1.
Mutants of β2-glycoprotein I: Their features and potent applications.
Shen, L, Azmi, NU, Tan, XW, Yasuda, S, Wahyuningsih, AT, Inagaki, J, Kobayashi, K, Ando, E, Sasaki, T, Matsuura, E
Best practice & research. Clinical rheumatology. 2018;(4):572-590
Abstract
β2-Glycoprotein I (β2GPI) is a highly-glycosylated plasma protein composed of five homologous domains which regulates coagulation, fibrinolysis, and/or angiogenesis by interacting to negatively charged hydrophobic molecules and/or with plasminogen and its metabolites. The present study focused on structural and functional characterization of β2GPI's domain I (DI) and V (DV). Through N-terminal amino acid sequencing, a novel plasmin-cleaved site at K287C288 was identified in DV. We further modified the intact DV by altering two amino acids at specific proteolytic cleavage sites to generate three stable DV mutants: DV(PP), (PE), and (AA). Results of both SDS-PAGE and MALDI-TOF-MS showed that all three DV mutants were more stable than the intact DV, and DV(PE) was predominantly resistant to proteolysis. Competitive ELISA assessed affinities of intact β2GPI and those mutants to cardiolipin. In culture system, all DV and DI mutants potently inhibited HUVEC's proliferation by 18-30% as compared to control. Only DI and nicked β2GPI showed significant inhibition in HUVEC's tube formation. Moreover, DV(PE)-coated affinity columns demonstrated its binding property towards anionic lipids and could substantially isolate anionic DOPS from zwitterionic DOPC as a purification model. In summary, the proteolytic resistant and unhindered phospholipid (PL) binding properties of DV(PE) have made it an appealing element for subsequent prospective studies. Future in-depth characterization and optimized applications of cleavage-resistant DV(PE) would complement its full capacity as a novel clinical modality in the field of vascular imaging and/or lipidomics studies.
2.
Practical analysis of specificity-determining residues in protein families.
Chagoyen, M, García-Martín, JA, Pazos, F
Briefings in bioinformatics. 2016;(2):255-61
Abstract
Determining the residues that are important for the molecular activity of a protein is a topic of broad interest in biomedicine and biotechnology. This knowledge can help understanding the protein's molecular mechanism as well as to fine-tune its natural function eventually with biotechnological or therapeutic implications. Some of the protein residues are essential for the function common to all members of a family of proteins, while others explain the particular specificities of certain subfamilies (like binding on different substrates or cofactors and distinct binding affinities). Owing to the difficulty in experimentally determining them, a number of computational methods were developed to detect these functional residues, generally known as 'specificity-determining positions' (or SDPs), from a collection of homologous protein sequences. These methods are mature enough for being routinely used by molecular biologists in directing experiments aimed at getting insight into the functional specificity of a family of proteins and eventually modifying it. In this review, we summarize some of the recent discoveries achieved through SDP computational identification in a number of relevant protein families, as well as the main approaches and software tools available to perform this type of analysis.
3.
Sequence-based protein superfamily classification using computational intelligence techniques: a review.
Vipsita, S, Rath, SK
International journal of data mining and bioinformatics. 2015;(4):424-57
Abstract
Protein superfamily classification deals with the problem of predicting the family membership of newly discovered amino acid sequence. Although many trivial alignment methods are already developed by previous researchers, but the present trend demands the application of computational intelligent techniques. As there is an exponential growth in size of biological database, retrieval and inference of essential knowledge in the biological domain become a very cumbersome task. This problem can be easily handled using intelligent techniques due to their ability of tolerance for imprecision, uncertainty, approximate reasoning, and partial truth. This paper discusses the various global and local features extracted from full length protein sequence which are used for the approximation and generalisation of the classifier. The various parameters used for evaluating the performance of the classifiers are also discussed. Therefore, this review article can show right directions to the present researchers to make an improvement over the existing methods.
4.
Protein Function Prediction: Problems and Pitfalls.
Pearson, WR
Current protocols in bioinformatics. 2015;:4.12.1-4.12.8
Abstract
The characterization of new genomes based on their protein sets has been revolutionized by new sequencing technologies, but biologists seeking to exploit new sequence information are often frustrated by the challenges associated with accurately assigning biological functions to newly identified proteins. Here, we highlight some of the challenges in functional inference from sequence similarity. Investigators can improve the accuracy of function prediction by (1) being conservative about the evolutionary distance to a protein of known function; (2) considering the ambiguous meaning of "functional similarity," and (3) being aware of the limitations of annotations in functional databases. Protein function prediction does not offer "one-size-fits-all" solutions. Prediction strategies work better when the idiosyncrasies of function and functional annotation are better understood.
5.
State-of-the-art bioinformatics protein structure prediction tools (Review).
Pavlopoulou, A, Michalopoulos, I
International journal of molecular medicine. 2011;(3):295-310
Abstract
Knowledge of the native structure of a protein could provide an understanding of the molecular basis of its function. However, in the postgenomics era, there is a growing gap between proteins with experimentally determined structures and proteins without known structures. To deal with the overwhelming data, a collection of automated methods as bioinformatics tools which determine the structure of a protein from its amino acid sequence have emerged. The aim of this paper is to provide the experimental biologists with a set of cutting-edge, carefully evaluated, user-friendly computational tools for protein structure prediction that would be helpful for the interpretation of their results and the rational design of new experiments.
6.
Supervised ensembles of prediction methods for subcellular localization.
Assfalg, J, Gong, J, Kriegel, HP, Pryakhin, A, Wei, T, Zimek, A
Journal of bioinformatics and computational biology. 2009;(2):269-85
Abstract
In the past decade, many automated prediction methods for the subcellular localization of proteins have been proposed, utilizing a wide range of principles and learning approaches. Based on an experimental evaluation of different methods and their theoretical properties, we propose to combine a well-balanced set of existing approaches to new, ensemble-based prediction methods. The experimental evaluation shows that our ensembles improve substantially over the underlying base methods.