Back

PyLipID: A Python package for analysis of protein-lipid interactions from MD simulations

Song, W.; Corey, R. A.; Ansell, B.; Cassidy, K.; Horrell, M.; Duncan, A.; Stansfeld, P. J.; Sansom, M.

2021-07-14 biophysics
10.1101/2021.07.14.452312 bioRxiv
Show abstract

Lipids play important modulatory and structural roles for membrane proteins. Molecular dynamics simulations are frequently used to provide insights into the nature of these proteinlipid interactions. Systematic comparative analysis requires tools that provide algorithms for objective assessment of such interactions. We introduce PyLipID, a python package for the identification and characterization of specific lipid interactions and binding sites on membrane proteins from molecular dynamics simulations. PyLipID uses a community analysis approach for binding site detection, calculating lipid residence times for both the individual protein residues and the detected binding sites. To assist structural analysis, PyLipID produces representative bound lipid poses from simulation data, using a density-based scoring function. To estimate residue contacts robustly, PyLipID uses a dual-cutoff scheme to differentiate between lipid conformational rearrangements whilst bound from full dissociation events. In addition to the characterization of protein-lipid interactions, PyLipID is applicable to analysis of the interactions of membrane proteins with other ligands. By combining automated analysis, efficient algorithms, and open-source distribution, PyLipID facilitates the systematic analysis of lipid interactions from large simulation datasets of multiple species of membrane proteins. ToC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/452312v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@1f6530corg.highwire.dtl.DTLVardef@c639d5org.highwire.dtl.DTLVardef@1fef967org.highwire.dtl.DTLVardef@a80446_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.