Selectivity of Polypeptide Binding to Nanoscale Substrates
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Selectivity of Polypeptide Binding to Nanoscale Substrates Steven R. Lustig and Anand Jagota Central Research & Development, E.I. du Pont de Nemours & Co., Inc. Experimental Station, Route 141 Wilmington, DE 19880-0356, U.S.A. ABSTRACT We present new computational methodology for designing polymers, such as polypeptides and polyelectrolytes, which can selectively recognize nanostructured substrates. The methodology applies to polymers which might be used to: control placement and assembly for electronic devices, template structure during materials synthesis, as well as add new biological and chemical functionality to surfaces. Optimization of the polymer configurational sequence permits enhancement of both binding energy on and binding selectivity between one or more atomistic surfaces. A novel Continuous Rotational Isomeric State (CRIS) method permits continuous backbone torsion sampling and is seen to be critical in binding optimization problems where chain flexibility is important. We illustrate selective polypeptide binding between either analytic, uniformly charged surfaces or atomistic GaAs(100), GaAs(110) and GaAs(111) surfaces. Computational results compare very favorably with prior experimental phage display observations [S.R. Whaley et al., Nature, 405, 665 (2000)] for GaAs substrates. Further investigation indicates that chain flexibility is important to exhibit selective binding between surfaces of similar charge density. Such chains begin with sequences which repel the surfaces, continue with sequences that attract the surface and end with sequences that neither attract nor repel strongly.
INTRODUCTION We present new computational methodology for designing polymers, such as polypeptides and polyelectrolytes, which can selectively recognize nanostructured substrates. The methodology applies to polymers which might be used to: control placement and assembly for electronic devices, template structure during materials synthesis, as well as add new biological and chemical functionality to surfaces. Optimization of the polymer configurational sequence permits enhancement of both binding energy on and binding selectivity between one or more atomistic surfaces. This optimization is enabled by combining highly-efficient, atomistic modeling of the polymer and surfaces with genetic mutation of the polymer configuration. The atomistic modeling permits the calculation of macromolecular statistics and thermodynamics of substrate binding, while genetic sequence mutation enables the search and enhancement of the desired polymer-surface interactions. Previous experimental works have demonstrated polypeptides with selectivity for binding to surfaces of metals and metal oxides [1-8] as well as a range of semiconductor surfaces [9]. Polypeptides which can recognize desired surfaces are typically selected from a library of several million candidates using either bare proteins or phages, often in the presence of surfactants or salts. These methods are often both practical and useful. There still exist several issues. First,
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