Conservation, Evolutionary: Difference between revisions
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===INTREPID=== | ===INTREPID=== | ||
In 2024, the INTREPID Server, formerly at the University of California, Berkeley, appears to be unavailable. | |||
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"[http://phylogenomics.berkeley.edu/INTREPID/index.html INTREPID] is an information-theoretic approach for functional site identification that exploits the information in large diverse multiple sequence alignments. INTREPID gathers homologs for a sequence using PSI-BLAST and estimates a phylogenetic tree. It then uses Jensen-Shannon divergence to measure the information for each position in the sequence at each subtree node encountered on a traversal of the phylogeny, tracing a path from the root to the leaf corresponding to the sequence of interest. Positions that are conserved across the entire family receive stronger scores than those that only become conserved within more closely related subgroups. This tree traversal produces a phylogenomic conservation score for each position in the MSA. INTREPID uses information from sequence only, and can thus be used when knowledge of structure is not available." (Quoted from the [http://phylogenomics.berkeley.edu/INTREPID/index.html INTREPID website].) | "[http://phylogenomics.berkeley.edu/INTREPID/index.html INTREPID] is an information-theoretic approach for functional site identification that exploits the information in large diverse multiple sequence alignments. INTREPID gathers homologs for a sequence using PSI-BLAST and estimates a phylogenetic tree. It then uses Jensen-Shannon divergence to measure the information for each position in the sequence at each subtree node encountered on a traversal of the phylogeny, tracing a path from the root to the leaf corresponding to the sequence of interest. Positions that are conserved across the entire family receive stronger scores than those that only become conserved within more closely related subgroups. This tree traversal produces a phylogenomic conservation score for each position in the MSA. INTREPID uses information from sequence only, and can thus be used when knowledge of structure is not available." (Quoted from the [http://phylogenomics.berkeley.edu/INTREPID/index.html INTREPID website].) | ||
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Evidence is provided that INTREPID out-performs ConSurf for predicting catalytic residues. | Evidence is provided that INTREPID out-performs ConSurf for predicting catalytic residues. | ||
Unlike ConSurf, INTREPID does not identify the [[#Locating Variable Patches|most variable residues]] in addition to the [[#Locating Conserved Patches|most conserved]]. | Unlike ConSurf, INTREPID does not identify the [[#Locating Variable Patches|most variable residues]] in addition to the [[#Locating Conserved Patches|most conserved]]. --> | ||
===xProtCAS=== | ===xProtCAS=== | ||
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===siteFiNDER|3D=== | ===siteFiNDER|3D=== | ||
In 2024, the siteFiNDER 3D Server, formerly at Yale University, appears to be unavailable. <!-- | |||
[http://sitefinder3d.mbb.yale.edu/ siteFiNDER|3D] performs ''conserved functional group'' (CFG) analysis. "CFG Analysis is a general method for predicting the location of functionally important sites within a target protein structure. Like other available structure/sequence analysis techniques, CFG Analysis exploits the evolutionary relationships present across groups of homologous proteins to identify regions that are likely to be of functional significance. However, this technique is particularly useful for situations where other methods fail, for instance when only a few or highly similar homologues can be identified." As its name implies, CFG analysis attempts to identify groups of conserved amino acids that together represent a functional site. In this respect, it goes beyond most other evolutionary conservation servers, which stop at assigning a conservation value to each amino acid. See the [http://consurfdb.tau.ac.il/comparison.php comparison of siteFiNDER|3D with ConSurf for cytochrome c]. | [http://sitefinder3d.mbb.yale.edu/ siteFiNDER|3D] performs ''conserved functional group'' (CFG) analysis. "CFG Analysis is a general method for predicting the location of functionally important sites within a target protein structure. Like other available structure/sequence analysis techniques, CFG Analysis exploits the evolutionary relationships present across groups of homologous proteins to identify regions that are likely to be of functional significance. However, this technique is particularly useful for situations where other methods fail, for instance when only a few or highly similar homologues can be identified." As its name implies, CFG analysis attempts to identify groups of conserved amino acids that together represent a functional site. In this respect, it goes beyond most other evolutionary conservation servers, which stop at assigning a conservation value to each amino acid. See the [http://consurfdb.tau.ac.il/comparison.php comparison of siteFiNDER|3D with ConSurf for cytochrome c]. | ||
This site provides links to several other software packages that predict functional sites, some of which are not further discussed in the present article. | This site provides links to several other software packages that predict functional sites, some of which are not further discussed in the present article. --> | ||
===HotPatch=== | ===HotPatch=== | ||
[http://hotpatch.mbi.ucla.edu/ HotPatch] <ref>PMID: 17451744</ref> "finds unusual patches on the surface of proteins, and computes just how unusual they are (patch rareness), and how likely each patch is to be of functional importance (functional confidence (FC).) The statistical analysis is done by comparing your protein's surface against the surfaces of a large set of proteins whose functional sites are known." One advantage of HotPatch is that sequence homologs are not required. See the [http://consurfdb.tau.ac.il/comparison.php comparison of HotPatch with ConSurf for cytochrome c]. | In 2024, the HotPatch Server, formerly at UCLA, appears to be unavailable. <!-- | ||
[http://hotpatch.mbi.ucla.edu/ HotPatch] <ref>PMID: 17451744</ref> "finds unusual patches on the surface of proteins, and computes just how unusual they are (patch rareness), and how likely each patch is to be of functional importance (functional confidence (FC).) The statistical analysis is done by comparing your protein's surface against the surfaces of a large set of proteins whose functional sites are known." One advantage of HotPatch is that sequence homologs are not required. See the [http://consurfdb.tau.ac.il/comparison.php comparison of HotPatch with ConSurf for cytochrome c]. --> | |||
===Evolutionary Trace Viewer=== | ===Evolutionary Trace Viewer=== | ||
[http:// | [http://evolution.lichtargelab.org/ETviewer Evolutionary Trace Viewer] (ETV).<!-- | ||
See the [http://consurfdb.tau.ac.il/comparison.php comparison of ETV with ConSurf for cytochrome c].--> | |||
<blockquote> | <blockquote> | ||
Comment by [[User:Eric Martz]], March, 2009: From the information provided on the ETV website, I found it quite difficult to understand what the ETV is doing, or how to use the viewer. An explanation in simple terms for non-specialists would be very useful. | Comment by [[User:Eric Martz]], March, 2009: From the information provided on the ETV website, I found it quite difficult to understand what the ETV is doing, or how to use the viewer. An explanation in simple terms for non-specialists would be very useful. | ||
</blockquote> | </blockquote> | ||
===EVcouplings / EVfold=== | ===EVcouplings / EVfold=== | ||