Biology subjects

Witten, J.

Publications and source records attributed to Witten, J..

3 recordsLinked to original sources

bioRxiv2019

Deep learning regression model for antimicrobial peptide design

Antimicrobial peptides (AMPs) are naturally occurring or synthetic peptides that show promise for treating antibiotic-resistant pathogens. Machine learning techniques are increasingly used to identify naturally occurring AMPs, but there is a dearth of purely computational methods to design novel effective AMPs, which would speed AMP development. We collected a large database, Giant Repository of AMP Activities (GRAMPA), containing AMP sequences and associated MICs. We designed a convolutional neural network to perform combined classification and regression on peptide sequences to quantitatively predict AMP activity against Escherichia coli. Our predictions outperformed the state of the art at AMP classification and were also effective at regression, for which there were no publicly available comparisons. We then used our model to design novel AMPs and experimentally demonstrated activity of these AMPs against the pathogens E. coli, Pseudomonas aeruginosa, and Staphylococcus aureus. Data, code, and neural network architecture and parameters are available at https://github.com/zswitten/Antimicrobial-Peptides.

bioinformatics↗

bioRxiv2019

Computational Insights Into Avidity of Polymeric Multivalent Binders

Multivalent binding interactions are commonly found throughout biology to enhance weak monovalent binding such as between glycoligands and protein receptors. Designing multivalent polymers to bind to viruses and toxic proteins is a promising avenue for inhibiting their attachment and subsequent infection of cells. Several studies have focused on oligomeric multivalent inhibitors and how changing parameters such as ligand shape and size, and linker length and flexibility affect binding. However, experimental studies of how larger structural parameters of multivalent polymers such as degree of polymerization affect binding avidity to targets have mixed results with some finding an improvement with longer polymers and some finding no effect. Here, we use Brownian dynamics simulations to provide a theoretical understanding of how degree of polymerization affects the binding avidity of multivalent polymers. We show that longer polymers increase binding avidity to multivalent targets, but reach a limit in binding avidity at high degrees of polymerization. We also show that when interacting with multiple targets simultaneously, longer polymers are able to use inter-target interactions to promote clustering and improve binding efficiency. We expect our results to narrow the design space for optimizing the structure and effectiveness of multivalent inhibitors, as well as be useful to understand biological design strategies for multivalent binding.

bioengineering↗

bioRxiv2016

Charge influences substrate recognition and self-assembly of hydrophobic FG sequences

The nuclear pore complex controls the passage of molecules via hydrophobic phenylalanine-glycine (FG) domains on nucleoporins. Such FG-domains consist of repeating units of FxFG, FG, or GLFG sequences, which can be interspersed with highly charged amino acid sequences. Despite the high density of charge exhibited in certain FG-domains, if and how charge influences FG-domain self-assembly and selective binding of nuclear transport receptors is largely unexplored. Studying how individual charged amino acids contribute to nuclear pore selectivity is challenging with modern in vivo and in vitro techniques due to the complexity of nucleoporin sequences. Here, we present a rationally designed approach to deconstruct essential components of nucleoporins down to 14 amino acid sequences. With these nucleoporin-based peptides, we systematically dissect how charge type and placement of charge influences self-assembly and selective binding of FG-containing gels. Specifically, we find that charge type determines which hydrophobic substrates FG sequences recognize while spatial localization of charge tunes hydrophobic self-assembly and receptor selectivity of FG sequences.

biophysics↗