AtbPpred: A Robust Sequence-Based Prediction of Anti-Tubercular Peptides Using Extremely Randomized Trees.
TL;DR: A two-layer machine learning (ML)-based predictor for the identification of anti-tubercular peptides (AtbPs) called AtbPpred is developed and it is anticipated that this predictor could be useful in the high-throughput prediction of AtbPs and also provide mechanistic insights into its functions.
read more
Abstract: Mycobacterium tuberculosis is one of the most dangerous pathogens in humans. It acts as an etiological agent of tuberculosis (TB), infecting almost one-third of the world's population. Owing to the high incidence of multidrug-resistant TB and extensively drug-resistant TB, there is an urgent need for novel and effective alternative therapies. Peptide-based therapy has several advantages, such as diverse mechanisms of action, low immunogenicity, and selective affinity to bacterial cell envelopes. However, the identification of anti-tubercular peptides (AtbPs) via experimentation is laborious and expensive; hence, the development of an efficient computational method is necessary for the prediction of AtbPs prior to both in vitro and in vivo experiments. To this end, we developed a two-layer machine learning (ML)-based predictor called AtbPpred for the identification of AtbPs. In the first layer, we applied a two-step feature selection procedure and identified the optimal feature set individually for nine different feature encodings, whose corresponding models were developed using extremely randomized tree (ERT). In the second-layer, the predicted probability of AtbPs from the above nine models were considered as input features to ERT and developed the final predictor. AtbPpred respectively achieved average accuracies of 88.3% and 87.3% during cross-validation and an independent evaluation, which were ~8.7% and 10.0% higher than the state-of-the-art method. Furthermore, we established a user-friendly webserver which is currently available at http://thegleelab.org/AtbPpred . We anticipate that this predictor could be useful in the high-throughput prediction of AtbPs and also provide mechanistic insights into its functions.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Machine intelligence in peptide therapeutics: A next-generation tool for rapid disease screening
TL;DR: Overall, it is shown that using ML models in peptide research can streamline the development of targeted peptide therapies and avoid the common pitfalls and challenges of using ML approaches for peptide therapeutics.
261
HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation
Md. Mehedi Hasan,Md. Mehedi Hasan,Nalini Schaduangrat,Shaherin Basith,Gwang Lee,Watshara Shoombuatong,Balachandran Manavalan +6 more
TL;DR: Performance comparisons over empirical cross-validation analysis, independent test, and case study against state-of-the-art methods demonstrate that HLPpred-Fuse consistently outperformed these methods in the identification of hemolytic activity.
191
OUP accepted manuscript
06 Jan 2022
TL;DR: Wang et al. as mentioned in this paper proposed a novel deep learning framework by utilizing the information bottleneck principle and transfer learning to predict the toxicity of peptides as well as proteins, which achieved a higher prediction performance than state-of-the-art methods on the peptide dataset.
131
Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework.
Md. Mehedi Hasan,Shaherin Basith,Mst. Shamima Khatun,Gwang Lee,Balachandran Manavalan,Hiroyuki Kurata +5 more
TL;DR: Ten different feature encoding schemes were explored, with the goal of capturing key characteristics around 6mA sites and Meta-i6mA was proposed that combined the baseline models using the meta-predictor approach and outperformed the existing predictors.
124
iUmami-SCM: A Novel Sequence-Based Predictor for Prediction and Analysis of Umami Peptides Using a Scoring Card Method with Propensity Scores of Dipeptides.
TL;DR: The first sequence-based predictor named iUmami-SCM is proposed, which serves as a powerful computational technique for large-scale umami peptide identification as well as facilitating the interpretation ofUmami peptides.
122
References
REVIEW : Recent advances in developing web-servers for predicting protein attributes
Kuo-Chen Chou,Hong-Bin Shen +1 more
TL;DR: In this minireview, a systematic introduction is presented to highlight the development of these web-servers by this group during the last three years.
A similarity-based method for prediction of drug side effects with heterogeneous information.
TL;DR: Results indicated that drug similarity in fingerprint was most related to the prediction of drug side effects and all drug properties gave less or more contributions.
In Silico Tools and Databases for Designing Peptide-Based Vaccine and Drugs.
Salman Sadullah Usmani,Rajesh Kumar,Sherry Bhalla,Vinod Kumar,Gajendra P. S. Raghava,Gajendra P. S. Raghava +5 more
TL;DR: A number of in silico tools have been developed to design immunotherapy as well as peptide-based drugs in the last two decades, which proved to be a catalyst in drug and vaccine designing.
Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms
TL;DR: A feature representation learning strategy that automatically learns the most discriminative features from existing feature descriptors in a supervised way is used, capable of effectively capturing the sequence determinants to represent the characteristics of QSPs, thereby contributing to the improved predictive performance.
Bastion3: A two-layer ensemble predictor of type III secreted effectors
Jiawei Wang,Jiahui Li,Jiahui Li,Bingjiao Yang,Ruopeng Xie,Tatiana T. Marquez-Lago,André Leier,Morihiro Hayashida,Tatsuya Akutsu,Yanju Zhang,Kuo-Chen Chou,Kuo-Chen Chou,Joel Selkrig,Tieli Zhou,Jiangning Song,Trevor Lithgow +15 more
TL;DR: Bastion3, a two-layer ensemble predictor developed to accurately identify type III secreted effectors from protein sequence data, is presented and further developed a user-friendly online toolkit, maximizing convenience for experimental scientists toward T3SE prediction.