Targeting pattern-recognition receptors to discover new small molecule immune modulators

Zhu, Gengzheng; Xu, Yao; Cen, Xiaohong; Nandakumar, Kutty Selva; Liu, Shuwen; Cheng, Kui

doi:10.1016/j.ejmech.2017.12.026

Cited by 53 publications

(45 citation statements)

References 73 publications

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“…Innate immunity is gaining more attention than adaptive immunity due to its role in combating the pathogens during the early stages of infection, while adaptive immunity comes later into the picture. Adaptive immunity comprises of receptors which are highly specific to antigens (61). In contrast, innate immunity consists of specialized receptors known as PRRs that recognize infectious pathogens and initiate inflammatory responses for their eradication (62).…”

Section: Discussionmentioning

confidence: 99%

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A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information

2020

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This study describes a method developed for predicting pattern recognition receptors (PRRs), which are an integral part of the immune system. The models developed here were trained and evaluated on the largest possible non-redundant PRRs, obtained from PRRDB 2.0, and non-pattern recognition receptors (Non-PRRs), obtained from Swiss-Prot. Firstly, a similarity-based approach using BLAST was used to predict PRRs and got limited success due to a large number of no-hits. Secondly, machine learningbased models were developed using sequence composition and achieved a maximum MCC of 0.63. In addition to this, models were developed using evolutionary information in the form of PSSM composition and achieved maximum MCC value of 0.66. Finally, we developed hybrid models that combined a similarity-based approach using BLAST and machine learning-based models. Our best model, which combined BLAST and PSSM based model, achieved a maximum MCC value of 0.82 with an AUROC value of 0.95, utilizing the potential of both similarity-based search and machine learning techniques. In order to facilitate the scientific community, we also developed a web server "PRRpred" based on the best model developed in this study (http://webs.iiitd.edu.in/ raghava/prrpred/).

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Section: Discussionmentioning

confidence: 99%

“…Several critical implications of PRRs have been reported in the past in the context of adjuvant designing, therapeutic targets, immunomodulator design, cancer immunotherapy, etc. (61,63,64). A comprehensive database of pathogen recognizing receptors such as PRRDB (34) is highly essential to understand innate immunity.…”

Section: Discussionmentioning

confidence: 99%

A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information

2020

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“…Innate immunity is gaining more attention than adaptive immunity due to its role in combating the pathogens during the early stages of infection, while adaptive immunity comes later into the picture. Adaptive immunity comprises of receptors which are highly specific to antigens (52). In contrast, innate immunity consists of specialized receptors known as pattern recognition receptors (PRRs) that recognize infectious pathogens and initiate inflammatory responses for their eradication (53).…”

Section: Discussionmentioning

confidence: 99%

“…Several critical implications of PRRs have been reported in the past in the context of adjuvant designing, therapeutic targets, immunomodulator design, cancer immunotherapy, etc. (52,54,55). A comprehensive database of pathogen recognizing receptors such as PRRDB (23) is highly essential to understand innate immunity.…”

Section: Discussionmentioning

confidence: 99%

A Hybrid Model for Predicting Pattern Recognition Receptors using Evolutionary Information

Kaur

Arora

Raghava

2019

Preprint

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This study describes a method developed for predicting pattern recognition receptors (PRRs), which are an integral part of the immune system. The models developed here were trained and evaluated on the largest possible non-redundant PRRs, and non-pattern recognition receptors (Non-PRRs) obtained from PRRDB 2.0. Firstly, a similarity-based approach using BLAST was used to predict PRRs and got limited success due to a large number of no-hits. Secondly, machine learning-based models were developed using sequence composition and achieved a maximum MCC of 0.63. In addition to this, models were developed using evolutionary information in the form of PSSM composition and achieved maximum MCC value of 0.66. Finally, we developed hybrid models that combined a similarity-based approach using BLAST and machine learningbased models. Our best model, which combined BLAST and PSSM based model, achieved a maximum MCC value of 0.82 with an AUROC value of 0.95, utilizing the potential of both similarity-based search and machine learning techniques. In order to facilitate the scientific community, we also developed a web server "PRRpred" based on the best model developed in this study (http://webs.iiitd.edu.in/raghava/prrpred/).

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“…At the effector level of arthritis, apart from targeting effector molecules, like C5 [ 67 , 153 ] and its break down product C5a [ 65 , 154 , 155 ], receptors (FcRs [ 156 ], TLRs [ 157 ]), transcription factors [ 158 , 159 ]), and cytokines, using different strategies and drugs [ 160 , 161 , 162 ], methods for direct targeting of pathogenic IgG antibodies could be attractive and optimal for therapeutic applications.…”

Section: Targeting Igg To Treat Antibody Dependent Pathologiesmentioning

confidence: 99%

Targeting IgG in Arthritis: Disease Pathways and Therapeutic Avenues

Nandakumar

2018

IJMS

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Rheumatoid arthritis (RA) is a polygenic and multifactorial syndrome. Many complex immunological and genetic interactions are involved in the final outcome of the clinical disease. Autoantibodies (rheumatoid factors, anti-citrullinated peptide/protein antibodies) are present in RA patients’ sera for a long time before the onset of clinical disease. Prior to arthritis onset, in the autoantibody response, epitope spreading, avidity maturation, and changes towards a pro-inflammatory Fc glycosylation phenotype occurs. Genetic association of epitope specific autoantibody responses and the induction of inflammation dependent and independent changes in the cartilage by pathogenic autoantibodies emphasize the crucial contribution of antibody-initiated inflammation in RA development. Targeting IgG by glyco-engineering, bacterial enzymes to specifically cleave IgG/alter N-linked Fc-glycans at Asn 297 or blocking the downstream effector pathways offers new avenues to develop novel therapeutics for arthritis treatment.

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Targeting pattern-recognition receptors to discover new small molecule immune modulators

Cited by 53 publications

References 73 publications

A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information

A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information

A Hybrid Model for Predicting Pattern Recognition Receptors using Evolutionary Information

Targeting IgG in Arthritis: Disease Pathways and Therapeutic Avenues

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