GNN Codon Adjacency Tunes Protein Translation.

The central dogma treats the ribosome as a molecular machine that reads one mRNA codon at a time as it adds each amino acid to its growing peptide chain. However, this and previous studies suggest that ribosomes actually perceive pairs of adjacent codons as they take three-nucleotide steps along the mRNA. We examined GNN codons, which we find are surprisingly overrepresented in eukaryote protein-coding open reading frames (ORFs), especially immediately after NNU codons. Ribosome profiling experiments in yeast revealed that ribosomes with NNU at their aminoacyl (A) site have particularly elevated densities when NNU is immediately followed (3') by a GNN codon, indicating slower mRNA threading of the NNU codon from the ribosome's A to peptidyl (P) sites. Moreover, if the assessment was limited to ribosomes that have only recently arrived at the next codon, by examining 21-nucleotide ribosome footprints (21-nt RFPs), elevated densities were observed for multiple codon classes when followed by GNN. This striking translation slowdown at adjacent 5'-NNN GNN codon pairs is likely mediated, in part, by the ribosome's CAR surface, which acts as an extension of the A-site tRNA anticodon during ribosome translocation and interacts through hydrogen bonding and pi stacking with the GNN codon. The functional consequences of 5'-NNN GNN codon adjacency are expected to influence the evolution of protein coding sequences.

Molecular dynamics of mismatch detection-How MutS uses indirect readout to find errors in DNA.

The mismatch repair protein MutS safeguards genomic integrity by finding and initiating repair of basepairing errors in DNA. Single-molecule studies show MutS diffusing on DNA, presumably scanning for mispaired/unpaired bases, and crystal structures show a characteristic "mismatch-recognition" complex with DNA enclosed within MutS and kinked at the site of error. But how MutS goes from scanning thousands of Watson-Crick basepairs to recognizing rare mismatches remains unanswered, largely because atomic-resolution data on the search process are lacking. Here, 10 µs all-atom molecular dynamics simulations of Thermus aquaticus MutS bound to homoduplex DNA and T-bulge DNA illuminate the structural dynamics underlying the search mechanism. MutS-DNA interactions constitute a multistep mechanism to check DNA over two helical turns for its 1) shape, through contacts with the sugar-phosphate backbone, 2) conformational flexibility, through bending/unbending engineered by large-scale motions of the clamp domain, and 3) local deformability, through basepair destabilizing contacts. Thus, MutS can localize a potential target by indirect readout due to lower energetic costs of bending mismatched DNA and identify a site that distorts easily due to weaker base stacking and pairing as a mismatch. The MutS signature Phe-X-Glu motif can then lock in the mismatch-recognition complex to initiate repair.

The CAR-mRNA Interaction Surface Is a Zipper Extension of the Ribosome A Site.

The ribosome CAR interaction surface behaves as an extension of the decoding center A site and has H-bond interactions with the +1 codon, which is next in line to enter the A site. Through molecular dynamic simulations, we investigated the codon sequence specificity of this CAR-mRNA interaction and discovered a strong preference for GCN codons, suggesting that there may be a sequence-dependent layer of translational regulation dependent on the CAR interaction surface. Dissection of the CAR-mRNA interaction through nucleotide substitution experiments showed that the first nucleotide of the +1 codon dominates over the second nucleotide position, consistent with an energetically favorable zipper-like activity that emanates from the A site through the CAR-mRNA interface. Moreover, the CAR/+1 codon interaction is affected by the identity of nucleotide 3 of +1 GCN codons, which influences the stacking of G and C. Clustering analysis suggests that the A-site decoding center adopts different neighborhood substates that depend on the identity of the +1 codon.

Arginine Methylation Regulates Ribosome CAR Function.

The ribosome CAR interaction surface is hypothesized to provide a layer of translation regulation through hydrogen-bonding to the +1 mRNA codon that is next to enter the ribosome A site during translocation. The CAR surface consists of three residues, 16S/18S rRNA C1054, A1196 (E. coli 16S numbering), and R146 of yeast ribosomal protein Rps3. R146 can be methylated by the Sfm1 methyltransferase which is downregulated in stressed cells. Through molecular dynamics analysis, we show here that methylation of R146 compromises the integrity of CAR by reducing the cation-pi stacking of the R146 guanidinium group with A1196, leading to reduced CAR hydrogen-bonding with the +1 codon. We propose that ribosomes assembled under stressed conditions have unmethylated R146, resulting in elevated CAR/+1 codon interactions, which tunes translation levels in response to the altered cellular context.

Toward a Universal Structural and Energetic Model for Prokaryotic Promoters.

With almost no consensus promoter sequence in prokaryotes, recruitment of RNA polymerase (RNAP) to precise transcriptional start sites (TSSs) has remained an unsolved puzzle. Uncovering the underlying mechanism is critical for understanding the principle of gene regulation. We attempted to search the hidden code in ∼16,500 promoters of 12 prokaryotes representing two kingdoms in their structure and energetics. Twenty-eight fundamental parameters of DNA structure including backbone angles, basepair axis, and interbasepair and intrabasepair parameters were used, and information was extracted from x-ray crystallography data. Three parameters (solvation energy, hydrogen-bond energy, and stacking energy) were selected for creating energetics profiles using in-house programs. DNA of promoter regions was found to be inherently designed to undergo a change in every parameter undertaken for the study, in all prokaryotes. The change starts from some distance upstream of TSSs and continues past some distance from TSS, hence giving a signature state to promoter regions. These signature states might be the universal hidden codes recognized by RNAP. This observation was reiterated when randomly selected promoter sequences (with little sequence conservation) were subjected to structure generation; all developed into very similar three-dimensional structures quite distinct from those of conventional B-DNA and coding sequences. Fine structural details at important motifs (viz. -11, -35, and -75 positions relative to TSS) of promoters reveal novel to our knowledge and pointed insights for RNAP interaction at these locations; it could be correlated with how some particular structural changes at the -11 region may allow insertion of RNAP amino acids in interbasepair space as well as facilitate the flipping out of bases from the DNA duplex.

p53 R175H hydrophobic patch and H-bond reorganization observed by MD simulation.

Molecular dynamics simulations probe the origins of aberrant functionality of R175H p53, which normally prevent tumorigenesis. This hotspot mutation exhibits loss of its essential zinc cofactor, aggregation, and activation of gain of function promoters, characteristics contributing to the loss of normal p53 activity. This study provided molecular level insight into the reorganization of the hydrogen bonding network and the formation of a hydrophobic patch on the surface of the protein. The hydrogen bonding network globally redistributes at the expense of the stability of the ß-sandwich structure, and surface residues reorganize to expose a 250 Å(2) hydrophobic patch of residues covering approximately 2% of the solvent accessible surface. These changes could both stabilize the protein in the conformation exposing the patch to solvent to mediate the reported aggregation, and cause a destabilization in the area associated with DNA binding residues to affect the specificity. The development of the patch prior to loss of zinc indicates that stabilizing the patch quickly may prevent zinc loss. Considerations for rational design of small molecule therapeutics in light of the structural insight has been discussed and it suggest the positive ring around the hydrophobic patch and conserved residues may constitute a druggable site.

Reconnaissance of Allostery via the Restoration of Native p53 DNA-Binding Domain Dynamics in Y220C Mutant p53 Tumor Suppressor Protein.

Allosteric regulation of protein dynamics infers a long-range deliberate propagation of information via micro- and macroscale interactions. The Y220C structural mutant is one of the most frequent cancerous p53 mutants. The mutation is distally located from the DNA-binding site of the p53 DNA-binding domain yet causes changes in DNA recognition. This system presents a unique opportunity to examine the allosteric control of mutated proteins under a drug design paradigm. We focus on the key case study of p53 Y220C mutation restoration by a series of new compounds suggested to have Y220C reactivation properties in comparison to our previous findings on the restorative potential of PK11000, a compound studied extensively for reactivation in vitro and in vivo. Previously, we implemented all-atom molecular dynamics (MD) simulations and our lab's techniques of MD-Sectors and MD-Markov state models on the wild type, the Y220C mutant, and Y220C with PK11000 to characterize the effector's restorative properties in terms of conformational dynamics and hydrogen bonding. In this study, we turn to probing the effects made by docking the battery of a new but less well-tested set of aminobenzothiazole derivative compounds reported by Baud et al., which show promise of Y220C rescue. We find that while complete and precise reconstitution of p53 WT molecular dynamics may not be observed as was the case with PK11000, dispersed local reconstitution of loop dynamics provides evidence of rescuing effects by aminobenzothiazole derivative N,2-dihydroxy-3,5-diiodo-4-(1H-pyrrol-1-yl)benzamide, Effector 22, like what we observed for PK11000. Generalizable insights into the mutation and allosteric reactivation of p53 by various effectors by reconstitution of WT dynamics observed in statistical conformational ensemble analysis and network inference are discussed, considering the development of allosteric drug design rooted in first principles.

Network Analysis of Molecular Dynamics Sectors in the p53 Protein.

Design of allosteric regulators is an emergent field in the area of drug discovery holding promise for currently untreated diseases. Allosteric regulators bind to a protein in one location and affect a distant site. The ubiquitous presence of allosteric effectors in biology and the success of serendipitously identified allosteric compounds point to the potential they hold. Although the mechanism of transmission of an allosteric signal is not unequivocally determined, one hypothesis suggests that groups of evolutionarily covarying residues within a protein, termed sectors, are conduits. A long-term goal of our lab is to allosterically modulate the activity of proteins by binding small molecules at points of allosteric control. However, methods to consistently identify such points remain unclear. Sector residues on the surfaces of proteins are a promising source of allosteric targets. Recently, we introduced molecular dynamics (MD)-based sectors; MD sectors capitalize on covariance of motion, in place of evolutionary covariance. By focusing on motional covariance, MD sectors tap into the framework of statistical mechanics afforded by the Boltzmann ensemble of structural conformations comprising the underlying data set. We hypothesized that the method of MD sectors can be used to identify a cohesive network of motionally covarying residues capable of transmitting an allosteric signal in a protein. While our initial qualitative results showed promise for the method to predict sectors, that a network of cohesively covarying residues had been produced remained an untested assumption. In this work, we apply network theory to rigorously analyze MD sectors, allowing us to quantitatively assess the biologically relevant property of network cohesiveness of sectors in the context of the tumor suppressor protein, p53. We revised the methodology for assessing and improving MD sectors. Specifically, we introduce a metric to calculate the cohesive properties of the network. Our new approach separates residues into two categories: sector residues and non-sector residues. The relatedness within each respective group is computed with a distance metric. Cohesive sector networks are identified as those that have high relatedness among the sector residues which exceeds the relatedness of the residues to the non-sector residues in terms of the correlation of motions. Our major finding was that the revised means of obtaining sectors was more efficacious than previous iterations, as evidenced by the greater cohesion of the networks. These results are discussed in the context of the development of allosteric regulators of p53 in particular and the expected applicability of the method to the drug design field in general.

Allosteric Signaling in PDZ Energetic Networks: Embedding Error Analysis.

Allosteric signaling in proteins has been known for some half a century, yet how the signal traverses the protein remains an active area of research. Recently, the importance of electrostatics to achieve long-range signaling has become increasingly appreciated. Our laboratory has been working on developing network approaches to capture such interactions. In this study, we turn our attention to the well-studied allosteric model protein, PDZ. We study the allosteric dynamics on a per-residue basis in key constructs involving the PDZ domain, its allosteric effector, and its peptide ligand. We utilize molecular dynamics trajectories to create the networks for the constructs to explore the allosteric effect by plotting the heat kernel results onto axes defined by principal components. We introduce a new metric to quantitate the volume sampled by a residue in the latent space. We relate our findings to PDZ and the greater field of allostery.

Insights into Rational Design of a New Class of Allosteric Effectors with Molecular Dynamics Markov State Models and Network Theory.

The development of drugs to restore protein function has been a major advance facilitated by molecular medicine. Allosteric regulation, a phenomenon widely observed in nature, in which a molecule binds to control a distance active site, holds great promise for regulating proteins, yet how to rationally design such a molecule remains a mystery. Over the past few years, we and others have developed several techniques based on molecular dynamics (MD) simulations: MD-Markov state models to capture global conformational substates, and network theory approach utilizing the interaction energy within the protein to confer local allosteric control. We focus on the key case study of the p53 Y220C mutation restoration by PK11000, a compound experimentally shown to reactivate p53 native function in Y220C mutant present tumors. We gain insights into the mutation and allosteric reactivation of the protein, which we anticipate will be applicable to de novo design to engineer new compounds not only for this mutation, but in other macromolecular systems as well.

Variable Regions of p53 Isoforms Allosterically Hard Code DNA Interaction.

Allosteric regulation of protein activity pervades biology as the "second secret of life." We have been examining the allosteric regulation and mutant reactivation of the tumor suppressor protein p53. We have found that generalizing the definition of allosteric effector to include entire proteins and expanding the meaning of binding site to include the interface of a transcription factor with its DNA to be useful in understanding the modulation of protein activity. Here, we cast the variable regions of p53 isoforms as allosteric regulators of p53 interactions with its consensus DNA. We implemented molecular dynamics simulations and our lab's new techniques of molecular dynamics (MD) sectors and MD-Markov state models to investigate the effects of nine naturally occurring splice variant isoforms of p53. We find that all of the isoforms differ from wild type in their dynamic properties and how they interact with the DNA. We consider the implications of these findings on allostery and cancer treatment.

Mutagenic Activation of Glutathione Peroxidase-4: Approaches toward Rational Design of Allosteric Drugs.

Glutathione peroxidase 4 (GPX4) reduces lipid hydroperoxides in lipid membranes, effectively inhibiting iron-dependent cell death or ferroptosis. The upregulation of the enzyme by the mutations at residues D21 and D23 has been suggested to be associated with higher protein activity, which confers more protection against neurodegenerative diseases such as Alzheimer's, Parkinson's, and Huntington's diseases. Therefore, it has become an attractive target for treating and preventing neurodegenerative diseases. However, identifying means of mimicking the beneficial effects of these mutations distant from the active site constitutes a formidable challenge in moving toward therapeutics. In this study, we explore using molecular dynamics simulations to computationally map the conformational and energetic landscape of the wild-type GPX4 protein and three mutant variants to identify the allosteric networks of the enzyme. We present the conformational dynamic profile providing the desired signature behavior of the enzyme. We also discuss the implications of these findings for drug design efforts.

Estradiol induces allosteric coupling and partitioning of sex-hormone-binding globulin monomers among conformational states.

Sex-hormone-binding globulin (SHBG) regulates the transport and bioavailability of estradiol. The dynamics of estradiol's binding to SHBG are incompletely understood, although it is believed that estradiol binds to each monomer of SHBG dimer with identical affinity (Kd ∼2 nM). Contrary to the prevalent view, we show that estradiol's binding to SHBG is nonlinear, and the "apparent" Kd changes with varying estradiol and SHBG concentrations. Estradiol's binding to each SHBG monomer influences residues in the ligand-binding pocket of both monomers and differentially alters the conformational and energy landscapes of both monomers. Monomers are not energetically or conformationally equivalent even in fully bound state. Estradiol's binding to SHBG involves bidirectional, inter-monomeric allostery that changes the distribution of both monomers among various energy and conformational states. Inter-monomeric allostery offers a mechanism to extend the binding range of SHBG and regulate hormone bioavailability as estradiol concentrations vary widely during life.

Homologs of the Tumor Suppressor Protein p53: A Bioinformatics Study for Drug Design.

Sequence and structure of proteins related to the tumor suppressor protein p53 were studied from the perspective of gaining insight for the development of therapeutic drugs. Our study addresses two major issues encumber bringing novel drugs to market: side effects and artifacts from animal models. In the first phase of our study, we performed a genome-wide search to identify potentially similar proteins to p53 that may be susceptible to off target effects. In the second phase, we chose a selection of common model organisms that could potentially be available to undergraduate researchers in the university setting to assess which ones utilize p53 most similar to humans on the basis of sequence homology and structural similarity from predicted structures. Our results confirm the proteins in the humans significantly similar to p53 are known paralogs within the p53 family. In considering model organisms, murine p53 bore great similarity to human p53 in terms of both sequence and structure, but others performed similarly well. We discuss the findings against the background of other structural benchmarks and point out potential benefits and drawbacks of various alternatives for use in future drug design pilot studies.

Spectral analysis of molecular dynamics simulations on PDZ: MD sectors.

The idea of protein "sectors" posits that sparse subsets of amino acid residues form cooperative networks that are key elements of protein stability, ligand binding, and allosterism. To date, protein sectors have been calculated by the statistical coupling analysis (SCA) method of Ranganathan and co-workers via the spectral analysis of conservation-weighted evolutionary covariance matrices obtained from a multiple sequence alignments of homologous families of proteins. SCA sectors, a knowledge-based protocol, have been indentified with functional properties and allosterism for a number of systems. In this study, we investigate the utility of the sector idea for the analysis of physics-based molecular dynamics (MD) trajectories of proteins. Our test case for this procedure is PSD95- PDZ3, one of the smallest proteins for which allosterism has been observed. It has served previously as a model system for a number of prediction algorithms, and is well characterized by X-ray crystallography, NMR spectroscopy and site specific mutagenisis. All-atom MD simulations were performed for a total of 500 nanoseconds using AMBER, and MD-calculated covariance matrices for the fluctuations of residue displacements and non-bonded interaction energies were subjected to spectral analysis in a manner analogous to that of SCA. The composition of MD sectors was compared with results from SCA, site specific mutagenesis, and allosterism. The concordance indicates that MD sectors are a viable protocol for analyzing MD trajectories and provide insight into the physical origin of the phenomenon.Communicated by Ramaswamy H. Sarma.

A Ribosome Interaction Surface Sensitive to mRNA GCN Periodicity.

A longstanding challenge is to understand how ribosomes parse mRNA open reading frames (ORFs). Significantly, GCN codons are over-represented in the initial codons of ORFs of prokaryote and eukaryote mRNAs. We describe a ribosome rRNA-protein surface that interacts with an mRNA GCN codon when next in line for the ribosome A-site. The interaction surface is comprised of the edges of two stacked rRNA bases: the Watson-Crick edge of 16S/18S rRNA C1054 and the adjacent Hoogsteen edge of A1196 (Escherichia coli 16S rRNA numbering). Also part of the interaction surface, the planar guanidinium group of a conserved Arginine (R146 of yeast ribosomal protein Rps3) is stacked adjacent to A1196. On its other side, the interaction surface is anchored to the ribosome A-site through base stacking of C1054 with the wobble anticodon base of the A-site tRNA. Using molecular dynamics simulations of a 495-residue subsystem of translocating ribosomes, we observed base pairing of C1054 to nucleotide G at position 1 of the next-in-line codon, consistent with previous cryo-EM observations, and hydrogen bonding of A1196 and R146 to C at position 2. Hydrogen bonding to both of these codon positions is significantly weakened when C at position 2 is changed to G, A or U. These sequence-sensitive mRNA-ribosome interactions at the C1054-A1196-R146 (CAR) surface potentially contribute to the GCN-mediated regulation of protein translation.

GCN sensitive protein translation in yeast.

Levels of protein translation by ribosomes are governed both by features of the translation machinery as well as sequence properties of the mRNAs themselves. We focus here on a striking three-nucleotide periodicity, characterized by overrepresentation of GCN codons and underrepresentation of G at the second position of codons, that is observed in Open Reading Frames (ORFs) of mRNAs. Our examination of mRNA sequences in Saccharomyces cerevisiae revealed that this periodicity is particularly pronounced in the initial codons-the ramp region-of ORFs of genes with high protein expression. It is also found in mRNA sequences immediately following non-standard AUG start sites, located upstream or downstream of the standard annotated start sites of genes. To explore the possible influences of the ramp GCN periodicity on translation efficiency, we tested edited ramps with accentuated or depressed periodicity in two test genes, SKN7 and HMT1. Greater conformance to (GCN)n was found to significantly depress translation, whereas disrupting conformance had neutral or positive effects on translation. Our recent Molecular Dynamics analysis of a subsystem of translocating ribosomes in yeast revealed an interaction surface that H-bonds to the +1 codon that is about to enter the ribosome decoding center A site. The surface, comprised of 16S/18S rRNA C1054 and A1196 (E. coli numbering) and R146 of ribosomal protein Rps3, preferentially interacts with GCN codons, and we hypothesize that modulation of this mRNA-ribosome interaction may underlie GCN-mediated regulation of protein translation. Integration of our expression studies with large-scale reporter studies of ramp sequence variants suggests a model in which the C1054-A1196-R146 (CAR) interaction surface can act as both an accelerator and braking system for ribosome translation.

Chemical principles additive model aligns low consensus DNA targets of p53 tumor suppressor protein.

Computational prediction of the interaction between protein transcription factors and their cognate DNA binding sites in genomic promoters constitutes a formidable challenge in two situations: when the number of discriminatory interactions is small compared to the length of the binding site, and when DNA binding sites possess a poorly conserved consensus binding motif. The transcription factor p53 tumor suppressor protein and its target DNA exhibit both of these issues. From crystal structure analysis, only three discriminatory amino acid side chains contact the DNA for a binding site spanning ten base pairs. Furthermore, our analysis of a dataset of genome wide fragments binding to p53 revealed many sequences lacking the expected consensus. The low information content leads to an overestimation of binding sites, and the lack of conservation equates to a computational alignment problem. Within a fragment of DNA known to bind to p53, computationally locating the position of the site equates to aligning the DNA with the binding interface. From a molecular perspective, that alignment implies a specification of which DNA bases are interacting with which amino acid side chains, and aligning many sequences to the same protein interface concomitantly produces a multiple sequence alignment. From this vantage, we propose to cast prediction of p53 binding sites as an alignment to the protein binding surface with the novel approach of optimizing the alignment of DNA fragments to the p53 binding interface based on chemical principles. A scoring scheme based on this premise was successfully implemented to score a dataset of biological DNA fragments known to contain p53 binding sites. The results illuminate the mechanism of recognition for the protein-DNA system at the forefront of cancer research. These findings implicate that p53 may recognize its target binding sites via several different mechanisms which may include indirect readout.

Molecular Dynamics-Markov State Model of Protein Ligand Binding and Allostery in CRIB-PDZ: Conformational Selection and Induced Fit.

Conformational selection and induced fit are well-known contributors to ligand binding and allosteric effects in proteins. Molecular dynamics (MD) simulations now enable the theoretical study of protein-ligand binding in terms of ensembles of interconverting microstates and the population shifts characteristic of "dynamical allostery." Here we investigate protein-ligand binding and allostery based on a Markov state model (MSM) with states and rates obtained from all-atom MD simulations. As an exemplary case, we consider the single domain protein par-6 PDZ with and without ligand and allosteric effector. This is one of the smallest proteins in which allostery has been experimentally observed. In spite of the increased complexity intrinsic to a statistical ensemble perspective, we find that conformational selection and induced fit mechanisms can be readily identified in the analysis. In the nonallosteric pathway, MD-MSM shows that PDZ binds ligand via conformational selection. However, the allosteric pathway requires an activation step that involves a conformational change induced by the allosteric effector Cdc42. Once in the allosterically activated state, we find that ligand binding can proceed by conformational selection. Our MD-MSM model predicts that allostery in this and possibly other systems involves both induced fit and conformational selection, not just one or the other.

Dependence of prevalence of contiguous pathways in proteins on structural complexity.

Allostery is a regulatory mechanism in proteins where an effector molecule binds distal from an active site to modulate its activity. Allosteric signaling may occur via a continuous path of residues linking the active and allosteric sites, which has been suggested by large conformational changes evident in crystal structures. An alternate possibility is that the signal occurs in the realm of ensemble dynamics via an energy landscape change. While the latter was first proposed on theoretical grounds, increasing evidence suggests that such a control mechanism is plausible. A major difficulty for testing the two methods is the ability to definitively determine that a residue is directly involved in allosteric signal transduction. Statistical Coupling Analysis (SCA) is a method that has been successful at predicting pathways, and experimental tests involving mutagenesis or domain substitution provide the best available evidence of signaling pathways. However, ascertaining energetic pathways which need not be contiguous is far more difficult. To date, simple estimates of the statistical significance of a pathway in a protein remain to be established. The focus of this work is to estimate such benchmarks for the statistical significance of contiguous pathways for the null model of selecting residues at random. We found that when 20% of residues in proteins are randomly selected, contiguous pathways at the 6 Å cutoff level were found with success rates of 51% in PDZ, 30% in p53, and 3% in MutS. The results suggest that the significance of pathways may have system specific factors involved. Furthermore, the possible existence of false positives for contiguous pathways implies that signaling could be occurring via alternate routes including those consistent with the energetic landscape model.