Biomedical subjects

Indraneel Majumdar

Publications and source records attributed to Indraneel Majumdar.

3 recordsLinked to original sources

PubMed2005

PALSSE: a program to delineate linear secondary structural elements from protein structures.

BACKGROUND: The majority of residues in protein structures are involved in the formation of alpha-helices and beta-strands. These distinctive secondary structure patterns can be used to represent a protein for visual inspection and in vector-based protein structure comparison. Success of such structural comparison methods depends crucially on the accurate identification and delineation of secondary structure elements. RESULTS: We have developed a method PALSSE (Predictive Assignment of Linear Secondary Structure Elements) that delineates secondary structure elements (SSEs) from protein Calpha coordinates and specifically addresses the requirements of vector-based protein similarity searches. Our program identifies two types of secondary structures: helix and beta-strand, typically those that can be well approximated by vectors. In contrast to traditional secondary structure algorithms, which identify a secondary structure state for every residue in a protein chain, our program attributes residues to linear SSEs. Consecutive elements may overlap, thus allowing residues located at the overlapping region to have more than one secondary structure type. CONCLUSION: PALSSE is predictive in nature and can assign about 80% of the protein chain to SSEs as compared to 53% by DSSP and 57% by P-SEA. Such a generous assignment ensures almost every residue is part of an element and is used in structural comparisons. Our results are in agreement with human judgment and DSSP. The method is robust to coordinate errors and can be used to define SSEs even in poorly refined and low-resolution structures. The program and results are available at http://prodata.swmed.edu/palsse/.

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PubMed2003

Structural classification of zinc fingers: survey and summary.

Zinc fingers are small protein domains in which zinc plays a structural role contributing to the stability of the domain. Zinc fingers are structurally diverse and are present among proteins that perform a broad range of functions in various cellular processes, such as replication and repair, transcription and translation, metabolism and signaling, cell proliferation and apoptosis. Zinc fingers typically function as interaction modules and bind to a wide variety of compounds, such as nucleic acids, proteins and small molecules. Here we present a comprehensive classification of zinc finger spatial structures. We find that each available zinc finger structure can be placed into one of eight fold groups that we define based on the structural properties in the vicinity of the zinc-binding site. Three of these fold groups comprise the majority of zinc fingers, namely, C2H2-like finger, treble clef finger and the zinc ribbon. Evolutionary relatedness of proteins within fold groups is not implied, but each group is divided into families of potential homologs. We compare our classification to existing groupings of zinc fingers and find that we define more encompassing fold groups, which bring together proteins whose similarities have previously remained unappreciated. We analyze functional properties of different zinc fingers and overlay them onto our classification. The classification helps in understanding the relationship between the structure, function and evolutionary history of these domains. The results are available as an online database of zinc finger structures.

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PubMed2003

CASP5 assessment of fold recognition target predictions.

We present an overview of the fifth round of Critical Assessment of Protein Structure Prediction (CASP5) fold recognition category. Prediction models were evaluated by using six different structural measures and four different alignment measures, and these scores were compared to those assigned manually over a diverse subset of target domains. Scores were combined to compare overall performance of participating groups and to estimate rank significance. The methods used by a few groups outperformed all other methods in terms of the evaluated criteria and could be considered state-of-the-art in structure prediction. We discuss a few examples of difficult fold recognition targets to highlight the progress of ab initio-type methods on difficult structure analogs and the difficulties of predicting multidomain targets and selecting prediction models. We also compared the results of manual groups to those of automatic servers evaluated in parallel by CAFASP, showing that the top performing automated server structure predictions approached those of the best manual predictors.

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