scholarly journals Predicting the impacts of mutations on protein-ligand binding affinity based on molecular dynamics simulations and machine learning methods

2020 ◽  
Vol 18 ◽  
pp. 439-454 ◽  
Author(s):  
Debby D. Wang ◽  
Le Ou-Yang ◽  
Haoran Xie ◽  
Mengxu Zhu ◽  
Hong Yan
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Ligand Binding ◽  
Molecular Dynamics Simulations ◽  
Binding Affinity ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Dynamics Simulations

Supervised Machine Learning Methods Applied to Predict Ligand- Binding Affinity

2017 ◽  
Vol 24 (23) ◽  
Author(s):  
Gabriela S. Heck ◽  
Val O. Pintro ◽  
Richard R. Pereira ◽  
Mauricio B. de Ávila ◽  
Nayara M.B. Levin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Identification of electroporation sites in the complex lipid organization of the plasma membrane

2021 ◽  
Author(s):  
Lea Rems ◽  
Xinru Tang ◽  
Fangwei Zhao ◽  
Sergio Perez-Conesa ◽  
Ilaria Testa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Plasma Membrane ◽  
Survival Analysis ◽  
Electric Field ◽  
Molecular Dynamics Simulations ◽  
Coarse Grained ◽  
Machine Learning Methods ◽  
Lipid Organization ◽  
Dynamics Simulations

The plasma membrane of a biological cell is a complex assembly of lipids and membrane proteins, which tightly regulate transmembrane transport. When a cell is exposed to a strong electric field, the membrane integrity becomes transiently disrupted by formation of transmembrane pores. This phenomenon, termed electroporation, is already utilized in many rapidly developing applications in medicine including gene therapy, cancer treatment, and treatment of cardiac arrythmias. However, the molecular mechanisms of electroporation are not yet sufficiently well understood; in particular, it is unclear where exactly pores form in the complex organization of the plasma membrane. In this study we combine coarse-grained molecular dynamics simulations, machine learning methods, and Bayesian survival analysis to identify how formation of pores depends on the local lipid organization. We show that pores do not form homogeneously across the membrane, but colocalize with domains that have specific features, the most important being high density of polyunsaturated lipids. We further show that knowing the lipid organization is sufficient to reliably predict poration sites with machine learning. However, by analysing poration kinetics with Bayesian survival analysis we then show that poration does not depend solely on local lipid arrangement, but also on membrane mechanical properties and the polarity of the electric field. Finally, we discuss how the combination of atomistic and coarse-grained molecular dynamics simulations, machine learning methods, and Bayesian survival analysis can guide the design of future experiments and help us to develop an accurate description of plasma membrane electroporation on the whole-cell level. Achieving this will allow us to shift the optimization of electroporation applications from blind trial-and-error approaches to mechanistic-driven design.

scholarly journals The Interplay of Cholesterol and Ligand Binding in hTSPO from Classical Molecular Dynamics Simulations

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1250
Author(s):  
Hien T. T. Lai ◽  
Alejandro Giorgetti ◽  
Giulia Rossetti ◽  
Toan T. Nguyen ◽  
Paolo Carloni ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Ligand Binding ◽  
Molecular Dynamics Simulations ◽  
Binding Site ◽  
Transmembrane Protein ◽  
Free Form ◽  
Experimental Investigations ◽  
Terminal Part ◽  
Dynamics Simulations ◽  
Cholesterol Binding

The translocator protein (TSPO) is a 18kDa transmembrane protein, ubiquitously present in human mitochondria. It is overexpressed in tumor cells and at the sites of neuroinflammation, thus representing an important biomarker, as well as a promising drug target. In mammalian TSPO, there are cholesterol–binding motifs, as well as a binding cavity able to accommodate different chemical compounds. Given the lack of structural information for the human protein, we built a model of human (h) TSPO in the apo state and in complex with PK11195, a molecule routinely used in positron emission tomography (PET) for imaging of neuroinflammatory sites. To better understand the interactions of PK11195 and cholesterol with this pharmacologically relevant protein, we ran molecular dynamics simulations of the apo and holo proteins embedded in a model membrane. We found that: (i) PK11195 stabilizes hTSPO structural fold; (ii) PK11195 might enter in the binding site through transmembrane helices I and II of hTSPO; (iii) PK11195 reduces the frequency of cholesterol binding to the lower, N–terminal part of hTSPO in the inner membrane leaflet, while this impact is less pronounced for the upper, C–terminal part in the outer membrane leaflet, where the ligand binding site is located; (iv) very interestingly, cholesterol most frequently binds simultaneously to the so-called CRAC and CARC regions in TM V in the free form (residues L150–X–Y152–X(3)–R156 and R135–X(2)–Y138–X(2)–L141, respectively). However, when the protein is in complex with PK11195, cholesterol binds equally frequently to the CRAC–resembling motif that we observed in TM I (residues L17–X(2)–F20–X(3)–R24) and to CRAC in TM V. We expect that the CRAC–like motif in TM I will be of interest in future experimental investigations. Thus, our MD simulations provide insight into the structural features of hTSPO and the previously unknown interplay between PK11195 and cholesterol interactions with this pharmacologically relevant protein.

Machine learning and molecular dynamics simulations assisted evolutionary design and discovery pipeline to screen the efficient small molecule acceptors for PTB7-Th based organic solar cells with over 15% efficiency

2022 ◽  
Author(s):  
Jin-Liang Wang ◽  
Asif Mahmood ◽  
Ahmad Irfan
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Solar Cells ◽  
Molecular Dynamics Simulations ◽  
Small Molecule ◽  
Organic Solar Cells ◽  
Evolutionary Design ◽  
New Materials ◽  
Dynamics Simulations ◽  
Discovery Pipeline

Organic solar cells are the most promising candidates for future commercialization. This goal can be quickly achieved by designing new materials and predicting their performance without experimentation to reduce the...

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