Motivation Protein–peptide interactions mediate a wide variety of cellular and biological functions. Methods for predicting these interactions have garnered a lot of interest over the past few years, as witnessed by the rapidly growing number of peptide-based therapeutic molecules currently in clinical trials. The size and flexibility of peptides has shown to be challenging for existing automated docking software programs. Results Here we present AutoDock CrankPep or ADCP in short, a novel approach to dock flexible peptides into rigid receptors. ADCP folds a peptide in the potential field created by the protein to predict the protein–peptide complex. We show that it outperforms leading peptide docking methods on two protein–peptide datasets commonly used for benchmarking docking methods: LEADS-PEP and peptiDB, comprised of peptides with up to 15 amino acids in length. Beyond these datasets, ADCP reliably docked a set of protein–peptide complexes containing peptides ranging in lengths from 16 to 20 amino acids. The robust performance of ADCP on these longer peptides enables accurate modeling of peptide-mediated protein–protein interactions and interactions with disordered proteins. Availability and implementation ADCP is distributed under the LGPL 2.0 open source license and is available at http://adcp.scripps.edu. The source code is available at https://github.com/ccsb-scripps/ADCP. Supplementary information Supplementary data are available at Bioinformatics online.
While amyriad non-coding RNAs are knowntobe essential in cellular processes and misregulated in diseases,the development of RNA-targeted small molecule probes has met with limited success.T oe lucidate the guiding principles for selective small molecule/RNArecognition, we analyzed cheminformatic and shape-based descriptors for 104 RNA-targeted ligands with demonstrated biological activity (RNA-targeted BIoactive ligaNd Database,R -BIND). We then compared R-BIND to both FDA-approved small molecule drugs and RNA ligands without reported bioactivity.S everal striking trends emerged for bioactive RNAligands,including:1)Compliance to medicinal chemistry rules,2 )distinctive structural features, and 3) enrichment in rod-like shapes over others.T his work provides unique insights that directly facilitate the selection and synthesis of RNA-targeted libraries with the goal of efficiently identifying selective small molecule ligands for therapeutically relevant RNAs.Supportinginformation and the ORCID identification number(s) for the author(s) of this article can be found under: https://doi.
The recently developed AlphaFold2 (AF2) algorithm predicts proteins’ 3D structures from amino acid sequences. The open AlphaFold protein structure database covers the complete human proteome. Using an industry-leading molecular docking method (Glide), we investigated the virtual screening performance of 37 common drug targets, each with an AF2 structure and known holo and apo structures from the DUD-E data set. In a subset of 27 targets where the AF2 structures are suitable for refinement, the AF2 structures show comparable early enrichment of known active compounds (avg. EF 1%: 13.0) to apo structures (avg. EF 1%: 11.4) while falling behind early enrichment of the holo structures (avg. EF 1%: 24.2). With an induced-fit protocol (IFD-MD), we can refine the AF2 structures using an aligned known binding ligand as the template to improve the performance in structure-based virtual screening (avg. EF 1%: 18.9). Glide-generated docking poses of known binding ligands can also be used as templates for IFD-MD, achieving similar improvements (avg. EF 1% 18.0). Thus, with proper preparation and refinement, AF2 structures show considerable promise for in silico hit identification.
While a new therapeutic cyclic peptide is approved nearly every year, docking large macrocycles has remained challenging. Here, we present a new version of our peptide docking software AutoDock CrankPep (ADCP), extended to dock peptides cyclized through their backbone and/or side chain disulfide bonds. We show that within the top 10 solutions, ADCP identifies the proper interactions for 71% of a data set of 38 complexes, thus making it a useful tool for rational peptide-based drug design.
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