Exploring additivity effects of double mutations on the binding affinity of protein-protein complexes

2018 ◽  
Vol 86 (5) ◽  
pp. 536-547 ◽  
Author(s):  
Sherlyn Jemimah ◽  
M. Michael Gromiha
Keyword(s):  
Binding Affinity ◽  
Protein Complexes

scholarly journals ISLAND: in-silico proteins binding affinity prediction using sequence information

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Wajid Arshad Abbasi ◽  
Adiba Yaseen ◽  
Fahad Ul Hassan ◽  
Saiqa Andleeb ◽  
Fayyaz Ul Amir Afsar Minhas
Keyword(s):  
Machine Learning ◽  
Protein Binding ◽  
Binding Affinity ◽  
State Of The Art ◽  
Protein Complexes ◽  
Protein Structures ◽  
Sequence Information ◽  
Binding Affinity Prediction ◽  
Generalization Performance ◽  
Affinity Prediction

Abstract Background Determining binding affinity in protein-protein interactions is important in the discovery and design of novel therapeutics and mutagenesis studies. Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures that limit their applicability to protein complexes with known structures. In this work, we explore sequence-based protein binding affinity prediction using machine learning. Method We have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the protein binding affinity. Results We present our findings that the true generalization performance of even the state-of-the-art sequence-only predictor is far from satisfactory and that the development of machine learning methods for binding affinity prediction with improved generalization performance is still an open problem. We have also proposed a sequence-based novel protein binding affinity predictor called ISLAND which gives better accuracy than existing methods over the same validation set as well as on external independent test dataset. A cloud-based webserver implementation of ISLAND and its python code are available at https://sites.google.com/view/wajidarshad/software. Conclusion This paper highlights the fact that the true generalization performance of even the state-of-the-art sequence-only predictor of binding affinity is far from satisfactory and that the development of effective and practical methods in this domain is still an open problem.

scholarly journals Structures of the human Pals1 PDZ domain with and without ligand suggest gated access of Crb to the PDZ peptide-binding groove

2015 ◽  
Vol 71 (3) ◽  
pp. 555-564 ◽  
Author(s):  
Marina E. Ivanova ◽  
Georgina C. Fletcher ◽  
Nicola O'Reilly ◽  
Andrew G. Purkiss ◽  
Barry J. Thompson ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Protein Interaction ◽  
Protein Complexes ◽  
Transmembrane Protein ◽  
Pdz Domain ◽  
Peptide Binding ◽  
Side Chain ◽  
Apical Plasma Membrane ◽  
Peptide Binding Groove ◽  
Binding Groove

Many components of epithelial polarity protein complexes possess PDZ domains that are required for protein interaction and recruitment to the apical plasma membrane. Apical localization of the Crumbs (Crb) transmembrane protein requires a PDZ-mediated interaction with Pals1 (protein-associated with Lin7, Stardust, MPP5), a member of the p55 family of membrane-associated guanylate kinases (MAGUKs). This study describes the molecular interaction between the Crb carboxy-terminal motif (ERLI), which is required forDrosophilacell polarity, and the Pals1 PDZ domain using crystallography and fluorescence polarization. Only the last four Crb residues contribute to Pals1 PDZ-domain binding affinity, with specificity contributed by conserved charged interactions. Comparison of the Crb-bound Pals1 PDZ structure with an apo Pals1 structure reveals a key Phe side chain that gates access to the PDZ peptide-binding groove. Removal of this side chain enhances the binding affinity by more than fivefold, suggesting that access of Crb to Pals1 may be regulated by intradomain contacts or by protein–protein interaction.

scholarly journals Optimization of Protein-Ligand Electrostatic Interactions Using an Alchemical Free-Energy Method

2019 ◽  
Author(s):  
Alexander Wade ◽  
David Huggins
Keyword(s):  
Free Energy ◽  
Binding Affinity ◽  
Energy Method ◽  
Electrostatic Interactions ◽  
Protein Complexes ◽  
Chemical Changes ◽  
Average Improvement ◽  
Explicit Solvent ◽  
Energy Perturbation ◽  
Alchemical Free Energy

<p>We present an alchemical free-energy method for optimizing the partial charges of a ligand to maximize the binding affinity with a receptor. This methodology can be applied to known ligand-protein complexes to determine an optimized set of ligand partial atomic changes. Three protein-ligand complexes have been optimized in this work: FXa, P38 and androgen receptor. The optimization of the ligand charges yielded improvements to binding affinity for all three systems. The sets of optimized charges can be used to identify design principles for chemical changes to the ligand which improve the binding affinity. In this work, beneficial chemical mutations are generated from these principles and the resulting molecules tested using free-energy perturbation calculations. We show that three quarters of our chemical changes are predicted to improve the binding affinity, with an average improvement of approximately 1 kcal/mol. The results demonstrate that charge optimization in explicit solvent is a useful tool for predicting beneficial chemical changes such as pyridinations, fluorinations, and oxygen to sulphur mutations. </p>

scholarly journals Contacts-based prediction of binding affinity in protein–protein complexes

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Anna Vangone ◽  
Alexandre MJJ Bonvin
Keyword(s):  
Binding Affinity ◽  
Conformational Changes ◽  
Protein Interactions ◽  
Prediction Accuracy ◽  
Protein Complexes ◽  
Structural Features ◽  
Specific Protein ◽  
Strong Impact ◽  
Protein Protein Interactions ◽  
Almost All

Almost all critical functions in cells rely on specific protein–protein interactions. Understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack a thorough understanding of the energetics of association of proteins. Here, we introduce a new and simple approach to predict binding affinity based on functional and structural features of the biological system, namely the network of interfacial contacts. We assess its performance against a protein–protein binding affinity benchmark and show that both experimental methods used for affinity measurements and conformational changes have a strong impact on prediction accuracy. Using a subset of complexes with reliable experimental binding affinities and combining our contacts and contact-types-based model with recent observations on the role of the non-interacting surface in protein–protein interactions, we reach a high prediction accuracy for such a diverse dataset outperforming all other tested methods.

UEP: an open-source and fast classifier for predicting the impact of mutations in protein–protein complexes

2020 ◽  
Author(s):  
Pep Amengual-Rigo ◽  
Juan Fernández-Recio ◽  
Victor Guallar
Keyword(s):  
Open Source ◽  
Binding Affinity ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Selection Procedure ◽  
Supplementary Information ◽  
Computational Time ◽  
Contact Potential ◽  
Three Body ◽  
The Impact

Abstract Motivation Single protein residue mutations may reshape the binding affinity of protein–protein interactions. Therefore, predicting its effects is of great interest in biotechnology and biomedicine. Unfortunately, the availability of experimental data on binding affinity changes upon mutation is limited, which hampers the development of new and more precise algorithms. Here, we propose UEP, a classifier for predicting beneficial and detrimental mutations in protein–protein complexes trained on interactome data. Results Regardless of the simplicity of the UEP algorithm, which is based on a simple three-body contact potential derived from interactome data, we report competitive results with the gold standard methods in this field with the advantage of being faster in terms of computational time. Moreover, we propose a consensus selection procedure by involving the combination of three predictors that showed higher classification accuracy in our benchmark: UEP, pyDock and EvoEF1/FoldX. Overall, we demonstrate that the analysis of interactome data allows predicting the impact of protein–protein mutations using UEP, a fast and reliable open-source code. Availability and implementation UEP algorithm can be found at: https://github.com/pepamengual/UEP. Supplementary information Supplementary data are available at Bioinformatics online.

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