<i>MuLiMs-MCoMPAs</i>: A Novel Multiplatform Framework to Compute Tensor Algebra-Based Three-Dimensional Protein Descriptors

Contreras-Torres, Ernesto; Marrero-Ponce, Yovani; Terán, Julio E.; García-Jacas, César R.; Brizuela, Carlos A.; Sánchez-Rodríguez, Juan Carlos

doi:10.1021/acs.jcim.9b00629

Cited by 8 publications

(10 citation statements)

References 49 publications

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“…In metadata networks, nodes are connected by a specific parameter in common, such as origin; the target against which they are assessed; functionality; the database where they come from; the cross-reference; Nterminus; C-terminus; or amino acid composition. In similarity networks, peptides are codified by descriptors, such as length, net charge, isoelectric point, molecular weight, Boman index, indices based on aggregation operators, hydrophobic moment, average hydrophilicity, hydrophobic periodicity, aliphatic index, and instability index [29,31,37]. Moreover, networks are visualized using different layouts, such as Fruchterman-Reingold [38].…”

Section: Starpep Toolbox Softwarementioning

confidence: 99%

A Novel Network Science and Similarity-Searching-Based Approach for Discovering Potential Tumor-Homing Peptides from Antimicrobials

et al. 2022

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Peptide-based drugs are promising anticancer candidates due to their biocompatibility and low toxicity. In particular, tumor-homing peptides (THPs) have the ability to bind specifically to cancer cell receptors and tumor vasculature. Despite their potential to develop antitumor drugs, there are few available prediction tools to assist the discovery of new THPs. Two webservers based on machine learning models are currently active, the TumorHPD and the THPep, and more recently the SCMTHP. Herein, a novel method based on network science and similarity searching implemented in the starPep toolbox is presented for THP discovery. The approach leverages from exploring the structural space of THPs with Chemical Space Networks (CSNs) and from applying centrality measures to identify the most relevant and non-redundant THP sequences within the CSN. Such THPs were considered as queries (Qs) for multi-query similarity searches that apply a group fusion (MAX-SIM rule) model. The resulting multi-query similarity searching models (SSMs) were validated with three benchmarking datasets of THPs/non-THPs. The predictions achieved accuracies that ranged from 92.64 to 99.18% and Matthews Correlation Coefficients between 0.894–0.98, outperforming state-of-the-art predictors. The best model was applied to repurpose AMPs from the starPep database as THPs, which were subsequently optimized for the TH activity. Finally, 54 promising THP leads were discovered, and their sequences were analyzed to encounter novel motifs. These results demonstrate the potential of CSNs and multi-query similarity searching for the rapid and accurate identification of THPs.

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Section: Starpep Toolbox Softwarementioning

confidence: 99%

A Novel Network Science and Similarity-Searching-Based Approach for Discovering Potential Tumor-Homing Peptides from Antimicrobials

et al. 2022

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“…Despite a great diversity of peptide features (classical and non-classical) that has been used in AMPs prediction/design, most of those features are sequences- or property- based; however, the 3D structural information of AMPs has not been deeply exploited for such aims [ 173 , 174 , 175 ]. Although experimental determinate 3D structures of AMPs are used in minor proportion than their sequences, the 3D structure prediction tools are becoming more accessible and less computational demanding when considering new advances in both software and computer architectures [ 176 , 177 ]. These facts will ease the gradually inclusion of 3D structural features in the prediction models.…”

Section: Future Research Directionsmentioning

confidence: 99%

Emerging Computational Approaches for Antimicrobial Peptide Discovery

et al. 2022

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In the last two decades many reports have addressed the application of artificial intelligence (AI) in the search and design of antimicrobial peptides (AMPs). AI has been represented by machine learning (ML) algorithms that use sequence-based features for the discovery of new peptidic scaffolds with promising biological activity. From AI perspective, evolutionary algorithms have been also applied to the rational generation of peptide libraries aimed at the optimization/design of AMPs. However, the literature has scarcely dedicated to other emerging non-conventional in silico approaches for the search/design of such bioactive peptides. Thus, the first motivation here is to bring up some non-standard peptide features that have been used to build classical ML predictive models. Secondly, it is valuable to highlight emerging ML algorithms and alternative computational tools to predict/design AMPs as well as to explore their chemical space. Another point worthy of mention is the recent application of evolutionary algorithms that actually simulate sequence evolution to both the generation of diversity-oriented peptide libraries and the optimization of hit peptides. Last but not least, included here some new considerations in proteogenomic analyses currently incorporated into the computational workflow for unravelling AMPs in natural sources.

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“…In metadata networks, nodes are connected by a specific parameter in common, such as origin, the target which assessed against, functionality, the database where they come from, the cross-reference, Nterminus, C-terminus, or amino acid composition. In similarity networks, peptides are codified by descriptors, such as length, net charge, isoelectric point, molecular weight, Boman index, indices based on aggregation operators, hydrophobic moment, average hydrophilicity, hydrophobic periodicity, aliphatic index, and instability index [29,31,37]. Moreover, networks are visualized using different layouts, such as Fruchterman Reingold [38].…”

Section: Starpep Toolbox Softwarementioning

confidence: 99%

A Novel Network Science and Similarity Searching-Based Approach for Discovering Potential Tumor Homing Peptides from Antimicrobials

Romero¹,

Marrero-Ponce²,

Rodríguez³

et al. 2022

Preprint

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Peptide-based drugs are promising anticancer candidates due to their biocompatibility, and low toxicity. Particularly, tumor homing peptides (THPs) have the ability to bind specifically to can-cer cells receptors and tumor vasculature. Despite their potential to develop antitumor drugs, there are few available prediction tools to assist the discovery of new THPs. Two webservers based on machine learning models are currently active, the TumorHPD (https://webs.iiitd.edu.in/raghava/tumorhpd) and the THPep (http://codes.bio/thpep), and more recently the SCMTHP (SCMTHP (pmlabstack.pythonanywhere.com), based on scoring card method. Herein, a novel method based on network science and similarity searching implemented in the starPep toolbox (http://mobiosd-hub.com/starpep/) is presented for THPs discovery. The approach leverages from exploring the structural space of THPs with Chemical Space Networks (CSNs) and from applying centrality measures to identify the most relevant and non-redundant THPs sequences within the CSN. Such THPs were considered as queries (Qs) for multi-query similarity searches that applies a group fusion (MAX-SIM rule) model. The resulting multi-query similarity searching models (SSMs) were validated with three benchmarking datasets of THPs/non-THPs. Predictions achieved accuracies ranged from 92.64 to 99.18% and Matthews Correlation Coefficients between 0.894-0.98, outperforming state-of-the-art predictors. The best model was applied to repurpose AMPs from the starPep database as THPs, which were subse-quently optimized for the TH activity. Finally, 54 promising THP leads were discovered, and their sequences were analyzed to encounter novel motifs. These results demonstrate the potential of CSNs and multi-query similarity searching for a rapid and accurate identification of THPs.

show abstract

MuLiMs-MCoMPAs: A Novel Multiplatform Framework to Compute Tensor Algebra-Based Three-Dimensional Protein Descriptors

Cited by 8 publications

References 49 publications

A Novel Network Science and Similarity-Searching-Based Approach for Discovering Potential Tumor-Homing Peptides from Antimicrobials

A Novel Network Science and Similarity-Searching-Based Approach for Discovering Potential Tumor-Homing Peptides from Antimicrobials

Emerging Computational Approaches for Antimicrobial Peptide Discovery

A Novel Network Science and Similarity Searching-Based Approach for Discovering Potential Tumor Homing Peptides from Antimicrobials

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