Computational design of the sequence and structure of a protein-binding peptide.
Ontology highlight
ABSTRACT:
SUBMITTER: Sammond DW
PROVIDER: S-EPMC3081598 | biostudies-literature | 2011 Mar
REPOSITORIES: biostudies-literature
Publications
Computational design of the sequence and structure of a protein-binding peptide.
Sammond Deanne W DW Bosch Dustin E DE Butterfoss Glenn L GL Purbeck Carrie C Machius Mischa M Siderovski David P DP Kuhlman Brian B
Journal of the American Chemical Society 20110309 12
The de novo design of protein-binding peptides is challenging because it requires the identification of both a sequence and a backbone conformation favorable for binding. We used a computational strategy that iterates between structure and sequence optimization to redesign the C-terminal portion of the RGS14 GoLoco motif peptide so that it adopts a new conformation when bound to Gα(i1). An X-ray crystal structure of the redesigned complex closely matches the computational model, with a backbone ...[more]
Similar Datasets
Project description:Sequence-specific DNA-binding proteins (DBPs) play critical roles in biology and biotechnology, and there has been considerable interest in the engineering of DBPs with new or altered specificities for genome editing and other applications. While there has been some success in reprogramming naturally occurring DBPs using selection methods, the computational design of new DBPs that recognize arbitrary target sites remains an outstanding challenge. We describe a computational method for the design of small DBPs that recognize specific target sequences through interactions with bases in the major groove.
2024-07-05 | GSE237017 | GEO
Project description:Computational design of sequence-specific DNA-binding proteins
| PRJNA993300 | ENA
Project description:Protein-protein interactions (PPIs) are involved at all levels of cellular organization, thus making the development of PPI inhibitors extremely valuable. The identification of selective inhibitors is challenging because of the shallow and extended nature of PPI interfaces. Inhibitors can be obtained by mimicking peptide binding epitopes in their bioactive conformation. For this purpose, several strategies have been evolved to enable a projection of side chain functionalities in analogy to peptide secondary structures, thereby yielding molecules that are generally referred to as peptidomimetics. Herein, we introduce a new classification of peptidomimetics (classes A-D) that enables a clear assignment of available approaches. Based on this classification, the Review summarizes strategies that have been applied for the structure-based design of PPI inhibitors through stabilizing or mimicking turns, β-sheets, and helices.
| S-EPMC4557054 | biostudies-literature
Project description:The binding of short disordered peptide stretches to globular protein domains is important for a wide range of cellular processes, including signal transduction, protein transport, and immune response. The often promiscuous nature of these interactions and the conformational flexibility of the peptide chain, sometimes even when bound, make the binding specificity of this type of protein interaction a challenge to understand. Here we develop and test a Monte Carlo-based procedure for calculating protein-peptide binding thermodynamics for many sequences in a single run. The method explores both peptide sequence and conformational space simultaneously by simulating a joint probability distribution which, in particular, makes searching through peptide sequence space computationally efficient. To test our method, we apply it to 3 different peptide-binding protein domains and test its ability to capture the experimentally determined specificity profiles. Insight into the molecular underpinnings of the observed specificities is obtained by analyzing the peptide conformational ensembles of a large number of binding-competent sequences. We also explore the possibility of using our method to discover new peptide-binding pockets on protein structures.
| S-EPMC3812049 | biostudies-literature
Project description:We describe a computational protocol, called DDMI, for redesigning scaffold proteins to bind to a specified region on a target protein. The DDMI protocol is implemented within the Rosetta molecular modeling program and uses rigid-body docking, sequence design, and gradient-based minimization of backbone and side-chain torsion angles to design low-energy interfaces between the scaffold and target protein. Iterative rounds of sequence design and conformational optimization were needed to produce models that have calculated binding energies that are similar to binding energies calculated for native complexes. We also show that additional conformation sampling with molecular dynamics can be iterated with sequence design to further lower the computed energy of the designed complexes. To experimentally test the DDMI protocol, we redesigned the human hyperplastic discs protein to bind to the kinase domain of p21-activated kinase 1 (PAK1). Six designs were experimentally characterized. Two of the designs aggregated and were not characterized further. Of the remaining four designs, three bound to the PAK1 with affinities tighter than 350 muM. The tightest binding design, named Spider Roll, bound with an affinity of 100 muM. NMR-based structure prediction of Spider Roll based on backbone and (13)C(beta) chemical shifts using the program CS-ROSETTA indicated that the architecture of human hyperplastic discs protein is preserved. Mutagenesis studies confirmed that Spider Roll binds the target patch on PAK1. Additionally, Spider Roll binds to full-length PAK1 in its activated state but does not bind PAK1 when it forms an auto-inhibited conformation that blocks the Spider Roll target site. Subsequent NMR characterization of the binding of Spider Roll to PAK1 revealed a comparably small binding 'on-rate' constant (<<10(5) M(-1) s(-1)). The ability to rationally design the site of novel protein-protein interactions is an important step towards creating new proteins that are useful as therapeutics or molecular probes.
| S-EPMC2903434 | biostudies-literature
Project description:Engineering specific interactions between proteins and small molecules is extremely useful for biological studies, as these interactions are essential for molecular recognition. Furthermore, many biotechnological applications are made possible by such an engineering approach, ranging from biosensors to the design of custom enzyme catalysts. Here, we present a novel method for the computational design of protein-small ligand binding named PocketOptimizer. The program can be used to modify protein binding pocket residues to improve or establish binding of a small molecule. It is a modular pipeline based on a number of customizable molecular modeling tools to predict mutations that alter the affinity of a target protein to its ligand. At its heart it uses a receptor-ligand scoring function to estimate the binding free energy between protein and ligand. We compiled a benchmark set that we used to systematically assess the performance of our method. It consists of proteins for which mutational variants with different binding affinities for their ligands and experimentally determined structures exist. Within this test set PocketOptimizer correctly predicts the mutant with the higher affinity in about 69% of the cases. A detailed analysis of the results reveals that the strengths of PocketOptimizer lie in the correct introduction of stabilizing hydrogen bonds to the ligand, as well as in the improved geometric complemetarity between ligand and binding pocket. Apart from the novel method for binding pocket design we also introduce a much needed benchmark data set for the comparison of affinities of mutant binding pockets, and that we use to asses programs for in silico design of ligand binding.
| S-EPMC3531388 | biostudies-literature
Project description:SummaryEver increasing amounts of protein structure data, combined with advances in machine learning, have led to the rapid proliferation of methods available for protein-sequence design. In order to utilize a design method effectively, it is important to understand the nuances of its performance and how it varies by design target. Here, we present PDBench, a set of proteins and a number of standard tests for assessing the performance of sequence-design methods. PDBench aims to maximize the structural diversity of the benchmark, compared with previous benchmarking sets, in order to provide useful biological insight into the behaviour of sequence-design methods, which is essential for evaluating their performance and practical utility. We believe that these tools are useful for guiding the development of novel sequence design algorithms and will enable users to choose a method that best suits their design target.Availability and implementationhttps://github.com/wells-wood-research/PDBench.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC9869650 | biostudies-literature
Project description:Protein quantitation via mass spectrometry relies on peptide proxies for the parent protein from which abundances are estimated. Owing to the variability in signal from individual peptides, accurate absolute quantitation usually relies on the addition of an external standard. Typically, this involves stable isotope-labeled peptides, delivered singly or as a concatenated recombinant protein. Consequently, the selection of the most appropriate surrogate peptides and the attendant design in recombinant proteins termed QconCATs are challenges for proteome science. QconCATs can now be built in a "a-la-carte" assembly method using synthetic biology: ALACATs. To assist their design, we present "AlacatDesigner", a tool that supports the peptide selection for recombinant protein standards based on the user's target protein. The user-customizable tool considers existing databases, occurrence in the literature, potential post-translational modifications, predicted miscleavage, predicted divergence of the peptide and protein quantifications, and ionization potential within the mass spectrometer. We show that peptide selections are enriched for good proteotypic and quantotypic candidates compared to empirical data. The software is freely available to use either via a web interface AlacatDesigner, downloaded as a Desktop application or imported as a Python package for the command line interface or in scripts.
| S-EPMC9903321 | biostudies-literature
Project description:Antibodies derived from non-human sources must be modified for therapeutic use so as to mitigate undesirable immune responses. While complementarity-determining region (CDR) grafting-based humanization techniques have been successfully applied in many cases, it remains challenging to maintain the desired stability and antigen binding affinity upon grafting. We developed an alternative humanization approach called CoDAH ("Computationally-Driven Antibody Humanization") in which computational protein design methods directly select sets of amino acids to incorporate from human germline sequences to increase humanness while maintaining structural stability. Retrospective studies show that CoDAH is able to identify variants deemed beneficial according to both humanness and structural stability criteria, even for targets lacking crystal structures. Prospective application to TZ47, a murine anti-human B7H6 antibody, demonstrates the approach. Four diverse humanized variants were designed, and all possible unique VH/VL combinations were produced as full-length IgG1 antibodies. Soluble and cell surface expressed antigen binding assays showed that 75% (6 of 8) of the computationally designed VH/VL variants were successfully expressed and competed with the murine TZ47 for binding to B7H6 antigen. Furthermore, 4 of the 6 bound with an estimated KD within an order of magnitude of the original TZ47 antibody. In contrast, a traditional CDR-grafted variant could not be expressed. These results suggest that the computational protein design approach described here can be used to efficiently generate functional humanized antibodies and provide humanized templates for further affinity maturation.
| S-EPMC5045135 | biostudies-literature
Project description:New protein-protein interactions (PPIs) are identified, but PPIs have different physicochemical properties compared with conventional targets, making it difficult to use small molecules. Peptides offer a new modality to target PPIs, but designing appropriate peptide sequences by computation is challenging. Recently, AlphaFold and RoseTTAFold have made it possible to predict protein structures from amino acid sequences with ultra-high accuracy, enabling de novo protein design. We designed peptides likely to have PPI as the target protein using the "binder hallucination" protocol of AfDesign, a de novo protein design method using AlphaFold. However, the solubility of the peptides tended to be low. Therefore, we designed a solubility loss function using solubility indices for amino acids and developed a solubility-aware AfDesign binder hallucination protocol. The peptide solubility in sequences designed using the new protocol increased with the weight of the solubility loss function; moreover, they captured the characteristics of the solubility indices. Moreover, the new protocol sequences tended to have higher affinity than random or single residue substitution sequences when evaluated by docking binding affinity. Our approach shows that it is possible to design peptide sequences that can bind to the interface of PPI while controlling solubility.
| S-EPMC9312799 | biostudies-literature