Pep–Whisperer: Inhibitory peptide design Hurwitz, Naama; Zaidman, Daniel; Wolfson, Haim J.
Proteins, structure, function, and bioinformatics,
November 2022, Volume:
90, Issue:
11
Journal Article
Peer reviewed
Designing peptides for protein–protein interaction inhibition is of significant interest in computer‐aided drug design. Such inhibitory peptides could mimic and compete with the binding of the ...partner protein to the inhibition target. Experimental peptide design is a laborious, time consuming, and expensive multi‐step process. Therefore, in silico peptide design can be beneficial for achieving this task. We present a novel algorithm, Pep–Whisperer, which aims to design inhibitory peptides for protein–protein interaction. The desirable peptides would have a relatively high predicted binding affinity to the target protein in a given protein–protein complex. The algorithm outputs linear peptides which are based on an initial template. The template could either be a peptide which is retrieved from the interaction site, or a patch of nonconsecutive amino acids from the protein–protein interface which is completed to a linear peptide by short polyalanine linkers. In addition, the algorithm takes into consideration the conservation of the amino acids in the ligand‐protein binding site by using evolutionary information for choosing the preferred amino acids in each position of the designed peptide. Our algorithm was able to design peptides with high predicted binding affinity to the target protein. The method is fully automated and available as a web server at http://bioinfo3d.cs.tau.ac.il/PepWhisperer/.
Design of peptide binders is an attractive strategy for targeting "undruggable" protein-protein interfaces. Current design protocols rely on the extraction of an initial sequence from one known ...protein interactor of the target protein, followed by in-silico or in-vitro mutagenesis-based optimization of its binding affinity. Wet lab protocols can explore only a minor portion of the vast sequence space and cannot efficiently screen for other desirable properties such as high specificity and low toxicity, while in-silico design requires intensive computational resources and often relies on simplified binding models. Yet, for a multivalent protein target, dozens to hundreds of natural protein partners already exist in the cellular environment. Here, we describe a peptide design protocol that harnesses this diversity via a machine learning generative model. After identifying putative natural binding fragments by literature and homology search, a compositional Restricted Boltzmann Machine is trained and sampled to yield hundreds of diverse candidate peptides. The latter are further filtered via flexible molecular docking and an in-vitro microchip-based binding assay. We validate and test our protocol on calcineurin, a calcium-dependent protein phosphatase involved in various cellular pathways in health and disease. In a single screening round, we identified multiple 16-length peptides with up to six mutations from their closest natural sequence that successfully interfere with the binding of calcineurin to its substrates. In summary, integrating protein interaction and sequence databases, generative modeling, molecular docking and interaction assays enables the discovery of novel protein-protein interaction modulators.
Structural details of protein-protein interactions are invaluable for understanding and deciphering biological mechanisms. Computational docking methods aim to predict the structure of a ...protein-protein complex given the structures of its single components. Protein flexibility and the absence of robust scoring functions pose a great challenge in the docking field. Due to these difficulties most of the docking methods involve a two-tier approach: coarse global search for feasible orientations that treats proteins as rigid bodies, followed by an accurate refinement stage that aims to introduce flexibility into the process. The FireDock web server, presented here, is the first web server for flexible refinement and scoring of protein-protein docking solutions. It includes optimization of side-chain conformations and rigid-body orientation and allows a high-throughput refinement. The server provides a user-friendly interface and a 3D visualization of the results. A docking protocol consisting of a global search by PatchDock and a refinement by FireDock was extensively tested. The protocol was successful in refining and scoring docking solution candidates for cases taken from docking benchmarks. We provide an option for using this protocol by automatic redirection of PatchDock candidate solutions to the FireDock web server for refinement. The FireDock web server is available at http://bioinfo3d.cs.tau.ac.il/FireDock/.
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•Protein structures are now widely available but functional annotations remain scarce.•Predicting the various binding sites of a protein enables elucidation of its function.•ScanNet ...is an interpretable geometric deep learning model for binding site prediction.•ScanNet outperforms previous approaches in terms of accuracy, speed and coverage.•A webserver for ScanNet, linked to both the PDB and AlphaFoldDB is made available.
Predicting the various binding sites of a protein from its structure sheds light on its function and paves the way towards design of interaction inhibitors. Here, we report ScanNet, a freely available web server for prediction of protein–protein, protein - disordered protein and protein - antibody binding sites from structure. ScanNet (Spatio-Chemical Arrangement of Neighbors Network) is an end-to-end, interpretable geometric deep learning model that learns spatio-chemical patterns directly from 3D structures. ScanNet consistently outperforms Machine Learning models based on handcrafted features and comparative modeling approaches. The web server is linked to both the PDB and AlphaFoldDB, and supports user-provided structure files. Predictions can be readily visualized on the website via the Molstar web app and locally via ChimeraX. ScanNet is available at http://bioinfo3d.cs.tau.ac.il/ScanNet/.
Motivation: Design of protein-protein interaction (PPI) inhibitors is a key challenge in structural bioinformatics and computer-aided drug design. Peptides, which partially mimic the interface area ...of one of the interacting proteins, are natural candidates to form protein-peptide complexes competing with the original PPI. The prediction of such complexes is especially challenging due to the high flexibility of peptide conformations.
Results: In this article, we present PepCrawler, a new tool for deriving binding peptides from protein-protein complexes and prediction of peptide-protein complexes, by performing high-resolution docking refinement and estimation of binding affinity. By using a fast path planning approach, PepCrawler rapidly generates large amounts of flexible peptide conformations, allowing backbone and side chain flexibility. A newly introduced binding energy funnel 'steepness score' was applied for the evaluation of the protein-peptide complexes binding affinity. PepCrawler simulations predicted high binding affinity for native protein-peptide complexes benchmark and low affinity for low-energy decoy complexes. In three cases, where wet lab data are available, the PepCrawler predictions were consistent with the data. Comparing to other state of the art flexible peptide-protein structure prediction algorithms, our algorithm is very fast, and takes only minutes to run on a single PC.
Availability:
http://bioinfo3d.cs.tau.ac.il/PepCrawler/
Contact:
eladdons@tau.ac.il; wolfson@tau.ac.il
Design of protein-protein interaction (PPI) inhibitors is a major challenge in Structural Bioinformatics. Peptides, especially short ones (5-15 amino acid long), are natural candidates for inhibition ...of protein-protein complexes due to several attractive features such as high structural compatibility with the protein binding site (mimicking the surface of one of the proteins), small size and the ability to form strong hotspot binding connections with the protein surface. Efficient rational peptide design is still a major challenge in computer aided drug design, due to the huge space of possible sequences, which is exponential in the length of the peptide, and the high flexibility of peptide conformations.
In this article we present PinaColada, a novel computational method for the design of peptide inhibitors for protein-protein interactions. We employ a version of the ant colony optimization heuristic, which is used to explore the exponential space (Formula: see text) of length n peptide sequences, in combination with our fast robotics motivated PepCrawler algorithm, which explores the conformational space for each candidate sequence. PinaColada is being run in parallel, on a DELL PowerEdge 2.8 GHZ computer with 20 cores and 256 GB memory, and takes up to 24 h to design a peptide of 5-15 amino acids length.
An online server available at: http://bioinfo3d.cs.tau.ac.il/PinaColada/.
danielza@post.tau.ac.il; wolfson@tau.ac.il.
Memdock is a tool for docking α-helical membrane proteins which takes into consideration the lipid bilayer environment. Given two α-helical membrane located protein molecules, the method outputs a ...list of potential complexes sorted by energy criteria. The program includes three steps: docking, refinement, and re-ranking of the results. All three docking steps have been customized to the membrane environment in order to improve performance and reduce program run-time. In this chapter, we describe the application of our web server, referred to as Memdock, for prediction of the docking complex for a pair of input membrane protein structures. Memdock is freely available for academic users without registration at http://bioinfo3d.cs.tau.ac.il/Memdock/index.html .
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