Physicochemical Features Of Proteins Research Articles

17O NMR Spectroscopy: A Novel Probe for Characterizing Protein Structure and Folding.

Simple SummaryOxygen is one of the most abundant atoms in the body. Biomolecules, including most proteins, contain a significant number of oxygen atoms, contributing to the maintenance of the structural and functional integrity of biomolecules. Despite these favorable attributes, detailed characterization of these atoms has been challenging, particularly because of the lack of an appropriate analytical tool. Here, we review recent developments in biomolecular 17O nuclear magnetic resonance spectroscopy, which can directly report the physicochemical properties of oxygen atoms in proteins or related biomolecules. We summarize recent studies that successfully employed this technique to elucidate various structural and functional features of proteins and protein complexes. Finally, we discuss a few promising benefits of this methodology, which we believe ensure its further development as a novel and powerful tool for investigating protein structure and folding.Oxygen is a key atom that maintains biomolecular structures, regulates various physiological processes, and mediates various biomolecular interactions. Oxygen-17 (17O), therefore, has been proposed as a useful probe that can provide detailed information about various physicochemical features of proteins. This is attributed to the facts that (1) 17O is an active isotope for nuclear magnetic resonance (NMR) spectroscopic approaches; (2) NMR spectroscopy is one of the most suitable tools for characterizing the structural and dynamical features of biomolecules under native-like conditions; and (3) oxygen atoms are frequently involved in essential hydrogen bonds for the structural and functional integrity of proteins or related biomolecules. Although 17O NMR spectroscopic investigations of biomolecules have been considerably hampered due to low natural abundance and the quadruple characteristics of the 17O nucleus, recent theoretical and technical developments have revolutionized this methodology to be optimally poised as a unique and widely applicable tool for determining protein structure and dynamics. In this review, we recapitulate recent developments in 17O NMR spectroscopy to characterize protein structure and folding. In addition, we discuss the highly promising advantages of this methodology over other techniques and explain why further technical and experimental advancements are highly desired.

Rcpi: R/Bioconductor package to generate various descriptors of proteins, compounds and their interactions.

In chemoinformatics and bioinformatics fields, one of the main computational challenges in various predictive modeling is to find a suitable way to effectively represent the molecules under investigation, such as small molecules, proteins and even complex interactions. To solve this problem, we developed a freely available R/Bioconductor package, called Compound-Protein Interaction with R (Rcpi), for complex molecular representation from drugs, proteins and more complex interactions, including protein-protein and compound-protein interactions. Rcpi could calculate a large number of structural and physicochemical features of proteins and peptides from amino acid sequences, molecular descriptors of small molecules from their topology and protein-protein interaction and compound-protein interaction descriptors. In addition to main functionalities, Rcpi could also provide a number of useful auxiliary utilities to facilitate the user's need. With the descriptors calculated by this package, the users could conveniently apply various statistical machine learning methods in R to solve various biological and drug research questions in computational biology and drug discovery. Rcpi is freely available from the Bioconductor site (http://bioconductor.org/packages/release/bioc/html/Rcpi.html).

PyDPI: Freely Available Python Package for Chemoinformatics, Bioinformatics, and Chemogenomics Studies

The rapidly increasing amount of publicly available data in biology and chemistry enables researchers to revisit interaction problems by systematic integration and analysis of heterogeneous data. Herein, we developed a comprehensive python package to emphasize the integration of chemoinformatics and bioinformatics into a molecular informatics platform for drug discovery. PyDPI (drug-protein interaction with Python) is a powerful python toolkit for computing commonly used structural and physicochemical features of proteins and peptides from amino acid sequences, molecular descriptors of drug molecules from their topology, and protein-protein interaction and protein-ligand interaction descriptors. It computes 6 protein feature groups composed of 14 features that include 52 descriptor types and 9890 descriptors, 9 drug feature groups composed of 13 descriptor types that include 615 descriptors. In addition, it provides seven types of molecular fingerprint systems for drug molecules, including topological fingerprints, electro-topological state (E-state) fingerprints, MACCS keys, FP4 keys, atom pair fingerprints, topological torsion fingerprints, and Morgan/circular fingerprints. By combining different types of descriptors from drugs and proteins in different ways, interaction descriptors representing protein-protein or drug-protein interactions could be conveniently generated. These computed descriptors can be widely used in various fields relevant to chemoinformatics, bioinformatics, and chemogenomics. PyDPI is freely available via https://sourceforge.net/projects/pydpicao/.

Classifying G-protein-coupled receptors to the finest subtype level

G-protein-coupled receptors (GPCRs) constitute a remarkable protein family of receptors that are involved in a broad range of biological processes. A large number of clinically used drugs elicit their biological effect via a GPCR. Thus, developing a reliable computational method for predicting the functional roles of GPCRs would be very useful in the pharmaceutical industry. Nowadays, researchers are more interested in functional roles of GPCRs at the finest subtype level. However, with the accumulation of many new protein sequences, none of the existing methods can completely classify these GPCRs to their finest subtype level. In this paper, a pioneer work was performed trying to resolve this problem by using a hierarchical classification method. The first level determines whether a query protein is a GPCR or a non-GPCR. If it is considered as a GPCR, it will be finally classified to its finest subtype level. GPCRs are characterized by 170 sequence-derived features encapsulating both amino acid composition and physicochemical features of proteins, and support vector machines are used as the classification engine. To test the performance of the present method, a non-redundant dataset was built which are organized at seven levels and covers more functional classes of GPCRs than existing datasets. The number of protein sequences in each level is 5956, 2978, 8079, 8680, 6477, 1580 and 214, respectively. By 5-fold cross-validation test, the overall accuracy of 99.56%, 93.96%, 82.81%, 85.93%, 94.1%, 95.38% and 92.06% were observed at each level. When compared with some previous methods, the present method achieved a consistently higher overall accuracy. The results demonstrate the power and effectiveness of the proposed method to accomplish the classification of GPCRs to the finest subtype level.

Propy: a tool to generate various modes of Chou’s PseAAC

Sequence-derived structural and physiochemical features have been frequently used for analysing and predicting structural, functional, expression and interaction profiles of proteins and peptides. To facilitate extensive studies of proteins and peptides, we developed a freely available, open source python package called protein in python (propy) for calculating the widely used structural and physicochemical features of proteins and peptides from amino acid sequence. It computes five feature groups composed of 13 features, including amino acid composition, dipeptide composition, tripeptide composition, normalized Moreau-Broto autocorrelation, Moran autocorrelation, Geary autocorrelation, sequence-order-coupling number, quasi-sequence-order descriptors, composition, transition and distribution of various structural and physicochemical properties and two types of pseudo amino acid composition (PseAAC) descriptors. These features could be generally regarded as different Chou's PseAAC modes. In addition, it can also easily compute the previous descriptors based on user-defined properties, which are automatically available from the AAindex database. The python package, propy, is freely available via http://code.google.com/p/protpy/downloads/list, and it runs on Linux and MS-Windows. Supplementary data are available at Bioinformatics online.

Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence

Sequence-derived structural and physicochemical features have been extensively used for analyzing and predicting structural, functional, expression and interaction profiles of proteins and peptides. PROFEAT has been developed as a web server for computing commonly used features of proteins and peptides from amino acid sequence. To facilitate more extensive studies of protein and peptides, numerous improvements and updates have been made to PROFEAT. We added new functions for computing descriptors of protein–protein and protein–small molecule interactions, segment descriptors for local properties of protein sequences, topological descriptors for peptide sequences and small molecule structures. We also added new feature groups for proteins and peptides (pseudo-amino acid composition, amphiphilic pseudo-amino acid composition, total amino acid properties and atomic-level topological descriptors) as well as for small molecules (atomic-level topological descriptors). Overall, PROFEAT computes 11 feature groups of descriptors for proteins and peptides, and a feature group of more than 400 descriptors for small molecules plus the derived features for protein–protein and protein–small molecule interactions. Our computational algorithms have been extensively tested and used in a number of published works for predicting proteins of specific structural or functional classes, protein–protein interactions, peptides of specific functions and quantitative structure activity relationships of small molecules. PROFEAT is accessible free of charge at http://bidd.cz3.nus.edu.sg/cgi-bin/prof/protein/profnew.cgi.

Prediction of highly-connected 'hub'-proteins in protein interaction networks using QSAR

Proteins that are most essential for functioning and viability of bacterial cell have been shown to exhibit larger number of interactions with other cell components. Thus, by identifying the most connected proteins (or hubs) in protein interaction networks (PINs), one may discover prospective drug targets that can be utilized to combat emergent and drug-resistant pathogens such as Methicillin-Resistant Staphylococcus aureus 252 (MRSA). The advantage of using such hub proteins as drug targets lies in their essentiality, non-replaceable position in the PIN and lower rate of mutation, which can help to counter bacterial resistance. However, finding or predicting such hub proteins remains a challenging task as the corresponding experiments are very costly, while traditional bioinformatics approaches generally fail in forecasting PIN data due to the general lack of agreement between the existing datasets [1]. Thus, we have decided to utilize various structural and physicochemical features of proteins, related to traditional QSAR properties for predicting highly connected proteins. Using our own in-house generated PIN for the MRSA cell we have trained a boosting tree-based classifier that uses 75 physical and chemical QSAR descriptors computed for all proteins in the interaction network [2]. The utilized parameters included molecular weight, net charge, isoelectric point, hydrophobicity, surface area, solvent accessibilities, electronegativity, secondary structure composition, surface coils and flexibility among other QSAR descriptors. The developed QSAR model has yielded a high prediction accuracy of 80% for the validation set and was used to predict additional hubs in the rest of the MRSA proteome. The predicted hubs have then been evaluated experimentally and 55% of them were confirmed as high interactors what corresponds to >5 fold dataset enrichment for potential hub-proteins provided by the developed QSAR model. Thus, the successful development of accurate hub classifiers demonstrated that highly-connected proteins tend to share certain structural and physicochemical features that can be characterized and quantified by conventional QSAR descriptors. It is anticipated that the developed hub classifiers will represent a useful tool for the prediction of highly-interacting proteins and can find broad application for planning and executing large-scale proteomic experiments and for identification of novel and prospective antibacterial drug targets – even in those organisms that currently lack protein interaction data.

Improving discrimination of outer membrane proteins by fusing different forms of pseudo amino acid composition

Integral membrane proteins are central to many cellular processes and constitute approximately 50% of potential targets for novel drugs. However, the number of outer membrane proteins (OMPs) present in the public structure database is very limited due to the difficulties in determining structure with experimental methods. Therefore, discriminating OMPs from non-OMPs with computational methods is of medical importance as well as genome sequencing necessity. In this study, some sequence-derived structural and physicochemical features of proteins were incorporated with amino acid composition to discriminate OMPs from non-OMPs using support vector machines. The discrimination performance of the proposed method is evaluated on a benchmark dataset of 208 OMPs, 673 globular proteins, and 206 α-helical membrane proteins. A high overall accuracy of 97.8% was observed in the 5-fold cross-validation test. In addition, the current method distinguished OMPs from globular proteins and α-helical membrane proteins with overall accuracies of 98.2 and 96.4%, respectively. The prediction performance is superior to the state-of-the-art methods in the literature. It is anticipated that the current method might be a powerful tool for the discrimination of OMPs.

PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence.

Sequence-derived structural and physicochemical features have frequently been used in the development of statistical learning models for predicting proteins and peptides of different structural, functional and interaction profiles. PROFEAT (Protein Features) is a web server for computing commonly-used structural and physicochemical features of proteins and peptides from amino acid sequence. It computes six feature groups composed of ten features that include 51 descriptors and 1447 descriptor values. The computed features include amino acid composition, dipeptide composition, normalized Moreau–Broto autocorrelation, Moran autocorrelation, Geary autocorrelation, sequence-order-coupling number, quasi-sequence-order descriptors and the composition, transition and distribution of various structural and physicochemical properties. In addition, it can also compute previous autocorrelations descriptors based on user-defined properties. Our computational algorithms were extensively tested and the computed protein features have been used in a number of published works for predicting proteins of functional classes, protein–protein interactions and MHC-binding peptides. PROFEAT is accessible at