Cloning the λ Switch: Digital and Markov Representations.

The lysis-lysogeny switch in E. coli due to infection from lambda phage has been extensively studied and explained by scientists of molecular biology. The bacterium either survives with the viral strand of deoxyribonucleic acid (DNA) or dies producing hundreds of viruses for propagation of infection. Many proteins transcribed after infection by λ phage take part in determining the fate of the bacterium, but two proteins that play a key role in this regard are the cI and cro dimers, which are transcribed off the viral DNA. This paper presents a novel modeling mechanism for the lysis-lysogeny switch, by transferring the interactions of the main proteins, the lambda right operator and promoter regions and the ribonucleic acid (RNA) polymerase, to a finite state machine (FSM), to determine cell fate. The FSM, and thus derived is implemented in field-programmable gate array (FPGA), and simulations have been run in random conditions. A Markov model has been created for the same mechanism. Steady state analysis has been conducted for the transition matrix of the Markov model, and the results have been generated to show the steady state probability of lysis with various model values. In this paper, it is hoped to lay down guidelines to convert biological processes into computing machines.

Maximizing Kinetic Information Gain of Markov State Models for Optimal Design of Spectroscopy Experiments.

Spectroscopic techniques such as Trp-Tyr quenching, luminescence resonance energy transfer, and triplet-triplet energy transfer are widely used for understanding the dynamic behavior of proteins. These experiments measure the relaxation of a particular labeled set of residue pairs, and the choice of residue pairs requires careful thought. As a result, experimentalists must pick residue pairs from a large pool of possibilities. In the current work, we show that molecular simulation datasets of protein dynamics can be used to systematically select an optimal set of residue positions to place probes for conducting spectroscopic experiments. The method described in this work, called Optimal Probes, can be used to rank trial sets of residue pairs in terms of their ability to capture the conformational dynamics of the protein. Optimal probes ensures two conditions: residue pairs capture the slow dynamics of the protein and their dynamics is not correlated for maximum information gain to score each trial set. Eventually, the highest scored set can be used for biophysical experiments to study the kinetics of the protein. The scoring methodology is based on kinetic network models of protein dynamics and a variational principle for molecular kinetics to optimize the hyperparameters used for the model. We also discuss that the scoring strategy used by Optimal Probes is the best possible way to ensure the ideal choice of residue pairs for experiments. We predict the best experimental probe positions for proteins λ-repressor, ß2-adrenergic receptor, and villin headpiece domain. These proteins have been well-studied and allow for a rigorous comparison of Optimal Probes predictions with already available experiments. Additionally, we also illustrate that our method can be used to predict the best choice for experiments by including any previous experiment choices available from other studies on the same protein. We consistently find that the best choice cannot be based on intuition or structural information such as distance difference between few known stable structures of the protein. Therefore, we show that incorporating protein dynamics could be used to maximize the information gain from experiments.

Complete Coupled Binding-Folding Pathway of the Intrinsically Disordered Transcription Factor Protein Brinker Revealed by Molecular Dynamics Simulations and Markov State Modeling.

Intrinsically disordered proteins (IDPs) make up a large class of proteins that lack stable structures in solution, existing instead as dynamic conformational ensembles. To perform their biological functions, many IDPs bind to other proteins or nucleic acids. Although IDPs are unstructured in solution, when they interact with binding partners, they fold into defined three-dimensional structures via coupled binding-folding processes. Because they frequently underlie IDP function, the mechanisms of this coupled binding-folding process are of great interest. However, given the flexibility inherent to IDPs and the sparse populations of intermediate states, it is difficult to reveal binding-folding pathways at atomic resolution using experimental methods. Computer simulations are another tool for studying these pathways at high resolution. Accordingly, we have applied 40 µs of unbiased molecular dynamics simulations and Markov state modeling to map the complete binding-folding pathway of a model IDP, the 59-residue C-terminal portion of the DNA binding domain of Drosophila melanogaster transcription factor Brinker (BrkDBD). Our modeling indicates that BrkDBD binds to its cognate DNA and folds in ∼50 µs by an induced fit mechanism, acquiring most of its stable secondary and tertiary structure only after it reaches the final binding site on the DNA. The protein follows numerous pathways en route to its bound and folded conformation, occasionally becoming stuck in kinetic traps. Each binding-folding pathway involves weakly bound, increasingly folded intermediate states located at different sites on the DNA surface. These findings agree with experimental data and provide additional insight into the BrkDBD folding mechanism and kinetics.

Quantitation of interactions between two DNA loops demonstrates loop domain insulation in E. coli cells.

Eukaryotic gene regulation involves complex patterns of long-range DNA-looping interactions between enhancers and promoters, but how these specific interactions are achieved is poorly understood. Models that posit other DNA loops--that aid or inhibit enhancer-promoter contact--are difficult to test or quantitate rigorously in eukaryotic cells. Here, we use the well-characterized DNA-looping proteins Lac repressor and phage λ CI to measure interactions between pairs of long DNA loops in E. coli cells in the three possible topological arrangements. We find that side-by-side loops do not affect each other. Nested loops assist each other's formation consistent with their distance-shortening effect. In contrast, alternating loops, where one looping element is placed within the other DNA loop, inhibit each other's formation, thus providing clear support for the loop domain model for insulation. Modeling shows that combining loop assistance and loop interference can provide strong specificity in long-range interactions.

Therapeutic assessment of cytochrome C for the prevention of obesity through endothelial cell-targeted nanoparticulate system.

Because the functional apoptosis-initiating protein, cytochrome C (CytC) is rapidly cleared from the circulation (t1/2 (half-life): 4 minutes), it cannot be used for in vivo therapy. We report herein on a hitherto unreported strategy for delivering exogenous CytC as a potential and safe antiobesity drug for preventing diet-induced obesity, the most common type of obesity in humans. The functional activity of CytC encapsulated in prohibitin (a white fat vessel-specific receptor)-targeted nanoparticles (PTNP) was evaluated quantitatively, as evidenced by the observations that CytC-loaded PTNP causes apoptosis in primary adipose endothelial cells in a dose-dependent manner, whereas CytC alone did not. The delivery of a single dose of CytC through PTNP into the circulation disrupted the vascular structure by the targeted apoptosis of adipose endothelial cells in vivo. Intravenous treatment of CytC-loaded PTNP resulted in a substantial reduction in obesity in high-fat diet (HFD) fed wild-type (wt) mice, as evidenced by the dose-dependent prevention of the percentage of increase in body weight and decrease in serum leptin levels. In addition, no detectable hepatotoxicity was found to be associated with this prevention. Thus, the finding highlights the promising potential of CytC for use as an antiobesity drug, when delivered through a nanosystem.

Theoretical and experimental relationships between percent inhibition and IC50 data observed in high-throughput screening.

The four-parameter logistic Hill equation models the theoretical relationship between inhibitor concentration and response and is used to derive IC(50) values as a measure of compound potency. This relationship is the basis for screening strategies that first measure percent inhibition at a single, uniform concentration and then determine IC(50) values for compounds above a threshold. In screening practice, however, a "good" correlation between percent inhibition values and IC(50) values is not always observed, and in the literature, there seems confusion about what correlation even to expect. We examined the relationship between percent inhibition data and IC(50) data in HDAC4 and ENPP2 high-throughput screening (HTS) data sets and compared our findings with a series of numerical simulations that allowed the investigation of the influence of parameters representing different types of uncertainties: variability in the screening concentration (related to solution library and compound characteristics, liquid handling), variations in Hill model parameters (related to interaction of compounds with target, type of assay), and influences of assay data quality parameters (related to assay and experimental design, liquid handling). In the different sensitivity analyses, we found that the typical variations of the actual compound concentrations in existing screening libraries generate the largest contributions to imperfect correlations. Excess variability in the ENPP2 assay above the values of the simulation model can be explained by compound aggregation artifacts.

Atomistic folding simulations of the five-helix bundle protein λ(6−85).

Protein folding is a classic grand challenge that is relevant to numerous human diseases, such as protein misfolding diseases like Alzheimer's disease. Solving the folding problem will ultimately require a combination of theory, simulation, and experiment, with theory and simulation providing an atomically detailed picture of both the thermodynamics and kinetics of folding and experimental tests grounding these models in reality. However, theory and simulation generally fall orders of magnitude short of biologically relevant time scales. Here we report significant progress toward closing this gap: an atomistic model of the folding of an 80-residue fragment of the λ repressor protein with explicit solvent that captures dynamics on a 10 milliseconds time scale. In addition, we provide a number of predictions that warrant further experimental investigation. For example, our model's native state is a kinetic hub, and biexponential kinetics arises from the presence of many free-energy basins separated by barriers of different heights rather than a single low barrier along one reaction coordinate (the previously proposed incipient downhill folding scenario).

Diffusive hidden Markov model characterization of DNA looping dynamics in tethered particle experiments.

In many biochemical processes, proteins bound to DNA at distant sites are brought into close proximity by loops in the underlying DNA. For example, the function of some gene-regulatory proteins depends on such 'DNA looping' interactions. We present a new technique for characterizing the kinetics of loop formation in vitro, as observed using the tethered particle method, and apply it to experimental data on looping induced by lambda repressor. Our method uses a modified ('diffusive') hidden Markov analysis that directly incorporates the Brownian motion of the observed tethered bead. We compare looping lifetimes found with our method (which we find are consistent over a range of sampling frequencies) to those obtained via the traditional threshold-crossing analysis (which can vary depending on how the raw data are filtered in the time domain). Our method does not involve any time filtering and can detect sudden changes in looping behavior. For example, we show how our method can identify transitions between long-lived, kinetically distinct states that would otherwise be difficult to discern.

Assessment of CD4+ T cells specific for the tumor antigen SSX-1 in cancer-free individuals.

Proteins encoded by genes of the SSX family are specifically expressed in tumors and are therefore relevant targets for cancer immunotherapy. One of the first identified family members, SSX-1, is expressed in a large fraction of synovial sarcomas as a fusion protein together with the product of the SYT gene. In addition, the full-length SSX-1 antigen is frequently expressed in tumors of several other histological types such as sarcoma, melanoma, hepatocellular carcinoma, ovarian cancer and myeloma. To date, however, SSX-1 specific T cell responses have not been investigated and no SSX-1 derived T cell epitopes have been described. Here, we have assessed the presence of CD4(+) T cells directed against the SSX-1 antigen in circulating lymphocytes of cancer-free individuals. After a single in vitro stimulation with a pool of peptides spanning the entire SSX-1 protein we could detect and isolate SSX-1-specific CD4(+) T cells from 5/5 donors analyzed. SSX-1-specific polyclonal populations isolated from these cultures recognized peptides located in three distinct regions of the protein containing clusters of sequences with significant predicted binding to frequently expressed MHC class II alleles. Characterization of specific clonal CD4(+) T cell populations derived from one donor allowed the identification of several naturally processed epitopes recognized in association with HLA-DR. These data document the existence of a significant repertoire of CD4(+) T cells specific for SSX-1 derived sequences in circulating lymphocytes of any individual that can be exploited for the development of both passive and active immunotherapeutic approaches to control disease evolution in cancer patients.

Predicting the effect of missense mutations on protein function: analysis with Bayesian networks.

BACKGROUND: A number of methods that use both protein structural and evolutionary information are available to predict the functional consequences of missense mutations. However, many of these methods break down if either one of the two types of data are missing. Furthermore, there is a lack of rigorous assessment of how important the different factors are to prediction. RESULTS: Here we use Bayesian networks to predict whether or not a missense mutation will affect the function of the protein. Bayesian networks provide a concise representation for inferring models from data, and are known to generalise well to new data. More importantly, they can handle the noisy, incomplete and uncertain nature of biological data. Our Bayesian network achieved comparable performance with previous machine learning methods. The predictive performance of learned model structures was no better than a naïve Bayes classifier. However, analysis of the posterior distribution of model structures allows biologically meaningful interpretation of relationships between the input variables. CONCLUSION: The ability of the Bayesian network to make predictions when only structural or evolutionary data was observed allowed us to conclude that structural information is a significantly better predictor of the functional consequences of a missense mutation than evolutionary information, for the dataset used. Analysis of the posterior distribution of model structures revealed that the top three strongest connections with the class node all involved structural nodes. With this in mind, we derived a simplified Bayesian network that used just these three structural descriptors, with comparable performance to that of an all node network.

Predicting stability of Arc repressor mutants with protein stochastic moments.

As more and more protein structures are determined and applied to drug manufacture, there is increasing interest in studying their stability. In this study, the stochastic moments ((SR)pi(k)) of 53 Arc repressor mutants were introduced as molecular descriptors modeling protein stability. The Linear Discriminant Analysis model developed correctly classified 43 out of 53, 81.13% of proteins according to their thermal stability. More specifically, the model classified 20/28 (71.4%) proteins with near wild-type stability and 23/25 (92%) proteins with reduced stability. Moreover, validation of the model was carried out by re-substitution procedures (81.0%). In addition, the stochastic moments based model compared favorably with respect to others based on physicochemical and geometric parameters such as D-Fire potential, surface area, volume, partition coefficient, and molar refractivity, which presented less than 77% of accuracy. This result illustrates the possibilities of the stochastic moments' method for the study of bioorganic and medicinal chemistry relevant proteins.

Markovian Backbone Negentropies: Molecular descriptors for protein research. I. Predicting protein stability in Arc repressor mutants.

As more and more protein structures are determined and applied to drug manufacture, there is increasing interest in studying their stability. In this sense, developing novel computational methods to predict and study protein stability in relation to their amino acid sequences has become a significant goal in applied Proteomics. In the study described here, Markovian Backbone Negentropies (MBN) have been introduced in order to model the effect on protein stability of a complete set of alanine substitutions in the Arc repressor. A total of 53 proteins were studied by means of Linear Discriminant Analysis using MBN as molecular descriptors. MBN are molecular descriptors based on a Markov chain model of electron delocalization throughout the protein backbone. The model correctly classified 43 out of 53 (81.13%) proteins according to their thermal stability. More specifically, the model classified 20/28 (71.4%) proteins with near wild-type stability and 23/25 (92%) proteins with reduced stability. Moreover, the model presented a good Mathew's regression coefficient of 0.643. Validation of the model was carried out by several Jackknife procedures. The method compares favorably with surface-dependent and thermodynamic parameter stability scoring functions. For instance, the D-FIRE potential classification function shows a level of good classification of 76.9%. On the other hand, surface, volume, logP, and molar refractivity show accuracies of 70.7, 62.3, 59.0, and 60.0%, respectively.

An assessment of proposed mechanisms for sensing hydrogen peroxide in mammalian systems.

Despite much recent interest in the biochemistry of reactive oxygen species, the mechanisms by which hydrogen peroxide (H2O2) functions in mammalian cells remain poorly defined. Proposed mechanisms for sensing H2O2 in mammalian cells include inactivation of protein tyrosine phosphatases and dual specificity phosphatases as well as inactivation of peroxiredoxins. In this critical review, proteins proposed to serve as sensors for H2O2 in mammals will be compared to peroxidases, catalases, and the bacterial H2O2 sensor OxyR for their ability to react with H2O2, in the context of our current knowledge concerning the concentrations of H2O2 present in cells.

Identification and analysis of chromodomain-containing proteins encoded in the mouse transcriptome.

The chromodomain is 40-50 amino acids in length and is conserved in a wide range of chromatic and regulatory proteins involved in chromatin remodeling. Chromodomain-containing proteins can be classified into families based on their broader characteristics, in particular the presence of other types of domains, and which correlate with different subclasses of the chromodomains themselves. Hidden Markov model (HMM)-generated profiles of different subclasses of chromodomains were used here to identify sequences encoding chromodomain-containing proteins in the mouse transcriptome and genome. A total of 36 different loci encoding proteins containing chromodomains, including 17 novel loci, were identified. Six of these loci (including three apparent pseudogenes, a novel HP1 ortholog, and two novel Msl-3 transcription factor-like proteins) are not present in the human genome, whereas the human genome contains four loci (two CDY orthologs and two apparent CDY pseudogenes) that are not present in mouse. A number of these loci exhibit alternative splicing to produce different isoforms, including 43 novel variants, some of which lack the chromodomain. The likely functions of these proteins are discussed in relation to the known functions of other chromodomain-containing proteins within the same family.

Design and folding of dimeric proteins.

In a similar way in which the folding of single-domain proteins provides an important test in the study of self-organization, the folding of homodimers constitutes a basic challenge in the quest for the mechanisms that are the basis of biological recognition. Dimerization is studied by following the evolution of two identical 20-letter amino acid chains within the framework of a lattice model and using Monte Carlo simulations. It is found that when design (evolution pressure) selects few, strongly interacting (conserved) amino acids to control the process, a three-state folding scenario follows, where the monomers first fold forming the halves of the eventual dimeric interface independently of each other, and then dimerize ("lock and key" kind of association). On the other hand, if design distributes the control of the folding process on a large number of (conserved) amino acids, a two-state folding scenario ensues, where dimerization takes place at the beginning of the process, resulting in an "induced type" of association. Making use of conservation patterns of families of analogous dimers, it is possible to compare the model predictions with the behavior of real proteins. It is found that theory provides an overall account of the experimental findings.

RADACK, a stochastic simulation of hydroxyl radical attack to DNA.

RADACK was conceived to simulate the radiation-induced attack to different DNA forms and complexes. It allows to separately calculate the probability of attack to each reactive atom of the sugar and of the base and takes into account the sequence-dependent structure of DNA as known from crystallographic or NMR studies or resulting from molecular modelling. The calculations are aimed to assess sequence-, structure- and ligand-dependent modulation of damages of sugar and bases, leading to single strand breaks (frank strand breaks, FSB) and alkali-labile base modifications (alkali-revealed breaks, ARB), respectively. The modelling procedure and the results of simulations for some representative structures (B, Z and quadruplex forms) are here described and discussed. The calculated relative probabilities of OH* radical attack to all reaction sites are compared to experimental FSB and ARB values. By a fitting procedure, the relative efficiencies of conversion of the C4' and C5'-centred radicals into FSB, epsilon (C4'): epsilon (C5'), and the relative efficiencies of base radicals- to- ARB conversion, epsilon(T) : epsilon(A) : epsilon(C) : epsilon(G), are then deduced for each DNA form. The ability of the model to account for the distribution of damages in DNA-ligand complexes is proven by its successful application to two DNA-protein systems : the lac repressor-lac operator complex and the nuclcosome core.

Radiosensitivity of DNA in a specific protein-DNA complex: the lac repressor-lac operator complex.

PURPOSE: To calculate the probability of radiation-induced frank strand breakage (FSB) at each nucleotide in the Escherichia coli lac repressor-lac operator system using a simulation procedure. To compare calculated and experimental results. To asses the contribution of DNA conformational changes and of the masking by the protein to DNA protection by the repressor. MATERIALS AND METHODS: Two structures of the complex were extracted from the PDB databank: crystallography- and NMR-based structures. Calculations were made of the accessibility of the atoms mainly involved in strand breakage (H4' and H5') to O&Hdot; and of the FSB probabilities, along: (1) DNA in the complex; (2) DNA in the complex depleted of the repressor; and (3) a linear DNA having the same sequence. An 80bp fragment bearing the operator was irradiated alone or in presence of the repressor. The relative probabilities of FSB at each nucleotide were determined using sequencing gel electrophoresis. RESULTS: Calculations predict modulation of the accessibility of H4' and H5' atoms and of the probabilities of FSB along the DNA fragments of complexes. This is due to the protein-induced conformational change and to masking by bound protein. The best agreement with the experimental FSB was observed for calculations that use the crystallography-based structure. CONCLUSIONS: For specific DNA-protein complexes, our calculations can predict the protein radiolytic footprints on DNA. They show the significant contribution of the protein-induced DNA conformational change to DNA protection.

Identification and ab initio simulations of early folding units in proteins.

The location of protein subunits that form early during folding, constituted of consecutive secondary structure elements with some intrinsic stability and favorable tertiary interactions, is predicted using a combination of threading algorithms and local structure prediction methods. Two folding units are selected among the candidates identified in a database of known protein structures: the fragment 15-55 of 434 cro, an all-alpha protein, and the fragment 1-35 of ubiquitin, an alpha/beta protein. These units are further analyzed by means of Monte Carlo simulated annealing using several database-derived potentials describing different types of interactions. Our results suggest that the local interactions along the chain dominate in the first folding steps of both fragments, and that the formation of some of the secondary structures necessarily occurs before structure compaction. These findings led us to define a prediction protocol, which is efficient to improve the accuracy of the predicted structures. It involves a first simulation with a local interaction potential only, whose final conformation is used as a starting structure of a second simulation that uses a combination of local interaction and distance potentials. The root mean square deviations between the coordinates of predicted and native structures are as low as 2-4 A in most trials. The possibility of extending this protocol to the prediction of full proteins is discussed. Proteins 2001;42:164-176.

Proteins of the endoplasmic-reticulum-associated degradation pathway: domain detection and function prediction.

Sequence database searches, using iterative-profile and Hidden-Markov-model approaches, were used to detect hitherto-undetected homologues of proteins that regulate the endoplasmic reticulum (ER)-associated degradation pathway. The translocon-associated subunit Sec63p (Sec=secretory) was shown to contain a domain of unknown function found twice in several Brr2p-like RNA helicases (Brr2=bad response to refrigeration 2). Additionally, Cue1p (Cue=coupling of ubiquitin conjugation to ER degradation), a yeast protein that recruits the ubiquitin-conjugating (UBC) enzyme Ubc7p to an ER-associated complex, was found to be one of a large family of putative scaffolding-domain-containing proteins that include the autocrine motility factor receptor and fungal Vps9p (Vps=vacuolar protein sorting). Two other yeast translocon-associated molecules, Sec72p and Hrd3p (Hrd=3-hydroxy-3-methylglutaryl-CoA reductase degradation), were shown to contain multiple tetratricopeptide-repeat-like sequences. From this observation it is suggested that Sec72p associates with a heat-shock protein, Hsp70, in a manner analogous to that known for Hop (Hsp70/Hsp90 organizing protein). Finally, the luminal portion of Ire1p (Ire=high inositol-requiring), thought to convey the sensing function of this transmembrane kinase and endoribonuclease, was shown to contain repeats similar to those in beta-propeller proteins. This finding hints at the mechanism by which Ire1p may sense extended unfolded proteins at the expense of compact folded molecules.