Optimization of protein–protein docking for predicting Fc–protein interactions

Abstract

have not yet been determined. Protein-protein docking could be used to investigate Fc-protein complexes; however, improved approaches are necessary to efficiently model such cases. In this study, a docking-based structural bioinformatics approach is developed for predicting the structures of Fcprotein complexes. Based on the available set of X-ray structures of Fc-protein complexes, three regions of the Fc, loosely corresponding to three turns within the structure, were defined as containing the essential features for protein recognition and used as restraints to filter the initial docking search.Rescoring the filtered poses with an optimal scoring strategy provided a success rate of approximately 80% of the test cases examined within the top ranked 20 poses, compared to approximately 20% by the initial unrestrained docking. The developed docking protocol provides a significant improvement over the initial unrestrained docking and will be valuable for predicting the structures of currently undetermined Fc-protein complexes, as well as in the design of peptides and proteins that target Fc.

An in-silico approach to develop of a multi-epitope vaccine candidate against SARS-CoV-2 envelope (E) protein

“…Based on the Ramachandran plot, 96% of the residues of re ned predicted model were found in favored and allowed regions with 3.5% outliers. Successful re ning might improve the applicability of template-based models by offering more reliable structures for functional analysis, molecular design or experimental structure determination 40 .In the context of structural vaccinology, a molecular docking study was needed to forecast the binding a nity of epitopes to the crystallized fragment (FC) of antibodies or MHC molecules 41,42 . To analyze the a nity of the nal multi-epitope vaccine to MHC molecules, we did 26 docking studies.…”

Vaccine Target Discovery

“…Pattern-based methods includes binding motifs, quantitative matrices, decision trees, artificial neural networks (ANNs), hidden Markov models (HMMs) and support vector machines (SVMs), among others. In contrast, the structure based methods are theoretically rooted and include homology modeling, docking and 3D threading techniques (Dominguez et al, 2003;De Vries et al, 2010;Agostino et al, 2016;Khan and Ranganathan, 2010;Liljeroos et al, 2015;. Although less accurate, pattern based approaches are over-represented in the literature due to higher complexity in development and longer computational time of the more accurate structure-based approaches (Ranganathan and Maria et al, 2017), including the sheer difference in the availability of linear versus structural data.…”

The Impact of Bioinformatics on Vaccine Design and Development

“…In the ield of structural vaccinology, molecular docking can be employed to predict the binding of epitopes to antibodies or to MHC receptors. Candidate antigens can be evaluated through the binding energy of the complex, and even mutations can be introduced to improve binding, but maintaining the speciicity of the immune response [37].…”

“…One study modiied the Fc portion of antibodies to increase binding of proteins to the antibodies' Fc. This approach is relevant to improve the functionality of designed antibodies, to study immune response evasion by some pathogens, and in biotechnology to purify antibodies or proteins [37].…”