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 Gibbs Free Energy, a Thermodynamic Measure of Protein–Protein Interactions, Correlates with Neurologic Disability

2021 ◽  
Vol 1 (3) ◽  
pp. 201-210
Author(s):  
Michael Keegan ◽  
Hava T. Siegelmann ◽  
Edward A. Rietman ◽  
Giannoula Lakka Klement ◽  
Jack A. Tuszynski
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Free Energy ◽  
Gibbs Free Energy ◽  
Protein Interactions ◽  
Neurological Diseases ◽  
Pearson Correlation ◽  
Brain Network ◽  
Network Organization ◽  
Protein Protein Interactions

Modern network science has been used to reveal new and often fundamental aspects of brain network organization in physiological as well as pathological conditions. As a consequence, these discoveries, which relate to network hierarchy, hubs and network interactions, have begun to change the paradigms of neurodegenerative disorders. In this paper, we explore the use of thermodynamics for protein–protein network interactions in Alzheimer’s disease (AD), Parkinson’s disease (PD), multiple sclerosis (MS), traumatic brain injury and epilepsy. To assess the validity of using network interactions in neurological diseases, we investigated the relationship between network thermodynamics and molecular systems biology for these neurological disorders. In order to uncover whether there was a correlation between network organization and biological outcomes, we used publicly available RNA transcription data from individual patients with these neurological conditions, and correlated these molecular profiles with their respective individual disability scores. We found a linear correlation (Pearson correlation of −0.828) between disease disability (a clinically validated measurement of a person’s functional status) and Gibbs free energy (a thermodynamic measure of protein–protein interactions). In other words, we found an inverse relationship between disease disability and thermodynamic energy. Because a larger degree of disability correlated with a larger negative drop in Gibbs free energy in a linear disability-dependent fashion, it could be presumed that the progression of neuropathology such as is seen in Alzheimer’s disease could potentially be prevented by therapeutically correcting the changes in Gibbs free energy.

scholarly journals Gibbs free energy of protein-protein interactions correlates with ATP production in cancer cells

2019 ◽  
Vol 45 (4) ◽  
pp. 423-430
Author(s):  
Stefan M. Golas ◽  
Amber N. Nguyen ◽  
Edward A. Rietman ◽  
Jack A. Tuszynski
Keyword(s):  
Free Energy ◽  
Cancer Cells ◽  
Gibbs Free Energy ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Atp Production

scholarly journals Gibbs Free Energy, a thermodynamic measure of protein-protein interactions, correlates with neurologic disability

2019 ◽  
Author(s):  
Michael Keegan ◽  
Hava T Siegelmann ◽  
Edward A Rietman ◽  
Giannola Lakka Klement
Keyword(s):  
Free Energy ◽  
Alzheimer Disease ◽  
Gibbs Free Energy ◽  
Protein Interactions ◽  
Pearson Correlation ◽  
Brain Network ◽  
Network Organization ◽  
Protein Protein Interactions ◽  
Degree Of Disability ◽  
Neurologic Disability

Background: Modern network science has been used to reveal new and often fundamental aspects of brain network organization in physiological as well as pathological conditions. As a consequence, these discoveries, which relate to network hierarchy, hubs and network interactions, begun to change the paradigms of neurodegenerative disorders. We therefore explored the use of thermodynamics for protein-protein network interactions in Alzheimer disease (AD), Parkinson disease (PD), multiple sclerosis (MS), traumatic brain injury and epilepsy. Methods: To assess the validity of using network interactions in neurological disease, we investigated the relationship between network thermodynamics and molecular systems biology for these neurological disorders. In order to uncover whether there was a correlation between network organization and biological outcomes, we used publicly available RNA transcription data from individual patients with these neurological conditions, and correlated these molecular profiles with their respective individual disability scores. Results: We found a linear correlation (Pearson correlation of -0.828) between disease disability (a clinically validated measurement of a person's functional status), and Gibbs free energy (a thermodynamic measure of protein-protein interactions). In other words, we found an inverse relationship between disease entropy and thermodynamic energy. Interpretation: Because a larger degree of disability correlated with a larger negative drop in Gibbs free energy in a linear, disability-dependent fashion, it could be presumed that the progression of neuropathology such as is seen in Alzheimer Disease, could potentially be prevented by therapeutically correcting the changes Gibbs free energy.

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 Analyzing Machupo virus-receptor binding by molecular dynamics simulations

2014 ◽  
Author(s):  
Austin G Meyer ◽  
Sara L Sawyer ◽  
Andrew D Ellington ◽  
Claus O Wilke
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Protein Interactions ◽  
Tight Binding ◽  
Free Energy Calculations ◽  
Virus Protein ◽  
Test Case ◽  
Protein Protein Interactions ◽  
Distance Curve ◽  
Machupo Virus

In many biological applications, we would like to be able to computationally predict mutational effects on affinity in protein-­protein interactions. However, many commonly used methods to predict these effects perform poorly in important test cases. In particular, the effects of multiple mutations, non­alanine substitutions, and flexible loops are difficult to predict with available tools and protocols. We present here an existing method applied in a novel way to a new test case; we interrogate affinity differences resulting from mutations in a host-­virus protein-­protein interface. We use steered molecular dynamics (SMD) to computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1). We then approximate affinity using the maximum applied force of separation and the area under the force-­versus-­distance curve. We find, even without the rigor and planning required for free energy calculations, that these quantities can provide novel biophysical insight into the GP1/hTfR1 interaction. First, with no prior knowledge of the system we can differentiate among wild type and mutant complexes. Moreover, we show that this simple SMD scheme correlates well with relative free energy differences computed via free energy perturbation. Second, although the static co-­crystal structure shows two large hydrogen-­bonding networks in the GP1/hTfR1 interface, our simulations indicate that one of them may not be important for tight binding. Third, one viral site known to be critical for infection may mark an important evolutionary suppressor site for infection-­resistant hTfR1 mutants. Finally, our approach provides a framework to compare the effects of multiple mutations, individually and jointly, on protein-protein interactions.

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.

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