Biomedical subjects

Kamalakar Gulukota

Publications and source records attributed to Kamalakar Gulukota.

3 recordsLinked to original sources

PubMed2004

Predicting GPCR-G-protein coupling using hidden Markov models.

MOTIVATION: Determining the coupling specificity of G-protein coupled receptors (GPCRs) is important for understanding the biology of this class of pharmacologically important proteins. Currently available in silico methods for predicting GPCR-G-protein coupling specificity have high error rate. METHOD: We introduce a new approach for creating hidden Markov models (HMMs) based on a first guess about the importance of various residues. We call these knowledge restricted HMMs to emphasize the fact that the state space of the HMM is restricted by the application of a priori knowledge. Specifically, we use only those amino acid residues of GPCRs which are likely to interact with G-proteins, namely those that are predicted to be in the intra-cellular loops. Furthermore, we concatenate these predicted loops into one sequence rather than considering them as four disparate units. This reduces the HMM state space by drastically decreasing the sequence length. RESULTS: Our knowledge restricted HMM based method to predict GPCR-G-protein coupling specificity has an error rate of <1%, when applied to a test set of GPCRs with known G-protein coupling specificity. AVAILABILITY: Academic users can get the data set mentioned herein and HMMs from the authors.

Binding Sites↗

PubMed2003

Immunoinformatics in personalized medicine.

Diagnosis of human disease has been undergoing steady improvement over the past few centuries. Many ailments that were once considered a single entity have been classified into finer categories on the basis of response to therapy (e.g. type I and type II diabetes), inheritance (e.g. familial and non-familial polyposis coli), histology (e.g. small cell and adenocarcinoma of lung) and most recently transcriptional profiling (e.g. leukaemia, lymphoma). The next dimension in this finer categorization appears to be the typing of the patient rather than the disease i.e. disease X in person of type Y. The problem of personalized medicine is to devise tests which predict the type of individual, especially where the type is correlated with response to therapy. Immunology has been at the forefront of personalized medicine for quite a while, even though the term is not often used in this connection. Blood grouping and cross-matching (for blood transfusion), and anaphylaxis test (for penicillin) are just two examples. In this paper I will argue that immunological tests have an important place in the future of personalized medicine. I will describe methods we developed for personalizing vaccines based on MHC allele frequencies in human populations and methods for predicting peptide binding to class I MHC molecules. In conclusion, I will argue that immunological tests, and consequently immunoinformatics, will play a big role in making personalized medicine a reality.

Alleles↗

PubMed2002

Characterization of mGluR5R, a novel, metabotropic glutamate receptor 5-related gene.

We report here the isolation of a novel gene termed mGluR5R (mGluR5-related). The N-terminus of mGluR5R is highly similar to the extracellular domain of metabotropic glutamate receptor 5 (mGluR5) whereas the C-terminus bears similarity to the testis-specific gene, RNF18. mGluR5R is expressed in the human CNS in a coordinate fashion with mGluR5. Although the sequence suggests that mGluR5R may be a secreted glutamate binding protein, we found that when expressed in HEK293 cells it was membrane associated and not secreted. Furthermore, mGluR5R was incapable of binding the metabotropic glutamate receptor class I selective agonist, quisqualate. Although mGluR5R could not form disulfide-mediated covalent homodimers, it was able to form a homomeric complex, presumably through noncovalent interactions. mGluR5R also formed noncovalent heteromeric associations with an engineered construct of the extracellular domain of mGluR5 as well as with full-length mGluR5 and mGluR1alpha. The ability of mGluR5R to associate with mGluR1alpha and mGluR5 suggests that it may be a modulator of class I metabotropic glutamate receptor function.

Amino Acid Sequence↗