Biomedical subjects

Asif M Khan

Publications and source records attributed to Asif M Khan.

7 recordsLinked to original sources

PubMed2006

Possible mechanism of pesticide toxicity-related oxidative stress leading to airway narrowing.

The study was conducted to assess the magnitude of oxidative stress and lung function abnormalities in 34 male pesticide sprayers on exposure to pesticides in mango plantations. Biochemical studies on blood antioxidant enzymes revealed an unchanged glutathione level and increased level of malondialdehyde (P < 0.001), which indicates that pesticide sprayers may have suffered from oxidative stress. Decreased acetyl-cholinesterase levels (P < 0.001) in sprayers compared to the controls suggest inhibition of cholinesterase activity. The present study shows that pesticide toxicity might lead to oxidative stress and airway narrowing resulting in decreased peak expiratory flow rate.

Adolescent↗

PubMed2006

Working conditions and health among employees at information technology--enabled services: a review of current evidence.

Workers in information technology (IT)-enabled services like business process outsourcing and call centers working with visual display units are reported to have various health and psycho-social disorders. Evidence from previously published studies in peer- reviewed journals and internet sources were examined to explore health disorders and psycho-social problems among personnel employed in IT-based services, for a systematic review on the topic. In addition, authors executed a questionnaire- based pilot study. The available literature and the pilot study, both suggest health disorders and psychosocial problems among workers of business process outsourcing. The details are discussed in the review.

Computer Terminals↗

PubMed2005

MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides.

MULTIPRED is a web-based computational system for the prediction of peptide binding to multiple molecules (proteins) belonging to human leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes. It uses hidden Markov models and artificial neural network methods as predictive engines. A novel data representation method enables MULTIPRED to predict peptides that promiscuously bind multiple HLA alleles within one HLA supertype. Extensive testing was performed for validation of the prediction models. Testing results show that MULTIPRED is both sensitive and specific and it has good predictive ability (area under the receiver operating characteristic curve A(ROC) > 0.80). MULTIPRED can be used for the mapping of promiscuous T-cell epitopes as well as the regions of high concentration of these targets--termed T-cell epitope hotspots. MULTIPRED is available at http://antigen.i2r.a-star.edu.sg/multipred/.

Computational Biology↗

PubMed2005

Neural models for predicting viral vaccine targets.

We applied artificial neural networks (ANN) for the prediction of targets of immune responses that are useful for study of vaccine formulations against viral infections. Using a novel data representation, we developed a system termed MULTIPRED that can predict peptide binding to multiple related human leukocyte antigens (HLA). This implementation showed high accuracy in the prediction of the promiscuous peptides that bind to five HLA-A2 allelic variants. MULTIPRED is useful for the identification of peptides that bind multiple HLA-A2 variants as a group. By implementing ANN as a classification engine, we enabled both the prediction of peptides binding to multiple individual HLA-A2 molecules and the prediction of promiscuous binders using a single model. The ANN MULTIPRED predicts peptide binding to HLA-A*0205 with excellent accuracy (area under the receiver operating characteristic curve--AROC>0.90), and to HLA-A*0201, HLA-A*0204 and HLA-A*0206 with high accuracy (AROC>0.85). Antigenic regions with high density of binders ("antigenic hot-spots") represent best targets for vaccine design. MULTIPRED not only predicts individual 9-mer binders but also predicts antigenic hot spots. Two HLA-A2 hot-spots in Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) membrane protein were predicted by using MULTIPRED.

Algorithms↗

PubMed2004

Systematic analysis of snake neurotoxins' functional classification using a data warehousing approach.

MOTIVATION: Sequence annotations, functional and structural data on snake venom neurotoxins (svNTXs) are scattered across multiple databases and literature sources. Sequence annotations and structural data are available in the public molecular databases, while functional data are almost exclusively available in the published articles. There is a need for a specialized svNTXs database that contains NTX entries, which are organized, well annotated and classified in a systematic manner. RESULTS: We have systematically analyzed svNTXs and classified them using structure-function groups based on their structural, functional and phylogenetic properties. Using conserved motifs in each phylogenetic group, we built an intelligent module for the prediction of structural and functional properties of unknown NTXs. We also developed an annotation tool to aid the functional prediction of newly identified NTXs as an additional resource for the venom research community. AVAILABILITY: We created a searchable online database of NTX proteins sequences (http://research.i2r.a-star.edu.sg/Templar/DB/snake_neurotoxin). This database can also be found under Swiss-Prot Toxin Annotation Project website (http://www.expasy.org/sprot/).

Animals↗

PubMed2004

CysView: protein classification based on cysteine pairing patterns.

CysView is a web-based application tool that identifies and classifies proteins according to their disulfide connectivity patterns. It accepts a dataset of annotated protein sequences in various formats and returns a graphical representation of cysteine pairing patterns. CysView displays cysteine patterns for those records in the data with disulfide annotations. It allows the viewing of records grouped by connectivity patterns. CysView's utility as an analysis tool was demonstrated by the rapid and correct classification of scorpion toxin entries from GenPept on the basis of their disulfide pairing patterns. It has proved useful for rapid detection of irrelevant and partial records, or those with incomplete annotations. CysView can be used to support distant homology between proteins. CysView is publicly available at http://research.i2r.a-star.edu.sg/CysView/.

Computer Graphics↗

PubMed2003

Bioinformatics for venom and toxin sciences.

Venomous animals produce a myriad of important pharmacological components. The individual components, or venoms (toxins), are used in ion channel and receptor studies, drug discovery, and formulation of insecticides. The toxin data are scattered across public databases which provide sequence and structural descriptions, but very limited functional annotation. The exponential growth of newly identified toxin data has created a need for better data management. Venominformatics is a systematic bioinformatics approach in which classified, consolidated and cleaned venom data are stored into repositories and integrated with advanced bioinformatics tools for the analysis of structure and function of toxins. Venominformatics complements experimental studies and helps reduce the number of essential experiments.

Animals↗