Biology subjects

Khramushin, A.

Publications and source records attributed to Khramushin, A..

4 recordsLinked to original sources

bioRxiv2021

PatchMAN docking: Modeling peptide-protein interactions in the context of the receptor surface

Peptide docking can be perceived as a subproblem of protein-protein docking. However, due to the short length and flexible nature of peptides, many do not adopt one defined conformation prior to binding. Therefore, to tackle a peptide docking problem, not only the relative orientation between the two partners, but also the bound conformation of the peptide needs to be modeled. Traditional peptide-centered approaches use information about the peptide sequence to generate a representative conformer ensemble, which can then be rigid body docked to the receptor. Alternatively, one may look at this problem from the viewpoint of the receptor, namely that the protein surface defines the peptide bound conformation.We present PatchMAN (Patch-Motif AligNments), a novel peptide docking approach which uses structural motifs to map the receptor surface with backbone scaffolds extracted from protein structures. On a non-redundant set of protein-peptide complexes, starting from free receptor structures, PatchMAN successfully models and identifies near-native peptide-protein complexes in 62% / 81% within 2.5[A] / 5[A] RMSD, with corresponding sampling in 81% / 100% of the cases, outperforming other approaches. PatchMAN leverages the observation that structural units of peptides with their binding pocket can be found not only within interfaces, but also within monomers. We show that the conformation of the bound peptide is sampled based on the structural context of the receptor only, without taking into account any sequence information. Beyond peptide docking, this approach opens exciting new avenues to study principles of peptide-protein association, and to the design of new peptide binders.

bioinformatics↗

bioRxiv2021

Harnessing protein folding neural networks for peptide-protein docking

Highly accurate protein structure predictions by the recently published deep neural networks such as AlphaFold2 and RoseTTAFold are truly impressive achievements, and will have a tremendous impact far beyond structural biology. If peptide-protein binding can be seen as a final complementing step in the folding of a protein monomer, we reasoned that these approaches might be applicable to the modeling of such interactions. We present a simple implementation of AlphaFold2 to model the structure of peptide-protein interactions, enabled by linking the peptide sequence to the protein c-terminus via a poly glycine linker. We show on a large non-redundant set of 162 peptide-protein complexes that peptide-protein interactions can indeed be modeled accurately. Importantly, prediction is fast and works without multiple sequence alignment information for the peptide partner. We compare performance on a smaller, representative set to the state-of-the-art peptide docking protocol PIPER-FlexPepDock, and describe in detail specific examples that highlight advantages of the two approaches, pointing to possible further improvements and insights in the modeling of peptide-protein interactions. Peptide-mediated interactions play important regulatory roles in functional cells. Thus the present advance holds much promise for significant impact, by bringing into reach a wide range of peptide-protein complexes, and providing important starting points for detailed study and manipulation of many specific interactions.

bioinformatics↗

bioRxiv2020

Defining binding motifs and dynamics of the multi-pocket FERM domain from ezrin, radixin, moesin and merlin

The ERM (ezrin, radixin and moesin) family of proteins and the related protein merlin participate in signaling events at the cell cortex. The proteins share an N-terminal FERM (band Four-point-one (4.1) ERM) domain comprised of three subdomains (F1, F2, and F3) that hold multiple binding sites for short linear peptide motifs. By screening the FERM domains of the ERMs and merlin against a phage library that display peptides representing the intrinsically disordered regions of the human proteome we identified more than 220 FERM binding peptides. The majority of the peptides contained an apparent Yx[FILV] motif, but ligands with alternative motifs were also found. Interactions with thirteen peptides were validated using a fluorescence polarization assay, and interactions with seven full-length proteins were validated through pull-down experiments. We investigated the energy landscapes of interactions between the moesin FERM domain and representative set of ligands using Rosetta FlexPepDock computational peptide docking protocols, which provide a detailed molecular understanding of the binding of peptides with distinct motifs (YxV and E[Y/F]xDFYDF) to different sites on the F3 subdomain. A third motif (FY[D/E]L(4-5x)PLxxx[L/V]) was proposed to bind more diffusely. By combining competition and modeling experiments, we further uncovered interdependencies between different types of ligands. The study expands the motif-based interactomes of the ERMs and merlin, and suggests that the FERM domain acts as a switchable interaction hub where one class of ligands to the F3 subdomain allosterically regulates binding of other F3 ligands.

biochemistry↗

bioRxiv2019

Protocols for all-atom reconstruction and high-resolution refinement of protein-peptide complex structures

Structural characterizations of protein-peptide complexes may require further improvements. These may include reconstruction of missing atoms and/or structure optimization leading to higher accuracy models. In this work, we describe a workflow that generates accurate structural models of peptide-protein complexes starting from protein-peptide models in C-alpha representation generated using CABS-dock molecular docking. First, protein-peptide models are reconstructed from their C-alpha traces to all-atom representation using MODELLER. Next, they are refined using RosettaFlexPepDock. The described workflow allows for reliable all-atom reconstruction of CABS-dock models and their further improvement to high-resolution models.

bioinformatics↗