The Experts below are selected from a list of 241551 Experts worldwide ranked by ideXlab platform
Jacquelyn S. Fetrow - One of the best experts on this subject based on the ideXlab platform.
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an atlas of peroxiredoxins created using an active Site profile based approach to Functionally relevant clustering of proteins
PLOS Computational Biology, 2017Co-Authors: Angela F Harper, Leslie B. Poole, Janelle B Leuthaeuser, Patricia C Babbitt, John H Morris, Thomas E Ferrin, Jacquelyn S. FetrowAbstract:Peroxiredoxins (Prxs or Prdxs) are a large protein superfamily of antioxidant enzymes that rapidly detoxify damaging peroxides and/or affect signal transduction and, thus, have roles in proliferation, differentiation, and apoptosis. Prx superfamily members are widespread across phylogeny and multiple methods have been developed to classify them. Here we present an updated atlas of the Prx superfamily identified using a novel method called MISST (Multi-level Iterative Sequence Searching Technique). MISST is an iterative search process developed to be both agglomerative, to add sequences containing similar Functional Site features, and divisive, to split groups when Functional Site features suggest distinct Functionally-relevant clusters. Superfamily members need not be identified initially-MISST begins with a minimal representative set of known structures and searches GenBank iteratively. Further, the method's novelty lies in the manner in which isoFunctional groups are selected; rather than use a single or shifting threshold to identify clusters, the groups are deemed isoFunctional when they pass a self-identification criterion, such that the group identifies itself and nothing else in a search of GenBank. The method was preliminarily validated on the Prxs, as the Prxs presented challenges of both agglomeration and division. For example, previous sequence analysis clustered the Prx Functional families Prx1 and Prx6 into one group. Subsequent expert analysis clearly identified Prx6 as a distinct Functionally relevant group. The MISST process distinguishes these two closely related, though Functionally distinct, families. Through MISST search iterations, over 38,000 Prx sequences were identified, which the method divided into six isoFunctional clusters, consistent with previous expert analysis. The results represent the most complete computational Functional analysis of proteins comprising the Prx superfamily. The feasibility of this novel method is demonstrated by the Prx superfamily results, laying the foundation for potential Functionally relevant clustering of the universe of protein sequences.
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Functional Site profiling and electrostatic analysis of cysteines modifiable to cysteine sulfenic acid.
Protein Science, 2008Co-Authors: Freddie R. Salsbury, Stacy T. Knutson, Leslie B. Poole, Jacquelyn S. FetrowAbstract:Cysteine sulfenic acid (Cys-SOH), a reversible modification, is a catalytic intermediate at enzyme active Sites, a sensor for oxidative stress, a regulator of some transcription factors, and a redox-signaling intermediate. This post-translational modification is not random: specific features near the cysteine control its reactivity. To identify features responsible for the propensity of cysteines to be modified to sulfenic acid, a list of 47 proteins (containing 49 known Cys-SOH Sites) was compiled. Modifiable cysteines are found in proteins from most structural classes and many Functional classes, but have no propensity for any one type of protein secondary structure. To identify features affecting cysteine reactivity, these Sites were analyzed using both Functional Site profiling and electrostatic analysis. Overall, the solvent exposure of modifiable cysteines is not different from the average cysteine. The combined sequence, structure, and electrostatic approaches reveal mechanistic determinants not obvious from overall sequence comparison, including: (1) pKas of some modifiable cysteines are affected by backbone features only; (2) charged residues are underrepresented in the structure near modifiable Sites; (3) threonine and other polar residues can exert a large influence on the cysteine pKa; and (4) hydrogen bonding patterns are suggested to be important. This compilation of Cys-SOH modification Sites and their features provides a quantitative assessment of previous observations and a basis for further analysis and prediction of these Sites. Agreement with known experimental data indicates the utility of this combined approach for identifying mechanistic determinants at protein Functional Sites.
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Current Protocols in Bioinformatics - Active Site profiling to identify protein Functional Sites in sequences and structures using the Deacon Active Site Profiler (DASP).
Current Protocols in Bioinformatics, 2006Co-Authors: Jacquelyn S. FetrowAbstract:Methods for the annotation and analysis of Functional Sites in proteins are an area of active research, and those methods that allow detailed characterization of Functional Site features are much needed. A Web Site application, DASP, which implements a previously described method (Cammer, et al., 2003) to allow users to create an active Site profile for any protein family, is described. Two protocols for Functional Site analysis of protein families using DASP are presented: 1) creation of Functional Site signatures and a profile from proteins of known structure and 2) utilization of the active Site profile to search sequences that contain fragments similar to those found in the Functional Site signatures. The active Site profile produced by Basic Protocol 1 allows the user to analyze the features of the Functional Site, i.e., those characteristics that are common across the family and those that are unique to one or several members of the family. The characteristics that are unique to a subfamily might be described as specificity determinants i.e., features that impart specificity to a particular function. Basic Protocol 2 provides instructions for searching for sequences that might contain a similar Functional Site. Keywords: active Site profiling; fuzzy Functional form; protein function prediction; active Site; Functional Site; Functional specificity determinants
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Active Site profiling to identify protein Functional Sites in sequences and structures using the Deacon Active Site Profiler (DASP).
Current protocols in bioinformatics, 2006Co-Authors: Jacquelyn S. FetrowAbstract:Methods for the annotation and analysis of Functional Sites in proteins are an area of active research, and those methods that allow detailed characterization of Functional Site features are much needed. A Web Site application, DASP, which implements a previously described method (Cammer, et al., 2003) to allow users to create an active Site profile for any protein family, is described. Two protocols for Functional Site analysis of protein families using DASP are presented: 1) creation of Functional Site signatures and a profile from proteins of known structure and 2) utilization of the active Site profile to search sequences that contain fragments similar to those found in the Functional Site signatures. The active Site profile produced by Basic Protocol 1 allows the user to analyze the features of the Functional Site, i.e., those characteristics that are common across the family and those that are unique to one or several members of the family. The characteristics that are unique to a subfamily might be described as specificity determinants i.e., features that impart specificity to a particular function. Basic Protocol 2 provides instructions for searching for sequences that might contain a similar Functional Site.
Zheng Rong Yang - One of the best experts on this subject based on the ideXlab platform.
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Peptide bioinformatics: peptide classification using peptide machines.
Methods in molecular biology (Clifton N.J.), 2008Co-Authors: Zheng Rong YangAbstract:Peptides scanned from whole protein sequences are the core information for many peptide bioinformatics research such as Functional Site prediction, protein structure identification, and protein function recognition. In these applications, we normally need to assign a peptide to one of the given categories using a computer model. They are therefore referred to as peptide classification applications. Among various machine learning approaches, including neural networks, peptide machines have demonstrated excellent performance in many applications. This chapter discusses the basic concepts of peptide classification, commonly used feature extraction methods, three peptide machines, and some important issues in peptide classification.
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Pattern Recognition Methods for Protein Functional Site Prediction
Current protein & peptide science, 2005Co-Authors: Zheng Rong Yang, Lipo Wang, Natasha Young, Dave Trudgian, Kuo-chen ChouAbstract:Protein Functional Site prediction is closely related to drug design, hence to public health. In order to save the cost and the time spent on identifying the Functional Sites in sequenced proteins in biology laboratory, computer programs have been widely used for decades. Many of them are implemented using the state-of-the-art pattern recognition algorithms, including decision trees, neural networks and support vector machines. Although the success of this effort has been obvious, advanced and new algorithms are still under development for addressing some difficult issues. This review will go through the major stages in developing pattern recognition algorithms for protein Functional Site prediction and outline the future research directions in this important area.
Haim J. Wolfson - One of the best experts on this subject based on the ideXlab platform.
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Recognition of Functional Sites in protein structures.
Journal of molecular biology, 2004Co-Authors: Alexandra Shulman-peleg, Ruth Nussinov, Haim J. WolfsonAbstract:Recognition of regions on the surface of one protein, that are similar to a binding Site of another is crucial for the prediction of molecular interactions and for Functional classifications. We first describe a novel method, SiteEngine, that assumes no sequence or fold similarities and is able to recognize proteins that have similar binding Sites and may perform similar functions. We achieve high efficiency and speed by introducing a low-resolution surface representation via chemically important surface points, by hashing triangles of physico-chemical properties and by application of hierarchical scoring schemes for a thorough exploration of global and local similarities. We proceed to rigorously apply this method to Functional Site recognition in three possible ways: first, we search a given Functional Site on a large set of complete protein structures. Second, a potential Functional Site on a protein of interest is compared with known binding Sites, to recognize similar features. Third, a complete protein structure is searched for the presence of an a priori unknown Functional Site, similar to known Sites. Our method is robust and efficient enough to allow computationally demanding applications such as the first and the third. From the biological standpoint, the first application may identify secondary binding Sites of drugs that may lead to side-effects. The third application finds new potential Sites on the protein that may provide targets for drug design. Each of the three applications may aid in assigning a function and in classification of binding patterns. We highlight the advantages and disadvantages of each type of search, provide examples of large-scale searches of the entire Protein Data Base and make Functional predictions.
Ponnampalam Gopalakrishnakone - One of the best experts on this subject based on the ideXlab platform.
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Functional Site of endogenous phospholipase A2 inhibitor from python serum
FEBS Journal, 2002Co-Authors: Mie Mie Su Thwin, Ramapatna L. Satish, Steven T. F. Chan, Ponnampalam GopalakrishnakoneAbstract:The Functional Site of ‘phospholipase A2 inhibitor from python’ (PIP) was predicted based on the hypothesis of proline brackets. Using different sources of secretory phospholipase A2 (sPLA2s) as enzyme, and [3H]arachidonate-labelled Escherichia coli as substrate, short synthetic peptides representing the proposed Site were examined for their secretory phospholipase A2 (sPLA2) inhibitory␣activity. A decapeptide P-PB.III proved to be the most potent of the tested peptides in inhibiting sPLA2 enzymatic activity in vitro, and exhibited striking anti-inflammatory effects in␣vivo in a mouse paw oedema model. P-PB.III inhibited the enzymatic activity of class I, II and III PLA2s, including that of human synovial fluid from arthritis patients. When tested by ELISA, biotinylated P-PB.III interacted positively with various PLA2s, suggesting that the specific region of PIP corresponding to P-PB.III, is likely to be involved in the PLA2–PLI interaction. The effect of P-PB.III on the peritoneal inflammatory response after surgical trauma in rats was also examined. P-PB.III effectively reduced the extent of postsurgical peritoneal adhesions as compared to controls. sPLA2 levels at seventh postoperative day in the peritoneal tissue of P-PB.III-treated rats were also significantly reduced (P
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Functional Site of endogenous phospholipase a2 inhibitor from python serum
FEBS Journal, 2002Co-Authors: Mie Mie Su Thwin, Ramapatna L. Satish, Steven T. F. Chan, Ponnampalam GopalakrishnakoneAbstract:The Functional Site of ‘phospholipase A2 inhibitor from python’ (PIP) was predicted based on the hypothesis of proline brackets. Using different sources of secretory phospholipase A2 (sPLA2s) as enzyme, and [3H]arachidonate-labelled Escherichia coli as substrate, short synthetic peptides representing the proposed Site were examined for their secretory phospholipase A2 (sPLA2) inhibitory␣activity. A decapeptide P-PB.III proved to be the most potent of the tested peptides in inhibiting sPLA2 enzymatic activity in vitro, and exhibited striking anti-inflammatory effects in␣vivo in a mouse paw oedema model. P-PB.III inhibited the enzymatic activity of class I, II and III PLA2s, including that of human synovial fluid from arthritis patients. When tested by ELISA, biotinylated P-PB.III interacted positively with various PLA2s, suggesting that the specific region of PIP corresponding to P-PB.III, is likely to be involved in the PLA2–PLI interaction. The effect of P-PB.III on the peritoneal inflammatory response after surgical trauma in rats was also examined. P-PB.III effectively reduced the extent of postsurgical peritoneal adhesions as compared to controls. sPLA2 levels at seventh postoperative day in the peritoneal tissue of P-PB.III-treated rats were also significantly reduced (P < 0.05) in comparison to those of the untreated controls. The present results shed additional insight on the essential structural elements for PLA2 binding, and may be useful as a basis for the design of novel therapeutic agents.
Kuo-chen Chou - One of the best experts on this subject based on the ideXlab platform.
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Pattern Recognition Methods for Protein Functional Site Prediction
Current protein & peptide science, 2005Co-Authors: Zheng Rong Yang, Lipo Wang, Natasha Young, Dave Trudgian, Kuo-chen ChouAbstract:Protein Functional Site prediction is closely related to drug design, hence to public health. In order to save the cost and the time spent on identifying the Functional Sites in sequenced proteins in biology laboratory, computer programs have been widely used for decades. Many of them are implemented using the state-of-the-art pattern recognition algorithms, including decision trees, neural networks and support vector machines. Although the success of this effort has been obvious, advanced and new algorithms are still under development for addressing some difficult issues. This review will go through the major stages in developing pattern recognition algorithms for protein Functional Site prediction and outline the future research directions in this important area.
