The Experts below are selected from a list of 45750 Experts worldwide ranked by ideXlab platform
Chris Sander - One of the best experts on this subject based on the ideXlab platform.
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a sequence Property approach to searching Protein databases
Journal of Molecular Biology, 1995Co-Authors: Uwe Hobohm, Chris SanderAbstract:Currently available sequence alignment programs are generally not capable of detecting functional and structural homologs in the twilight zone of sequence similarity, i.e. when the sequence identity falls below about 25%. Here we attempt to detect such weak similarities using an approach based on a notion of Protein sequence similarity radically different from that used in sequential alignment. The approach defines Protein sequence dissimilarity (or distance) as a weighted sum of differences of compositional properties such as singlet and doublet amino acid composition, molecular weight, isoelectric point (Protein Property search or PropSearch). With PropSearch, either single sequences can be used for a database query, or multiple sequences can be merged into an "average" sequence reflecting the average composition of a Protein family. First, we show that members of structural Protein families have a low mutual PropSearch distance when the weights are optimized to discriminate maximally between structural families. Second, we demonstrate the results of database searches using the PropSearch method. Such searches are very rapid when scanning a preprocessed database and do not require alignments. In cases in which conventional alignment tools fail to detect similarities, PropSearch can be used to generate hypotheses about possible structural or functional relationships between a new sequence and sequences in the database.
Guohui Lin - One of the best experts on this subject based on the ideXlab platform.
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ppt db the Protein Property prediction and testing database
Nucleic Acids Research, 2007Co-Authors: David S Wishart, David Arndt, Mark V Berjanskii, An Chi Guo, Yi Shi, Savita Shrivastava, Jianjun Zhou, You Zhou, Guohui LinAbstract:The Protein Property prediction and testing database (PPT-DB) is a database housing nearly 30 carefully curated databases, each of which contains commonly predicted Protein Property information. These properties include both structural (i.e. secondary structure, contact order, disulfide pairing) and dynamic (i.e. order parameters, B-factors, folding rates) features that have been measured, derived or tabulated from a variety of sources. PPT-DB is designed to serve two purposes. First it is intended to serve as a centralized, up-to-date, freely downloadable and easily queried repository of predictable or ‘derived’ Protein Property data. In this role, PPT-DB can serve as a one-stop, fully standardized repository for developers to obtain the required training, testing and validation data needed for almost any kind of Protein Property prediction program they may wish to create. The second role that PPT-DB can play is as a tool for homology-based Protein Property prediction. Users may query PPT-DB with a sequence of interest and have a specific Property predicted using a sequence similarity search against PPT-DB's extensive collection of Proteins with known properties. PPT-DB exploits the well-known fact that Protein structure and dynamic properties are highly conserved between homologous Proteins. Predictions derived from PPT-DB's similarity searches are typically 85–95% correct (for categorical predictions, such as secondary structure) or exhibit correlations of >0.80 (for numeric predictions, such as accessible surface area). This performance is 10–20% better than what is typically obtained from standard ‘ab initio’ predictions. PPT-DB, its prediction utilities and all of its contents are available at http://www.pptdb.ca
Frances H Arnold - One of the best experts on this subject based on the ideXlab platform.
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in the light of directed evolution pathways of adaptive Protein evolution
Proceedings of the National Academy of Sciences of the United States of America, 2009Co-Authors: Jesse D Bloom, Frances H ArnoldAbstract:Directed evolution is a widely-used engineering strategy for improving the stabilities or biochemical functions of Proteins by repeated rounds of mutation and selection. These experiments offer empirical lessons about how Proteins evolve in the face of clearly-defined laboratory selection pressures. Directed evolution has revealed that single amino acid mutations can enhance properties such as catalytic activity or stability and that adaptation can often occur through pathways consisting of sequential beneficial mutations. When there are no single mutations that improve a particular Protein Property experiments always find a wealth of mutations that are neutral with respect to the laboratory-defined measure of fitness. These neutral mutations can open new adaptive pathways by at least 2 different mechanisms. Functionally-neutral mutations can enhance a Protein's stability, thereby increasing its tolerance for subsequent functionally beneficial but destabilizing mutations. They can also lead to changes in “promiscuous” functions that are not currently under selective pressure, but can subsequently become the starting points for the adaptive evolution of new functions. These lessons about the coupling between adaptive and neutral Protein evolution in the laboratory offer insight into the evolution of Proteins in nature.
Herschel Rabitz - One of the best experts on this subject based on the ideXlab platform.
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enhancing the efficiency of directed evolution in focused enzyme libraries by the adaptive substituent reordering algorithm
Chemistry: A European Journal, 2012Co-Authors: Xiaojiang Feng, Joaquin Sanchis, Manfred T Reetz, Herschel RabitzAbstract:Directed evolution is a broadly successful strategy for Protein engineering in the quest to enhance the stereoselectivity, activity, and thermostability of enzymes. To increase the efficiency of directed evolution based on iterative saturation mutagenesis, the adaptive substituent reordering algorithm (ASRA) is introduced here as an alternative to traditional quantitative structure-activity relationship (QSAR) methods for identifying potential Protein mutants with desired properties from minimal sampling of focused libraries. The operation of ASRA depends on identifying the underlying regularity of the Protein Property landscape, allowing it to make predictions without explicit knowledge of the structure-Property relationships. In a proof-of-principle study, ASRA identified all or most of the best enantioselective mutants among the synthesized epoxide hydrolase from Aspergillus niger, in the absence of peptide seeds with high E-values. ASRA even revealed a laboratory error from irregularities of the reordered E-value landscape alone.
Uwe Hobohm - One of the best experts on this subject based on the ideXlab platform.
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a sequence Property approach to searching Protein databases
Journal of Molecular Biology, 1995Co-Authors: Uwe Hobohm, Chris SanderAbstract:Currently available sequence alignment programs are generally not capable of detecting functional and structural homologs in the twilight zone of sequence similarity, i.e. when the sequence identity falls below about 25%. Here we attempt to detect such weak similarities using an approach based on a notion of Protein sequence similarity radically different from that used in sequential alignment. The approach defines Protein sequence dissimilarity (or distance) as a weighted sum of differences of compositional properties such as singlet and doublet amino acid composition, molecular weight, isoelectric point (Protein Property search or PropSearch). With PropSearch, either single sequences can be used for a database query, or multiple sequences can be merged into an "average" sequence reflecting the average composition of a Protein family. First, we show that members of structural Protein families have a low mutual PropSearch distance when the weights are optimized to discriminate maximally between structural families. Second, we demonstrate the results of database searches using the PropSearch method. Such searches are very rapid when scanning a preprocessed database and do not require alignments. In cases in which conventional alignment tools fail to detect similarities, PropSearch can be used to generate hypotheses about possible structural or functional relationships between a new sequence and sequences in the database.
