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Designing antimicrobial peptides: form follows function
Multidrug-resistant bacteria are a severe threat to public health. Conventional antibiotics are becoming increasingly ineffective as a result of resistance, and it is imperative to find newExpand
Computer-based de novo design of drug-like molecules
This review outlines the various design concepts and highlights current developments in computer-based de novo design of hit and lead structure candidates for drug discovery projects. Expand
Prediction of Type III Secretion Signals in Genomes of Gram-Negative Bacteria
It is demonstrated that the signals encoded in the sequences of type III secretion system effectors can be consistently recognized and predicted by machine learning techniques and provides a substantial basis for the identification of exported pathogenic proteins as targets for future therapeutic intervention. Expand
Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification
Although SVM outperformed the ANN classifiers with regard to overall prediction accuracy, both methods were shown to complement each other, as the sets of true positives, false positives, true negatives, and false negatives produced by the two classifiers were not identical. Expand
Support vector machine applications in bioinformatics.
The theory and main principles of the SVM approach are outlined, and successful applications in traditional areas of bioinformatics research are described, including neural network and SVM models to identify small organic molecules that potentially modulate the function of G-protein coupled receptors. Expand
Properties and prediction of mitochondrial transit peptides from Plasmodium falciparum.
A neural network approach for the prediction of mitochondrial transit peptides (mTPs) from the malaria-causing parasite Plasmodium falciparum is presented, and a distinct amino acid usage pattern has been found in protein encoding regions of P. falcIParum. Expand
PocketPicker: analysis of ligand binding-sites with shape descriptors
PocketPicker is an automated grid-based technique for the prediction of protein binding pockets that specifies the shape of a potential binding-site with regard to its buriedness and a descriptor that translates the arrangement of grid points delineating a detected binding- site into a correlation vector is introduced. Expand
Helicobacter pylori HtrA is a new secreted virulence factor that cleaves E‐cadherin to disrupt intercellular adhesion
An entirely new function of HtrA is described and it is identified as a new secreted virulence factor from Helicobacter pylori, which cleaves the ectodomain of the cell‐adhesion protein E‐cadherin. Expand
Distinct Roles of Secreted HtrA Proteases from Gram-negative Pathogens in Cleaving the Junctional Protein and Tumor Suppressor E-cadherin*
HtrA-mediated E-cadherin cleavage is a prevalent pathogenic mechanism of multiple Gram-negative bacteria representing an attractive novel target for therapeutic intervention to combat bacterial infections. Expand
Artificial neural networks for computer-based molecular design.
  • G. Schneider, P. Wrede
  • Computer Science, Medicine
  • Progress in biophysics and molecular biology
  • 1 November 1998
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. Expand