6.2.2. Definition of Effective Properties 3064 6.3. Response Properties to Magnetic Fields 3066 6.3.1. Nuclear Shielding 3066 6.3.2. Indirect Spin−Spin Coupling 3067 6.3.3. EPR Parameters 3068 6.4. Properties of Chiral Systems 3069 6.4.1. Electronic Circular Dichroism (ECD) 3069 6.4.2. Optical Rotation (OR) 3069 6.4.3. VCD and VROA 3070 7. Continuum and Discrete Models 3071 7.1. Continuum Methods within MD and MC Simulations 3072

/pdf/quantum-mechanical-continuum-solvation-models-3edv963g14.pdf

Quantum mechanical continuum solvation models.

Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes

Motivation: Homology models of proteins are of great interest for planning and analysing biological experiments when no experimental three-dimensional structures are available. Building homology models requires specialized programs and up-to-date sequence and structural databases. Integrating all required tools, programs and databases into a single web-based workspace facilitates access to homology modelling from a computer with web connection without the need of downloading and installing large program packages and databases.

Results: SWISS-MODEL workspace is a web-based integrated service dedicated to protein structure homology modelling. It assists and guides the user in building protein homology models at different levels of complexity. A personal working environment is provided for each user where several modelling projects can be carried out in parallel. Protein sequence and structure databases necessary for modelling are accessible from the workspace and are updated in regular intervals. Tools for template selection, model building and structure quality evaluation can be invoked from within the workspace. Workflow and usage of the workspace are illustrated by modelling human Cyclin A1 and human Transmembrane Protease 3.

Availability: The SWISS-MODEL workspace can be accessed freely at http://swissmodel.expasy.org/workspace/

Contact: Torsten.Schwede@unibas.ch

Supplementary information: Supplementary data are available at Bioinformatics online.

/pdf/the-swiss-model-workspace-a-web-based-environment-for-1jba36oddx.pdf

The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling

SWISS-MODEL (http://swissmodel.expasy.org) is a server for automated comparative modeling of three-dimensional (3D) protein structures. It pioneered the field of automated modeling starting in 1993 and is the most widely-used free web-based automated modeling facility today. In 2002 the server computed 120 000 user requests for 3D protein models. SWISS-MODEL provides several levels of user interaction through its World Wide Web interface: in the 'first approach mode' only an amino acid sequence of a protein is submitted to build a 3D model. Template selection, alignment and model building are done completely automated by the server. In the 'alignment mode', the modeling process is based on a user-defined target-template alignment. Complex modeling tasks can be handled with the 'project mode' using DeepView (Swiss-PdbViewer), an integrated sequence-to-structure workbench. All models are sent back via email with a detailed modeling report. WhatCheck analyses and ANOLEA evaluations are provided optionally. The reliability of SWISS-MODEL is continuously evaluated in the EVA-CM project. The SWISS-MODEL server is under constant development to improve the successful implementation of expert knowledge into an easy-to-use server.

SWISS-MODEL: an automated protein homology-modeling server

Several means for improving the performance and training of neural networks for classification are proposed Crossvalidation is used as a tool for optimizing network parameters and architecture It is shown that the remaining residual generalization error can be reduced by invoking ensembles of similar networks >

/pdf/neural-network-ensembles-4ujxrg94mn.pdf

Neural network ensembles

The specificity of the enzyme(s) catalysing the covalent link between the hydroxyl side chains of serine or threonine and the sugar moiety N-acetylgalactosamine (GalNAc) is unknown. Pattern recognition by artificial neural networks and weight matrix algorithms was performed to determine the exact position of in vivo O-linked GalNAc-glycosylated serine and threonine residues from the primary sequence exclusively. The acceptor sequence context for O-glycosylation of serine was found to differ from that of threonine and the two types were therefore treated separately. The context of the sites showed a high abundance of proline, serine and threonine extending far beyond the previously reported region covering positions -4 through +4 relative to the glycosylated residue. The O-glycosylation sites were found to cluster and to have a high abundance in the N-terminal part of the protein. The sites were also found to have an increased preference for three different classes of beta-turns. No simple consensus-like rule could be deduced for the complex glycosylation sequence acceptor patterns. The neural networks were trained on the hitherto largest data material consisting of 48 carefully examined mammalian glycoproteins comprising 264 O-glycosylation sites. For detection neural network algorithms were much more reliable than weight matrices. The networks correctly found 60-95% of the O-glycosylated serine/threonine residues and 88-97% of the non-glycosylated residues in two independent test sets of known glycoproteins. A computer server using E-mail for prediction of O-glycosylation sites has been implemented and made publicly available. The Internet address is NetOglyc@cbs.dtu.dk.

/pdf/prediction-of-o-glycosylation-of-mammalian-proteins-3g9aopbfeg.pdf

Prediction of O-glycosylation of mammalian proteins: specificity patterns of UDP-GalNAc:polypeptide N-acetylgalactosaminyltransferase.

It is shown that microwave irradiation can affect the kinetics of the folding process of some globular proteins, especially beta-lactoglobulin. At low temperature the folding from the cold denatured phase of the protein is enhanced, while at a higher temperature the denaturation of the protein from its folded state is enhanced. In the latter case, a negative temperature gradient is needed for the denaturation process, suggesting that the effects of the microwaves are nonthermal. This supports the notion that coherent topological excitations can exist in proteins. The application of microwaves hold promises for a wide range of biotechnological applications, such as protein synthesis, protein aggregation, etc., and may have implications for biological systems as well.

/pdf/microwave-enhanced-folding-and-denaturation-of-globular-4kv7tcw6vf.pdf

Microwave-enhanced folding and denaturation of globular proteins.

https://febs.onlinelibrary.wiley.com/doi/pdf/10.1016/0014-5793%2888%2981066-4

Protein secondary structure and homology by neural networks The α-helices in rhodopsin

We predict interatomic Calpha distances by two independent data driven methods. The first method uses statistically derived probability distributions of the pairwise distance between two amino acids, whilst the latter method consists of a neural network prediction approach equipped with windows taking the context of the two residues into account. These two methods are used to predict whether distances in independent test sets were above or below given thresholds. We investigate which distance thresholds produce the most information-rich constraints and, in turn, the optimal performance of the two methods. The predictions are based on a data set derived using a new threshold which defines when sequence similarity implies structural similarity. We show that distances in proteins are predicted more accurately by neural networks than by probability density functions. We show that the accuracy of the predictions can be further increased by using sequence profiles. A threading method based on the predicted distances is presented. A homepage with software, predictions and data related to this paper is available at http://www.cbs.dtu.dk/services/CPHmodels/.

/pdf/protein-distance-constraints-predicted-by-neural-networks-2koq5oos5v.pdf

Protein distance constraints predicted by neural networks and probability density functions.

Secondary structure prediction in- volving up to 800 neural network predictions has been developed, by use of novel methods such as output expansion and a unique balloting procedure. An overall performance of 77.2%- 80.2% (77.9%- 80.6% mean per-chain) for three-state (helix, strand, coil) prediction was obtained when evaluated on a commonly used set of 126 protein chains. The method uses profiles made by position-specific scoring matri- ces as input, while at the output level it predicts on three consecutive residues simultaneously. The pre- dictions arise from tenfold, cross validated training and testing of 1032 protein sequences, using a scheme with primary structure neural networks followed by structure filtering neural networks. With respect to blind prediction, this work is prelimi- nary and awaits evaluation by CASP4. Proteins 2000;41:17-20. © 2000 Wiley-Liss, Inc.

Henrik Bohr

Papers

Prediction of O-glycosylation of mammalian proteins: specificity patterns of UDP-GalNAc:polypeptide N-acetylgalactosaminyltransferase.

Microwave-enhanced folding and denaturation of globular proteins.

Protein secondary structure and homology by neural networks The α-helices in rhodopsin

Protein distance constraints predicted by neural networks and probability density functions.

Prediction of protein secondary structure at 80% accuracy