Subtilases: the superfamily of subtilisin-like serine proteases.

Subtilases are members of the clan (or superfamily) of subtilisin-like serine proteases. Over 200 subtilases are presently known, more than 170 of which with their complete amino acid sequence. In this update of our previous overview (Siezen RJ, de Vos WM, Leunissen JAM, Dijkstra BW, 1991, Protein Eng 4:719-731), details of more than 100 new subtilases discovered in the past five years are summarized, and amino acid sequences of their catalytic domains are compared in a multiple sequence alignment. Based on sequence homology, a subdivision into six families is proposed. Highly conserved residues of the catalytic domain are identified, as are large or unusual deletions and insertions. Predictions have been updated for Ca(2+)-binding sites, disulfide bonds, and substrate specificity, based on both sequence alignment and three-dimensional homology modeling.     

Importance of the release of strand 1C to the polymerization mechanism of inhibitory serpins.

Serpin polymerization is the underlying cause of several diseases, including thromboembolism, emphysema, liver cirrhosis, and angioedema. Understanding the structure of the polymers and the mechanism of polymerization is necessary to support rational design of therapeutic agents. Here we show that polymerization of antithrombin is sensitive to the addition of synthetic peptides that interact with the structure. A 12-m34 peptide (homologous to P14-P3 of antithrombin reactive loop), representing the entire length of s4A, prevented polymerization totally. A 6-mer peptide (homologous to P14-P9 of antithrombin) not only allowed polymerization to occur, but induced it. This effect could be blocked by the addition of a 5-mer peptide with s1C sequence of antithrombin or by an unrelated peptide representing residues 26-31 of cholecystokinin. The s1C or cholecystokinin peptide alone was unable to form a complex with native antithrombin. Moreover, an active antitrypsin double mutant, Pro 361-->Cys, Ser 283-->Cys, was engineered for the purpose of forming a disulfide bond between s1C and s2C to prevent movement of s1C. This mutant was resistant to polymerization if the disulfide bridge was intact, but, under reducing conditions, it regained the potential to polymerize. We have also modeled long-chain serpin polymers with acceptable stereochemistry using two previously proposed loop-A-sheet and loop-C-sheet polymerization mechanisms and have shown both to be sterically feasible, as are "mixed" linear polymers. We therefore conclude that the release of strand 1C must be an element of the mechanism of serpin polymerization.     

Homology modeling of histidine-containing phosphocarrier protein and eosinophil-derived neurotoxin: Construction of models and comparison with experiment

Homology modeling methods have been used to construct models of two proteins—the histidine-containing phosphocarrier protein (HPr) from Mycoplasma capricolum and human eosinophil-derived neurotoxin (EDN). Comparison of the models with the subsequently determined X-ray crystal structures indicates that the core regions of both proteins are reasonably well reproduced, although the template structures are closer to the X-ray structures in these regions—possible enhancements are discussed. The conformations of most of the side chains in the core of HPr are well reproduced in the modeled structure. As expected, the conformations of surface side chains in this protein differ significantly from the X-ray structure. The loop regions of EDN were incorrectly modeled—reasons for this and possible enhancements are discussed. © 1995 Wiley-Liss, Inc.     

Progress in fold recognition

The prediction experiment reveals that fold recognition has become a powerful tool in structural biology. We applied our fold recognition technique to 13 target sequences. In two cases, replication terminating protein and prosequence of subtilisin, the predicted structures are very similar to the experimentally determined folds. For the first time, in a public blind test, the unknown structures of proteins have been predicted ahead of experiment to an accuracy approaching molecular detail. In two other cases the approximate folds have been predicted correctly. According to the assessors there were 12 recognizable folds among the target proteins. In our postprediction analysis we find that in 7 cases our fold recognition technique is successful. In several of the remaining cases the predicted folds have interesting features in common with the experimental results. We present our procedure, discuss the results, and comment on several fundamental and technical problems encountered in fold recognition. © 1995 Wiley-Liss, Inc.     

Homology modeling by the ICM method

Five models have been built by the ICM method for the Comparative Modeling section of the Meeting on the Critical Assessment of Techniques for Protein Structure Prediction. The targets have homologous proteins with known three-dimensional structure with sequence identity ranging from 25 to 77%. After alignment of the target sequence with the related three-dimensional structure, the modeling procedure consists of two subproblems: side-chain prediction and loop prediction. The ICM method approaches these problems with the following steps: (1) a starting model is created based on the homologous structure with the conserved portion fixed and the noncon-served portion having standard covalent geometry and free torsion angles; (2) the Biased Probability Monte Carlo (BPMC) procedure is applied to search the subspaces of either all the nonconservative side-chain torsion angles or torsion angles in a loop backbone and surrounding side chains. A special algorithm was designed to generate low-energy loop deformations. The BPMC procedure globally optimizes the energy function consisting of ECEPP/3 and solvation energy terms. Comparison of the predictions with the NMR or crystallographic solutions reveals a high proportion of correctly predicted side chains. The loops were not correctly predicted because imprinted distortions of the backbone increased the energy of the near-native conformation and thus made the solution unrecognizable. Interestingly, the energy terms were found to be reliable and the sampling of conformational space sufficient. The implications of this finding for the strategies of future comparative modeling are discussed. © 1995 Wiley-Liss, Inc.     

A model for the effects of primary substrates on the kinetics of reductive dehalogenation

A kinetic model that describes substrate interactions during reductive dehalogenation reactions is developed. This model describes how the concentrations of primary electron-donor and -acceptor substrates affect the rates of reductive dehalogenation reactions. A basic model, which considers only exogenous electron-donor and -acceptor substrates, illustrates the fundamental interactions that affect reductive dehalogenation reaction kinetics. Because this basic model cannot accurately describe important phenomena, such as reductive dehalogenation that occurs in the absence of exogenous electron donors, it is expanded to include an endogenous electron donor and additional electron acceptor reactions. This general model more accurately reflects the behavior that has been observed for reductive dehalogenation reactions. Under most conditions, primary electron-donor substrates stimulate the reductive dehalogenation rate, while primary electron acceptors reduce the reaction rate. The effects of primary substrates are incorporated into the kinetic parameters for a Monod-like rate expression. The apparent maximum rate of reductive dehalogenation (q _{m, ap} ) and the apparent half-saturation concentration (K _ap ) increase as the electron donor concentration increases. The electron-acceptor concentration does not affect q _{m, ap} , but K _ap is directly proportional to its concentration.Definitions for model parameters RX halogenated aliphatic substrate - E-M ⁿ reduced dehalogenase - E-M ⁿ⁺² oxidized dehalogenase - [E-M ⁿ ] steady-state concentration of the reduced dehalogenase (moles of reduced dehalogenase per unit volume) - [E-M ⁿ⁺²] steady-state concentration of the oxidized dehalogenase (moles of reduced dehalogenase per unit volume) - DH₂ primary exogenous electron-donor substrate - A primary exogenous electron-acceptor substrate - A2 second primary exogenous electron-acceptor substrate - X biomass concentration (biomass per unit volume) - f fraction of biomass that is comprised of the dehalogenase (moles of dehalogenase per unit biomass) - stoichiometric coefficient for the reductive dehalogenation reaction (moles of dehalogenase oxidized per mole of halogenated substrate reduced) - stoichiometric coefficient for oxidation of the primary electron donor (moles of dehalogenase reduced per mole of donor oxidized) - stoichiometric coefficient for oxidation of the endogenous electron donor (moles of dehalogenase reduced per unit biomass oxidized) - stoichiometric coefficient for reduction of the primary electron acceptor (moles of dehalogenase oxidized per mole of acceptor reduced) - stoichiometric coefficient for reduction of the second electron acceptor (moles of dehalogenase oxidized per mole of acceptor reduced) - r _RX rate of the reductive dehalogenation reaction (moles of halogenated substrate reduced per unit volume per unit time) - r _d1 rate of oxidation of the primary exogenous electron donor (moles of donor oxidized per unit volume per unit time) - r _d2 rate of oxidation of the endogenous electron donor (biomass oxidized per unit volume per unit time) - r _a1 rate of reduction of the primary exogenous electron acceptor (moles of acceptor reduced per unit volume per unit time) - r _a2 rate of reduction of the second primary electron acceptor (moles of acceptor reduced per unit volume per unit time) - k _RX mixed second-order rate coefficient for the reductive dehalogenation reaction (volume per mole dehalogenase per unit time) - k _d1 mixed-second-order rate coefficient for oxidation of the primary electron donor (volume per mole dehalogenase per unit time) - k _d2 mixed-second-order rate coefficient for oxidation of the endogenous electron donor (volume per mole dehalogenase per unit time) - b first-order biomass decay coefficient (biomass oxidized per unit biomass per unit time) - k _a1 mixed-second-order rate coefficient for reduction of the primary electron acceptor (volume per mole dehalogenase per unit time) - k _a2 mixed-second-order rate coefficient for reduction of the second primary electron acceptor (volume per mole dehalogenase per unit time) - q _m,ap apparent maximum specific rate of reductive dehalogenation (moles of RX per unit biomass per unit time) - K _ap apparent half-saturation concentration for the halogenated aliphatic substrate (moles of RX per unit volume) - k _ap apparent pseudo-first-order rate coefficient for reductive dehalogenation (volume per unit biomass per unit time)     

Constrained C-terminal hexapeptide neurotensin analogues containing a 3-oxoindolizidine skeleton

Summary In order to enforce different spatial orientations in the C-terminal hexapeptide of neurotensin (NT_8–13) and to gain information about the importance of the 10–11 peptide bond for binding to NT receptors, the Pro¹⁰-Tyr¹¹ fragment has been replaced with (2R,8S,8aR)-, (2S,8S,8aR)-, (2S,8S,8aS)-, (2S,8R,8aS)- and (2R,8R,8aS)-8-amino-2-benzyl-3-oxoindolizidine-2-carboxylic acid. Molecular dynamics calculations and energy minimization studies have shown that, contrarily to the Pro-Tyr moiety, none of these indolizidines display a tendency to adopt type I and III -turns, but those having (8S,8aR) or (8R,8aS) stereochemistry essentially adopt extended conformations and the (8S,8aS) stereoisomer prefers a nonstandard folding. The four diastereomeric NT_8–13 analogues incorporating (8S,8aR) or (8R,8aS) indolizidines displayed binding affinities for the brain NT receptor similar to that of [Ala¹¹]-NT_8–13 and only five- to ninefold lower than that of the corresponding analogue, [Phe¹¹]NT_8–13. Although this slight decrease could be attributed to differences in conformational behavior between these constrained NT_8–13 analogues and [Phe¹¹]NT_8–13 or NT_8–13, it is not clear whether the -turn around Pro¹⁰-AA¹¹ (AA=Phe, Tyr) is conserved upon receptor binding. An excessive restriction in the motions of the aromatic side chain, imposed by the highly steric constraint of the indolizidine moiety, emerges as an alternative explanation. The findings reported here demonstrate the possibility of replacing the Pro¹⁰-Tyr¹¹ dipeptide in NT_8–13 with a non-peptide residue without affecting considerably the affinity for brain NT receptors.     

A minimal,compartmental model for a dendritic origin of bistability of motoneuron firing patterns

Various nonlinear regenerative responses, including plateau potentials and bistable repetitive firing modes, have been observed in motoneurons under certain conditions. Our simulation results support the hypothesis that these responses are due to plateau-generating currents in the dendrites, consistent with a major role for a noninactivating calcium L-type current as suggested by experiments. Bistability as observed in the soma of low- and higher-frequency spiking or, under TTX, of near resting and depolarized plateau potentials, occurs because the dendrites can be in a near resting or depolarized stable steady state. We formulate and study a two-compartment minimal model of a motoneuron that segregates currents for fast spiking into a soma-like compartment and currents responsible for plateau potentials into a dendrite-like compartment. Current flows between compartments through a coupling conductance, mimicking electrotonic spread. We use bifurcation techniques to illuminate how the coupling strength affects somatic behavior. We look closely at the case of weak coupling strength to gain insight into the development of bistable patterns. Robust somatic bistability depends on the electrical separation since it occurs only for weak to moderate coupling conductance. We also illustrate that hysteresis of the two spiking states is a natural consequence of the plateau behavior in the dendrite compartment.     

A multivariate approach to the selection of biological reserves

Multivariate analysis provides an effective context for the examination of some significant aspects of biodiversity and conservation. The framework is a multidimensional space that integrates sample sites, taxa and environments. This approach enables terms such as representativeness, complementarity and irreplaceability to be integrated within an intuitive and practical framework for reserve design. Cluster analysis is proposed to determine what is there by defining a set of complementary clusters. These clusters are sampled in a representative manner; from the core outward. The degree of irreplaceability of a site is defined as the multivariate distance of each potential reserve site to its nearest neighbour.     

Building proteins from C alpha coordinates using the dihedral probability grid Monte Carlo method.

Dihedral probability grid Monte Carlo (DPG-MC) is a general-purpose method of conformational sampling that can be applied to many problems in peptide and protein modeling. Here we present the DPG-MC method and apply it to predicting complete protein structures from C alpha coordinates. This is useful in such endeavors as homology modeling, protein structure prediction from lattice simulations, or fitting protein structures to X-ray crystallographic data. It also serves as an example of how DPG-MC can be applied to systems with geometric constraints. The conformational propensities for individual residues are used to guide conformational searches as the protein is built from the amino-terminus to the carboxyl-terminus. Results for a number of proteins show that both the backbone and side chain can be accurately modeled using DPG-MC. Backbone atoms are generally predicted with RMS errors of about 0.5 A (compared to X-ray crystal structure coordinates) and all atoms are predicted to an RMS error of 1.7 A or better.