Here, we report the determination of the affinity of a selected aptamer and theophylline via biolayer interferometry (BLI) experiments. Additionally, using surface-enhanced Raman spectroscopy (SERS), the conformational changes on theophylline-aptamer binding were identified from differences in the SER spectra. Finally, molecular dynamics (MD) simulations were used to identify the specific conformational changes of the aptamer during the binding process. Such a combined BLI-SERS-MD method provides an in-depth understanding of the theophylline-aptamer binding processes and a comprehensive explanation for conformational changes, which helps to select, design, and modify an aptamer with high affinity and specificity. It can also be used as a scheme for the study of other aptamer-ligand interactions, which can be applied to the detection, sensing, clinical diagnosis, and treatment of diseases.

Effect of mutations on binding of ligands to guanine riboswitch probed by free energy perturbation and molecular dynamics simulations

Riboswitches can regulate gene expression by direct and specific interactions with ligands and have recently attracted interest as potential drug targets for antibacterial. In this work, molecular dynamics (MD) simulations, free energy perturbation (FEP) and molecular mechanics generalized Born surface area (MM-GBSA) methods were integrated to probe the effect of mutations on the binding of ligands to guanine riboswitch (GR). The results not only show that binding free energies predicted by FEP and MM-GBSA obtain an excellent correlation, but also indicate that mutations involved in the current study can strengthen the binding affinity of ligands GR. Residue-based free energy decomposition was applied to compute ligand-nucleotide interactions and the results suggest that mutations highly affect interactions of ligands with key nucleotides U22, U51 and C74. Dynamics analyses based on MD trajectories indicate that mutations not only regulate the structural flexibility but also change the internal motion modes of GR, especially for the structures J12, J23 and J31, which implies that the aptamer domain activity of GR is extremely plastic and thus readily tunable by nucleotide mutations. This study is expected to provide useful molecular basis and dynamics information for the understanding of the function of GR and possibility as potential drug targets for antibacterial.

Theoretical understanding of the thermodynamics and interactions in transcriptional regulator TtgR-ligand binding

The transcriptional regulator TtgR belongs to the TetR family of transcriptional repressors. It depresses the transcription of the TtgABC operon and itself and thus regulates the extrusion of noxious chemicals with efflux pumps in bacterial cells. As the ligand-binding domain of TtgR is rather flexible, it can bind with a number of structurally diverse ligands, such as antibiotics, flavonoids and aromatic solvents. In the current work, we perform equilibrium and nonequilibrium alchemical free energy simulation to predict the binding affinities of a series of ligands targeting the TtgR protein and an agreement between the theoretical prediction and the experimental result is observed. End-point methods MM/PBSA and MM/GBSA are also employed for comparison. We further study the interaction maps and contacts between the protein and the ligand and identify important interactions in the protein-ligand binding cases. The dynamics fluctuation and secondary structures are also investigated. The current work sheds light on atomic and thermodynamic understanding of the TtgR-ligand interactions.

Large scale relative protein ligand binding affinities using non-equilibrium alchemy

Ligand binding affinity calculations based on molecular dynamics (MD) simulations and non-physical (alchemical) thermodynamic cycles have shown great promise for structure-based drug design. However, their broad uptake and impact is held back by the notoriously complex setup of the calculations. Only a few tools other than the free energy perturbation approach by Schrödinger Inc. (referred to as FEP+) currently enable end-to-end application. Here, we present for the first time an approach based on the open-source software pmx that allows to easily set up and run alchemical calculations for diverse sets of small molecules using the GROMACS MD engine. The method relies on theoretically rigorous non-equilibrium thermodynamic integration (TI) foundations, and its flexibility allows calculations with multiple force fields. In this study, results from the Amber and Charmm force fields were combined to yield a consensus outcome performing on par with the commercial FEP+ approach. A large dataset of 482 perturbations from 13 different protein-ligand datasets led to an average unsigned error (AUE) of 3.64 ± 0.14 kJ mol-1, equivalent to Schrödinger's FEP+ AUE of 3.66 ± 0.14 kJ mol-1. For the first time, a setup is presented for overall high precision and high accuracy relative protein-ligand alchemical free energy calculations based on open-source software.

Performance evaluation of molecular docking and free energy calculations protocols using the D3R Grand Challenge 4 dataset

Using the D3R Grand Challenge 4 dataset containing Beta-secretase 1 (BACE) and Cathepsin S (CatS) inhibitors, we have evaluated the performance of our in-house docking workflow that involves in the first step the selection of the most suitable docking software for the system of interest based on structural and functional information available in public databases, followed by the docking of the dataset to predict the binding modes and ranking of ligands. The macrocyclic nature of the BACE ligands brought additional challenges, which were dealt with by a careful preparation of the three-dimensional input structures for ligands. This provided top-performing predictions for BACE, in contrast with CatS, where the predictions in the absence of guiding constraints provided poor results. These results highlight the importance of previous structural knowledge that is needed for correct predictions on some challenging targets. After the end of the challenge, we also carried out free energy calculations (i.e. in a non-blinded manner) for CatS using the pmx software and several force fields (AMBER, Charmm). Using knowledge-based starting pose construction allowed reaching remarkable accuracy for the CatS free energy estimates. Interestingly, we show that the use of a consensus result, by averaging the results from different force fields, increases the prediction accuracy.

Can All-Atom Molecular Dynamics Simulations Quantitatively Describe Homeodomain-DNA Binding Equilibria?

We systematically investigate the applicability of a molecular dynamics-based setup for the calculations of standard binding free energies of biologically relevant protein-DNA complexes. The free energies are extracted from a potential of mean force calculated using umbrella sampling simulations. Two protein-DNA systems derived from a homeodomain transcription factor complex are studied in order to investigate the binding of both disordered and globular proteins. Free energies and trajectories obtained using two modern molecular mechanical force fields are compared to each other and to experimental data. The temperature dependence of the calculated standard binding free energies is investigated by performing all simulations over a range of temperatures. We show that the values of standard binding free energies obtained from these simulations are overestimated compared to experimental results. Significant differences are observed between the two protein-DNA systems and between the two force fields, which are explained by different propensities to form inter- and intramolecular contacts. The number of protein-DNA contacts increases with increasing temperature, in agreement with the experimentally known temperature dependence of enthalpies of binding. However, conclusions about the temperature dependence of the standard binding free energies cannot be made with confidence, as the differences among the values are on the order of statistical uncertainty.

Accurate Estimation of Ligand Binding Affinity Changes upon Protein Mutation

The design of proteins with novel ligand-binding functions holds great potential for application in biomedicine and biotechnology. However, our ability to engineer ligand-binding proteins is still limited, and current approaches rely primarily on experimentation. Computation could reduce the cost of the development process and would allow rigorous testing of our understanding of the principles governing molecular recognition. While computational methods have proven successful in the early stages of the discovery process, optimization approaches that can quantitatively predict ligand affinity changes upon protein mutation are still lacking. Here, we assess the ability of free energy calculations based on first-principles statistical mechanics, as well as the latest Rosetta protocols, to quantitatively predict such affinity changes on a challenging set of 134 mutations. After evaluating different protocols with computational efficiency in mind, we investigate the performance of different force fields. We show that both the free energy calculations and Rosetta are able to quantitatively predict changes in ligand binding affinity upon protein mutations, yet the best predictions are the result of combining the estimates of both methods. These closely match the experimentally determined ΔΔG values, with a root-mean-square error of 1.2 kcal/mol for the full benchmark set and of 0.8 kcal/mol for a subset of protein systems providing the most reproducible results. The currently achievable accuracy offers the prospect of being able to employ computation for the optimization of ligand-binding proteins as well as the prediction of drug resistance.

Molecular basis for the increased affinity of an RNA recognition motif with re-engineered specificity: A molecular dynamics and enhanced sampling simulations study

The RNA recognition motif (RRM) is the most common RNA binding domain across eukaryotic proteins. It is therefore of great value to engineer its specificity to target RNAs of arbitrary sequence. This was recently achieved for the RRM in Rbfox protein, where four mutations R118D, E147R, N151S, and E152T were designed to target the precursor to the oncogenic miRNA 21. Here, we used a variety of molecular dynamics-based approaches to predict specific interactions at the binding interface. Overall, we have run approximately 50 microseconds of enhanced sampling and plain molecular dynamics simulations on the engineered complex as well as on the wild-type Rbfox·pre-miRNA 20b from which the mutated systems were designed. Comparison with the available NMR data on the wild type molecules (protein, RNA, and their complex) served to establish the accuracy of the calculations.

Free energy calculations suggest that further improvements in affinity and selectivity are achieved by the S151T replacement.

RNA is an outstanding target for oncological intervention. Engineering the most common RNA binding motif in human proteins (called RRM) so as to bind to a specific RNA has an enormous pharmacological potential. Yet, it is highly non trivial to design RRM-bearing protein variants with RNA selectivity and affinity sufficiently high for clinical applications. Here we present an extensive molecular simulation study which shed light on the exquisite molecular recognition of the empirically-engineered complex between the RRM-bearing protein Rbfox and its RNA target pre-miR21. The simulations allow predicting a variant, the S151T, which may lead to further enhancement of selectivity and affinity for pre-miR21.

Consistent Prediction of Mutation Effect on Drug Binding in HIV-1 Protease Using Alchemical Calculations

Despite a large number of antiretroviral drugs targeting HIV-1 protease for inhibition, mutations in this protein during the course of patient treatment can render them inefficient. This emerging resistance inspired numerous computational studies of the HIV-1 protease aimed at predicting the effect of mutations on drug binding in terms of free binding energy Δ G, as well as in mechanistic terms. In this study, we analyze ten different protease-inhibitor complexes carrying major resistance-associated mutations (RAMs) G48V, I50V, and L90M using molecular dynamics simulations. We demonstrate that alchemical free energy calculations can consistently predict the effect of mutations on drug binding. By explicitly probing different protonation states of the catalytic aspartic dyad, we reveal the importance of the correct choice of protonation state for the accuracy of the result. We also provide insight into how different mutations affect drug binding in their specific ways, with the unifying theme of how all of them affect the crucial drug binding regions of the protease.