In recent years, machine-learning-based scoring functions have significantly improved the scoring power. However, many of these methods do not perform well in distinguishing the native structure from docked decoy poses due to the lack of decoy structural information in their training data. Here, we developed a machine-learning model, named DeepBSP, that can directly predict the root mean square deviation (rmsd) of a ligand docking pose with reference to its native binding pose. Unlike the binding affinity, the rmsd between the docking poses with reference to their native structures can be straightforwardly determined. By training on a generated data set with 11,925 native complexes and more than 165,000 docked poses, our model shows excellent docking power on our test set and also on the CASF-2016 docking decoy set compared to other major scoring functions. Thus, by combining molecular dockings that generate many poses with the application of DeepBSP, one can more accurately predict the best binding pose that is closest to the native complex structure. This DeepBSP model shall be very useful in picking out poses close to their natives from many poses generated from a dock application.
The emergence and worldwide spread of multi-drug resistant bacteria makes an urgent challenge for the development of novel antibacterial agents. A perspective weapon to fight against severe infections caused by drug-resistant microorganisms is antimicrobial peptides (AMPs). AMPs are a diverse class of naturally occurring molecules that are produced as a first line of defense by all multi-cellular organisms. Limited by the number of experimental determinate 3D structure, most of the prediction or classification methods of AMPs were based on 2D descriptors, including sequence, amino acid composition, peptide net charge, hydrophobicity, amphiphilic, etc. Due to the rapid development of structural simulation methods, predicted models of proteins (or peptides) have been successfully applied in structure based drug design, for example as targets of virtual ligand screening. Here, we establish the activity prediction model based on the predicted 3D structure of AMPs molecule. To our knowledge, it is the first report of prediction method based on 3D descriptors of AMPs. Novel AMPs were designed by using the model, and their antibacterial effect was measured by in vitro experiments.
Mitochondrial
serine hydroxymethyl transferase isoform 2 (SHMT2) has attracted increasing
attention as a pivotal catalyzing regulator of the serine/glycine
pathway in the one-carbon metabolism of cancer cells. However, few
inhibitors that target this potential anticancer target have been
discovered. Quantitative characterization of the interactions between
SHMT2 and its known inhibitors should benefit future discovery of
novel inhibitors. In this study, we employed a recently developed
alanine-scanning-interaction-entropy method to quantitatively calculate
the residue-specific binding free energy of 28 different SHMT2 inhibitors
that originate from the same skeleton. Major contributing residues
from SHMT2 and chemical groups from the inhibitors were identified,
and the binding energy of each residue was quantitatively determined,
revealing essential features of the protein-inhibitor interaction.
The most important contributing residue is Y105 of the B chain followed
by L166 of the A chain. The calculated protein–ligand binding
free energies are in good agreement with the experimental results
and showed better correlation and smaller errors compared with those
obtained using the conventional MM/GBSA with the normal mode method.
These results may aid the rational design of more effective SHMT2
inhibitors.
In
this study, we developed a new physical-based scoring function,
Atom Pair-Based Scoring function (APBScore), which includes pairwise
van der Waals (VDW), electrostatic interaction, and hydrogen bond
energies between the receptor and ligand. Despite the simple form
of this scoring function, the tests of APBScore on several benchmark
datasets show its excellent performance in scoring as compared to
other widely used traditional scoring functions. Particularly, the
scoring performance of APBScore is among the top-ranking scoring functions
for complexes with zinc/ligand interactions. In addition to the scoring
power, APBScore also shows good performance in ranking and docking
as compared to some traditional scoring functions. In addition, the
APBScore is sensitive to receptor/ligand atomic collisions and therefore
can correctly identify decoy complex structures with atomic collisions.
These features are the result of optimizing atom-pair VDW interactions,
performing structural minimization of the initial structures, and
treating zinc/ligand interactions more accurately. The source code
of APBScore is available at .
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