Knowledge-based energy functions for computational studies of proteins

This chapter discusses theoretical framework and methods for developing knowledge-based potential functions essential for protein structure prediction, protein-protein interaction, and protein sequence design. We discuss in some details about the Miyazawa-Jernigan contact statistical potential, distance-dependent statistical potentials, as well as geometric statistical potentials. We also describe a geometric model for developing both linear and non-linear potential functions by optimization. Applications of knowledge-based potential functions in protein-decoy discrimination, in protein-protein interactions, and in protein design are then described. Several issues of knowledge-based potential functions are finally discussed.Comment: 57 pages, 6 figures. To be published in a book by Springe

Structural transitions and energy landscape for cowpea chlorotic mottle virus capsid mechanics from nanoindentation in vitro and in silico

Physical properties of capsids of plant and animal viruses are important factors in capsid self-assembly, survival of viruses in the extracellular environment, and their cell infectivity. Combined AFM experiments and computational modeling on subsecond timescales of the indentation nanomechanics of Cowpea Chlorotic Mottle Virus capsid show that the capsid’s physical properties are dynamic and local characteristics of the structure, which change with the depth of indentation and depend on the magnitude and geometry of mechanical input. Under large deformations, the Cowpea Chlorotic Mottle Virus capsid transitions to the collapsed state without substantial local structural alterations. The enthalpy change in this deformation state ΔHind = 11.5–12.8 MJ/mol is mostly due to large-amplitude out-of-plane excitations, which contribute to the capsid bending; the entropy change TΔSind = 5.1–5.8 MJ/mol is due to coherent in-plane rearrangements of protein chains, which mediate the capsid stiffening. Direct coupling of these modes defines the extent of (ir)reversibility of capsid indentation dynamics correlated with its (in)elastic mechanical response to the compressive force. This emerging picture illuminates how unique physico-chemical properties of protein nanoshells help define their structure and morphology, and determine their viruses’ biological functio

Order Statistics Theory of Unfolding of Multimeric Proteins

Dynamic force spectroscopy has become indispensable for the exploration of the mechanical properties of proteins. In force-ramp experiments, performed by utilizing a time-dependent pulling force, the peak forces for unfolding transitions in a multimeric protein (D)N are used to map the free energy landscape for unfolding for a protein domain D. We show that theoretical modeling of unfolding transitions based on combining the observed first (f1), second (f2), …, Nth (fN) unfolding forces for a protein tandem of fixed length N, and pooling the force data for tandems of different length, n1 < n2 < … < N, leads to an inaccurate estimation of the distribution of unfolding forces for the protein D, ψD(f). This problem can be overcome by using Order statistics theory, which, in conjunction with analytically tractable models, can be used to resolve the molecular characteristics that determine the unfolding micromechanics. We present a simple method of estimation of the parent distribution, ψD(f), based on analyzing the force data for a tandem (D)n of arbitrary length n. Order statistics theory is exemplified through a detailed analysis and modeling of the unfolding forces obtained from pulling simulations of the monomer and oligomers of the all-β-sheet WW domain