Use of restrained molecular dynamics in water to determine three-dimensional protein structure: prediction of the three-dimensional structure of Ecballium elaterium trypsin inhibitor II

Refinement of distance geometry (DG) structures of EETI-II (Heitz et al.: Biochemistry 28:2392-2398, 1989), a member of the squash family trypsin inhibitor, have been carried out by restrained molecular dynamics (RMD) in water. The resulting models show better side chain apolar/polar surface ratio and estimated solvation free energy than structures refined "in vacuo." The consistent lower values of residual NMR constraint violations, apolar/polar surface ratio, and solvation free energy for one of these refined structures allowed prediction of the 3D folding and disulfide connectivity of EETI-II. Except for the few first residues for which no NMR constraints were available, this computer model fully agreed with X-ray structures of CMTI-I (Bode et al.: FEBS Lett. 242:285-292, 1989) and EETI-II complexed with trypsin that appeared after the RMD simulation was completed. Restrained molecular dynamics in water is thus proved to be highly valuable for refinement of DG structures. Also, the successful use of apolar/polar surface ratio and of solvation free energy reinforce the analysis of Novotny et al. (Proteins 4:19-30, 1988) and shows that these criteria are useful indicators of correct versus misfolded models.     

Monte carlo simulations of protein folding. II. Application to protein A,ROP, and crambin

The hierarchy of lattice Monte Carlo models described in the accompanying paper (Kolinski, A., Skolnick, J. Monte Carlo simulations of protein folding. I. Lattice model and interaction scheme. Proteins 18:338–352, 1994) is applied to the simulation of protein folding and the prediction of 3-dimensional structure. Using sequence information alone, three proteins have been successfully folded: the B domain of staphylococcal protein A, a 120 residue, monomeric version of ROP dimer, and crambin. Starting from a random expanded conformation, the model proteins fold along relatively well-defined folding pathways. These involve a collection of early intermediates, which are followed by the final (and rate-determining) transition from compact intermediates closely resembling the molten globule state to the native-like state. The predicted structures are rather unique, with native-like packing of the side chains. The accuracy of the predicted native conformations is better than those obtained in previous folding simulations. The best (but by no means atypical) folds of protein A have a coordinate rms of 2.25 Å from the native Cα trace, and the best coordinate rms from crambin is 3.18 Å. For ROP monomer, the lowest coordinate rms from equivalent Cαs of ROP dimer is 3.65 Å. Thus, for two simple helical proteins and a small α/β protein, the ability to predict protein structure from sequence has been demonstrated. © 1994 John Wiley & Sons, Inc.     

Five-stranded β-sheet sandwiched with two α-helices: A structural link between restriction endonucleases EcoRI and EcoRV

Examination of crystal structures of restriction endonucleases EcoRI and EcoRV complexes with their cognate DNA revealed a common structural element, which forms the core of both proteins. This element consists of a five-stranded β-sheet and two α-helices packed against it and could be described as α–β sandwich in which helices and β-strands lie in two stacked layers. While the spatial structure of this α–β sandwich is conserved in both enzymes, there are no detectable similarities between amino acid sequences except of a few residues involved in active site formation. Probably, other restriction endonucleases which have similar organization of the active site might possess similar structural element regardless of DNA sequence recognized and recognition elements in the enzyme used. © 1994 Wiley-Liss, Inc.     

Discrimination of folding types of globular proteins based on average distance maps constructed from their sequences

It has been shown that probable portions which form contacts in a protein can be predicted by means of an average distance map (ADM) as well as regular structures (-helices and -turns) defined as short-range compact regions (Kikuchiet al., 1988a,c). In this paper, we analyze the occurrence of those portions and short-range compact regions on ADMs for various proteins regarding their folding types. We have found out that each folding type of proteins shows characteristic distribution of such parts on ADMS. We also discuss the possibility of the prediction of folding types of proteins by ADMs.     

Structural analysis based on state-space modeling.

A new method has been developed to compute the probability that each amino acid in a protein sequence is in a particular secondary structural element. Each of these probabilities is computed using the entire sequence and a set of predefined structural class models. This set of structural classes is patterned after Jane Richardson''s taxonomy for the domains of globular proteins. For each structural class considered, a mathematical model is constructed to represent constraints on the pattern of secondary structural elements characteristic of that class. These are stochastic models having discrete state spaces (referred to as hidden Markov models by researchers in signal processing and automatic speech recognition). Each model is a mathematical generator of amino acid sequences; the sequence under consideration is modeled as having been generated by one model in the set of candidates. The probability that each model generated the given sequence is computed using a filtering algorithm. The protein is then classified as belonging to the structural class having the most probable model. The secondary structure of the sequence is then analyzed using a "smoothing" algorithm that is optimal for that structural class model. For each residue position in the sequence, the smoother computes the probability that the residue is contained within each of the defined secondary structural elements of the model. This method has two important advantages: (1) the probability of each residue being in each of the modeled secondary structural elements is computed using the totality of the amino acid sequence, and (2) these probabilities are consistent with prior knowledge of realizable domain folds as encoded in each model. As an example of the method''s utility, we present its application to flavodoxin, a prototypical alpha/beta protein having a central beta-sheet, and to thioredoxin, which belongs to a similar structural class but shares no significant sequence similarity.     

Protein conformational landscapes: Energy minimization and clustering of a long molecular dynamics trajectory

Using energy minimization and cluster analysis, we have analyzed a 1020 ps molecular dynamics trajectory of solvated bovine pancreatic trypsin inhibitor. Elucidation of conformational sub states in this way both illustrates the degree of conformational convergence in the simulation and reduces the structural data to a tractable subset. The relative movement of structures upon energy minimization was used to estimate the sizes of features on the protein potential energy surface. The structures were analyzed using their pairwise root-mean-square C_α deviations, which gave a global measure of conformational changes that would not be apparent by monitoring single degrees of freedom. At time scales of 0.1 ps, energy minimization detected sharp transitions between energy minima separated by 0.1 Å rms deviation. Larger conformational clusters containing these smaller minima and separated by 0.25 Å were seen at 1 ps time scales. Both of these small features of the conformational landscape were characterized by movements in loop regions associated with small, correlated backbone dihedral angle shifts. On a nanosecond time scale, the main features of the protein energy landscape were clusters separated by over 0.7 Å rms deviation, with only seven of these sub states visited over the 1 ns trajectory. These substates, discernible both before and after energy minimization, differ mainly in a monotonic pivot of the loop residues 11–18 over the course of the simulation. This loop contains lysine 17, which specifically binds to trypsin in the active site. The trajectory did not return to previously visited clusters, indicating that this trajectory has not been shown to have completely sampled the conformational substates available to it. Because the apparent convergence to a single region of conformation space depends on both the time scale of observation and the size of the conformational features examined, convergence must be operationally defined within the context of the simulation. © 1995 Wiley-Liss, Inc.     

Chemical induction of β-carotene biosynthesis

Marsh white seedless grapefruit were treated with the 2-diethylaminoethanol esters of the following acids: benzoic, phenylacetic, hydrocinnamic, 4-phenylbutyric, 5-phenylvaleric, valeric, hexanoic, heptanoic, octanoic, nonanoic, 5-chlorovaleric, cyclohexanecarboxylic, phenoxyacetic, p-chlorophenoxyacetic, 3-phenoxypropionic, cinnamic and p-chlorocinnamic. Several of these esters, in particular the hexanoate, 4-phenylbutyrate and cinnamate, caused the accumulation of large amounts of β-carotene. The effects of the hexanoate and of 2-phenoxytriethylamine, which causes only lycopene accumulation, were studied as functions of time. The hexanoate caused the rapid accumulation of lycopene during the first day. The amount of lycopene then began to decrease and that of β-carotene increased until, after 14 days, β-carotene was the major pigment. 2-Phenoxytriethylamine caused rapid lycopene accumulation during the first day and a slow steady increase afterwards. Thus, the mode of action of the β-carotene inducers may be similar to that of the lycopene inducers except that the former are probably rapidly hydrolysed by the esterase(s) in the flavedo, so that they no longer inhibit the cyclase(s), and β-carotene is accumulated at the expanse of lycopene.     

Differences between genetic and physical centromere distances in the case of two genes for male sterility in barley

Summary Linkage studies with thirty translocations (one of the two chromosomes involved being number 4) in relation to msg24 (chromosome 4) and thirteen translocations (one of the two chromosomes involved being number 6) in relation to msg6 (chromosome 6) show without exception close linkage for all combinations tested. The results indicate that both genes are located genetically in or close to the centromere regions of their chromosomes.Cytological analysis of two BTT stocks (balanced tertiary trisomics) ascertained the respective chromosome arms (both msg24 and msg6 on the short arms) and revealed marked differences between genetic and physical centromere distances. The reason is obviously the high content of centromeric heterochromatin occupying both the chromosome arms involved.     

Structure--activity relationships of chemical inducers of carotenoid biosynthesis

Fifteen amines having a profound effect on carotenogenesis in Marsh seedless grapefruit are reported. The compounds fall into three series: Et₂N(CH₂)_nMe (n = 4–8), Et₂N(CH₂)_nPh (n = 1–5), and Et₂NCH₂CH₂OC₆H₄R (R=H, p-Me, p-Et, p-iso-Pr, p-tert-Bu), There was up to an 11-fold increase in the total carotene content. Lycopene, not normally accumulated, became a major pigment. The inducing ability of the amines on carotenoid biosynthesis is correlated with the octanol-water partition coefficient. The mode of action appears to be similar to that of 2-(4-chlorophenylthio)triethylamine hydrochloride.     

Influence of systems biology response and environmental exposure level on between-subject variability in breath and blood biomarkers

To explain the underlying causes of apparently stochastic disease, current research is focusing on systems biology approaches wherein individual genetic makeup and specific ‘gene–environment’ interactions are considered. This is an extraordinarily complex task because both the environmental exposure profiles and the specific genetic susceptibilities presumably have large variance components. In this article, the focus is on the initial steps along the path to disease outcome namely environmental uptake, biologically available dose, and preclinical effect. The general approach is to articulate a conceptual model and identify biomarker measurements that could populate the model with hard data. Between-subject variance components from different exposure studies are used to estimate the source and magnitude of the variability of biomarker measurements. The intent is to determine the relative effects of different biological media (breath or blood), environmental compounds and their metabolites, different concentration levels, and levels of environmental exposure control. Examples are drawn from three distinct exposure biomarker studies performed by the US Environmental Protection Agency that studied aliphatic and aromatic hydrocarbons, trichloroethylene and methyl tertiary butyl ether. All results are based on empirical biomarker measurements of breath and blood from human subjects; biological specimens were collected under appropriate Institutional Review Board protocols with informed consent of the subjects. The ultimate goal of this work is to develop a framework for eventually assessing the total susceptibility ranges along the toxicological pathway from exposure to effect. The investigation showed that exposures are a greater contributor to biomarker variance than are internal biological parameters.