Protein-DNA interfaces: a molecular dynamics analysis of time-dependent recognition processes for three transcription factors

CHIMERA_NA: A Customizable Mutagenesis Tool for Structural Manipulations in Nucleic Acids and Their Complexes

Studying the structure and dynamics of nucleic acids and their complexes is crucial for understanding fundamental biological processes and developing therapeutic interventions. However, the limited availability of experimentally characterized nucleic acid structures poses a challenge for exploring their properties comprehensively. To address this, we developed a customizable mutagenesis tool, CHIMERA_NA, to manipulate nucleic acid structures and their complexes. Utilizing the user-friendly CHIMERA_NA, researchers can perform mutations in nucleic acid structures, enabling the exploration of diverse structural configurations and dynamic behaviors. The tool offers the flexibility to generate all possible combinations of mutations or specific user-defined mutations based on research requirements. CHIMERA_NA leverages the capabilities of UCSF Chimera software, a widely used platform for molecular structure analysis, to facilitate the generation of mutations in nucleic acids. Our tool modifies the reference structure of nucleic acids or their complexes to generate initial coordinates of mutated structures/complexes within seconds for further computational exploration. This capability allows users to extend their investigations beyond structural repositories, enabling the study of DNA/RNA drug recognition, nucleic acid-protein interactions, and the intrinsic structural and dynamic properties of nucleic acids. By providing a user-friendly and customizable approach to nucleic acid mutagenesis, CHIMERA_NA contributes to advancing our understanding of nucleic acid biology and facilitating drug discovery efforts targeting nucleic acid-based mechanisms. CHIMERA_NA is freely available in the Supporting Information of this article.

Impact of subtype C-specific amino acid variants on HIV-1 Tat-TAR interaction: insights from molecular modelling and dynamics

BACKGROUND

HIV-1 produces Tat, a crucial protein for transcription, viral replication, and CNS neurotoxicity. Tat interacts with TAR, enhancing HIV reverse transcription. Subtype C Tat variants (C31S, R57S, Q63E) are associated with reduced transactivation and neurovirulence compared to subtype B. However, their precise impact on Tat-TAR binding is unclear. This study investigates how these substitutions affect Tat-TAR interaction.

METHODS

We utilized molecular modelling techniques, including MODELLER, to produce precise three-dimensional structures of HIV-1 Tat protein variants. We utilized Tat subtype B as the reference or wild type, and generated Tat variants to mirror those amino acid variants found in Tat subtype C. Subtype C-specific amino acid substitutions were selected based on their role in the neuropathogenesis of HIV-1. Subsequently, we conducted molecular docking of each Tat protein variant to TAR using HDOCK, followed by molecular dynamic simulations.

RESULTS

Molecular docking results indicated that Tat subtype B (TatWt) showed the highest affinity for the TAR element (-262.07), followed by TatC31S (-261.61), TatQ63E (-256.43), TatC31S/R57S/Q63E (-238.92), and TatR57S (-222.24). However, binding free energy analysis showed higher affinities for single variants TatQ63E (-349.2 ± 10.4 kcal/mol) and TatR57S (-290.0 ± 9.6 kcal/mol) compared to TatWt (-247.9 ± 27.7 kcal/mol), while TatC31S and TatC31S/R57SQ/63E showed lower values. Interactions over the protein trajectory were also higher for TatQ63E and TatR57S compared to TatWt, TatC31S, and TatC31S/R57SQ/63E, suggesting that modifying amino acids within the Arginine/Glutamine-rich region notably affects TAR interaction. Single amino acid mutations TatR57S and TatQ63E had a significant impact, while TatC31S had minimal effect. Introducing single amino acid variants from TatWt to a more representative Tat subtype C (TatC31S/R57SQ/63E) resulted in lower predicted binding affinity, consistent with previous findings.

CONCLUSIONS

These identified amino acid positions likely contribute significantly to Tat-TAR interaction and the differential pathogenesis and neuropathogenesis observed between subtype B and subtype C. Additional experimental investigations should prioritize exploring the influence of these amino acid signatures on TAR binding to gain a comprehensive understanding of their impact on viral transactivation, potentially identifying them as therapeutic targets.

Dynamics of Protein-RNA Interfaces Using All-Atom Molecular Dynamics Simulations

Facing the current challenges posed by human health diseases requires the understanding of cell machinery at a molecular level. The interplay between proteins and RNA is key for any physiological phenomenon, as well protein-RNA interactions. To understand these interactions, many experimental techniques have been developed, spanning a very wide range of spatial and temporal resolutions. In particular, the knowledge of tridimensional structures of protein-RNA complexes provides structural, mechanical, and dynamical pieces of information essential to understand their functions. To get insights into the dynamics of protein-RNA complexes, we carried out all-atom molecular dynamics simulations in explicit solvent on nine different protein-RNA complexes with different functions and interface size by taking into account the bound and unbound forms. First, we characterized structural changes upon binding and, for the RNA part, the change in the puckering. Second, we extensively analyzed the interfaces, their dynamics and structural properties, and the structural waters involved in the binding, as well as the contacts mediated by them. Based on our analysis, the interfaces rearranged during the simulation time showing alternative and stable residue-residue contacts with respect to the experimental structure.

Brewing COFFEE: A Sequence-Specific Coarse-Grained Energy Function for Simulations of DNA-Protein Complexes

DNA-protein interactions are pervasive in a number of biophysical processes ranging from transcription and gene expression to chromosome folding. To describe the structural and dynamic properties underlying these processes accurately, it is important to create transferable computational models. Toward this end, we introduce Coarse-grained Force Field for Energy Estimation, COFFEE, a robust framework for simulating DNA-protein complexes. To brew COFFEE, we integrated the energy function in the self-organized polymer model with side-chains for proteins and the three interaction site model for DNA in a modular fashion, without recalibrating any of the parameters in the original force-fields. A unique feature of COFFEE is that it describes sequence-specific DNA-protein interactions using a statistical potential (SP) derived from a data set of high-resolution crystal structures. The only parameter in COFFEE is the strength (λDNAPRO) of the DNA-protein contact potential. For an optimal choice of λDNAPRO, the crystallographic B-factors for DNA-protein complexes with varying sizes and topologies are quantitatively reproduced. Without any further readjustments to the force-field parameters, COFFEE predicts scattering profiles that are in quantitative agreement with small-angle X-ray scattering experiments, as well as chemical shifts that are consistent with NMR. We also show that COFFEE accurately describes the salt-induced unraveling of nucleosomes. Strikingly, our nucleosome simulations explain the destabilization effect of ARG to LYS mutations, which do not alter the balance of electrostatic interactions but affect chemical interactions in subtle ways. The range of applications attests to the transferability of COFFEE, and we anticipate that it would be a promising framework for simulating DNA-protein complexes at the molecular length-scale.

Probing the Nucleic Acid Flexibility to Disarm the Viral Counter-Defense Machinery: Design and Characterization of Potent p19 Inhibitors

Plant viruses are highly destructive and significant contributors to several global pandemics and epidemics in plants. A viral disease outbreak in plants can cause a scarcity of food supply and is a severe concern to humanity. The siRNA (small interfering RNA)-mediated RNA-induced silencing complex (RISC) formation is a primary defense mechanism in plants against viruses, where the RISC binds and degrades viral mRNAs. As a counter-defense, many viruses encode RNA-silencing suppressor proteins (e.g., the p19 protein from the Tombusviridae family) for viral proliferation in plants. The functional form of p19 (homodimer) binds to plant siRNA with high affinities, thereby interrupting the RISC formation and thus preventing the viral mRNA silencing in plants. By altering the RISC formation, the p19 protein helps the virus invasion in the plant and ultimately stunts host growth. In this study, we designed several modified siRNA-based molecules for p19 inhibition. The viral p19 protein is known to interact predominantly through H-bonds with 2'-OH and phosphates of the plant siRNA. We utilized this information and in silico-designed flexible substituents of siRNA, where we removed the C2'-C3' bond in each nucleotide unit. We performed all-atom explicit-solvent molecular dynamics simulations (400 ns, 3 replicates each) for control/modified siRNA─p19 complexes (8 in total) followed by energetic estimations. Strikingly, in a few modified complexes, the siRNA not only retained the double-helical structural integrity but also displayed remarkably enhanced p19 binding compared to the control siRNA; hence, we consider it important to perform biological and chemical in vitro and in vivo studies on proposed flexible nucleic acids as p19 inhibitors for crop protection.

Toll-like receptor-guided therapeutic intervention of human cancers: molecular and immunological perspectives

Toll-like receptors (TLRs) serve as the body's first line of defense, recognizing both pathogen-expressed molecules and host-derived molecules released from damaged or dying cells. The wide distribution of different cell types, ranging from epithelial to immune cells, highlights the crucial roles of TLRs in linking innate and adaptive immunity. Upon stimulation, TLRs binding mediates the expression of several adapter proteins and downstream kinases, that lead to the induction of several other signaling molecules such as key pro-inflammatory mediators. Indeed, extraordinary progress in immunobiological research has suggested that TLRs could represent promising targets for the therapeutic intervention of inflammation-associated diseases, autoimmune diseases, microbial infections as well as human cancers. So far, for the prevention and possible treatment of inflammatory diseases, various TLR antagonists/inhibitors have shown to be efficacious at several stages from pre-clinical evaluation to clinical trials. Therefore, the fascinating role of TLRs in modulating the human immune responses at innate as well as adaptive levels directed the scientists to opt for these immune sensor proteins as suitable targets for developing chemotherapeutics and immunotherapeutics against cancer. Hitherto, several TLR-targeting small molecules (e.g., Pam3CSK4, Poly (I:C), Poly (A:U)), chemical compounds, phytocompounds (e.g., Curcumin), peptides, and antibodies have been found to confer protection against several types of cancers. However, administration of inappropriate doses of such TLR-modulating therapeutics or a wrong infusion administration is reported to induce detrimental outcomes. This review summarizes the current findings on the molecular and structural biology of TLRs and gives an overview of the potency and promises of TLR-directed therapeutic strategies against cancers by discussing the findings from established and pipeline discoveries.

Brewing COFFEE: A sequence-specific coarse-grained energy function for simulations of DNA-protein complexes

DNA-protein interactions are pervasive in a number of biophysical processes ranging from transcription, gene expression, to chromosome folding. To describe the structural and dynamic properties underlying these processes accurately, it is important to create transferable computational models. Toward this end, we introduce Coarse grained force field for energy estimation, COFFEE, a robust framework for simulating DNA-protein complexes. To brew COFFEE, we integrated the energy function in the Self-Organized Polymer model with Side Chains for proteins and the Three Interaction Site model for DNA in a modular fashion, without re-calibrating any of the parameters in the original force-fields. A unique feature of COFFEE is that it describes sequence-specific DNA-protein interactions using a statistical potential (SP) derived from a dataset of high-resolution crystal structures. The only parameter in COFFEE is the strength (λ D N A P R O ) of the DNA-protein contact potential. For an optimal choice of λ D N A P R O , the crystallographic B-factors for DNA-protein complexes, with varying sizes and topologies, are quantitatively reproduced. Without any further readjustments to the force-field parameters, COFFEE predicts the scattering profiles that are in quantitative agreement with SAXS experiments as well as chemical shifts that are consistent with NMR. We also show that COFFEE accurately describes the salt-induced unraveling of nucleosomes. Strikingly, our nucleosome simulations explain the destabilization effect of ARG to LYS mutations, which does not alter the balance of electrostatic interactions, but affects chemical interactions in subtle ways. The range of applications attests to the transferability of COFFEE, and we anticipate that it would be a promising framework for simulating DNA-protein complexes at the molecular length-scale.

A dynamical view of protein-protein complexes: Studies by molecular dynamics simulations

Protein-protein interactions are at the basis of many protein functions, and the knowledge of 3D structures of protein-protein complexes provides structural, mechanical and dynamical pieces of information essential to understand these functions. Protein-protein interfaces can be seen as stable, organized regions where residues from different partners form non-covalent interactions that are responsible for interaction specificity and strength. They are commonly described as a peripheral region, whose role is to protect the core region that concentrates the most contributing interactions, from the solvent. To get insights into the dynamics of protein-protein complexes, we carried out all-atom molecular dynamics simulations in explicit solvent on eight different protein-protein complexes of different functional class and interface size by taking into account the bound and unbound forms. On the one hand, we characterized structural changes upon binding of the proteins, and on the other hand we extensively analyzed the interfaces and the structural waters involved in the binding. Based on our analysis, in 6 cases out of 8, the interfaces rearranged during the simulation time, in stable and long-lived substates with alternative residue-residue contacts. These rearrangements are not restricted to side-chain fluctuations in the periphery but also affect the core interface. Finally, the analysis of the waters at the interface and involved in the binding pointed out the importance to take into account their role in the estimation of the interaction strength.

Molecular dynamics simulations reveal the selectivity mechanism of structurally similar agonists to TLR7 and TLR8

TLR7 and TLR8 are key members of the Toll-like receptor family, playing crucial roles in the signaling pathways of innate immunity, and thus become attractive therapeutic targets of many diseases including infections and cancer. Although TLR7 and TLR8 show a high degree of sequence homology, their biological response to small molecule binding is very different. Aiming to understand the mechanism of selective profiles of small molecule modulators against TLR7 and TLR8, we carried out molecular dynamic simulations on three imidazoquinoline derivatives bound to the receptors separately. They are Resiquimod (R), Hybrid-2 (H), and Gardiquimod (G), selective agonists of TLR7 and TLR8. Our MD trajectories indicated that in the complex of TLR7-R and TLR7-G, the two chains forming the TLR7 dimer tended to remain “open” conformation, while the rest systems maintained in the closed format. The agonists R, H, and G developed conformational deviation mainly on the aliphatic tail. Furthermore, we attempted to quantify the selectivity between TLR7 and TLR8 by binding free energies via MM-GBSA method. It showed that the three selected modulators were more favorable for TLR7 than TLR8, and the ranking from the strongest to the weakest was H, R and G, aligning well with experimental data. In the TLR7, the flexible and hydrophobic aliphatic side chain of H has stronger van der Waals interactions with V381 and F351 but only pick up interaction with one amino acid residue i.e. Y353 of TLR8. Unsurprisingly, the positively charged side chain of G has less favorable interaction with I585 of TLR7 and V573 of TLR8 explaining G is weak agonist of both TLR7 and TLR8. All three imidazoquinoline derivatives can form stable hydrogen bonds with D555 of TLR7 and the corresponding D543 of TLR8. In brief, the set of total 400ns MD studies sheds light on the potential selectivity mechanisms of agonists towards TLR7 and TLR8, indicating the van der Waals interaction as the driving force for the agonists binding, thus provides us insights for designing more potent and selective modulators to cooperate with the hydrophobic nature of the binding pocket.

Tenuous transcriptional threshold of human sex determination. II. SRY exploits water-mediated clamp at the edge of ambiguity

Y-encoded transcription factor SRY initiates male differentiation in therian mammals. This factor contains a high-mobility-group (HMG) box, which mediates sequence-specific DNA binding with sharp DNA bending. A companion article in this issue described sex-reversal mutations at box position 72 (residue 127 in human SRY), invariant as Tyr among mammalian orthologs. Although not contacting DNA, the aromatic ring seals the domain's minor wing at a solvent-exposed junction with a basic tail. A seeming paradox was posed by the native-like biochemical properties of inherited Swyer variant Y72F: its near-native gene-regulatory activity is consistent with the father's male development, but at odds with the daughter's XY female somatic phenotype. Surprisingly, aromatic rings (Y72, F72 or W72) confer higher transcriptional activity than do basic or polar side chains generally observed at solvated DNA interfaces (Arg, Lys, His or Gln). Whereas biophysical studies (time-resolved fluorescence resonance energy transfer and heteronuclear NMR spectroscopy) uncovered only subtle perturbations, dissociation of the Y72F complex was markedly accelerated relative to wild-type. Studies of protein-DNA solvation by molecular-dynamics (MD) simulations of an homologous high-resolution crystal structure (SOX18) suggest that Y72 para-OH anchors a network of water molecules at the tail-DNA interface, perturbed in the variant in association with nonlocal conformational fluctuations. Loss of the Y72 anchor among SRY variants presumably "unclamps" its basic tail, leading to (a) rapid DNA dissociation despite native affinity and (b) attenuated transcriptional activity at the edge of sexual ambiguity. Conservation of Y72 suggests that this water-mediated clamp operates generally among SRY and metazoan SOX domains.

Revealing atomic-scale molecular diffusion of a plant-transcription factor WRKY domain protein along DNA

In transcription factors’ search for target genes, one-dimensional diffusion of the protein along DNA is essential. Experimentally, it remains challenging to resolve the individual diffusional steps of protein on DNA. Here, we report mainly all-atom equilibrium simulations of a WRKY domain protein in association with and diffusion along DNA. We demonstrate a complete stepping cycle of the protein for one base pair on DNA within microseconds, along with stochastic motions. Processive protein diffusions on DNA have been further sampled in a coarse-grained model. We have also found preferential DNA-strand association of the domain protein, which becomes most prominent at specific DNA binding, and it can be common for small-domain proteins to balance movements on the DNA with the sequence recognition.

Transcription factor (TF) target search on genome is highly essential for gene expression and regulation. High-resolution determination of TF diffusion along DNA remains technically challenging. Here, we constructed a TF model system using the plant WRKY domain protein in complex with DNA from crystallography and demonstrated microsecond diffusion dynamics of WRKY on DNA by employing all-atom molecular-dynamics (MD) simulations. Notably, we found that WRKY preferentially binds to one strand of DNA with significant energetic bias compared with the other, or nonpreferred strand. The preferential DNA-strand binding becomes most prominent in the static process, from nonspecific to specific DNA binding, but less distinct during diffusive movements of the domain protein on the DNA. Remarkably, without employing acceleration forces or bias, we captured a complete one-base-pair stepping cycle of the protein tracking along major groove of DNA with a homogeneous poly-adenosine sequence, as individual hydrogen bonds break and reform at the protein–DNA binding interface. Further DNA-groove tracking motions of the protein forward or backward, with occasional sliding as well as strand crossing to minor groove of DNA, were also captured. The processive diffusion of WRKY along DNA has been further sampled via coarse-grained MD simulations. The study thus provides structural dynamics details on diffusion of a small TF domain protein, suggests how the protein approaches a specific recognition site on DNA, and supports further high-precision experimental detection. The stochastic movements revealed in the TF diffusion also provide general clues about how other protein walkers step and slide along DNA.

The ancillary N-terminal region of the yeast AP-1 transcription factor Yap8 contributes to its DNA binding specificity

Activator protein 1 (AP-1) is one of the largest families of basic leucine zipper (bZIP) transcription factors in eukaryotic cells. How AP-1 proteins achieve target DNA binding specificity remains elusive. In Saccharomyces cerevisiae, the AP-1-like protein (Yap) family comprises eight members (Yap1 to Yap8) that display distinct genomic target sites despite high sequence homology of their DNA binding bZIP domains. In contrast to the other members of the Yap family, which preferentially bind to short (7–8 bp) DNA motifs, Yap8 binds to an unusually long DNA motif (13 bp). It has been unclear what determines this unique specificity of Yap8. In this work, we use molecular and biochemical analyses combined with computer-based structural design and molecular dynamics simulations of Yap8–DNA interactions to better understand the structural basis of DNA binding specificity determinants. We identify specific residues in the N-terminal tail preceding the basic region, which define stable association of Yap8 with its target promoter. We propose that the N-terminal tail directly interacts with DNA and stabilizes Yap8 binding to the 13 bp motif. Thus, beside the core basic region, the adjacent N-terminal region contributes to alternative DNA binding selectivity within the AP-1 family.

Atomic-resolution mapping of transcription factor-DNA interactions by femtosecond laser crosslinking and mass spectrometry

Transcription factors (TFs) regulate target genes by specific interactions with DNA sequences. Detecting and understanding these interactions at the molecular level is of fundamental importance in biological and clinical contexts. Crosslinking mass spectrometry is a powerful tool to assist the structure prediction of protein complexes but has been limited to the study of protein-protein and protein-RNA interactions. Here, we present a femtosecond laser-induced crosslinking mass spectrometry (fliX-MS) workflow, which allows the mapping of protein-DNA contacts at single nucleotide and up to single amino acid resolution. Applied to recombinant histone octamers, NF1, and TBP in complex with DNA, our method is highly specific for the mapping of DNA binding domains. Identified crosslinks are in close agreement with previous biochemical data on DNA binding and mostly fit known complex structures. Applying fliX-MS to cells identifies several bona fide crosslinks on DNA binding domains, paving the way for future large scale ex vivo experiments.

Conformational tuning of a DNA-bound transcription factor

Transcription factors are involved in many cellular processes that take place remote from their cognate DNA sequences. The efficiencies of these activities are thus in principle counteracted by high binding affinities of the factors to their cognate DNAs. Models such as facilitated diffusion or dissociation address this apparent contradiction. We show that the MYC associated transcription factor X (MAX) undergoes nanoscale conformational fluctuations in the DNA-bound state, which is consistent with facilitated dissociation from or diffusion along DNA strands by transiently reducing binding energies. An integrative approach involving EPR, NMR, crystallographic and molecular dynamics analyses demonstrates that the N-terminal domain of MAX constantly opens and closes around a bound DNA ligand thereby dynamically tuning the binding epitope and the mode of interaction.

DNA-protein interaction: identification, prediction and data analysis

Life in living organisms is dependent on specific and purposeful interaction between other molecules. Such purposeful interactions make the various processes inside the cells and the bodies of living organisms possible. DNA-protein interactions, among all the types of interactions between different molecules, are of considerable importance. Currently, with the development of numerous experimental techniques, diverse methods are convenient for recognition and investigating such interactions. While the traditional experimental techniques to identify DNA-protein complexes are time-consuming and are unsuitable for genome-scale studies, the current high throughput approaches are more efficient in determining such interaction at a large-scale, but they are clearly too costly to be practice for daily applications. Hence, according to the availability of much information related to different biological sequences and clearing different dimensions of conditions in which such interactions are formed, with the developments related to the computer, mathematics, and statistics motivate scientists to develop bioinformatics tools for prediction the interaction site(s). Until now, there has been much progress in this field. In this review, the factors and conditions governing the interaction and the laboratory techniques for examining such interactions are addressed. In addition, developed bioinformatics tools are introduced and compared for this reason and, in the end, several suggestions are offered for the promotion of such tools in prediction with much more precision.

Trinucleotide Base Pair Stacking Free Energy for Understanding TF-DNA Recognition and the Functions of SNPs

Single nucleotide polymorphisms (SNPs) affect base pair stacking, which is the primary factor for maintaining the stability of DNA. However, the mechanism of how SNPs lead to phenotype variations is still unclear. In this work, we connected SNPs and base pair stacking by a 3-mer base pair stacking free energy matrix. The SNPs with large base pair stacking free energy differences led to phenotype variations. A molecular dynamics (MD) simulation was then applied. Our results showed that base pair stacking played an important role in the transcription factor (TF)-DNA interaction. Changes in DNA structure mainly originate from TF-DNA interactions, and with the increased base pair stacking free energy, the structure of DNA approaches its free type, although its binding affinity was increased by the SNP. In addition, quantitative models using base pair stacking features revealed that base pair stacking can be used to predict TF binding specificity. As such, our work combined knowledge from bioinformatics and structural biology and provided a new understanding of the relationship between SNPs and phenotype variations. The 3-mer base pair stacking free energy matrix is useful in high-throughput screening of SNPs and predicting TF-DNA binding affinity.

Molecular Modeling Applied to Nucleic Acid-Based Molecule Development

Molecular modeling by means of docking and molecular dynamics (MD) has become an integral part of early drug discovery projects, enabling the screening and enrichment of large libraries of small molecules. In the past decades, special emphasis was drawn to nucleic acid (NA)-based molecules in the fields of therapy, diagnosis, and drug delivery. Research has increased dramatically with the advent of the SELEX (systematic evolution of ligands by exponential enrichment) technique, which results in single-stranded DNA or RNA sequences that bind with high affinity and specificity to their targets. Herein, we discuss the role and contribution of docking and MD to the development and optimization of new nucleic acid-based molecules. This review focuses on the different approaches currently available for molecular modeling applied to NA interaction with proteins. We discuss topics ranging from structure prediction to docking and MD, highlighting their main advantages and limitations and the influence of flexibility on their calculations.

How a single 5-methylation of cytosine regulates the recognition of C/EBPβ transcription factor: a molecular dynamic simulation study

CpG methylation can regulate gene expression by altering the specific binding of protein and DNA. In order to understand how a single 5mC regulates protein-DNA interactions, we have compared the structures and dynamics of CEBP/βprotein-DNA complexes before and after methylation, and the results indicate that even a single 5mC can regulate protein-DNA recognition by steric-hindrance effect of methyl group and changing the hydrogen bond interactions. The interactions between the methyl group, mCpG motif, and the conserved residue arginine make the protein read out the variation of local environment, which further enhances the specific recognition and affects the base pair stacking. The stacking interactions can propagate along the backbone of DNA and lead to long-range allosteric effects, including obvious conformational variations for DNA base pairs.

Lysine Side-Chain Dynamics in the Binding Site of Homeodomain/DNA Complexes As Observed by NMR Relaxation Experiments and Molecular Dynamics Simulations

An important but poorly characterized contribution to the thermodynamics of protein-DNA interactions is the loss of entropy that occurs from restricting the conformational freedom of amino acid side chains. The effect of restricting the flexibility of several side chains at a protein-DNA interface may be comparable in many cases to the other factors that determine the binding thermodynamics and may, therefore, play a key role in dictating the binding affinity and/or specificity. Because the entropic contributions, including the presence and influence of side-chain dynamics, are especially difficult to estimate based on structural information, it is important to pursue experimental and theoretical studies that can provide direct information regarding these issues. We report on studies of a model system, the homeodomain/DNA complex, focusing on the Lys50 class of homeodomains where a key lysine residue in position 50 was shown previously to be critical for binding site specificity. NMR methodology was employed for determining the dynamics of lysine side-chain amino groups via 15N relaxation measurements in the Lys50-class homeodomains from the Drosophila protein Bicoid and the human protein Pitx2. In the case of Pitx2, complexes with both a consensus and a nonconsensus DNA binding site were examined. NMR-derived order parameters indicated moderate to substantial conformational freedom for the lysine NH3+ group in the complexes studied. To complement the experimental NMR measurements, molecular dynamics simulations were performed for the consensus complexes to gain further, detailed insights regarding the dynamics of the Lys50 side chain and other important residues in the protein-DNA interface.

Decomposing protein-DNA binding and recognition using simplified protein models

We analyze the role of different physicochemical factors in protein/DNA binding and recognition by comparing the results from all-atom molecular dynamics simulations with simulations using simplified protein models. These models enable us to separate the role of specific amino acid side chains, formal amino acid charges and hydrogen bonding from the effects of the low-dielectric volume occupied by the protein. Comparisons are made on the basis of the conformation of DNA after protein binding, the ionic distribution around the complex and the sequence specificity. The results for four transcription factors, binding in either the minor or major grooves of DNA, show that the protein volume and formal charges, with one exception, play a predominant role in binding. Adding hydrogen bonding and a very small number of key amino acid side chains at the all-atom level yields results in DNA conformations and sequence recognition close to those seen in the reference all-atom simulations.