Abstract
BackgroundWhile a large body of work exists on comparing and benchmarking descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work a total of 13 amino acid descriptor sets have been benchmarked with respect to their ability of establishing bioactivity models. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI, BLOSUM, a novel protein descriptor set (termed ProtFP (4 variants)), and in addition we created and benchmarked three pairs of descriptor combinations. Prediction performance was evaluated in seven structure-activity benchmarks which comprise Angiotensin Converting Enzyme (ACE) dipeptidic inhibitor data, and three proteochemometric data sets, namely (1) GPCR ligands modeled against a GPCR panel, (2) enzyme inhibitors (NNRTIs) with associated bioactivities against a set of HIV enzyme mutants, and (3) enzyme inhibitors (PIs) with associated bioactivities on a large set of HIV enzyme mutants.ResultsThe amino acid descriptor sets compared here show similar performance (<0.1 log units RMSE difference and <0.1 difference in MCC), while errors for individual proteins were in some cases found to be larger than those resulting from descriptor set differences ( > 0.3 log units RMSE difference and >0.7 difference in MCC). Combining different descriptor sets generally leads to better modeling performance than utilizing individual sets. The best performers were Z-scales (3) combined with ProtFP (Feature), or Z-Scales (3) combined with an average Z-Scale value for each target, while ProtFP (PCA8), ST-Scales, and ProtFP (Feature) rank last.ConclusionsWhile amino acid descriptor sets capture different aspects of amino acids their ability to be used for bioactivity modeling is still – on average – surprisingly similar. Still, combining sets describing complementary information consistently leads to small but consistent improvement in modeling performance (average MCC 0.01 better, average RMSE 0.01 log units lower). Finally, performance differences exist between the targets compared thereby underlining that choosing an appropriate descriptor set is of fundamental for bioactivity modeling, both from the ligand- as well as the protein side.
Highlights
While a large body of work exists on comparing and benchmarking descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking
70–30 validation on G protein-coupled receptor (GPCR) ligands In a similar spirit to the validation on Angiotensin Converting Enzyme (ACE) inhibitors, a similar 70–30 validation was performed on the GPCR set
In this case a classification model was employed and performance was expressed as mean sensitivity and mean Matthews correlation coefficient (MCC) for all descriptor sets in the study [32]
Summary
While a large body of work exists on comparing and benchmarking descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. The technique is similar to Quantitative Structure-Activity Relationship (QSAR) modeling but expands on its ligand-only nature in that it takes both ligand- and target space into account when generating bioactivity models. This enables PCM to explain bioactivity based on chemical properties (features of the ligand) in combination with particular protein properties (features of the target). PCM models are able to extrapolate in both the chemical (ligand) as well as the biological (target) domain (under the limitations of the data and the models constructed), as shown in previous work [5,6,7]. For a further rationale of the current work the reader is referred to the companion paper [21]
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