scholarly journals Evolution and Functional Information

2017 ◽  
Author(s):  
Matthew K. Matlock ◽  
S. Joshua Swamidass
Keyword(s):  
Mutual Information ◽  
Protein Sequence ◽  
Mutational Bias ◽  
Functional Information ◽  
Sequence Alignments ◽  
Multifunctional Proteins ◽  
True Number ◽  
The Fantastic ◽  
Random Searches

Abstract“Functional Information”—estimated from the mutual information of protein sequence alignments—has been proposed as a reliable way of estimating the number of proteins with a specified function and the consequent difficulty of evolving a new function. The fantastic rarity of functional proteins computed by this approach emboldens some to argue that evolution is impossible. Random searches, it seems, would have no hope of finding new functions. Here, we use simulations to demonstrate that sequence alignments are a poor estimate functional information. The mutual information of sequence alignments fantastically underestimates of the true number of functional proteins, because it also is strongly influenced by a family’s history, mutational bias, and selection. Regardless, even if functional information could be reliably calculated, it tells us nothing about the difficulty of evolving new functions, because it does not estimate the distance between a new function and existing functions. The pervasive observation of multifunctional proteins suggests that functions are actually ver close to one another and abundant. Multifunctional proteins would be impossible if the FI argument against evolution were true.

UniprotR: Retrieving and visualizing protein sequence and functional information from Universal Protein Resource (UniProt knowledgebase)

2020 ◽  
Vol 213 ◽  
pp. 103613 ◽  
Author(s):  
Mohamed Soudy ◽  
Ali Mostafa Anwar ◽  
Eman Ali Ahmed ◽  
Aya Osama ◽  
Shahd Ezzeldin ◽  
...  
Keyword(s):  
Protein Sequence ◽  
Functional Information ◽  
Universal Protein

scholarly journals Crystallization of an atypical short-chain dehydrogenase fromVibrio vulnificuslacking the conserved catalytic tetrad

2012 ◽  
Vol 68 (7) ◽  
pp. 771-774 ◽  
Author(s):  
Geraldine Buysschaert ◽  
Kenneth Verstraete ◽  
Savvas N. Savvides ◽  
Bjorn Vergauwen
Keyword(s):  
Protein Sequence ◽  
Scientific Literature ◽  
Vibrio Vulnificus ◽  
Short Chain ◽  
Reduced Form ◽  
Molecular Replacement ◽  
Structural Studies ◽  
Sequence Alignments ◽  
Crystal Forms ◽  
Short Chain Dehydrogenase

Short-chain dehydrogenases/reductases (SDRs) are a rapidly expanding superfamily of enzymes that are found in all kingdoms of life. Hallmarked by a highly conserved Asn-Ser-Tyr-Lys catalytic tetrad, SDRs have a broad substrate spectrum and play diverse roles in key metabolic processes. Locus tag VVA1599 inVibrio vulnificusencodes a short-chain dehydrogenase (hereafter referred to as SDRvv) which lacks the signature catalytic tetrad of SDR members. Structure-based protein sequence alignments have suggested that SDRvv may harbour a unique binding site for its nicotinamide cofactor. To date, structural studies of SDRs with altered catalytic centres are underrepresented in the scientific literature, thus limiting understanding of their spectrum of substrate and cofactor preferences. Here, the expression, purification and crystallization of recombinant SDRvv are presented. Two well diffracting crystal forms could be obtained by cocrystallization in the presence of the reduced form of the phosphorylated nicotinamide cofactor NADPH. The collected data were of sufficient quality for successful structure determination by molecular replacement and subsequent refinement. This work sets the stage for deriving the identity of the natural substrate of SDRvv and the structure–function landscape of typical and atypical SDRs.

scholarly journals PROMALS web server for accurate multiple protein sequence alignments

2007 ◽  
Vol 35 (Web Server) ◽  
pp. W649-W652 ◽  
Author(s):  
J. Pei ◽  
B.-H. Kim ◽  
M. Tang ◽  
N. V. Grishin
Keyword(s):  
Protein Sequence ◽  
Web Server ◽  
Sequence Alignments ◽  
Multiple Protein

Analysis and prediction of functional sub-types from protein sequence alignments

2000 ◽  
Vol 303 (1) ◽  
pp. 61-76 ◽  
Author(s):  
Sridhar S. Hannenhalli ◽  
Robert B. Russell
Keyword(s):  
Protein Sequence ◽  
Sequence Alignments

scholarly journals HPMV: Human protein mutation viewer — relating sequence mutations to protein sequence architecture and function changes

2015 ◽  
Vol 13 (05) ◽  
pp. 1550028 ◽  
Author(s):  
Westley Arthur Sherman ◽  
Durga Bhavani Kuchibhatla ◽  
Vachiranee Limviphuvadh ◽  
Sebastian Maurer-Stroh ◽  
Birgit Eisenhaber ◽  
...  
Keyword(s):  
Protein Sequence ◽  
Genetic Disorders ◽  
Human Protein ◽  
Post Translational Modification ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Sequence Architecture ◽  
Protein Mutation ◽  
Architectural Features ◽  
Protein Mutations

Next-generation sequencing advances are rapidly expanding the number of human mutations to be analyzed for causative roles in genetic disorders. Our Human Protein Mutation Viewer (HPMV) is intended to explore the biomolecular mechanistic significance of non-synonymous human mutations in protein-coding genomic regions. The tool helps to assess whether protein mutations affect the occurrence of sequence-architectural features (globular domains, targeting signals, post-translational modification sites, etc.). As input, HPMV accepts protein mutations — as UniProt accessions with mutations (e.g. HGVS nomenclature), genome coordinates, or FASTA sequences. As output, HPMV provides an interactive cartoon showing the mutations in relation to elements of the sequence architecture. A large variety of protein sequence architectural features were selected for their particular relevance to mutation interpretation. Clicking a sequence feature in the cartoon expands a tree view of additional information including multiple sequence alignments of conserved domains and a simple 3D viewer mapping the mutation to known PDB structures, if available. The cartoon is also correlated with a multiple sequence alignment of similar sequences from other organisms. In cases where a mutation is likely to have a straightforward interpretation (e.g. a point mutation disrupting a well-understood targeting signal), this interpretation is suggested. The interactive cartoon can be downloaded as standalone viewer in Java jar format to be saved and viewed later with only a standard Java runtime environment. The HPMV website is: http://hpmv.bii.a-star.edu.sg/ .

scholarly journals Using homology relations within a database markedly boosts protein sequence similarity search

2015 ◽  
Vol 112 (22) ◽  
pp. 7003-7008 ◽  
Author(s):  
Jing Tong ◽  
Ruslan I. Sadreyev ◽  
Jimin Pei ◽  
Lisa N. Kinch ◽  
Nick V. Grishin
Keyword(s):  
Protein Sequence ◽  
Sequence Similarity ◽  
Large Fraction ◽  
Query Sequence ◽  
Homology Search ◽  
Sequence Alignments ◽  
Current Sequence ◽  
Detection Quality ◽  
And Function ◽  
Precision Rate

Inference of homology from protein sequences provides an essential tool for analyzing protein structure, function, and evolution. Current sequence-based homology search methods are still unable to detect many similarities evident from protein spatial structures. In computer science a search engine can be improved by considering networks of known relationships within the search database. Here, we apply this idea to protein-sequence–based homology search and show that it dramatically enhances the search accuracy. Our new method, COMPADRE (COmparison of Multiple Protein sequence Alignments using Database RElationships) assesses the relationship between the query sequence and a hit in the database by considering the similarity between the query and hit’s known homologs. This approach increases detection quality, boosting the precision rate from 18% to 83% at half-coverage of all database homologs. The increased precision rate allows detection of a large fraction of protein structural relationships, thus providing structure and function predictions for previously uncharacterized proteins. Our results suggest that this general approach is applicable to a wide variety of methods for detection of biological similarities. The web server is available at prodata.swmed.edu/compadre.

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