Intermolecular Contact Potentials for Protein–Protein Interactions Extracted from Binding Free Energy Changes upon Mutation

2013 ◽  
Vol 9 (8) ◽  
pp. 3715-3727 ◽  
Author(s):  
Iain H. Moal ◽  
Juan Fernandez-Recio
Keyword(s):  
Free Energy ◽  
Protein Interactions ◽  
Binding Free Energy ◽  
Protein Protein Interactions ◽  
Intermolecular Contact

scholarly journals Generating quantitative binding landscapes through fractional binding selections, deep sequencing and data normalization

2019 ◽  
Author(s):  
Michael Heyne ◽  
Niv Papo ◽  
Julia Shifman
Keyword(s):  
Free Energy ◽  
Protein Interactions ◽  
Binding Free Energy ◽  
Surface Display ◽  
Yeast Surface Display ◽  
Protein Protein Interactions ◽  
Protein Mutants ◽  
Display Technology ◽  
Data Points ◽  
Affinity Interaction

AbstractQuantifying the effects of various mutations on binding free energy is crucial for understanding the evolution of protein-protein interactions and would greatly facilitate protein engineering studies. Yet, measuring changes in binding free energy (ΔΔGbind) remains a tedious task that requires expression of each mutant, its purification, and affinity measurements. We developed a new approach that allows us to quantify ΔΔGbindfor thousands of protein mutants in one experiment. Our protocol combines protein randomization, Yeast Surface Display technology, Next Generation Sequencing, and a few experimental ΔΔGbinddata points on purified proteins to generate ΔΔGbindvalues for the remaining numerous mutants of the same protein complex. Using this methodology, we comprehensively map the single-mutant binding landscape of one of the highest-affinity interaction between BPTI and Bovine Trypsin. We show that ΔΔGbindfor this interaction could be quantified with high accuracy over the range of 12 kcal/mol displayed by various BPTI single mutants.

scholarly journals SAAMBE-SEQ: a sequence-based method for predicting mutation effect on protein–protein binding affinity

2020 ◽  
Author(s):  
Gen Li ◽  
Swagata Pahari ◽  
Adithya Krishna Murthy ◽  
Siqi Liang ◽  
Robert Fragoza ◽  
...  
Keyword(s):  
Free Energy ◽  
Protein Binding ◽  
Binding Affinity ◽  
Protein Interactions ◽  
Structural Information ◽  
Binding Free Energy ◽  
Supplementary Information ◽  
Sequence Information ◽  
Protein Protein Interactions ◽  
Genome Scale

Abstract Motivation Vast majority of human genetic disorders are associated with mutations that affect protein–protein interactions by altering wild-type binding affinity. Therefore, it is extremely important to assess the effect of mutations on protein–protein binding free energy to assist the development of therapeutic solutions. Currently, the most popular approaches use structural information to deliver the predictions, which precludes them to be applicable on genome-scale investigations. Indeed, with the progress of genomic sequencing, researchers are frequently dealing with assessing effect of mutations for which there is no structure available. Results Here, we report a Gradient Boosting Decision Tree machine learning algorithm, the SAAMBE-SEQ, which is completely sequence-based and does not require structural information at all. SAAMBE-SEQ utilizes 80 features representing evolutionary information, sequence-based features and change of physical properties upon mutation at the mutation site. The approach is shown to achieve Pearson correlation coefficient (PCC) of 0.83 in 5-fold cross validation in a benchmarking test against experimentally determined binding free energy change (ΔΔG). Further, a blind test (no-STRUC) is compiled collecting experimental ΔΔG upon mutation for protein complexes for which structure is not available and used to benchmark SAAMBE-SEQ resulting in PCC in the range of 0.37–0.46. The accuracy of SAAMBE-SEQ method is found to be either better or comparable to most advanced structure-based methods. SAAMBE-SEQ is very fast, available as webserver and stand-alone code, and indeed utilizes only sequence information, and thus it is applicable for genome-scale investigations to study the effect of mutations on protein–protein interactions. Availability and implementation SAAMBE-SEQ is available at http://compbio.clemson.edu/saambe_webserver/indexSEQ.php#started. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Mapping, Structure and Modulation of PPI

2021 ◽  
Vol 9 ◽  
Author(s):  
Elisa Martino ◽  
Sara Chiarugi ◽  
Francesco Margheriti ◽  
Gianpiero Garau
Keyword(s):  
Free Energy ◽  
Protein Interactions ◽  
Hot Spots ◽  
Molecular Level ◽  
Protein Complexes ◽  
Binding Free Energy ◽  
Detailed Knowledge ◽  
Protein Protein Interactions ◽  
Advanced Technologies ◽  
Successful Design

Because of the key relevance of protein–protein interactions (PPI) in diseases, the modulation of protein-protein complexes is of relevant clinical significance. The successful design of binding compounds modulating PPI requires a detailed knowledge of the involved protein-protein system at molecular level, and investigation of the structural motifs that drive the association of the proteins at the recognition interface. These elements represent hot spots of the protein binding free energy, define the complex lifetime and possible modulation strategies. Here, we review the advanced technologies used to map the PPI involved in human diseases, to investigate the structure-function features of protein complexes, and to discover effective ligands that modulate the PPI for therapeutic intervention.

scholarly journals Thermodynamic measures of cancer: Gibbs free energy and entropy of protein–protein interactions

2016 ◽  
Vol 42 (3) ◽  
pp. 339-350 ◽  
Author(s):  
Edward A. Rietman ◽  
John Platig ◽  
Jack A. Tuszynski ◽  
Giannoula Lakka Klement
Keyword(s):  
Free Energy ◽  
Gibbs Free Energy ◽  
Protein Interactions ◽  
Protein Protein Interactions

scholarly journals Decreased Interactions between Calmodulin and a Mutant Huntingtin Model Might Reduce the Cytotoxic Level of Intracellular Ca2+: A Molecular Dynamics Study

2021 ◽  
Vol 22 (16) ◽  
pp. 9025
Author(s):  
Sanda Nastasia Moldovean ◽  
Vasile Chiş
Keyword(s):  
Molecular Dynamics ◽  
Protein Interactions ◽  
Structural Changes ◽  
Binding Free Energy ◽  
Point Mutations ◽  
Antagonistic Effect ◽  
Free Energy Calculations ◽  
Point Of View ◽  
Mutant Huntingtin ◽  
Protein Protein Interactions

Mutant huntingtin (m-HTT) proteins and calmodulin (CaM) co-localize in the cerebral cortex with significant effects on the intracellular calcium levels by altering the specific calcium-mediated signals. Furthermore, the mutant huntingtin proteins show great affinity for CaM that can lead to a further stabilization of the mutant huntingtin aggregates. In this context, the present study focuses on describing the interactions between CaM and two huntingtin mutants from a biophysical point of view, by using classical Molecular Dynamics techniques. The huntingtin models consist of a wild-type structure, one mutant with 45 glutamine residues and the second mutant with nine additional key-point mutations from glutamine residues into proline residues (9P(EM) model). Our docking scores and binding free energy calculations show higher binding affinities of all HTT models for the C-lobe end of the CaM protein. In terms of dynamic evolution, the 9P(EM) model triggered great structural changes into the CaM protein’s structure and shows the highest fluctuation rates due to its structural transitions at the helical level from α-helices to turns and random coils. Moreover, our proposed 9P(EM) model suggests much lower interaction energies when compared to the 45Qs-HTT mutant model, this finding being in good agreement with the 9P(EM)’s antagonistic effect hypothesis on highly toxic protein–protein interactions.

scholarly journals SKEMPI 2.0: An updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation

2018 ◽  
Author(s):  
Justina Jankauskaitė ◽  
Brian Jiménez-García ◽  
Justas Dapkūnas ◽  
Juan Fernández-Recio ◽  
Iain H. Moal
Keyword(s):  
Binding Energy ◽  
Protein Interactions ◽  
Binding Free Energy ◽  
Molecular Complexes ◽  
Protein Protein Interactions ◽  
Binding Thermodynamics ◽  
Binding Data ◽  
Enthalpy And Entropy Changes ◽  
Enthalpy And Entropy ◽  
The Relationship

AbstractMotivationUnderstanding the relationship between the sequence, structure, binding energy, binding kinetics and binding thermodynamics of protein-protein interactions is crucial to understanding cellular signaling, the assembly and regulation of molecular complexes, the mechanisms through which mutations lead to disease, and protein engineering.ResultsWe present SKEMPI 2.0, a major update to our database of binding free energy changes upon mutation for structurally resolved protein-protein interactions. This version now contains manually curated binding data for 7085 mutations, an increase of 133%, including changes in kinetics for 1844 mutations, enthalpy and entropy changes for 443 mutations, and 440 mutations which abolish detectable binding.AvailabilityThe database is available at https://life.bsc.es/pid/skempi2/

scholarly journals Structural and Computational Characterization of Disease-Related Mutations Involved in Protein-Protein Interfaces

2019 ◽  
Vol 20 (7) ◽  
pp. 1583 ◽  
Author(s):  
Dàmaris Navío ◽  
Mireia Rosell ◽  
Josu Aguirre ◽  
Xavier de la Cruz ◽  
Juan Fernández-Recio
Keyword(s):  
Protein Interactions ◽  
Computational Analysis ◽  
Molecular Level ◽  
Binding Free Energy ◽  
Evolutionary Conservation ◽  
Core Region ◽  
Protein Protein Interactions ◽  
The Core ◽  
Protein Interfaces

One of the known potential effects of disease-causing amino acid substitutions in proteins is to modulate protein-protein interactions (PPIs). To interpret such variants at the molecular level and to obtain useful information for prediction purposes, it is important to determine whether they are located at protein-protein interfaces, which are composed of two main regions, core and rim, with different evolutionary conservation and physicochemical properties. Here we have performed a structural, energetics and computational analysis of interactions between proteins hosting mutations related to diseases detected in newborn screening. Interface residues were classified as core or rim, showing that the core residues contribute the most to the binding free energy of the PPI. Disease-causing variants are more likely to occur at the interface core region rather than at the interface rim (p < 0.0001). In contrast, neutral variants are more often found at the interface rim or at the non-interacting surface rather than at the interface core region. We also found that arginine, tryptophan, and tyrosine are over-represented among mutated residues leading to disease. These results can enhance our understanding of disease at molecular level and thus contribute towards personalized medicine by helping clinicians to provide adequate diagnosis and treatments.

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