PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions

Dong, Jie; Yao, Zhi‐Jiang; Zhang, Lin; Luo, Feijun; Lin, Qinlu; Lu, Aiping; Chen, Alex F.; Cao, Dong‐Sheng

doi:10.1186/s13321-018-0270-2

Cited by 114 publications

(92 citation statements)

References 57 publications

(44 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, Pfeature is complementary to PROFEAT but not alternate or replacement. Similarly, PyBioMed is a recently developed python library with the capability of computing features of biological molecules and interaction samples (Dong et al, 2018). A module named "PyProtein" is developed in PyBioMed which is used to calculate protein features (structural and physicochemical properties).…”

Section: Discussionmentioning

confidence: 99%

“…This part basically covers already existing methods of protein/peptide feature extraction (Dong et al, 2018). This module have seven sub modules to compute complex composition based features, following is brief description of each features.…”

Section: Miscellaneousmentioning

confidence: 99%

“…In past, numerous web servers, standalone software and libraries has been developed that includes PROFEAT (Li et al, 2006), PyBioMed (Dong et al, 2018), iFeature (Chen et al, 2018), protr (Xiao et al, 2015), Rcpi (Cao et al, 2015), propy (Cao, Xu, et al, 2013); for computing features of a protein. Despite number of methods have been developed in past, still number of important features have not been integrated in any software, particularly features required for annotating protein at residue level.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Computing wide range of protein/peptide features from their sequence and structure

Pande

Patiyal

Lathwal

et al. 2019

Preprint

View full text Add to dashboard Cite

MotivationIn last three decades, a wide range of protein descriptors/features have been discovered to annotate a protein with high precision. A wide range of features have been integrated in numerous software packages (e.g., PROFEAT, PyBioMed, iFeature, protr, Rcpi, propy) to predict function of a protein. These features are not suitable to predict function of a protein at residue level such as prediction of ligand binding residues, DNA interacting residues, post translational modification etc. ResultsIn order to facilitate scientific community, we have developed a software package that computes more than 50,000 features, important for predicting function of a protein and its residues. It has five major modules for computing; composition-based features, binary profiles, evolutionary information, structure-based features and patterns. The composition-based module allows user to compute; i) simple compositions like amino acid, dipeptide, tripeptide; ii) Properties based compositions; iii) Repeats and distribution of amino acids; iv) Shannon entropy to measure the low complexity regions; iv) Miscellaneous compositions like pseudo amino acid, autocorrelation, conjoint triad, quasi-sequence order. Binary profile of amino acid sequences provides complete information including order of residues or type of residues; specifically, suitable to predict function of a protein at residue level. Pfeature allows one to compute evolutionary informationbased features in form of PSSM profile generated using PSIBLAST. Structure based module allows computing structure-based features, specifically suitable to annotate chemically modified peptides/proteins. Pfeature also allows generating overlapping patterns and feature from whole protein or its parts (e.g., N-terminal, C-terminal). In summary, Pfeature comprises of almost all features used till now, for predicting function of a protein/peptide including its residues. AvailabilityIt is available in form of a web server, named as Pfeature (https://webs.iiitd.edu.in/raghava/pfeature/), as well as python library and standalone package (https://github.com/raghavagps/Pfeature) suitable for Windows, Ubuntu, Fedora, MacOS and Centos based operating system.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Miscellaneousmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computing wide range of protein/peptide features from their sequence and structure

Pande

Patiyal

Lathwal

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Wang et al (2017) published POSSUM as a public web server as well as a Perl/Python-based tool, executable via the command line [143]. PyBioMed is another Python library foremost aiming cheminformaticians, owing to the fact, that many molecular encodings are implemented, e.g., topological descriptors, applicable in QSAR studies [144]. Nevertheless, Dong et al (2018) rounded out this package with a variety of amino acid encodings and additional tools, such as sequence and structure retrieval [144].…”

Section: Databases and Packagesmentioning

confidence: 99%

“…PyBioMed is another Python library foremost aiming cheminformaticians, owing to the fact, that many molecular encodings are implemented, e.g., topological descriptors, applicable in QSAR studies [144]. Nevertheless, Dong et al (2018) rounded out this package with a variety of amino acid encodings and additional tools, such as sequence and structure retrieval [144]. Recently, Chen et al (2018) published iFeature, which is accessible as a Python package and web server [145].…”

Section: Databases and Packagesmentioning

confidence: 99%

Encodings and models for antimicrobial peptide classification for multi-resistant pathogens

Spänig

Heider

2019

BioData Mining

View full text Add to dashboard Cite

Antimicrobial peptides (AMPs) are part of the inherent immune system. In fact, they occur in almost all organisms including, e.g., plants, animals, and humans. Remarkably, they show effectivity also against multi-resistant pathogens with a high selectivity. This is especially crucial in times, where society is faced with the major threat of an ever-increasing amount of antibiotic resistant microbes. In addition, AMPs can also exhibit antitumor and antiviral effects, thus a variety of scientific studies dealt with the prediction of active peptides in recent years. Due to their potential, even the pharmaceutical industry is keen on discovering and developing novel AMPs. However, AMPs are difficult to verify in vitro, hence researchers conduct sequence similarity experiments against known, active peptides. Unfortunately, this approach is very time-consuming and limits potential candidates to sequences with a high similarity to known AMPs. Machine learning methods offer the opportunity to explore the huge space of sequence variations in a timely manner. These algorithms have, in principal, paved the way for an automated discovery of AMPs. However, machine learning models require a numerical input, thus an informative encoding is very important. Unfortunately, developing an appropriate encoding is a major challenge, which has not been entirely solved so far. For this reason, the development of novel amino acid encodings is established as a stand-alone research branch. The present review introduces state-of-the-art encodings of amino acids as well as their properties in sequence and structure based aggregation. Moreover, albeit a wellchosen encoding is essential, performant classifiers are required, which is reflected by a tendency towards specifically designed models in the literature. Furthermore, we introduce these models with a particular focus on encodings derived from support vector machines and deep learning approaches. Albeit a strong focus has been set on AMP predictions, not all of the mentioned encodings have been elaborated as part of antimicrobial research studies, but rather as general protein or peptide representations.

show abstract

AI‐Powered Mining of Highly Customized and Superior ESIPT‐Based Fluorescent Probes

Huang,

Fang

et al. 2024

Advanced Science

View full text Add to dashboard Cite

Excited‐state intramolecular proton transfer (ESIPT) has attracted great attention in fluorescent sensors and luminescent materials due to its unique photobiological and photochemical features. However, the current structures are far from meeting the specific demands for ESIPT molecules in different scenarios; the try‐and‐error development method is labor‐intensive and costly. Therefore, it is imperative to devise novel approaches for the exploration of promising ESIPT fluorophores. This research proposes an artificial intelligence approach aiming at exploring ESIPT molecules efficiently. The first high‐quality ESIPT dataset and a multi‐level prediction system are constructed that realized accurate identification of ESIPT molecules from a large number of compounds under a stepwise distinguishing from conventional molecules to fluorescent molecules and then to ESIPT molecules. Furthermore, key structural features that contributed to ESIPT are revealed by using the SHapley Additive exPlanations (SHAP) method. Then three strategies are proposed to ensure the ESIPT process while keeping good safety, pharmacokinetic properties, and novel structures. With these strategies, >700 previously unreported ESIPT molecules are screened from a large pool of 570 000 compounds. The ESIPT process and biosafety of optimal molecules are successfully validated by quantitative calculation and experiment. This novel approach is expected to bring a new paradigm for exploring ideal ESIPT molecules.

show abstract

PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions

Cited by 114 publications

References 57 publications

Computing wide range of protein/peptide features from their sequence and structure

Computing wide range of protein/peptide features from their sequence and structure

Encodings and models for antimicrobial peptide classification for multi-resistant pathogens

AI‐Powered Mining of Highly Customized and Superior ESIPT‐Based Fluorescent Probes

Contact Info

Product

Resources

About