Application of amino acid occurrence for discriminating different folding types of globular proteins

<p>Abstract</p> <p>Background</p> <p>Predicting the three-dimensional structure of a protein from its amino acid sequence is a long-standing goal in computational/molecular biology. The discrimination of different structural classes and folding types are intermediate steps in protein structure prediction.</p> <p>Results</p> <p>In this work, we have proposed a method based on linear discriminant analysis (LDA) for discriminating 30 different folding types of globular proteins using amino acid occurrence. Our method was tested with a non-redundant set of 1612 proteins and it discriminated them with the accuracy of 38%, which is comparable to or better than other methods in the literature. A web server has been developed for discriminating the folding type of a query protein from its amino acid sequence and it is available at http://granular.com/PROLDA/.</p> <p>Conclusion</p> <p>Amino acid occurrence has been successfully used to discriminate different folding types of globular proteins. The discrimination accuracy obtained with amino acid occurrence is better than that obtained with amino acid composition and/or amino acid properties. In addition, the method is very fast to obtain the results.</p

TMFunction: database for functional residues in membrane proteins

We have developed the database TMFunction, which is a collection of more than 2900 experimentally observed functional residues in membrane proteins. Each entry includes the numerical values for the parameters IC50 (measure of the effectiveness of a compound in inhibiting biological function), Vmax (maximal velocity of transport), relative activity of mutants with respect to wild-type protein, binding affinity, dissociation constant, etc., which are important for understanding the sequence–structure–function relationship of membrane proteins. In addition, we have provided information about name and source of the protein, Uniprot and Protein Data Bank codes, mutational and literature information. Furthermore, TMFunction is linked to related databases and other resources. We have set up a web interface with different search and display options so that users have the ability to get the data in several ways. TMFunction is freely available at http://tmbeta-genome.cbrc.jp/TMFunction/

NETASA: neural network based prediction of solvent accessibility

Motivation: Prediction of the tertiary structure of a protein from its amino acid sequence is one of the most important problems in molecular biology. The successful prediction of solvent accessibility will be very helpful to achieve this goal. In the present work, we have implemented a server, NETASA for predicting solvent accessibility of amino acids using our newly optimized neural network algorithm. Several new features in the neural network architecture and training method have been introduced, and the network learns faster to provide accuracy values, which are comparable or better than other methods of ASA prediction. Results: Prediction in two and three state classification systems with several thresholds are provided. Our prediction method achieved the accuracy level upto 90% for training and 88% for test data sets. Three state prediction results provide a maximum 65% accuracy for training and 63% for the test data. Applicability of neural networks for ASA prediction has been confirmed with a larger data set and wider range of state thresholds. Salient differences between a linear and exponential network for ASA prediction have been analysed

Native geometry and the dynamics of protein folding

In this paper we investigate the role of native geometry on the kinetics of protein folding based on simple lattice models and Monte Carlo simulations. Results obtained within the scope of the Miyazawa-Jernigan indicate the existence of two dynamical folding regimes depending on the protein chain length. For chains larger than 80 amino acids the folding performance is sensitive to the native state's conformation. Smaller chains, with less than 80 amino acids, fold via two-state kinetics and exhibit a significant correlation between the contact order parameter and the logarithmic folding times. In particular, chains with N=48 amino acids were found to belong to two broad classes of folding, characterized by different cooperativity, depending on the contact order parameter. Preliminary results based on the G\={o} model show that the effect of long range contact interaction strength in the folding kinetics is largely dependent on the native state's geometry.Comment: Proceedings of the BIFI 2004 - I International Conference, Zaragoza (Spain) Biology after the genome: a physical view. To appear in Biophysical Chemistr

PDBTM: Protein Data Bank of transmembrane proteins after 8 years

The PDBTM database (available at http://pdbtm .enzim.hu), the first comprehensive and up-to-date transmembrane protein selection of the Protein Data Bank, was launched in 2004. The database was created and has been continuously updated by the TMDET algorithm that is able to distinguish between transmembrane and non-transmembrane proteins using their 3D atomic coordinates only. The TMDET algorithm can locate the spatial positions of transmembrane proteins in lipid bilayer as well. During the last 8 years not only the size of the PDBTM database has been steadily growing from ~400 to 1700 entries but also new structural elements have been identified, in addition to the well-known a-helical bundle and b-barrel structures. Numerous ‘exotic’ transmembrane protein structures have been solved since the first release, which has made it necessary to define these new structural elements, such as membrane loops or interfacial helices in the database. This article reports the new features of the PDBTM database that have been added since its first release, and our current efforts to keep the database up-to-date and easy to use so that it may continue to serve as a fundamental resource for the scientific community

PINT: Protein–protein Interactions Thermodynamic Database

The first release of Protein–protein Interactions Thermodynamic Database (PINT) contains >1500 data of several thermodynamic parameters along with sequence and structural information, experimental conditions and literature information. Each entry contains numerical data for the free energy change, dissociation constant, association constant, enthalpy change, heat capacity change and so on of the interacting proteins upon binding, which are important for understanding the mechanism of protein–protein interactions. PINT also includes the name and source of the proteins involved in binding, their Protein Information Resource, SWISS-PROT and Protein Data Bank (PDB) codes, secondary structure and solvent accessibility of residues at mutant positions, measuring methods, experimental conditions, such as buffers, ions and additives, and literature information. A WWW interface facilitates users to search data based on various conditions, feasibility to select the terms for output and different sorting options. Further, PINT is cross-linked with other related databases, PIR, SWISS-PROT, PDB and NCBI PUBMED literature database. The database is freely available a

TMBETA-NET: discrimination and prediction of membrane spanning β-strands in outer membrane proteins

We have developed a web-server, TMBETA-NET for discriminating outer membrane proteins and predicting their membrane spanning β-strand segments. The amino acid compositions of globular and outer membrane proteins have been systematically analyzed and a statistical method has been proposed for discriminating outer membrane proteins. The prediction of membrane spanning segments is mainly based on feed forward neural network and refined with β-strand length. Our program takes the amino acid sequence as input and displays the type of the protein along with membrane-spanning β-strand segments as a stretch of highlighted amino acid residues. Further, the probability of residues to be in transmembrane β-strand has been provided with a coloring scheme. We observed that outer membrane proteins were discriminated with an accuracy of 89% and their membrane spanning β-strand segments at an accuracy of 73% just from amino acid sequence information. The prediction server is available at

CUPSAT: prediction of protein stability upon point mutations

CUPSAT (Cologne University Protein Stability Analysis Tool) is a web tool to analyse and predict protein stability changes upon point mutations (single amino acid mutations). This program uses structural environment specific atom potentials and torsion angle potentials to predict ΔΔG, the difference in free energy of unfolding between wild-type and mutant proteins. It requires the protein structure in Protein Data Bank format and the location of the residue to be mutated. The output consists information about mutation site, its structural features (solvent accessibility, secondary structure and torsion angles), and comprehensive information about changes in protein stability for 19 possible substitutions of a specific amino acid mutation. Additionally, it also analyses the ability of the mutated amino acids to adapt the observed torsion angles. Results were tested on 1538 mutations from thermal denaturation and 1603 mutations from chemical denaturation experiments. Several validation tests (split-sample, jack-knife and k-fold) were carried out to ensure the reliability, accuracy and transferability of the prediction method that gives >80% prediction accuracy for most of these validation tests. Thus, the program serves as a valuable tool for the analysis of protein design and stability. The tool is accessible from the link