Protein Structure Prediction
Mostrando 37-48 de 281 artigos, teses e dissertações.
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37. GeneSilico protein structure prediction meta-server
Rigorous assessments of protein structure prediction have demonstrated that fold recognition methods can identify remote similarities between proteins when standard sequence search methods fail. It has been shown that the accuracy of predictions is improved when refined multiple sequence alignments are used instead of single sequences and if different method
Oxford University Press.
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38. HYPROSP: a hybrid protein secondary structure prediction algorithm—a knowledge-based approach
We develop a knowledge-based approach (called PROSP) for protein secondary structure prediction. The knowledge base contains small peptide fragments together with their secondary structural information. A quantitative measure M, called match rate, is defined to measure the amount of structural information that a target protein can extract from the knowledge
Oxford University Press.
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39. Improved prediction of protein secondary structure by use of sequence profiles and neural networks.
The explosive accumulation of protein sequences in the wake of large-scale sequencing projects is in stark contrast to the much slower experimental determination of protein structures. Improved methods of structure prediction from the gene sequence alone are therefore needed. Here, we report a substantial increase in both the accuracy and quality of secondar
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40. Assessment of some problems associated with prediction of the three-dimensional structure of a protein from its amino-acid sequence.
It is shown that most present empirical prediction algorithms provide information about the conformational states of individual residues, but give little information about the three-dimensional structure of a protein. It is necessary to predict the conformational state of every residue before the resulting structure can serve as a starting conformation to co
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41. Water in protein structure prediction
Proteins have evolved to use water to help guide folding. A physically motivated, nonpairwise-additive model of water-mediated interactions added to a protein structure prediction Hamiltonian yields marked improvement in the quality of structure prediction for larger proteins. Free energy profile analysis suggests that long-range water-mediated potentials gu
National Academy of Sciences.
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42. Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue–residue contacts
Motivation:Correct prediction of residue–residue contacts in proteins that lack good templates with known structure would take ab initio protein structure prediction a large step forward. The lack of correct contacts, and in particular long-range contacts, is considered the main reason why these methods often fail.
Oxford University Press.
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43. PROTINFO: secondary and tertiary protein structure prediction
Information about the secondary and tertiary structure of a protein sequence can greatly assist biologists in the generation and testing of hypotheses, as well as design of experiments. The PROTINFO server enables users to submit a protein sequence and request a prediction of the three-dimensional (tertiary) structure based on comparative modeling, fold gene
Oxford University Press.
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44. BPROMPT: a consensus server for membrane protein prediction
Protein structure prediction is a cornerstone of bioinformatics research. Membrane proteins require their own prediction methods due to their intrinsically different composition. A variety of tools exist for topology prediction of membrane proteins, many of them available on the Internet. The server described in this paper, BPROMPT (Bayesian PRediction Of Me
Oxford University Press.
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45. Reconstruction and Stability of Secondary Structure Elements in the Context of Protein Structure Prediction
Efficient and accurate reconstruction of secondary structure elements in the context of protein structure prediction is the major focus of this work. We present a novel approach capable of reconstructing α-helices and β-sheets in atomic detail. The method is based on Metropolis Monte Carlo simulations in a force field of empirical potentials that are desig
The Biophysical Society.
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46. The PredictProtein server
PredictProtein (PP, http://cubic.bioc.columbia.edu/pp/) is an internet service for sequence analysis and the prediction of aspects of protein structure and function. Users submit protein sequence or alignments; the server returns a multiple sequence alignment, PROSITE sequence motifs, low-complexity regions (SEG), ProDom domain assignments, nuclear localisat
Oxford University Press.
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47. The PredictProtein server
PredictProtein (http://www.predictprotein.org) is an Internet service for sequence analysis and the prediction of protein structure and function. Users submit protein sequences or alignments; PredictProtein returns multiple sequence alignments, PROSITE sequence motifs, low-complexity regions (SEG), nuclear localization signals, regions lacking regular struct
Oxford University Press.
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48. Comprehensive de novo structure prediction in a systems-biology context for the archaea Halobacterium sp. NRC-1
Simultaneous analysis of the association network, coordinated mRNA level changes in microarray experiments and genome-wide structure prediction of the three-dimensional structure of 1,185 proteins and protein domains provided insights into the roles of several Halobacterium NRC-1 proteins of previously unknown function.
BioMed Central.