Toward coherent O-band data center interconnects

Upcoming generations of coherent intra/inter data center interconnects currently lack a clear path toward a reduction of cost and power consumption,which are the driving factors for these data links.In this work,the tradeoffs associated with a transition from coherent C-band to O-band silicon photonics are addressed and evaluated.The discussion includes the fundamental components of coherent data links,namely the optical components,fiber link and transceivers.As a major component of these links,a monolithic silicon photonic BiCMOS O-band coherent receiver is evaluated for its potential performance and compared to an analogous C-band device.

finDr:A web server for in silico D-peptide ligand identification

In the rapidly expanding field of peptide therapeutics,the short in vivo half-life of peptides represents a considerable limitation for drug action.D-peptides,consisting entirely of the dextrorotatory enantiomers of naturally occurring levorotatory amino acids(AAs),do not suffer from these shortcomings as they are intrinsically resistant to proteolytic degradation,resulting in a favourable pharmacokinetic profile.To experimentally identify D-peptide binders to interesting therapeutic targets,so-called mirror-image phage display is typically performed,whereby the target is synthesized in D-form and L-peptide binders are screened as in conventional phage display.This technique is extremely powerful,but it requires the synthesis of the target in D-form,which is challenging for large proteins.Here we present finDr,a novel web server for the computational identification and optimization of D-peptide ligands to any protein structure(https://findr.biologie.uni-freiburg.de/).finDr performs molecular docking to virtually screen a library of helical 12-mer peptides extracted from the RCSB Protein Data Bank(PDB)for their ability to bind to the target.In a separate,heuristic approach to search the chemical space of 12-mer peptides,finDr executes a customizable evolutionary algorithm(EA)for the de novo identification or optimization of D-peptide ligands.As a proof of principle,we demonstrate the validity of our approach to predict optimal binders to the pharmacologically relevant target phenol soluble modulin alpha 3(PSMα3),a toxin of methicillin-resistant Staphylococcus aureus(MRSA).We validate the predictions using in vitro binding assays,supporting the success of this approach.Compared to conventional methods,finDr provides a low cost and easy-to-use alternative for the identification of D-peptide ligands against protein targets of choice without size limitation.We believe finDr will facilitate D-peptide discovery with implications in biotechnology and biomedicine.