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 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 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 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.

Effect of Intestinal Flora Clearance on Liver Proteomics in Mice

2019 ◽  
Vol 16 (3) ◽  
pp. 199-209
Author(s):  
Zhenghu Jia ◽  
Hui Liu ◽  
Mei Song ◽  
Chengmao Yang ◽  
Yapu Zhao ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Concanavalin A ◽  
Intestinal Flora ◽  
Label Free ◽  
Protein Protein Interactions ◽  
Rt Pcr ◽  
Proteome Level ◽  
And Control ◽  
The Relationship ◽  
Free Quantification

Background: Intestinal flora dynamically affects the host&#039;s systemic immune system. Liver is one of the organs that may be affected by intestinal microbiota. </P><P> Materials and Methods: In this study, we aimed to identify proteome level differences between liver tissue from mice cleared intestinal flora and control using tandem mass spectrometry (LC-MS/MS) and label free quantification. Additionally, protein-protein interactions were mapped by STRING, and also, the enrichment of inflammation-related signaling pathways and biological processes was identified using GO and IPA network system. RT-PCR and Western blot were used for validation of the proteomics findings. Results: Our study demonstrated that mice with cleared intestinal flora exhibited decreased sensitivity to Concanavalin A induced acute hepatitis. 324 Proteins in liver were differently expressed after intestinal flora clearance for one week while 210 proteins were differently expressed after intestinal flora clearance for two weeks. Furthermore, five of the identified proteins were validated by western blotting and further investigated by semi-quantitative RT-PCR. Conclusion: Our results showed that intestinal flora clearance in mice could reduce sensitivity to Concanavalin A induced liver injury and influence the expression of proteins in liver, which provides a clue for studying the relationship between gut bacteria and Concanavalin A induced hepatitis.

Chemical Thermodynamics

2021 ◽  
pp. 344-364
Author(s):  
Christopher O. Oriakhi
Keyword(s):  
Free Energy ◽  
Phase Change ◽  
Equilibrium Constant ◽  
Gibbs Free Energy ◽  
Chemical Thermodynamics ◽  
Laws Of Thermodynamics ◽  
Core Concepts ◽  
Enthalpy And Entropy Changes ◽  
Enthalpy And Entropy ◽  
The Relationship

Chemical Thermodynamics discusses the fundamental laws of thermodynamics along with their relationships to heat, work, enthalpy, entropy, and temperature. Predicting the direction of a spontaneous change and calculating the change in entropy of a reaction are core concepts. The relationship between entropy, free energy and work is covered. The Gibbs free energy is used quantitatively to predict if reactions or processes are going to be exothermic and spontaneous or endothermic under the stated conditions. Also explored are the enthalpy and entropy changes during a phase change. Finally the Gibbs free energy of a chemical reaction is related to its equilibrium constant and the temperature.

Frataxin, a molecule of mystery: trading stability for function in its iron-binding site

2010 ◽  
Vol 426 (2) ◽  
pp. e1-e3 ◽  
Author(s):  
Darius J. R. Lane ◽  
Des R. Richardson
Keyword(s):  
Binding Site ◽  
Protein Interactions ◽  
Mitochondrial Protein ◽  
Iron Binding ◽  
Protein Protein Interactions ◽  
Iron Sulfur Cluster ◽  
Downstream Effects ◽  
Biochemical Journal ◽  
And Function ◽  
The Relationship

What are the structural implications for iron binding by frataxin, the mitochondrial protein whose decreased expression results in Friedreich's ataxia? Though frataxin has been shown to be essential for proper handling of iron within mitochondria (e.g. for iron–sulfur cluster and haem biosynthesis), its exact molecular function remains unclear. In this issue of the Biochemical Journal, Correia and colleagues investigate the relationship between structure and function at the putative iron-binding site of Yfh1 (yeast frataxin). Using a host of Yfh1 combination point mutants, the authors observe that the presence of a semi-conserved pocket of negative charge within the ‘acidic ridge’ region (thought to be responsible for iron binding) only mildly enhances Yfh1's ability to bind iron, though it does significantly increase the protein's structural flexibility. The general emerging view is that frataxin's keystone role in mitochondrial iron metabolism depends on iron binding. This appears to have downstream effects on protein–protein interactions that are crucial for frataxin function. The current results reveal a somewhat delicate relationship between iron binding and structural plasticity that may help unravel the enigma of frataxin's metabolic roles.

scholarly journals Prediction and validation of protein–protein interactors from genome-wide DNA-binding data using a knowledge-based machine-learning approach

Open Biology ◽  
2016 ◽  
Vol 6 (9) ◽  
pp. 160183 ◽  
Author(s):  
Ashley J. Waardenberg ◽  
Bernou Homan ◽  
Stephanie Mohamed ◽  
Richard P. Harvey ◽  
Romaric Bouveret
Keyword(s):  
Machine Learning ◽  
Dna Binding ◽  
Protein Interactions ◽  
Heart Development ◽  
Machine Learning Algorithms ◽  
Protein Protein Interactions ◽  
Knowledge Based ◽  
Genome Wide ◽  
Binding Data ◽  
Gene Regulatory

The ability to accurately predict the DNA targets and interacting cofactors of transcriptional regulators from genome-wide data can significantly advance our understanding of gene regulatory networks. NKX2-5 is a homeodomain transcription factor that sits high in the cardiac gene regulatory network and is essential for normal heart development. We previously identified genomic targets for NKX2-5 in mouse HL-1 atrial cardiomyocytes using DNA-adenine methyltransferase identification (DamID). Here, we apply machine learning algorithms and propose a knowledge-based feature selection method for predicting NKX2-5 protein : protein interactions based on motif grammar in genome-wide DNA-binding data. We assessed model performance using leave-one-out cross-validation and a completely independent DamID experiment performed with replicates. In addition to identifying previously described NKX2-5-interacting proteins, including GATA, HAND and TBX family members, a number of novel interactors were identified, with direct protein : protein interactions between NKX2-5 and retinoid X receptor (RXR), paired-related homeobox (PRRX) and Ikaros zinc fingers (IKZF) validated using the yeast two-hybrid assay. We also found that the interaction of RXRα with NKX2-5 mutations found in congenital heart disease (Q187H, R189G and R190H) was altered. These findings highlight an intuitive approach to accessing protein–protein interaction information of transcription factors in DNA-binding experiments.

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