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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. Author summary: Peptides that efficiently bind a target protein and interfere with its native protein-protein interactions are attractive reagents for basic research and therapeutic applications. However, rational peptide design remains challenging, as i) exhaustive exploration of the vast sequence space is impossible, ii) generically, there is a mismatch between selection criteria and target objectives, and iii) additional constraints such as low toxicity are frequently critical. Here, we present an integrative peptide design protocol based on a sequence generative model trained on native protein interactors of the target. We tested our protocol on Calcineurin, a serine/threonine phosphatase involved in multiple health and disease pathways. We showed that the generative model i) enables extensive exploration of the sequence space, ii) approximates well binding affinity to the target, and iii) yields highly diverse candidate sequences. After further selection via molecular docking and high-throughput binding assay, we found that 70% of the designed peptides successfully interfered with Cn-substrate interactions. Our integrative protocol could thus be broadly applicable to the rational design of protein-protein interaction disruptors. [ABSTRACT FROM AUTHOR]

Motivation: A highly efficient template-based protein-protein docking algorithm, nicknamed SnapDock, is presented. It employs a Geometric Hashing-based structural alignment scheme to align the target proteins to the interfaces of non-redundant protein-protein interface libraries. Docking of a pair of proteins utilizing the 22 600 interface PIFACE library is performed in<2 min on the average. A flexible version of the algorithm allowing hinge motion in one of the proteins is presented as well. Results: To evaluate the performance of the algorithm a blind re-modelling of 3547 PDB complexes, which have been uploaded after the PIFACE publication has been performed with success ratio of about 35%. Interestingly, a similar experiment with the template free PatchDock docking algorithm yielded a success rate of about 23% with roughly 1/3 of the solutions different from those of SnapDock. Consequently, the combination of the two methods gave a 42% success ratio. [ABSTRACT FROM AUTHOR]

Motivation: A wide range of fundamental biological processes are mediated by membrane proteins. Despite their large number and importance, less than 1% of all 3D protein structures deposited in the Protein Data Bank are of membrane proteins. This is mainly due to the challenges of crystallizing such proteins or performing NMR spectroscopy analyses. All the more so, there is only a small number of membrane protein-protein complexes with known structure. Therefore, developing computational tools for docking membrane proteins is crucial. Numerous methods for docking globular proteins exist, however few have been developed especially for membrane proteins and designed to address docking within the lipid bilayer environment. Results: We present a novel algorithm, Memdock, for docking α-helical membrane proteins which takes into consideration the lipid bilayer environment for docking as well as for refining and ranking the docking candidates. We show that our algorithm improves both the docking accuracy and the candidates ranking compared to a standard protein-protein docking algorithm. [ABSTRACT FROM AUTHOR]

Motivation: 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. Results: 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 (20n) 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 2.8GHZ computer with 20 cores and 256GB memory, and takes up to 24 h to design a peptide of 5-15 amino acids length. [ABSTRACT FROM AUTHOR]

Protein-protein interactions are central to cell function. In order to fully understand these interactions, one has to elucidate the three-dimensional structures of the underlying complexes. While experimental methods have advanced significantly in the last decade, there are still few structures of protein complexes in the Protein Data Bank. Reliable computational techniques are required to fill in this gap. Better understanding of protein-protein interactions has also opened a large number of potential targets for the pharmaceutical industry, which previously viewed these interactions as 'undruggable'. In this review, we focus on the algorithms developed by the Tel Aviv University Structural Bioinformatics (Bioinfo3D) Lab to model protein-protein interactions, and on a preliminary attempt to search for peptide inhibitors for these interactions. All the algorithms presented are among the fastest available today and can be accessed via the group web server. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

Background: The study of gene mutants and their interactions is fundamental to understanding gene function and backup mechanisms within the cell. The recent availability of large scale genetic interaction networks in yeast and worm allows the investigation of the biological mechanisms underlying these interactions at a global scale. To date, less than 2% of the known genetic interactions in yeast or worm can be accounted for by sequence similarity. Results: Here, we perform a genome-scale structural comparison among protein pairs in the two species. We show that significant fractions of genetic interactions involve structurally similar proteins, spanning 7-10% and 14% of all known interactions in yeast and worm, respectively. We identify several structural features that are predictive of genetic interactions and show their superiority over sequence-based features. Conclusion: Structural similarity is an important property that can explain and predict genetic interactions. According to the available data, the most abundant mechanism for genetic interactions among structurally similar proteins is a common interacting partner shared by two genetically interacting proteins. [ABSTRACT FROM AUTHOR]