In a recent Science paper, Sarel Fleishman et al. report the de-novo computational design of a protein interface to specifically target and tightly bind a surface patch of the flu hemaglutinin protein. We interview Sarel to get the insights from behind the scenes and the outlook for this exciting approach.
There are several forms of peptide-protein interactions, one of which are globular PPIs mediated by a dominant linear peptide at the interface. To what extent could peptides extracted from a globular protein monomer be used to inhibit the interaction to its partner? In this work, we have investigated the possibility of deriving peptides from the interface of globular proteins to design inhibitors that would compete with their native interaction.
How can peptides overcome the entropic cost involved in switching from an unstructured, flexible peptide to a rigid, well-defined bound structure? What are the strategies used by peptides in? order to bind their protein receptor? How is this different than protein-protein interactions? In this work we performed A structure-based analysis of peptide-protein interactions to try and answer these questions.
Traditionally, computational protein design efforts have been directed at calculating a single sequence predicted to fold to a particular target structure. Recently, however, a number of conceptual generalizations have been pursued, ranging from the use of backbone flexibility, off-rotamer side chain flexibility, negative design, multi-body potentials, conformational free energy, and prediction of sequence profiles. Below I present our state-of-the-art research whose goal is to understand how protein sequences are optimized to be compatible with binding multiple partners with high affinity. – By Menachem Fromer.
Last month in Nature Chemical Biology, Dan Mandell and Tanja Kortemme from UCSF published a great review of current research in macromolecular modeling and experimentation: Computer-aided design of functional protein interactions. The review focuses on protein-protein interactions and interfaces but also covers the general field of macromolecular modeling in some depth. It includes sections that introduce basic concepts in modeling and tables that provide a succinct snapshot of recent accomplishments and how they tie in to the greater field.
Having finished reviewing a review, I will be following Borges’ lead and review papers from the literature that have not yet been written. Any paper suggestions?
This recent mini-review by Stein et al. focuses on the mechanisms that enable dynamic, transient, short lived interactions in cellular networks. Of special interest are the always popular “motif recognition domain”-“short flexible peptide” interactions. However, post translational modifications and regulation by disorder are also discussed. We concise the review further to some basic/interesting/anecdotal/”pondering worthy” points.
The task of predicting the interface of a given protein using only the structure of the unbound protein, is an important goal. Many groups have attempted tackling this problem from different fronts and using different approaches. We preset here 10 popular protein-protein interface prediction servers. This might prove especially helpful to those who take part in the ongoing CAPRI round.
Incorporation of flexibility into docking simulations and accounting for backbone conformational changes during association is probably the toughest problem protein-protein dockers are facing today. Normal Mode Analysis is lately the answer of many groups to this problem. We hereby review two approaches of incorporating NMA into docking simulations, reflecting the induced fit vs. conformational sampling association mechanisms.
In a research published at Cell, Skerker et al. performs a computational co-variation analysis on a dataset of two-component system sequences. This results in a set of putative specificity determining residues. The authors then demonstrate how they can successfully use these to modulate Histidine Kinase-Response Regulator interactions both in-vitro and in-vivo. By Nir London