Protein molecular surface mapped at different geometrical resolutions

Protein Adsorption on Solid Surfaces: Data Mining, Database, Molecular Surface-Derived Properties, and Semiempirical Relationships

Protein adsorption on solid surfaces is a process relevant to biological, medical, industrial, and environmental applications. Despite this wide interest and advancement in measurement techniques, the complexity of protein adsorption has frustrated its accurate prediction. To address this challenge, here, data regarding protein adsorption reported in the last four decades was collected, checked for completeness and correctness, organized, and archived in an upgraded, freely accessible Biomolecular Adsorption Database, which is equivalent to a large-scale, ad hoc, crowd-sourced multifactorial experiment. The shape and physicochemical properties of the proteins present in the database were quantified on their molecular surfaces using an in-house program (ProMS) operating as an add-on to the PyMol software. Machine learning-based analysis indicated that protein adsorption on hydrophobic and hydrophilic surfaces is modulated by different sets of operational, structural, and molecular surface-based physicochemical parameters. Separately, the adsorption data regarding four "benchmark" proteins, i.e., lysozyme, albumin, IgG, and fibrinogen, was processed by piecewise linear regression with the protein monolayer acting as breakpoint, using the linearization of the Langmuir isotherm formalism, resulting in semiempirical relationships predicting protein adsorption. These relationships, derived separately for hydrophilic and hydrophobic surfaces, described well the protein concentration on the surface as a function of the protein concentration in solution, adsorbing surface contact angle, ionic strength, pH, and temperature of the carrying fluid, and the difference between pH and the isoelectric point of the protein. When applying the semiempirical relationships derived for benchmark proteins to two other "test" proteins with known PDB structure, i.e., β-lactoglobulin and α-lactalbumin, the errors of this extrapolation were found to be in a linear relationship with the dissimilarity between the benchmark and the test proteins. The work presented here can be used for the estimation of operational parameters modulating protein adsorption for various applications such as diagnostic devices, pharmaceuticals, biomaterials, or the food industry.

A novel computational strategy for defining the minimal protein molecular surface representation

Most proteins perform their biological function by interacting with one or more molecular partners. In this respect, characterizing local features of the molecular surface, that can potentially be involved in the interaction with other molecules, represents a step forward in the investigation of the mechanisms of recognition and binding between molecules. Predictive methods often rely on extensive samplings of molecular patches with the aim to identify hot spots on the surface. In this framework, analysis of large proteins and/or many molecular dynamics frames is often unfeasible due to the high computational cost. Thus, finding optimal ways to reduce the number of points to be sampled maintaining the biological information (including the surface shape) carried by the molecular surface is pivotal. In this perspective, we here present a new theoretical and computational algorithm with the aim of defining a set of molecular surfaces composed of points not uniformly distributed in space, in such a way as to maximize the information of the overall shape of the molecule by minimizing the number of total points. We test our procedure’s ability in recognizing hot-spots by describing the local shape properties of portions of molecular surfaces through a recently developed method based on the formalism of 2D Zernike polynomials. The results of this work show the ability of the proposed algorithm to preserve the key information of the molecular surface using a reduced number of points compared to the complete surface, where all points of the surface are used for the description. In fact, the methodology shows a significant gain of the information stored in the sampling procedure compared to uniform random sampling.

Power Law Behavior in Protein Desorption Kinetics Originating from Sequential Binding and Unbinding

The study of protein adsorption at the single molecule level has recently revealed that the adsorption is reversible, but with a long-tailed residence time distribution which can be approximated with a sum of exponential functions putatively related to distinct adsorption sites. Here it is proposed that the shape of the residence time distribution results from an adsorption process with sequential and reversible steps that contribute to overall binding strength resembling "zippering". In this model, the survival function of the residence time distribution of single proteins varies from an exponential distribution for a single adsorption step to a power law distribution with exponent -1/2 for a large number of adsorption steps. The adsorption of fluorescently labeled fibrinogen to glass surfaces is experimentally studied with single molecule imaging. The experimental residence time distribution can be readily fit by the proposed model. This demonstrates that the observed long residence times can arise from stepwise adsorption rather than rare but strong binding sites and provides guidance for the control of protein adsorption to biomaterials.

Mapping hydrophobicity on the protein molecular surface at atom-level resolution

A precise representation of the spatial distribution of hydrophobicity, hydrophilicity and charges on the molecular surface of proteins is critical for the understanding of the interaction with small molecules and larger systems. The representation of hydrophobicity is rarely done at atom-level, as this property is generally assigned to residues. A new methodology for the derivation of atomic hydrophobicity from any amino acid-based hydrophobicity scale was used to derive 8 sets of atomic hydrophobicities, one of which was used to generate the molecular surfaces for 35 proteins with convex structures, 5 of which, i.e., lysozyme, ribonuclease, hemoglobin, albumin and IgG, have been analyzed in more detail. Sets of the molecular surfaces of the model proteins have been constructed using spherical probes with increasingly large radii, from 1.4 to 20 Å, followed by the quantification of (i) the surface hydrophobicity; (ii) their respective molecular surface areas, i.e., total, hydrophilic and hydrophobic area; and (iii) their relative densities, i.e., divided by the total molecular area; or specific densities, i.e., divided by property-specific area. Compared with the amino acid-based formalism, the atom-level description reveals molecular surfaces which (i) present an approximately two times more hydrophilic areas; with (ii) less extended, but between 2 to 5 times more intense hydrophilic patches; and (iii) 3 to 20 times more extended hydrophobic areas. The hydrophobic areas are also approximately 2 times more hydrophobicity-intense. This, more pronounced "leopard skin"-like, design of the protein molecular surface has been confirmed by comparing the results for a restricted set of homologous proteins, i.e., hemoglobins diverging by only one residue (Trp37). These results suggest that the representation of hydrophobicity on the protein molecular surfaces at atom-level resolution, coupled with the probing of the molecular surface at different geometric resolutions, can capture processes that are otherwise obscured to the amino acid-based formalism.