scholarly journals IDOPS, a Profile HMM-Based Tool to Detect Pesticidal Sequences and Compare Their Genetic Context

2021 ◽  
Vol 12 ◽  
Author(s):  
Stefani Díaz-Valerio ◽  
Anat Lev Hacohen ◽  
Raphael Schöppe ◽  
Heiko Liesegang
Keyword(s):  
Markov Models ◽  
Insect Pests ◽  
Crop Protection ◽  
Resource Center ◽  
Uniprotkb Database ◽  
Genomic Environment ◽  
Profile Hmm ◽  
Detection Unit ◽  
Structural Classes ◽  
Genetic Context

Biopesticide-based crop protection is constantly challenged by insect resistance. Thus, expansion of available biopesticides is crucial for sustainable agriculture. Although Bacillus thuringiensis is the major agent for pesticide bioprotection, the number of bacteria species synthesizing proteins with biopesticidal potential is much higher. The Bacterial Pesticidal Protein Resource Center (BPPRC) offers a database of sequences for the control of insect pests, grouped in structural classes. Here we present IDOPS, a tool that detects novel biopesticidal sequences and analyzes them within their genetic environment. The backbone of the IDOPS detection unit is a curated collection of high-quality hidden Markov models that is in accordance with the BPPRC nomenclature. IDOPS was positively benchmarked with BtToxin_Digger and Cry_Processor. In addition, a scan of the UniProtKB database using the IDOPS models returned an abundance of new pesticidal protein candidates distributed across all of the structural groups. Gene expression depends on the genomic environment, therefore, IDOPS provides a comparative genomics module to investigate the genetic regions surrounding pesticidal genes. This feature enables the investigation of accessory elements and evolutionary traits relevant for optimal toxin expression and functional diversification. IDOPS contributes and expands our current arsenal of pesticidal proteins used for crop protection.

Adaptive Mechanisms of Insect Pests Against Plant Protease Inhibitors and Future Prospects Related to Crop Protection: A Review

2015 ◽  
Vol 22 (2) ◽  
pp. 149-163 ◽  
Author(s):  
Maria Macedo ◽  
Caio de Oliveira ◽  
Poliene Costa ◽  
Elaine Castelhano ◽  
Marcio Silva-Filho
Keyword(s):  
Protease Inhibitors ◽  
Insect Pests ◽  
Crop Protection ◽  
Future Prospects ◽  
Adaptive Mechanisms ◽  
Plant Protease

scholarly journals Recent Advances in Engineered Nanoparticles for RNAi-Mediated Crop Protection Against Insect Pests

2021 ◽  
Vol 3 ◽  
Author(s):  
Charlotte E. Pugsley ◽  
R. E. Isaac ◽  
Nicholas J. Warren ◽  
Olivier J. Cayre
Keyword(s):  
Nucleic Acid ◽  
Insect Pests ◽  
Research Effort ◽  
Insect Pest ◽  
Crop Protection ◽  
Mrna Degradation ◽  
Engineered Nanoparticles ◽  
Future Research ◽  
Therapeutic Applications ◽  
Rnai Response

Since the discovery of RNA interference (RNAi) in the nematode worm Caenorhabditis elegans in 1998 by Fire and Mello et al., strides have been made in exploiting RNAi for therapeutic applications and more recently for highly selective insect pest control. Although triggering mRNA degradation in insects through RNAi offers significant opportunities in crop protection, the application of environmental naked dsRNA is often ineffective in eliciting a RNAi response that results in pest lethality. There are many possible reasons for the failed or weak induction of RNAi, with predominant causes being the degradation of dsRNA in the formulated pesticide, in the field or in the insect once ingested, poor cuticular and oral uptake of the nucleic acid and sometimes the lack of an innate strong systemic RNAi response. Therefore, in the last 10 years significant research effort has focused on developing methods for the protection and delivery of environmental dsRNA to enable RNAi-induced insect control. This review focuses on the design and synthesis of vectors (vehicles that are capable of carrying and protecting dsRNA) that successfully enhance mRNA degradation via the RNAi machinery. The majority of solutions exploit the ability of charged polymers, both synthetic and natural, to complex with dsRNA, but alternative nanocarriers such as clay nanosheets and liposomal vesicles have also been developed. The various challenges of dsRNA delivery and the obstacles in the development of well-designed nanoparticles that act to protect the nucleic acid are highlighted. In addition, future research directions for improving the efficacy of RNA-mediated crop protection are anticipated with inspiration taken from polymeric architectures constructed for RNA-based therapeutic applications.

scholarly journals SHsearch - a method for fast remote homology detection

2018 ◽  
Author(s):  
Mohamed Baddar
Keyword(s):  
Markov Models ◽  
Sequence Similarity ◽  
Protein Homology ◽  
Homology Detection ◽  
Remote Homology ◽  
Sensitivity Loss ◽  
Computationally Intensive ◽  
Profile Hmm ◽  
Programming Algorithms ◽  
Remote Homology Detection

Remote homology detection is the problem of detecting homology in cases of low sequence similarity. It is a hard computational problem with no approach that works well in all cases. Methods based on profile hidden Markov models (HMM) often exhibit relatively higher sensitivity for detecting remote homologies than commonly used approaches. However, calculating similarity scores in profile HMM methods is computationally intensive as they use dynamic programming algorithms. In this paper we introduce SHsearch: a new method for remote protein homology detection. Our method is implemented as a modification of HHsearch: a remote protein homology detection method based on comparing two profile HMMs. The motivation for modification was to reduce the run time of HHsearch significantly with minimal sensitivity loss. SHsearch focuses on comparing the important submodels of the query and database HMMs instead of comparing the complete models. Hence, SHsearch achieves a significant speedup over HHsearch with minimal loss in sensitivity. On SCOP 1.63, SHsearch achieved 88X speedup with 8.2% loss in sensitivity with respect to HHsearch at error rate of 10%, which deemed to be an acceptable tradeoff.

scholarly journals Epidemiology and forecasting of insect-pests and diseases for value-added agro-advisory

MAUSAM ◽  
2021 ◽  
Vol 67 (1) ◽  
pp. 267-276
Author(s):  
AMRENDER KUMAR ◽  
A. K. JAIN ◽  
B. K. BHATTACHARYA ◽  
VINOD KUMAR ◽  
A. K. MISHRA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Insect Pests ◽  
Plant Protection ◽  
Crop Protection ◽  
Value Added ◽  
Accurate Estimation ◽  
Learning Approach ◽  
Pests And Diseases ◽  
Functional Relationships ◽  
Machine Learning Approach

Models are means to capture, condense and organize knowledge. These are expressions, which represent relationship between various components of a system. A well-tested weather-based model can be an effective scientific tool for forewarning insect-pests and diseases in advance so that timely plant protection measures could be taken up. Various types of techniques have been developed for the purpose. The simplest technique forms the class of thumb rules, which are based on experience. Though these do not have much scientific background but are extensively used to provide quick forewarning of the menace. Another tool in practice is regression model that represents relationship between two or more variables so that one variable can be predicted from the other (s). Linear and non-linear regression models have been widely used in studying relationship of insect-pests and diseases with time and weather variables (as such or in some transformed forms). With the advent of computers more sophisticated techniques such as simulation modelling and machine learning approach such as decision tree induction algorithms, genetic algorithms, neural networks, rough sets, etc. have been explored. A number of simulation models have been developed all over the world for quantifying effects of various factors including weather on agriculture.  These may provide a good forecast but require detailed data base, which may not be available. Machine learning approach has recently received some attention. As opposed to traditional model-based methods, machine learning approach is self adaptive methods in that there are a few a priori assumptions about the models for problem(s) under study. This technique learns more from examples and captures subtle functional relationships among the data even if the underlying relationships are unknown or hard to describe.  This modelling approach with ability to learn from experience is very useful for many practical problems provided enough data are available. Remotely sensed data can provide useful information relating to area under the crop and also the condition thereof. It has certain advantages over land use statistics due to multi-spectral, synoptic and repetitive coverage. An attempt has been made for accurate estimation of area affected by insect-pests and diseases in crops along with accurate assessment of damage due to the same are possible for providing compensation to farmers. In this study, an Integrated Decision Support System (IDSS) for Crop Protection Services is also discussed.  

scholarly journals SHsearch: a method for fast remote homology detection

2018 ◽  
Author(s):  
Mohamed Baddar
Keyword(s):  
Markov Models ◽  
Sequence Similarity ◽  
Protein Homology ◽  
Homology Detection ◽  
Remote Homology ◽  
Sensitivity Loss ◽  
Computationally Intensive ◽  
Profile Hmm ◽  
Programming Algorithms ◽  
Remote Homology Detection

Remote homology detection is the problem of detecting homology in cases of low sequence similarity. It is a hard computational problem with no approach that works well in all cases. Methods based on profile hidden Markov models (HMM) often exhibit relatively higher sensitivity for detecting remote homologies than commonly used approaches. However, calculating similarity scores in profile HMM methods is computationally intensive as they use dynamic programming algorithms. In this paper we introduce SHsearch: a new method for remote protein homology detection. Our method is implemented as a modification of HHsearch: a remote protein homology detection method based on comparing two profile HMMs. The motivation for modification was to reduce the run time of HHsearch significantly with minimal sensitivity loss. SHsearch focuses on comparing the important submodels of the query and database HMMs instead of comparing the complete models. Hence, SHsearch achieves a significant speedup over HHsearch with minimal loss in sensitivity. On SCOP 1.63, SHsearch achieved 88X speedup with 8.2% loss in sensitivity with respect to HHsearch at error rate of 10%, which deemed to be an acceptable tradeoff.

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