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Biomedical subjects

P Wrede

Publications and source records attributed to P Wrede.

At least 19 recordsLinked to original sources
PubMed1998

Peptide design by artificial neural networks and computer-based evolutionary search.

A technique for systematic peptide variation by a combination of rational and evolutionary approaches is presented. The design scheme consists of five consecutive steps: (i) identification of a "seed peptide" with a desired activity, (ii) generation of variants selected from a physicochemical space around the seed peptide, (iii) synthesis and testing of this biased library, (iv) modeling of a quantitative sequence-activity relationship by an artificial neural network, and (v) de novo design by a computer-based evolutionary search in sequence space using the trained neural network as the fitness function. This strategy was successfully applied to the identification of novel peptides that fully prevent the positive chronotropic effect of anti-beta1-adrenoreceptor autoantibodies from the serum of patients with dilated cardiomyopathy. The seed peptide, comprising 10 residues, was derived by epitope mapping from an extracellular loop of human beta1-adrenoreceptor. A set of 90 peptides was synthesized and tested to provide training data for neural network development. De novo design revealed peptides with desired activities that do not match the seed peptide sequence. These results demonstrate that computer-based evolutionary searches can generate novel peptides with substantial biological activity.

Amino Acid Sequence↗
PubMed1998

Peptide design aided by neural networks: biological activity of artificial signal peptidase I cleavage sites.

De novo designed signal peptidase I cleavage sites were tested for their biological activity in vivo in an Escherichia coli expression and secretion system. The artificial cleavage site sequences were generated by two different computer-based design techniques, a simple statistical method, and a neural network approach. In previous experiments, a neural network was used for feature extraction from a set of known signal peptidase I cleavage sites and served as the fitness function in an evolutionary design cycle leading to idealized cleavage site sequences. The cleavage sites proposed by the two algorithms were active in vivo as predicted. There seems to be an interdependence between several cleavage site features for the constitution of sequences recognized by signal peptidase. It is concluded that neural networks are useful tools for sequence-oriented peptide design.

Amino Acid Sequence↗
PubMed1998

Artificial neural networks for computer-based molecular design.

The theory of artificial neural networks is briefly reviewed focusing on supervised and unsupervised techniques which have great impact on current chemical applications. An introduction to molecular descriptors and representation schemes is given. In addition, worked examples of recent advances in this field are highlighted and pioneering publications are discussed. Applications of several types of artificial neural networks to compound classification, modelling of structure-activity relationships, biological target identification, and feature extraction from biopolymers are presented and compared to other techniques. Advantages and limitations of neural networks for computer-aided molecular design and sequence analysis are discussed.

Computer-Aided Design↗
PubMed1998

Feature-extraction from endopeptidase cleavage sites in mitochondrial targeting peptides.

Cleavage sites in nuclear-encoded mitochondrial protein targeting peptides (mTPs) from mammals, yeast, and plants have been analysed for characteristic physicochemical features using statistical methods, perceptrons, multilayer neural networks, and self-organizing feature maps. Three different sequence motifs were found, revealing loosely defined arginine motifs with Arg in positions -10, -3, and -2. A self-organizing feature map was able to cluster these three types of endopeptidase target sites but did not identify any species-specific characteristics in mTPs. Neural networks were used to define local sequence features around precursor cleavage sites.

Amino Acid Sequence↗
PubMed1996

Structure optimization of an artificial neural filter detecting membrane-spanning amino acid sequences.

An artificial neural network has been developed for the recognition and prediction of transmembrane regions in the amino acid sequences of human integral membrane proteins. It provides an additional prediction method besides the common hydrophobicity analysis by statistical means. Membrane/nonmembrane transition regions are predicted with 92% accuracy in both training and independent test data. The method used for the development of the neural filter is the algorithm of structure evolution. It subjects both the architecture and parameters of the system to a systematical optimization process and carries out local search in the respective structure and parameter spaces. The training technique of incomplete induction as part of the structure evolution provides for a comparatively general solution of the problem that is described by input-output relations only. Seven physiochemical side-chain properties were used to encode the amino acid sequences. It was found that geometric parameters like side-chain volume, bulkiness, or surface area are of minor importance. The properties polarity, refractivity, and hydrophobicity, however, turned out to support feature extraction. It is concluded that membrane transition regions in proteins are encoded in sequences as a characteristic feature based on the respective side-chain properties. The method of structure evolution is described in detail for this particular application and suggestions for further development of amino acid sequence filters are made.

Algorithms↗
PubMed1996

Local structural motifs of protein backbones are classified by self-organizing neural networks.

Important and relevant information is expected to be encoded in local structural elements of proteins. An unsupervised learning algorithm (Kohonen algorithm) was applied to the representation and unbiased classification of local backbone structures contained in a set of proteins. Training yielded a two-dimensional Kohonen feature map with 100 different structural motifs including certain helical and strand structures. All motifs were represented in a phi-psi-plot and some of them as a three-dimensional model. The course of structural motifs along the backbone of four selected proteins (cytochrome b5, cytochrome b562, lysozyme, gamma crystallin) was investigated in detail. Trajectories and histograms visualizing the abundance of characteristic motifs allowed for the distinction between different types of protein overall folds. It is demonstrated how the histograms may be used to construct a structural similarity matrix for proteins. The Kohonen algorithm provides a simple procedure for classification of local protein structures independent of any a priori knowledge of leading structural motifs. Training of the Kohonen network leads to the generation of "consensus structures' serving for the task of classification.

Algorithms↗
PubMed1995

Development of simple fitness landscapes for peptides by artificial neural filter systems.

The applicability of artificial neural filter systems as fitness functions for sequence-oriented peptide design was evaluated. Two example applications were selected: classification of dipeptides according to their hydrophobicity and classification of proteolytic cleavage-sites of protein precursor sequences according to their mean hydrophobicities and mean side-chain volumes. The cleavage-sites covered 12 residues. In the dipeptide experiments the objective was to separate a selected set of molecules from all other possible dipeptide sequences. Perceptrons, feedforward networks with one hidden layer, and a hybrid network were applied. The filters were trained by a (1, lambda) evolution strategy. Two types of network units employing either a sigmoidal or a unimodal transfer function were used in the feedforward filters, and their influence on classification was investigated. The two-layer hybrid network employed gaussian activation functions. To analyze classification of the different filter systems, their output was plotted in the two-dimensional sequence space. The diagrams were interpreted as fitness landscapes qualifying the markedness of a characteristic peptide feature which can be used as a guide through sequence space for rational peptide design. It is demonstrated that the applicability of neural filter systems as a heuristic method for sequence optimization depends on both the appropriate network architecture and selection of representative sequence data. The networks with unimodal activation functions and the hybrid networks both led to a number of local optima. However, the hybrid networks produced the best prediction results. In contrast, the filters with sigmoidal activation produced good reclassification results leading to fitness landscapes lacking unreasonable local optima. Similar results were obtained for classification of both dipeptides and cleavage-site sequences.

Amino Acid Sequence↗
PubMed1995

Peptide design in machina: development of artificial mitochondrial protein precursor cleavage sites by simulated molecular evolution.

Artificial neural networks were used for extraction of characteristic physiochemical features from mitochondrial matrix metalloprotease target sequences. The amino acid properties hydrophobicity and volume were used for sequence encoding. A window of 12 residues was employed, encompassing positions -7 to +5 of precursors with cleavage sites. Two sets of noncleavage site examples were selected for network training which was performed by an evolution strategy. The weight vectors of the optimized networks were visualized and interpreted by Hinton diagrams. A neural filter system consisting of 13 perceptron-type networks accurately classified the data. It served as the fitness function in a simulated molecular evolution procedure for sequence-oriented de novo design of idealized cleavage sites. A detailed description of the strategy is given. Several putative high-quality cleavage sites were obtained revealing the critical nature of the residues in the positions -2 and -5. Charged residues seem to have a major influence on cleavage site function.

Amino Acid Sequence↗
PubMed1994

A neural network model for the prediction of membrane-spanning amino acid sequences.

The architecture and weights of an artificial neural network model that predicts putative transmembrane sequences have been developed and optimized by the algorithm of structure evolution. The resulting filter is able to classify membrane/nonmembrane transition regions in sequences of integral human membrane proteins with high accuracy. Similar results have been obtained for both training and test set data, indicating that the network has focused on general features of transmembrane sequences rather than specializing on the training data. Seven physicochemical amino acid properties have been used for sequence encoding. The predictions are compared to hydrophobicity plots.

Algorithms↗
PubMed1994

The rational design of amino acid sequences by artificial neural networks and simulated molecular evolution: de novo design of an idealized leader peptidase cleavage site.

A method for the rational design of locally encoded amino acid sequence features using artificial neural networks and a technique for simulating molecular evolution has been developed. De novo in machine design of Escherichia coli leader peptidase (SP1) cleavage sites serves as an example application. A modular neural network system that employs sequence descriptions in terms of physicochemical properties has been trained on the recognition of characteristic cleavage site features. It is used for sequence qualification in the design cycle, representing the sequence fitness function. Starting from a random sequence several cleavage site sequences were generated by a simulated molecular evolution technique. It is based on a simple genetic algorithm that takes the quality values calculated by the artificial neural network as a heuristic for inductive sequence optimization. Simulated in vivo mutation and selection allows the identification of predominant sequence positions in Escherichia coli signal peptide cleavage site regions (positions -2 and -6). Various amino acid distance maps are used to define metrics for the step size of mutations. Position-specific mutability values indicate sequence positions exposed to high or low selection pressure in the simulations. The use of several distance maps leads to different courses of optimization and to various idealized sequences. It is concluded that amino acid distances are context dependent. Furthermore, a method for identification of local optima during sequence optimization is presented.

Algorithms↗
PubMed1994

Artificial neural networks and simulated molecular evolution are potential tools for sequence-oriented protein design.

The potential of artificial neural filter systems for feature extraction from amino acid sequences is discussed. Analysis of signal peptidase I cleavage-sites in protein precursor sequences serves as an example application. Trained neural networks can be used as the fitness function in an evolutionary protein design cycle termed 'simulated molecular evolution' which is an entirely computer-based method for the rational design of locally encoded amino acid sequence features. The design procedure itself is regarded as an optimization process which can follow several schemes. Gradient search, diffusive search, and evolution strategy have been compared with regard to their usefulness for optimization. It turns out that gradient search is well suited for optimization in smooth fitness landscapes without local minima, whereas evolution strategy seems to be a method of choice for optimization in a high-dimensional multimodal search space. This is concluded from optimization experiments using a multimodal example function.

Amino Acid Sequence↗
PubMed1993

Analysis of cleavage-site patterns in protein precursor sequences with a perceptron-type neural network.

A method for feature extraction from protein sequences has been developed which is based on an artificial neural filter system. Amino acid sequences are analyzed with regard to physicochemical residue properties. This alternative representation of a sequence allows an interpretation of the networks' weight values in a comprehensive and biochemically meaningful way by displaying the optimized network weights in Hinton diagrams. Signal peptidase cleavage sites of E.coli periplasmic proteins, human mitochondrial precursors and chloroplast precursors from spinach have been investigated. The network for E.coli periplasmic protein precursors classified both training and test data with 100% accuracy. The interpretation of its network weights clearly confirms the "-3,-1 rule" and the existence of a hydrophobic core region starting at position -6. Further striking features and dominant positions can be found for all three types of cleavage sites.

Algorithms↗
PubMed1993

Development of artificial neural filters for pattern recognition in protein sequences.

Four different artificial neural network architectures have been tested for their suitability to extract and predict sequence features. For optimization of the network weights an evolutionary computing method has been applied. The networks have feedforward architecture and provide adaptive neural filter systems for pattern recognition in primary structures and sequence classification. The recognition and prediction of signal peptidase cleavage sites of E. coli periplasmic protein precursors serves as an example for filter development. The primary structures are represented by seven physicochemical residue properties. This amino acid description provides the feature space for network optimization. The properties hydrophobicity, hydrophilicity, side-chain volume, and polarity allowed an accurate classification of the data. A three-layer network architecture reached a learning success of 100%; the highest prediction accuracy in an independent test set of sequences was 97%. This network architecture appears to be most suited for the analysis of E. coli signal peptidase cleavage sites. Further suggestions about the design and future applications of artificial neural networks for protein sequence analysis are made.

Bacterial Proteins↗
PubMed1992

In vitro synthesis of bacterio-opsin: integration into microsomal membranes.

The translation and membrane integration of bacterio-opsin from Halobacterium salinarium were investigated. Plasmids containing the bacterio-opsin-gene with or without its original presequence were transcribed with the T7-RNA-polymerase and translated in vitro in a wheat germ system. The integration of the expressed bacterio-opsin into dog pancreas microsomes was studied. Both precursor bacterio-opsin and mature bacterio-opsin integrate into the eukaryotic membrane.

Animals↗
PubMed1989

Genetic transfer of the pigment bacteriorhodopsin into the eukaryote Schizosaccharomyces pombe.

The gene encoding for bacterio-opsin (bop gene) from Halobacterium halobium has been introduced in a yeast expression vector. After transformation in Schizosaccharomyces pombe, bacterio-opsin (BO) is expressed and was detected by antisera. The precursor protein of BO (pre-BO) is processed by cleavage of amino acids at the N-terminal end as in H. halobium. Addition of the chromophore, retinal, to the culture medium results in a slight purple colour of the yeast cells indicating the in vivo regeneration of BO to bacteriorhodopsin (BR) and its incorporation into membranes. Therefore, in contrast to the expression in E. coli, isolation of the membrane protein and reconstitution in lipid vesicles is not necessary for functional analysis. The kinetics of the ground state signal of the photocycle BR in protoplasts is demonstrated by flash spectroscopy and is comparable to that of the natural system. The present investigation shows for the first time the transfer of an energy converting protein from archaebacteria to eukaryotes by genetic techniques. This is a basis for further studies on membrane biogenesis, genetics, and bioenergetics by analysis of in vivo active mutants.

Bacteriorhodopsins↗
PubMed1988

Bacteriorhodopsin precursor is processed in two steps.

Besides the Coomassie-blue-stained band corresponding to mature bacterioopsin two additional bands of slightly higher apparent molecular masses were observed in purple membrane preparations from Halobacterium halobium by SDS-PAGE. The staining intensity within the triple band pattern varied with the age of the cell culture. For cells in the stationary growth phase the lower band, corresponding to mature bacterioopsin, is the predominant one. Immunodetection and site-specific proteolysis with papain identified the upper band as originating from the previously described precursor of bacterioopsin with its 13-amino-acid-long N-terminal presequence. Our results suggest that the intermediate band is due to a modified precursor of bacterioopsin with a truncated presequence of about eight amino acids. A two-step mechanism for the processing of pre-bacterioopsin to the mature protein in this archaebacterium is proposed.

Amino Acid Sequence↗