Polyacrylonitrile, poly(styrene-acrylonitrile) and poly(acrylonitrile-butadiene-styrene) complexes with transition metal salts have been synthesized. The synthesis were attempted by carrying out free radical polymerization reactions in aqueous transition metal salt solutions (ZnCl 2 , CuCl 2 , CoCl 2 , CoSO 4 , NiSO 4 , CuSO 4 ) and by addition of the transition metal salts to the reaction mixture in the final stage of the polymerization. It is shown experimentally and theoretically that the first method is successful only when zinc chloride is used, while the cobalt, nickel, copper complexes are formed through the addition of the salts to the ready polymers. Reactions that take place during studied complexations were investigated by means of ab initio quantum chemistry calculations, thermodynamic parameters for these reactions were determined.

MotivationAs more data of experimentally determined protein structures is becoming available, data-driven models to describe protein sequence-structure relationship become more feasible. Within this space, the amino acid sequence design of protein-protein interactions has still been a rather challenging sub-problem with very low success rates - yet it is central for the most biological processes.ResultsWe developed an attention-based deep learning model inspired by algorithms used for image-caption assignments for sequence design of peptides or protein fragments. These interaction fragments are derived from and represent core parts of protein-protein interfaces. Our trained model allows the one-sided design of a given protein fragment which can be applicable for the redesign of protein-interfaces or the de novo design of new interactions fragments. Here we demonstrate its potential by recapitulating naturally occurring protein-protein interactions including antibody-antigen complexes. The designed interfaces capture essential native interactions with high prediction accuracy and have native-like binding affinities. It further does not need precise backbone location, making it an attractive tool for working with de novo design of protein-protein interactions.AvailabilityThe source code of the method is available at https://github.com/strauchlab/iNNterfaceDesignSupplementary informationSupplementary data are available at Bioinformatics online.

to protein characterization and interpretation of results; Aided in manuscript preparation. Rachel A. Jones Lipinsky: Conducted chemical synthesis and characterization. Noah R. Leigh: Contributed to the automation aspect of the screen, and helped facilitate the screen. Raman G. Kutty: Contributed to the development and optimization of the pNPP assay; intellectual contribution to project design and interpretation of results. Daniel S. Sem: Key role in guiding group members in the characterization of DUSP5 enzyme kinetics and activity; aided in all aspects of project direction, compiled, prepared and edited the manuscript. Rajendra Rathore: Key role in guiding group members in the characterization of DUSP5/ERK interactions via molecular dynamic simulations. Ramani Ramchandran: Key role in guiding group members in the characterization of DUSP5 activity and purification; aided in all aspects of project direction, compiled, prepared and edited the manuscript.

Background: The mitogen-activated protein kinase (MAPK) pathway is functionally generic and critical in maintaining physiological homeostasis and normal tissue development. This pathway is under tight regulation, which is in part mediated by dual-specific phosphatases (DUSPs), which dephosphorylate serine, threonine, and tyrosine residues of the ERK family of proteins. DUSP5 is of high clinical interest because of mutations we identified in this protein in patients with vascular anomalies. Unlike other DUSPs, DUSP5 has unique specificity toward substrate pERK1/2. Using molecular docking and simulation strategies, we previously showed that DUSP5 has two pockets, which are utilized in a specific fashion to facilitate specificity toward catalysis of its substrate pERK1/2. Remarkably, most DUSPs share high similarity in their catalytic sites. Studying the catalytic domain of DUSP5 and identifying amino acid residues that are important for dephosphorylating pERK1/2 could be critical in developing small molecules for therapies targeting DUSP5. Results: In this study, we utilized computational modeling to identify and predict the importance of two conserved amino acid residues, H262 and S270, in the DUSP5 catalytic site. Modeling studies predicted that catalytic activity of DUSP5 would be altered if these critical conserved residues were mutated. We next generated independent Glutathione-S-Transferase (GST)-tagged full-length DUSP5 mutant proteins carrying specific mutations H262F and S270A in the phosphatase domain. Biochemical analysis was performed on these purified proteins, and consistent with our computational prediction, we observed altered enzyme activity kinetic profiles for both mutants with a synthetic small molecule substrate (pNPP) and the physiological relevant substrate (pERK) when compared to wild type GST-DUSP5 protein. Conclusion: Our molecular modeling and biochemical studies combined demonstrate that enzymatic activity of phosphatases can be manipulated by mutating specific conserved amino acid residues in the catalytic site (phosphatase domain). This strategy could facilitate generation of small molecules that will serve as agonists/antagonists of DUSP5 activity.

Motivation As more data of experimentally determined protein structures are becoming available, data-driven models to describe protein sequence-structure relationships become more feasible. Within this space, the amino acid sequence design of protein-protein interactions is still a rather challenging subproblem with very low success rates – yet, it is central to most biological processes. Results We developed an attention-based deep learning model inspired by algorithms used for image-caption assignments to design peptides or protein fragment sequences. Our trained model can be applied for the redesign of natural protein-interfaces or the designed protein interaction fragments. Here, we validate the potential by recapitulating naturally occurring protein-protein interactions including antibody-antigen complexes. The designed interfaces accurately capture essential native interactions and have comparable native-like binding affinities in silico. Furthermore, our model does not need a precise backbone location, making it an attractive tool for working with de novo design of protein-protein interactions. Availability The source code of the method is available at https://github.com/strauchlab/iNNterfaceDesign Supplementary information Supplementary data is available at Bioinformatics online.