• La Universidad
    • Historia
    • Rectoría
    • Autoridades
    • Secretaría General
    • Pastoral UC
    • Organización
    • Hechos y cifras
    • Noticias UC
  • 2011-03-15-13-28-09
  • Facultades
    • Agronomía e Ingeniería Forestal
    • Arquitectura, Diseño y Estudios Urbanos
    • Artes
    • Ciencias Biológicas
    • Ciencias Económicas y Administrativas
    • Ciencias Sociales
    • College
    • Comunicaciones
    • Derecho
    • Educación
    • Filosofía
    • Física
    • Historia, Geografía y Ciencia Política
    • Ingeniería
    • Letras
    • Matemáticas
    • Medicina
    • Química
    • Teología
    • Sede regional Villarrica
  • 2011-03-15-13-28-09
  • Organizaciones vinculadas
  • 2011-03-15-13-28-09
  • Bibliotecas
  • 2011-03-15-13-28-09
  • Mi Portal UC
  • 2011-03-15-13-28-09
  • Correo UC
- Repository logo
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Yкраї́нська
  • Log in
    Log in
    Have you forgotten your password?
Repository logo
  • Communities & Collections
  • All of DSpace
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Yкраї́нська
  • Log in
    Log in
    Have you forgotten your password?
  1. Home
  2. Browse by Author

Browsing by Author "Sali, A."

Now showing 1 - 5 of 5
Results Per Page
Sort Options
  • No Thumbnail Available
    Item
    A composite score for predicting errors in protein structure models
    (2006) Eramian, D.; Shen, M. Y.; Devos D.; Melo Ledermann, Francisco Javier; Sali, A.; Marti Renom, M. A.
    Reliable prediction of model accuracy is an important unsolved problem in protein structure modeling. To address this problem, we studied 24 individual assessment scores, including physics-based energy functions, statistical potentials, and machine learning–based scoring functions. Individual scores were also used to construct ∼85,000 composite scoring functions using support vector machine (SVM) regression. The scores were tested for their abilities to identify the most native-like models from a set of 6000 comparative models of 20 representative protein structures. Each of the 20 targets was modeled using a template of <30% sequence identity, corresponding to challenging comparative modeling cases. The best SVM score outperformed all individual scores by decreasing the average RMSD difference between the model identified as the best of the set and the model with the lowest RMSD (ΔRMSD) from 0.63 Å to 0.45 Å, while having a higher Pearson correlation coefficient to RMSD (r = 0.87) than any other tested score. The most accurate score is based on a combination of the DOPE non-hydrogen atom statistical potential; surface, contact, and combined statistical potentials from MODPIPE; and two PSIPRED/DSSP scores. It was implemented in the SVMod program, which can now be applied to select the final model in various modeling problems, including fold assignment, target–template alignment, and loop modeling.
  • No Thumbnail Available
    Item
    Comparative protein structure modeling of genes and genomes
    (2000) Martí-Renom, M. A.; Stuart, A. C.; Fiser, A.; Sánchez, R.; Melo Ledermann, Francisco Javier; Sali, A.
    Comparative modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target–template alignment, model building, and model evaluation. The number of protein sequences that can be modeled and the accuracy of the predictions are increasing steadily because of the growth in the number of known protein structures and because of the improvements in the modeling software. Further advances are necessary in recognizing weak sequence–structure similarities, aligning sequences with structures, modeling of rigid body shifts, distortions, loops and side chains, as well as detecting errors in a model. Despite these problems, it is currently possible to model with useful accuracy significant parts of approximately one third of all known protein sequences. The use of individual comparative models in biology is already rewarding and increasingly widespread. A major new challenge for comparative modeling is the integration of it with the torrents of data from genome sequencing projects as well as from functional and structural genomics. In particular, there is a need to develop an automated, rapid, robust, sensitive, and accurate comparative modeling pipeline applicable to whole genomes. Such large-scale modeling is likely to encourage new kinds of applications for the many resulting models, based on their large number and completeness at the level of the family, organism, or functional network.
  • Loading...
    Thumbnail Image
    Item
    Fold assessment for comparative protein structure modeling
    (2007) Melo Ledermann, Francisco Javier; Sali, A.
    Accurate and automated assessment of both geometrical errors and incompleteness of comparative protein structure models is necessary for an adequate use of the models. Here, we describe a composite score for discriminating between models with the correct and incorrect fold. To find an accurate composite score, we designed and applied a genetic algorithm method that searched for a most informative subset of 21 input model features as well as their optimized nonlinear transformation into the composite score. The 21 input features included various statistical potential scores, stereochemistry quality descriptors, sequence alignment scores, geometrical descriptors, and measures of protein packing. The optimized composite score was found to depend on (1) a statistical potential z-score for residue accessibilities and distances, (2) model compactness, and (3) percentage sequence identity of the alignment used to build the model. The accuracy of the composite score was compared with the accuracy of assessment by single and combined features as well as by other commonly used assessment methods. The testing set was representative of models produced by automated comparative modeling on a genomic scale. The composite score performed better than any other tested score in terms of the maximum correct classification rate (i.e., 3.3% false positives and 2.5% false negatives) as well as the sensitivity and specificity across the whole range of thresholds. The composite score was implemented in our program MODELLER-8 and was used to assess models in the MODBASE database that contains comparative models for domains in approximately 1.3 million protein sequences.
  • Loading...
    Thumbnail Image
    Item
    MODBASE: a database of annotated comparative protein structure models and associated resources
    (2006) Pieper, U.; Eswar, N.; Davis, F. P.; Braberg, H.; Madhusudhan, M. S.; Rossi, A.; Marti Renom, M.; Karchin, R.; Webb, B. M.; Eramian, D.; Shen, M. Y.; Kelly, L.; Melo Ledermann, Francisco Javier; Sali, A.
    MODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models for all available protein sequences that can be matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on MODELLER for fold assignment, sequence–structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE is updated regularly to reflect the growth in protein sequence and structure databases, and improvements in the software for calculating the models. MODBASE currently contains 3 094 524 reliable models for domains in 1 094 750 out of 1 817 889 unique protein sequences in the UniProt database (July 5, 2005); only models based on statistically significant alignments and models assessed to have the correct fold despite insignificant alignments are included. MODBASE also allows users to generate comparative models for proteins of interest with the automated modeling server MODWEB (http://salilab.org/modweb). Our other resources integrated with MODBASE include comprehensive databases of multiple protein structure alignments (DBAli, http://salilab.org/dbali), structurally defined ligand binding sites and structurally defined binary domain interfaces (PIBASE, http://salilab.org/pibase) as well as predictions of ligand binding sites, interactions between yeast proteins, and functional consequences of human nsSNPs (LS-SNP, http://salilab.org/LS-SNP).
  • Loading...
    Thumbnail Image
    Item
    Statistical potentials for fold assessment
    (2002) Melo Ledermann, Francisco Javier; Sánchez, R.; Sali, A.
    A protein structure model generally needs to be evaluated to assess whether or not it has the correct fold. To improve fold assessment, four types of a residue-level statistical potential were optimized, including distance-dependent, contact, ϕ/Ψ dihedral angle, and accessible surface statistical potentials. Approximately 10,000 test models with the correct and incorrect folds were built by automated comparative modeling of protein sequences of known structure. The criterion used to discriminate between the correct and incorrect models was the Z-score of the model energy. The performance of a Z-score was determined as a function of many variables in the derivation and use of the corresponding statistical potential. The performance was measured by the fractions of the correctly and incorrectly assessed test models. The most discriminating combination of any one of the four tested potentials is the sum of the normalized distance-dependent and accessible surface potentials. The distance-dependent potential that is optimal for assessing models of all sizes uses both Cα and Cβ atoms as interaction centers, distinguishes between all 20 standard residue types, has the distance range of 30 Å, and is derived and used by taking into account the sequence separation of the interacting atom pairs. The terms for the sequentially local interactions are significantly less informative than those for the sequentially nonlocal interactions. The accessible surface potential that is optimal for assessing models of all sizes uses Cβ atoms as interaction centers and distinguishes between all 20 standard residue types. The performance of the tested statistical potentials is not likely to improve significantly with an increase in the number of known protein structures used in their derivation. The parameters of fold assessment whose optimal values vary significantly with model size include the size of the known protein structures used to derive the potential and the distance range of the accessible surface potential. Fold assessment by statistical potentials is most difficult for the very small models. This difficulty presents a challenge to fold assessment in large-scale comparative modeling, which produces many small and incomplete models. The results described in this study provide a basis for an optimal use of statistical potentials in fold assessment.

Bibliotecas - Pontificia Universidad Católica de Chile- Dirección oficinas centrales: Av. Vicuña Mackenna 4860. Santiago de Chile.

  • Cookie settings
  • Privacy policy
  • End User Agreement
  • Send Feedback