scholarly journals Response to comments on ‘Empirical comparison of web-based antimicrobial peptide prediction tools’

2018 ◽  
Vol 35 (15) ◽  
pp. 2695-2696
Author(s):  
Musa Nur Gabere ◽  
William Stafford Noble
Keyword(s):  
Antimicrobial Peptide ◽  
Empirical Comparison ◽  
Web Based ◽  
Prediction Tools ◽  
Peptide Prediction

Comment on: ‘Empirical comparison of web-based antimicrobial peptide prediction tools’

2018 ◽  
Vol 35 (15) ◽  
pp. 2692-2694 ◽  
Author(s):  
Boris Vishnepolsky ◽  
Malak Pirtskhalava
Keyword(s):  
Antimicrobial Peptide ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Empirical Comparison ◽  
Web Based ◽  
Prediction Tools ◽  
Peptide Prediction

Abstract Supplementary information: Supplementary data are available at Bioinformatics online.

scholarly journals Empirical comparison of web-based antimicrobial peptide prediction tools

2017 ◽  
Vol 33 (13) ◽  
pp. 1921-1929 ◽  
Author(s):  
Musa Nur Gabere ◽  
William Stafford Noble
Keyword(s):  
Antimicrobial Peptide ◽  
Empirical Comparison ◽  
Web Based ◽  
Prediction Tools ◽  
Peptide Prediction

scholarly journals Empirical Comparison and Analysis of Web-Based DNA N4-Methylcytosine Site Prediction Tools

2020 ◽  
Vol 22 ◽  
pp. 406-420 ◽  
Author(s):  
Balachandran Manavalan ◽  
Md. Mehedi Hasan ◽  
Shaherin Basith ◽  
Vijayakumar Gosu ◽  
Tae-Hwan Shin ◽  
...  
Keyword(s):  
Empirical Comparison ◽  
Web Based ◽  
Site Prediction ◽  
Prediction Tools ◽  
Comparison And Analysis

Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools

2019 ◽  
Vol 21 (2) ◽  
pp. 408-420 ◽  
Author(s):  
Ran Su ◽  
Jie Hu ◽  
Quan Zou ◽  
Balachandran Manavalan ◽  
Leyi Wei
Keyword(s):  
Prediction Models ◽  
Cell Penetrating Peptides ◽  
Web Based ◽  
Practical Applications ◽  
Prediction Tools ◽  
Cell Penetrating ◽  
Potential Applications ◽  
Peptide Prediction

Abstract Cell-penetrating peptides (CPPs) facilitate the delivery of therapeutically relevant molecules, including DNA, proteins and oligonucleotides, into cells both in vitro and in vivo. This unique ability explores the possibility of CPPs as therapeutic delivery and its potential applications in clinical therapy. Over the last few decades, a number of machine learning (ML)-based prediction tools have been developed, and some of them are freely available as web portals. However, the predictions produced by various tools are difficult to quantify and compare. In particular, there is no systematic comparison of the web-based prediction tools in performance, especially in practical applications. In this work, we provide a comprehensive review on the biological importance of CPPs, CPP database and existing ML-based methods for CPP prediction. To evaluate current prediction tools, we conducted a comparative study and analyzed a total of 12 models from 6 publicly available CPP prediction tools on 2 benchmark validation sets of CPPs and non-CPPs. Our benchmarking results demonstrated that a model from the KELM-CPPpred, namely KELM-hybrid-AAC, showed a significant improvement in overall performance, when compared to the other 11 prediction models. Moreover, through a length-dependency analysis, we find that existing prediction tools tend to more accurately predict CPPs and non-CPPs with the length of 20–25 residues long than peptides in other length ranges.

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes

2019 ◽  
Vol 26 (21) ◽  
pp. 3890-3910 ◽  
Author(s):  
Branislava Gemovic ◽  
Neven Sumonja ◽  
Radoslav Davidovic ◽  
Vladimir Perovic ◽  
Nevena Veljkovic
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Experimental Studies ◽  
Protein Protein Interactions ◽  
Web Based ◽  
Prediction Tools ◽  
Physiological Processes ◽  
Modern Drug ◽  
Ppi Prediction

Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies.

scholarly journals Recent trends in antimicrobial peptide prediction using machine learning techniques

2017 ◽  
Vol 13 (12) ◽  
pp. 415-416 ◽  
Author(s):  
Yash Shah ◽  
◽  
Deepak Sehgal ◽  
Jayaraman K Valadi ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Antimicrobial Peptide ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Recent Trends ◽  
Peptide Prediction

Discovering New In Silico Tools for Antimicrobial Peptide Prediction

2012 ◽  
Vol 13 (9) ◽  
pp. 1148-1157 ◽  
Author(s):  
Marc Torrent ◽  
M. Victoria Nogues ◽  
Ester Boix
Keyword(s):  
Antimicrobial Peptide ◽  
In Silico ◽  
Peptide Prediction ◽  
In Silico Tools

Development of Antimicrobial Peptide Prediction Tool for Aquaculture Industries

2016 ◽  
Vol 8 (3) ◽  
pp. 141-149 ◽  
Author(s):  
Aditi Gautam ◽  
Asuda Sharma ◽  
Sarika Jaiswal ◽  
Samar Fatma ◽  
Vasu Arora ◽  
...  
Keyword(s):  
Antimicrobial Peptide ◽  
Prediction Tool ◽  
Peptide Prediction

Progress in the development of antimicrobial peptide prediction tools

2020 ◽  
Vol 21 ◽  
Author(s):  
Chunyan Ao ◽  
Yu Zhang ◽  
Dapeng Li ◽  
Yuming Zhao ◽  
Quan Zou
Keyword(s):  
Antimicrobial Activities ◽  
Rapid Identification ◽  
Research Community ◽  
Research Field ◽  
Innate Immune ◽  
Computational Tools ◽  
Prediction Tools ◽  
Wide Range ◽  
Peptide Prediction ◽  
Significant Attention

: Antimicrobial peptides (AMPs) are natural polypeptides with antimicrobial activities and are found in most organisms. AMPs are evolutionarily conservative components that belong to the innate immune system and show potent activity against bacteria, fungi, viruses and in some cases display antitumor activity. Thus, AMPs are major candidates in the development of new antibacterial reagents. In the last few decades, AMPs have attracted significant attention from the research community. During the early stages of the development of this research field, AMPs were experimentally identified, which is an expensive and time-consuming procedure. Therefore, research and development (R&D) of fast, highly efficient computational tools for predicting AMPs has enabled the rapid identification and analysis of new AMPs from a wide range of organisms. Moreover, these computational tools have allowed researchers to better understand the activities of AMPs, which has promoted R&D of antibacterial drugs. In this review, we systematically summarize AMP prediction tools and their corresponding algorithms used.

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