ir-HSP: Improved Recognition of Heat Shock Proteins, Their Families and Sub-types Based On g-Spaced Di-peptide Features and Support Vector Machine

Meher, Prabina Kumar; Sahu, Tanmaya Kumar; Gahoi, Shachi; Rao, Atmakuri Ramakrishna

doi:10.3389/fgene.2017.00235

Cited by 18 publications

(15 citation statements)

References 91 publications

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“…Protein and peptide sequence-based classification methods have been extensively developed to improve the understanding of the functionality of polypeptides [ 65 , 66 ]. By using rough set theory, our method builds rules that distinguish between active antibacterial peptides from inactive antibacterial peptides.…”

Section: Discussionmentioning

confidence: 99%

Antimicrobial peptide similarity and classification through rough set theory using physicochemical boundaries

et al. 2018

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BackgroundAntimicrobial peptides attract considerable interest as novel agents to combat infections. Their long-time potency across bacteria, viruses and fungi as part of diverse innate immune systems offers a solution to overcome the rising concerns from antibiotic resistance. With the rapid increase of antimicrobial peptides reported in the databases, peptide selection becomes a challenge. We propose similarity analyses to describe key properties that distinguish between active and non-active peptide sequences building upon the physicochemical properties of antimicrobial peptides. We used an iterative supervised machine learning approach to classify active peptides from inactive peptides with low false discovery rates in a relatively short computational search time.ResultsBy generating explicit boundaries, our method defines new categories of active and inactive peptides based on their physicochemical properties. Consequently, it describes physicochemical characteristics of similarity among active peptides and the physicochemical boundaries between active and inactive peptides in a single process. To build the similarity boundaries, we used the rough set theory approach; to our knowledge, this is the first time that this approach has been used to classify peptides. The modified rough set theory method limits the number of values describing a boundary to a user-defined limit. Our method is optimized for specificity over selectivity. Noting that false positives increase activity assays while false negatives only increase computational search time, our method provided a low false discovery rate. Published datasets were used to compare our rough set theory method to other published classification methods and based on this comparison, we achieved high selectivity and comparable sensitivity to currently available methods.ConclusionsWe developed rule sets that define physicochemical boundaries which allow us to directly classify the active sequences from inactive peptides. Existing classification methods are either sequence-order insensitive or length-dependent, whereas our method generates the rule sets that combine order-sensitive descriptors with length-independent descriptors. The method provides comparable or improved performance to currently available methods. Discovering the boundaries of physicochemical properties may lead to a new understanding of peptide similarity.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2514-6) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Antimicrobial peptide similarity and classification through rough set theory using physicochemical boundaries

et al. 2018

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“…Heat shock proteins (HSPs) are a group of highly conserved, abundantly expressed proteins with diverse functions (216), including the assembly and sequestering of multi-protein complexes, transportation of polypeptide chains across cellular membranes, regulation of protein folding (217)(218)(219)(220)(221), and certain functions that protect against stress-induced injury (137). HSPs are known as molecular chaperones and are organized into six general families according to their molecular weight and activity: HSP20, HSP40, HSP60, HSP70, HSP90, and HSP100 (222). Typically, they are proteins constitutively expressed in the cytoplasm that co-localize to the nucleus under stress induced by physical and chemical insult.…”

Section: Parathion and Malathion Cause Genomic Instabilitymentioning

confidence: 99%

Signs of carcinogenicity induced by parathion, malathion, and estrogen in human breast epithelial cells (Review)

2021

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Cancer development is a multistep process that may be induced by a variety of compounds. Environmental substances, such as pesticides, have been associated with different human diseases. Organophosphorus pesticides (OPs) are among the most commonly used insecticides. Despite the fact that organophosphorus has been associated with an increased risk of cancer, particularly hormone-mediated cancer, few prospective studies have examined the use of individual insecticides. Reported results have demonstrated that OPs and estrogen induce a cascade of events indicative of the transformation of human breast epithelial cells. In vitro studies analyzing an immortalized non-tumorigenic human breast epithelial cell line may provide us with an approach to analyzing cell transformation under the effects of OPs in the presence of estrogen. The results suggested hormone-mediated effects of these insecticides on the risk of cancer among women. It can be concluded that, through experimental models, the initiation of cancer can be studied by analyzing the steps that transform normal breast cells to malignant ones through certain substances, such as pesticides and estrogen. Such substances cause genomic instability, and therefore tumor formation in the animal, and signs of carcinogenesis in vitro. Cancer initiation has been associated with an increase in genomic instability, indicated by the inactivation of tumor-suppressor genes and activation of oncogenes in the presence of malathion, parathion, and estrogen. In the present study, a comprehensive summary of the impact of OPs in human and rat breast cancer, specifically their effects on the cell cycle, signaling pathways linked to epidermal growth factor, drug metabolism, and genomic instability in an MCF-10F estrogen receptor-negative breast cell line is provided.

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“…To include all possible HSP transcripts/genes, we included the proteins from all three searches, and comparative heatmaps were plotted, as explained in [29]. In addition, due to the limitations of database updates, a machine-learning method developed to derive features for HSPs from primary sequences, ir-HSP [30], was also used to predict HSPs.…”

Section: Hsp Ortholog Searchmentioning

confidence: 99%

Expression of Heat Shock Proteins in Thermally Challenged Pacific Abalone Haliotis discus hannai

Kyeong

Kim

Shin

et al. 2019

Genes

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Summer mortality, caused by thermal conditions, is the biggest threat to abalone aquaculture production industries. Various measures have been taken to mitigate this issue by adjusting the environment; however, the cellular processes of Pacific abalone (Haliotis discus hannai) have been overlooked due to the paucity of genetic information. The draft genome of H. discus hannai has recently been reported, prompting exploration of the genes responsible for thermal regulation in Pacific abalone. In this study, 413 proteins were systematically annotated as members of the heat shock protein (HSP) super families, and among them 26 HSP genes from four Pacific abalone tissues (hemocytes, gill, mantle, and muscle) were differentially expressed under cold and heat stress conditions. The co-expression network revealed that HSP expression patterns were tissue-specific and similar to those of other shellfish inhabiting intertidal zones. Finally, representative HSPs were selected at random and their expression patterns were identified by RNA sequencing and validated by qRT-PCR to assess expression significance. The HSPs expressed in hemocytes were highly similar in both analyses, suggesting that hemocytes could be more reliable samples for validating thermal condition markers compared to other tissues.

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ir-HSP: Improved Recognition of Heat Shock Proteins, Their Families and Sub-types Based On g-Spaced Di-peptide Features and Support Vector Machine

Cited by 18 publications

References 91 publications

Antimicrobial peptide similarity and classification through rough set theory using physicochemical boundaries

Antimicrobial peptide similarity and classification through rough set theory using physicochemical boundaries

Signs of carcinogenicity induced by parathion, malathion, and estrogen in human breast epithelial cells (Review)

Expression of Heat Shock Proteins in Thermally Challenged Pacific Abalone Haliotis discus hannai

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