Computationally-Aided Modeling of Hsp70–Client Interactions: Past, Present, and Future

Abstract

Hsp70 molecular chaperones play central roles in maintaining a healthy cellular proteome. Hsp70s function by binding to short peptide sequences in incompletely folded client proteins, thus preventing them from misfolding and/or aggregating, and in many cases holding them in a state that is competent for subsequent processes like translocation across membranes. There is considerable interest in predicting the sites where Hsp70s may bind their clients, as the ability to do so sheds light on the cellular functions of the chaperone. In addition, the capacity of the Hsp70 chaperone family to bind to a broad array of clients and to identify accessible sequences that enable discrimination of those that are folded from those that are not fully folded, which is essential to their cellular roles, is a fascinating puzzle in molecular recognition. In this article we discuss efforts to harness computational modeling with input from experimental data to develop a predictive understanding of the promiscuous yet selective binding of Hsp70 molecular chaperones to accessible sequences within their client proteins. We trace how an increasing understanding of the complexities of Hsp70−client interactions has led computational modeling to new underlying assumptions and design features. We describe the trend from purely data-driven analysis toward increased reliance on physics-based modeling that deeply integrates structural information and sequence-based functional data with physics-based binding energies. Notably, new experimental insights are adding to our understanding of the molecular origins of "selective promiscuity" in substrate binding by Hsp70 chaperones and challenging the underlying assumptions and design used in earlier predictive models. Taking the new experimental findings together with exciting progress in computational modeling of protein structures leads us to foresee a bright future for a predictive understanding of selective-yet-promiscuous binding exploited by Hsp70 molecular chaperones; the resulting new insights will also apply to substrate binding by other chaperones and by signaling proteins.

Role of Hsp70 in Post-Translational Protein Targeting: Tail-Anchored Membrane Proteins and Beyond

“…The function of Hsp70 relies on the ability of its substrate binding domain (SBD) to bind short extended segments of sequences enriched in hydrophobic residues (often flanked by basic amino acids) that are exposed on client proteins [ 5 , 8 , 9 , 10 ]. Client affinity is regulated by the ATPase cycle in the nucleotide binding domain (NBD) of Hsp70 and is tightly coupled to interaction with co-chaperones [ 1 , 2 , 3 , 8 ].…”

Distribution and solvent exposure of Hsp70 chaperone binding sites across the Escherichia coli proteome

“…More recently, other Hsp70 binding‐site prediction algorithms were developed. A review by Nordquist et al summarizes and compares all available Hsp70 binding‐site prediction methods, including sequence‐based, structure‐based, and molecular dynamics‐based approaches 42–46 . For instance, the Limbo algorithm combines sequence information based on experimental binding assays with structural information from molecular modeling via the FoldX force field 44 .…”

“…31 In all, the computational tool by Rüdiger et al is extremely powerful because it is reliable, accurate and easy to use. While this algorithm is not capable of identifying structural details including substrate binding registry and orientation, 42 these two characteristics are not relevant to the present study.…”

“…A review by Nordquist et al summarizes and compares all available Hsp70 binding-site prediction methods, including sequence-based, structure-based, and molecular dynamics-based approaches. [42][43][44][45][46] For instance, the Limbo algorithm combines sequence information based on experimental binding assays with structural information from molecular modeling via the FoldX force field. 44 Importantly, Limbo binding profiles are consistent with the Hsp70 binding preferences reported by Rüdiger et al (Figure 1C).…”

“…All three above approaches yield similar scores for Hsp70 binding at the residue-specific level, and are comparably reliable. 42 The sequence-based algorithm by Rüdiger et al, however, is simpler.…”