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Protein adsorption at solid-liquid interfaces is critical to many applications, including biomaterials, protein microarrays
and lab-on-a-chip devices. Despite this general interest, and a large amount of research in the last half a century, protein
adsorption cannot be predicted with an engineering level, design-orientated accuracy. Here we describe a Biomolecular
Adsorption Database (BAD), freely available online, which archives the published protein adsorption data. Piecewise
linear regression with breakpoint applied to the data in the BAD suggests that the input variables to protein adsorption,
i.e., protein concentration in solution; protein descriptors derived from primary structure (number of residues, protein
hydrophobicity and spread of amino acid hydrophobicity, isoelectric point); surface descriptors (contact angle); and fluid
environment descriptors (pH, ionic strength), correlate well with the output variable - the protein concentration on the
surface. Furthermore, neural network analysis revealed that the size of the BAD makes it sufficiently representative, with
a neural network-based predictive error of 5% or less. Interestingly, a consistently better fit is obtained if the BAD is
divided into two separate subsets representing protein adsorption on hydrophilic and hydrophobic surfaces. Based on
these findings, selected entries from the BAD have been used to construct neural network-based estimation routines,
which predict the amount of adsorbed protein, the thickness of the absorbed layer and the surface tension of the proteincovered
surface. While the BAD is of general interest, the prediction of the thickness and the surface tension of the
protein-covered layers are of particular relevance to the design of microfluidics devices.
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