Order independent structural alignment of circularly permuted proteins

Circular permutation connects the N and C termini of a protein and concurrently cleaves elsewhere in the chain, providing an important mechanism for generating novel protein fold and functions. However, their in genomes is unknown because current detection methods can miss many occurances, mistaking random repeats as circular permutation. Here we develop a method for detecting circularly permuted proteins from structural comparison. Sequence order independent alignment of protein structures can be regarded as a special case of the maximum-weight independent set problem, which is known to be computationally hard. We develop an efficient approximation algorithm by repeatedly solving relaxations of an appropriate intermediate integer programming formulation, we show that the approximation ratio is much better then the theoretical worst case ratio of $r = 1/4$. Circularly permuted proteins reported in literature can be identified rapidly with our method, while they escape the detection by publicly available servers for structural alignment.Comment: 5 pages, 3 figures, Accepted by IEEE-EMBS 2004 Conference Proceeding

Protein Functional Surfaces: Global Shape Matching and Local Spatial Alignments of Ligand Binding Sites

<p>Abstract</p> <p>Background</p> <p>Protein surfaces comprise only a fraction of the total residues but are the most conserved functional features of proteins. Surfaces performing identical functions are found in proteins absent of any sequence or fold similarity. While biochemical activity can be attributed to a few key residues, the broader surrounding environment plays an equally important role.</p> <p>Results</p> <p>We describe a methodology that attempts to optimize two components, global shape and local physicochemical texture, for evaluating the similarity between a pair of surfaces. Surface shape similarity is assessed using a three-dimensional object recognition algorithm and physicochemical texture similarity is assessed through a spatial alignment of conserved residues between the surfaces. The comparisons are used in tandem to efficiently search the Global Protein Surface Survey (GPSS), a library of annotated surfaces derived from structures in the PDB, for studying evolutionary relationships and uncovering novel similarities between proteins.</p> <p>Conclusion</p> <p>We provide an assessment of our method using library retrieval experiments for identifying functionally homologous surfaces binding different ligands, functionally diverse surfaces binding the same ligand, and binding surfaces of ubiquitous and conformationally flexible ligands. Results using surface similarity to predict function for proteins of unknown function are reported. Additionally, an automated analysis of the ATP binding surface landscape is presented to provide insight into the correlation between surface similarity and function for structures in the PDB and for the subset of protein kinases.</p

Predicting HLA Class I Non-Permissive Amino Acid Residues Substitutions

Prediction of peptide binding to human leukocyte antigen (HLA) molecules is essential to a wide range of clinical entities from vaccine design to stem cell transplant compatibility. Here we present a new structure-based methodology that applies robust computational tools to model peptide-HLA (p-HLA) binding interactions. The method leverages the structural conservation observed in p-HLA complexes to significantly reduce the search space and calculate the system’s binding free energy. This approach is benchmarked against existing p-HLA complexes and the prediction performance is measured against a library of experimentally validated peptides. The effect on binding activity across a large set of high-affinity peptides is used to investigate amino acid mismatches reported as high-risk factors in hematopoietic stem cell transplantation.</p

Inferring Functional Relationships of Proteins from Local Sequence and Spatial Surface Patterns

es, and for further inquiries on evolutionary origins of structural elements important for protein function. q 2003 Elsevier Ltd. All rights reserved. Keywords: protein surface; surface pattern; protein function; pocket sequence; pocket shape *Corresponding author Introduction With rapid progress in the determination of protein structures, 1,2 protein structural analysis has become an important source of information for understanding functional roles of proteins. Conservation of protein structures often reveals very distant evolutionary relationships, which are otherwise difficult to detect by sequence analysis alone. Analysis of protein structure can provide insightful ideas about the biochemical functions and mechanisms of proteins (e.g. active sites, catalytic residues, and substrate interactions). 9--11 An important approach of studying protein structures is fold analysis. Identifying the correct tertiary fold of protein is often helpful for inferring protein func

pvSOAR: detecting similar surface patterns of pocket and void surfaces of amino acid residues on proteins

Detecting similar protein surfaces provides an important route for discovering unrecognized or novel functional relationship between proteins. The web server pvSOAR (pocket and void Surfaces Of Amino acid Residues) provides an online resource to identify similar protein surface regions. pvSOAR can take a structure either uploaded by a user or obtained from the Protein Data Bank, and identifies similar surface patterns based on geometrically defined pockets and voids. It provides several search modes to compare protein surfaces by similarity in local sequence, local shape and local orientation. Statistically significant search results are reported for visualization and interactive exploration. pvSOAR can be used to predict biological functions of proteins with known three-dimensional structures but unknown biological roles. It can also be used to study functional relationship between proteins and for exploration of the evolutionary origins of structural elements important for protein function. We present an example using pvSOAR to explore the biological roles of a protein whose structure was solved by the structural genomics project. The pvSOAR web server is available at http://pvsoar.bioengr.uic.edu/

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surface analysis for function annotatio

Liang J: Order independent structural alignment of circularly permuted proteins

Abstract–Circular permutation connects the N and C termini of a protein and concurrently cleaves elsewhere in the chain, providing an important mechanism for generating novel protein fold and functions. However, their in genomes is unknown because current detection methods can miss many occurances, mistaking random repeats as circular permutation. Here we develop a method for detecting circularly permuted proteins from structural comparison. Sequence order independent alignment of protein structures can be regarded as a special case of the maximum-weight independent set problem, which is known to be computationally hard. We develop an efficient approximation algorithm by repeatedly solving relaxations of an appropriate intermediate integer programming formulation, we show that the approximation ratio is much better then the theoretical worst case ratio of ¦ § © � �. Circularly permuted proteins reported in literature can be identified rapidly with our method, while they escape the detection by publicly available servers for structural alignment. Keywords–circular permuations, integer programming, linear programming, protein structure alignmen

Predicting HLA Class I Non-Permissive Amino Acid Residues Substitutions

<div><p>Prediction of peptide binding to human leukocyte antigen (HLA) molecules is essential to a wide range of clinical entities from vaccine design to stem cell transplant compatibility. Here we present a new structure-based methodology that applies robust computational tools to model peptide-HLA (p-HLA) binding interactions. The method leverages the structural conservation observed in p-HLA complexes to significantly reduce the search space and calculate the system’s binding free energy. This approach is benchmarked against existing p-HLA complexes and the prediction performance is measured against a library of experimentally validated peptides. The effect on binding activity across a large set of high-affinity peptides is used to investigate amino acid mismatches reported as high-risk factors in hematopoietic stem cell transplantation.</p> </div