Displaying publications 1 - 20 of 115 in total

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  1. Naseer S, Ali RF, Khan YD, Dominic PDD
    J Biomol Struct Dyn, 2022;40(22):11691-11704.
    PMID: 34396935 DOI: 10.1080/07391102.2021.1962738
    Lysine glutarylation is a post-translation modification which plays an important regulatory role in a variety of physiological and enzymatic processes including mitochondrial functions and metabolic processes both in eukaryotic and prokaryotic cells. This post-translational modification influences chromatin structure and thereby results in global regulation of transcription, defects in cell-cycle progression, DNA damage repair, and telomere silencing. To better understand the mechanism of lysine glutarylation, its identification in a protein is necessary, however, experimental methods are time-consuming and labor-intensive. Herein, we propose a new computational prediction approach to supplement experimental methods for identification of lysine glutarylation site prediction by deep neural networks and Chou's Pseudo Amino Acid Composition (PseAAC). We employed well-known deep neural networks for feature representation learning and classification of peptide sequences. Our approach opts raw pseudo amino acid compositions and obsoletes the need to separately perform costly and cumbersome feature extraction and selection. Among the developed deep learning-based predictors, the standard neural network-based predictor demonstrated highest scores in terms of accuracy and all other performance evaluation measures and outperforms majority of previously reported predictors without requiring expensive feature extraction process. iGluK-Deep:Computational Identification of lysine glutarylationsites using deep neural networks with general Pseudo Amino Acid Compositions Sheraz Naseer, Rao Faizan Ali, Yaser Daanial Khan, P.D.D DominicCommunicated by Ramaswamy H. Sarma.
    Matched MeSH terms: Computational Biology/methods
  2. Chew TH, Joyce-Tan KH, Akma F, Shamsir MS
    Bioinformatics, 2011 May 1;27(9):1320-1.
    PMID: 21398666 DOI: 10.1093/bioinformatics/btr109
    birgHPC, a bootable Linux Live CD has been developed to create high-performance clusters for bioinformatics and molecular dynamics studies using any Local Area Network (LAN)-networked computers. birgHPC features automated hardware and slots detection as well as provides a simple job submission interface. The latest versions of GROMACS, NAMD, mpiBLAST and ClustalW-MPI can be run in parallel by simply booting the birgHPC CD or flash drive from the head node, which immediately positions the rest of the PCs on the network as computing nodes. Thus, a temporary, affordable, scalable and high-performance computing environment can be built by non-computing-based researchers using low-cost commodity hardware.
    Matched MeSH terms: Computational Biology/methods*
  3. Tan CH, Tan KY, Fung SY, Tan NH
    BMC Genomics, 2015;16:687.
    PMID: 26358635 DOI: 10.1186/s12864-015-1828-2
    The king cobra (Ophiophagus hannah) is widely distributed throughout many parts of Asia. This study aims to investigate the complexity of Malaysian Ophiophagus hannah (MOh) venom for a better understanding of king cobra venom variation and its envenoming pathophysiology. The venom gland transcriptome was investigated using the Illumina HiSeq™ platform, while the venom proteome was profiled by 1D-SDS-PAGE-nano-ESI-LCMS/MS.
    Matched MeSH terms: Computational Biology/methods
  4. Roslan R, Othman RM, Shah ZA, Kasim S, Asmuni H, Taliba J, et al.
    Comput Biol Med, 2010 Jun;40(6):555-64.
    PMID: 20417930 DOI: 10.1016/j.compbiomed.2010.03.009
    Protein-protein interactions (PPIs) play a significant role in many crucial cellular operations such as metabolism, signaling and regulations. The computational methods for predicting PPIs have shown tremendous growth in recent years, but problem such as huge false positive rates has contributed to the lack of solid PPI information. We aimed at enhancing the overlap between computational predictions and experimental results in an effort to partially remove PPIs falsely predicted. The use of protein function predictor named PFP() that are based on shared interacting domain patterns is introduced in this study with the purpose of aiding the Gene Ontology Annotations (GOA). We used GOA and PFP() as agents in a filtering process to reduce false positive pairs in the computationally predicted PPI datasets. The functions predicted by PFP() were extracted from cross-species PPI data in order to assign novel functional annotations for the uncharacterized proteins and also as additional functions for those that are already characterized by the GO (Gene Ontology). The implementation of PFP() managed to increase the chances of finding matching function annotation for the first rule in the filtration process as much as 20%. To assess the capability of the proposed framework in filtering false PPIs, we applied it on the available S. cerevisiae PPIs and measured the performance in two aspects, the improvement made indicated as Signal-to-Noise Ratio (SNR) and the strength of improvement, respectively. The proposed filtering framework significantly achieved better performance than without it in both metrics.
    Matched MeSH terms: Computational Biology/methods*
  5. Loh SY, Jahans-Price T, Greenwood MP, Greenwood M, Hoe SZ, Konopacka A, et al.
    eNeuro, 2017 12 21;4(6).
    PMID: 29279858 DOI: 10.1523/ENEURO.0243-17.2017
    The supraoptic nucleus (SON) is a group of neurons in the hypothalamus responsible for the synthesis and secretion of the peptide hormones vasopressin and oxytocin. Following physiological cues, such as dehydration, salt-loading and lactation, the SON undergoes a function related plasticity that we have previously described in the rat at the transcriptome level. Using the unsupervised graphical lasso (Glasso) algorithm, we reconstructed a putative network from 500 plastic SON genes in which genes are the nodes and the edges are the inferred interactions. The most active nodal gene identified within the network was Caprin2. Caprin2 encodes an RNA-binding protein that we have previously shown to be vital for the functioning of osmoregulatory neuroendocrine neurons in the SON of the rat hypothalamus. To test the validity of the Glasso network, we either overexpressed or knocked down Caprin2 transcripts in differentiated rat pheochromocytoma PC12 cells and showed that these manipulations had significant opposite effects on the levels of putative target mRNAs. These studies suggest that the predicative power of the Glasso algorithm within an in vivo system is accurate, and identifies biological targets that may be important to the functional plasticity of the SON.
    Matched MeSH terms: Computational Biology/methods*
  6. Alballa M, Aplop F, Butler G
    PLoS One, 2020;15(1):e0227683.
    PMID: 31935244 DOI: 10.1371/journal.pone.0227683
    Transporters mediate the movement of compounds across the membranes that separate the cell from its environment and across the inner membranes surrounding cellular compartments. It is estimated that one third of a proteome consists of membrane proteins, and many of these are transport proteins. Given the increase in the number of genomes being sequenced, there is a need for computational tools that predict the substrates that are transported by the transmembrane transport proteins. In this paper, we present TranCEP, a predictor of the type of substrate transported by a transmembrane transport protein. TranCEP combines the traditional use of the amino acid composition of the protein, with evolutionary information captured in a multiple sequence alignment (MSA), and restriction to important positions of the alignment that play a role in determining the specificity of the protein. Our experimental results show that TranCEP significantly outperforms the state-of-the-art predictors. The results quantify the contribution made by each type of information used.
    Matched MeSH terms: Computational Biology/methods*
  7. Seah CS, Kasim S, Saedudin RR, Md Fudzee MF, Mohamad MS, Hassan R, et al.
    Pak J Pharm Sci, 2019 May;32(3 Special):1395-1408.
    PMID: 31551221
    Numerous cancer studies have combined different datasets for the prognosis of patients. This study incorporated four networks for significant directed random walk (sDRW) to predict cancerous genes and risk pathways. The study investigated the feasibility of cancer prediction via different networks. In this study, multiple micro array data were analysed and used in the experiment. Six gene expression datasets were applied in four networks to study the effectiveness of the networks in sDRW in terms of cancer prediction. The experimental results showed that one of the proposed networks is outstanding compared to other networks. The network is then proposed to be implemented in sDRW as a walker network. This study provides a foundation for further studies and research on other networks. We hope these finding will improve the prognostic methods of cancer patients.
    Matched MeSH terms: Computational Biology/methods*
  8. Al-Khatib RM, Rashid NA, Abdullah R
    J Biomol Struct Dyn, 2011 Aug;29(1):1-26.
    PMID: 21696223
    The secondary structure of RNA pseudoknots has been extensively inferred and scrutinized by computational approaches. Experimental methods for determining RNA structure are time consuming and tedious; therefore, predictive computational approaches are required. Predicting the most accurate and energy-stable pseudoknot RNA secondary structure has been proven to be an NP-hard problem. In this paper, a new RNA folding approach, termed MSeeker, is presented; it includes KnotSeeker (a heuristic method) and Mfold (a thermodynamic algorithm). The global optimization of this thermodynamic heuristic approach was further enhanced by using a case-based reasoning technique as a local optimization method. MSeeker is a proposed algorithm for predicting RNA pseudoknot structure from individual sequences, especially long ones. This research demonstrates that MSeeker improves the sensitivity and specificity of existing RNA pseudoknot structure predictions. The performance and structural results from this proposed method were evaluated against seven other state-of-the-art pseudoknot prediction methods. The MSeeker method had better sensitivity than the DotKnot, FlexStem, HotKnots, pknotsRG, ILM, NUPACK and pknotsRE methods, with 79% of the predicted pseudoknot base-pairs being correct.
    Matched MeSH terms: Computational Biology/methods*
  9. Yew SM, Chan CL, Kuan CS, Toh YF, Ngeow YF, Na SL, et al.
    BMC Genomics, 2016 Feb 03;17:91.
    PMID: 26842951 DOI: 10.1186/s12864-016-2409-8
    Ochroconis mirabilis, a recently introduced water-borne dematiaceous fungus, is occasionally isolated from human skin lesions and nails. We identified an isolate of O. mirabilis from a skin scraping with morphological and molecular studies. Its genome was then sequenced and analysed for genetic features related to classification and biological characteristics.
    Matched MeSH terms: Computational Biology/methods
  10. Gan HM, Gan HY, Tan MH, Penny SS, Willan RC, Austin CM
    PMID: 25648928 DOI: 10.3109/19401736.2015.1007355
    The complete mitochondrial genome of the commercially and ecologically important and internationally vulnerable giant clam Tridacna squamosa was recovered by genome skimming using the MiSeq platform. The T. squamosa mitogenome has 20,930 base pairs (62.35% A+T content) and is made up of 12 protein-coding genes, 2 ribosomal subunit genes, 24 transfer RNAs, and a 2594 bp non-coding AT-rich region. The mitogenome has a relatively large insertion in the atp6 gene. This is the first mitogenome to be sequenced from the genus Tridacna, and the family Tridacnidae and represents a new gene order.
    Matched MeSH terms: Computational Biology/methods
  11. Grandjean F, Tan MH, Gan HY, Gan HM, Austin CM
    PMID: 25738217 DOI: 10.3109/19401736.2015.1018207
    The Austropotamobius pallipes complete mitogenome has been recovered using Next-Gen sequencing. Our sample of A. pallipes has a mitogenome of 15,679 base pairs (68.44% A + T content) made up of 13 protein-coding genes, 2 ribosomal subunit genes, 22 transfer RNAs, and a 877 bp non-coding AT-rich region. This is the first mitogenome sequenced for a crayfish from the family Astacidae and the 4(th) for northern hemisphere genera.
    Matched MeSH terms: Computational Biology/methods
  12. Angers-Loustau A, Petrillo M, Bengtsson-Palme J, Berendonk T, Blais B, Chan KG, et al.
    F1000Res, 2018;7.
    PMID: 30026930 DOI: 10.12688/f1000research.14509.2
    Next-Generation Sequencing (NGS) technologies are expected to play a crucial role in the surveillance of infectious diseases, with their unprecedented capabilities for the characterisation of genetic information underlying the virulence and antimicrobial resistance (AMR) properties of microorganisms.  In the implementation of any novel technology for regulatory purposes, important considerations such as harmonisation, validation and quality assurance need to be addressed.  NGS technologies pose unique challenges in these regards, in part due to their reliance on bioinformatics for the processing and proper interpretation of the data produced.  Well-designed benchmark resources are thus needed to evaluate, validate and ensure continued quality control over the bioinformatics component of the process.  This concept was explored as part of a workshop on "Next-generation sequencing technologies and antimicrobial resistance" held October 4-5 2017.   Challenges involved in the development of such a benchmark resource, with a specific focus on identifying the molecular determinants of AMR, were identified. For each of the challenges, sets of unsolved questions that will need to be tackled for them to be properly addressed were compiled. These take into consideration the requirement for monitoring of AMR bacteria in humans, animals, food and the environment, which is aligned with the principles of a "One Health" approach.
    Matched MeSH terms: Computational Biology/methods*
  13. Yeo JG, Wasser M, Kumar P, Pan L, Poh SL, Ally F, et al.
    Nat Biotechnol, 2020 06;38(6):679-684.
    PMID: 32440006 DOI: 10.1038/s41587-020-0532-1
    Matched MeSH terms: Computational Biology/methods*
  14. Packiam KAR, Ramanan RN, Ooi CW, Krishnaswamy L, Tey BT
    Appl Microbiol Biotechnol, 2020 Apr;104(8):3253-3266.
    PMID: 32076772 DOI: 10.1007/s00253-020-10454-w
    Over the past few decades, Escherichia coli (E. coli) remains the most favorable host among the microbial cell factories for the production of soluble recombinant proteins. Recombinant protein production (RPP) via E. coli is optimized at the level of gene expression (expression level) and the process condition of fermentation (process level). Presently, the reported studies do not give a clear view on the selection of methods employed in the optimization of RPP. Here, we have reviewed various optimization methods and their preferences with respect to the factors at expression and process levels to achieve the optimal levels of soluble RPP. With a greater understanding of these optimization methods, we proposed a stepwise methodology linking the factors from both levels for optimizing the production of soluble recombinant protein in E. coli. The proposed methodology is further explained through five sets of examples demonstrating the optimization of RPP at both expression and process levels.Key Points• Stepwise methodology of optimizing recombinant protein production is proposed.• In silico tools can facilitate the optimization of gene- and protein-based factors.• Optimization of gene- and protein-based factors aids host-vector selection.• Statistical optimization is preferred for achieving optimal levels of process factors.
    Matched MeSH terms: Computational Biology/methods*
  15. Hawari AH, Mohamed-Hussein ZA
    BMC Bioinformatics, 2010;11:83.
    PMID: 20144236 DOI: 10.1186/1471-2105-11-83
    The development and simulation of dynamic models of terpenoid biosynthesis has yielded a systems perspective that provides new insights into how the structure of this biochemical pathway affects compound synthesis. These insights may eventually help identify reactions that could be experimentally manipulated to amplify terpenoid production. In this study, a dynamic model of the terpenoid biosynthesis pathway was constructed based on the Hybrid Functional Petri Net (HFPN) technique. This technique is a fusion of three other extended Petri net techniques, namely Hybrid Petri Net (HPN), Dynamic Petri Net (HDN) and Functional Petri Net (FPN).
    Matched MeSH terms: Computational Biology/methods*
  16. Abidin SAZ, Othman I, Naidu R
    Methods Mol Biol, 2021;2211:233-240.
    PMID: 33336281 DOI: 10.1007/978-1-0716-0943-9_16
    Shotgun proteomics has been widely applied to study proteins in complex biological samples. Combination of high-performance liquid chromatography with mass spectrometry has allowed for comprehensive protein analysis with high resolution, sensitivity, and mass accuracy. Prior to mass spectrometry analysis, proteins are extracted from biological samples and subjected to in-solution trypsin digestion. The digested proteins are subjected for clean-up and injected into the liquid chromatography-mass spectrometry system for peptide mass identification. Protein identification is performed by analyzing the mass spectrometry data on a protein search engine software such as PEAKS studio loaded with protein database for the species of interest. Results such as protein score, protein coverage, number of peptides, and unique peptides identified will be obtained and can be used to determine proteins identified with high confidence. This method can be applied to understand the proteomic changes or profile brought by bio-carrier-based therapeutics in vitro. In this chapter, we describe methods in which proteins can be extracted for proteomic analysis using a shotgun approach. The chapter outlines important in vitro techniques and data analysis that can be applied to investigate the proteome dynamics.
    Matched MeSH terms: Computational Biology/methods
  17. Fotoohifiroozabadi S, Mohamad MS, Deris S
    J Bioinform Comput Biol, 2017 Apr;15(2):1750004.
    PMID: 28274174 DOI: 10.1142/S0219720017500044
    Protein structure alignment and comparisons that are based on an alphabetical demonstration of protein structure are more simple to run with faster evaluation processes; thus, their accuracy is not as reliable as three-dimension (3D)-based tools. As a 1D method candidate, TS-AMIR used the alphabetic demonstration of secondary-structure elements (SSE) of proteins and compared the assigned letters to each SSE using the [Formula: see text]-gram method. Although the results were comparable to those obtained via geometrical methods, the SSE length and accuracy of adjacency between SSEs were not considered in the comparison process. Therefore, to obtain further information on accuracy of adjacency between SSE vectors, the new approach of assigning text to vectors was adopted according to the spherical coordinate system in the present study. Moreover, dynamic programming was applied in order to account for the length of SSE vectors. Five common datasets were selected for method evaluation. The first three datasets were small, but difficult to align, and the remaining two datasets were used to compare the capability of the proposed method with that of other methods on a large protein dataset. The results showed that the proposed method, as a text-based alignment approach, obtained results comparable to both 1D and 3D methods. It outperformed 1D methods in terms of accuracy and 3D methods in terms of runtime.
    Matched MeSH terms: Computational Biology/methods*
  18. Khang TF, Mohd Puaad NAD, Teh SH, Mohamed Z
    J Forensic Sci, 2021 May;66(3):960-970.
    PMID: 33438785 DOI: 10.1111/1556-4029.14655
    Wing shape variation has been shown to be useful for delineating forensically important fly species in two Diptera families: Calliphoridae and Sarcophagidae. Compared to DNA-based identification, the cost of geometric morphometric data acquisition and analysis is relatively much lower because the tools required are basic, and stable softwares are available. However, to date, an explicit demonstration of using wing geometric morphometric data for species identity prediction in these two families remains lacking. Here, geometric morphometric data from 19 homologous landmarks on the left wing of males from seven species of Calliphoridae (n = 55), and eight species of Sarcophagidae (n = 40) were obtained and processed using Generalized Procrustes Analysis. Allometric effect was removed by regressing centroid size (in log10 ) against the Procrustes coordinates. Subsequently, principal component analysis of the allometry-adjusted Procrustes variables was done, with the first 15 principal components used to train a random forests model for species prediction. Using a real test sample consisting of 33 male fly specimens collected around a human corpse at a crime scene, the estimated percentage of concordance between species identities predicted using the random forests model and those inferred using DNA-based identification was about 80.6% (approximate 95% confidence interval = [68.9%, 92.2%]). In contrast, baseline concordance using naive majority class prediction was 36.4%. The results provide proof of concept that geometric morphometric data has good potential to complement morphological and DNA-based identification of blow flies and flesh flies in forensic work.
    Matched MeSH terms: Computational Biology/methods*
  19. Ramly B, Afiqah-Aleng N, Mohamed-Hussein ZA
    Int J Mol Sci, 2019 Jun 18;20(12).
    PMID: 31216618 DOI: 10.3390/ijms20122959
    Based on clinical observations, women with polycystic ovarian syndrome (PCOS) are prone to developing several other diseases, such as metabolic and cardiovascular diseases. However, the molecular association between PCOS and these diseases remains poorly understood. Recent studies showed that the information from protein-protein interaction (PPI) network analysis are useful in understanding the disease association in detail. This study utilized this approach to deepen the knowledge on the association between PCOS and other diseases. A PPI network for PCOS was constructed using PCOS-related proteins (PCOSrp) obtained from PCOSBase. MCODE was used to identify highly connected regions in the PCOS network, known as subnetworks. These subnetworks represent protein families, where their molecular information is used to explain the association between PCOS and other diseases. Fisher's exact test and comorbidity data were used to identify PCOS-disease subnetworks. Pathway enrichment analysis was performed on the PCOS-disease subnetworks to identify significant pathways that are highly involved in the PCOS-disease associations. Migraine, schizophrenia, depressive disorder, obesity, and hypertension, along with twelve other diseases, were identified to be highly associated with PCOS. The identification of significant pathways, such as ribosome biogenesis, antigen processing and presentation, and mitophagy, suggest their involvement in the association between PCOS and migraine, schizophrenia, and hypertension.
    Matched MeSH terms: Computational Biology/methods
  20. Khan S, Zakariah M, Rolfo C, Robrecht L, Palaniappan S
    Oncotarget, 2017 May 09;8(19):30830-30843.
    PMID: 27027344 DOI: 10.18632/oncotarget.8306
    Although the idea of bacteria causing different types of cancer has exploded about century ago, the potential mechanisms of carcinogenesis is still not well established. Many reports showed the involvement of M. hominis in the development of prostate cancer, however, mechanistic approach for growth and development of prostate cancer has been poorly understood. In the current study, we predicted M. hominis proteins targeting in the mitochondria and cytoplasm of host cells and their implication in prostate cancer. A total of 77 and 320 proteins from M. hominis proteome were predicted to target in the mitochondria and cytoplasm of host cells respectively. In particular, various targeted proteins may interfere with normal growth behaviour of host cells, thereby altering the decision of programmed cell death. Furthermore, we investigated possible mechanisms of the mitochondrial and cytoplasmic targeted proteins of M. hominis in etiology of prostate cancer by screening the whole proteome.
    Matched MeSH terms: Computational Biology/methods
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