SAP (SLAM-associated protein) is a small lymphocyte-specific signalling molecule that is defective or absent in patients with X-linked lymphoproliferative syndrome (XLP). Consistent with its single src homology 2 (SH2) domain architecture and unusually high affinity for SLAM (also called CD150), SAP has been suggested to function by blocking binding of SHP-2 or other SH2-containing signalling proteins to SLAM receptors. Additionally, SAP has recently been shown to be required for recruitment and activation of the Src-family kinase FynT after SLAM ligation. This signalling 'adaptor' function has been difficult to conceptualize, because unlike typical SH2-adaptor proteins, SAP contains only a single SH2 domain and lacks other recognized protein interaction domains or motifs. Here, we show that the SAP SH2 domain binds to the SH3 domain of FynT and directly couples FynT to SLAM. The crystal structure of a ternary SLAM-SAP-Fyn-SH3 complex reveals that SAP binds the FynT SH3 domain through a surface-surface interaction that does not involve canonical SH3 or SH2 binding interactions. The observed mode of binding to the Fyn-SH3 domain is expected to preclude the auto-inhibited conformation of Fyn, thereby promoting activation of the kinase after recruitment. These findings broaden our understanding of the functional repertoire of SH3 and SH2 domains.

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The Third Modeling Workshop focusing on bioprocess modeling was held in Kenilworth, NJ in May 2019. A summary of these Workshop proceedings is captured in this manuscript. Modeling is an active area of research within the biotechnology community, and there is a critical need to assess the current state and opportunities for continued investment to realize the full potential of models, including resource and time savings. Beyond individual presentations and topics of novel interest, a

A main requirement for the implementation of model-based process development in industry is the capability of the model to predict high protein load densities. The frequently used steric mass action isotherm assumes a thermodynamically ideal system and, hence constant activity coefficients. In this manuscript, an industrial antibody purification problem under high load conditions is considered where this assumption does not hold. The high protein load densities, as commonly applied in industrial downstream processing, may lead to complex elution peak shapes. Using Mollerup's generalized ion-exchange isotherm (GIEX), the observed elution peak shapes could be modeled. To this end, the GIEX isotherm introduced two additional parameters to approximate the asymmetric activity coefficient. The effects of these two parameters on the curvature of the adsorption isotherm and the resulting chromatogram are investigated. It could be shown that they can be determined by inverse peak fitting and conform with the mechanistic demands of model-based process development.

Biopharmaceutical product and process development do not yet take advantage of predictive computational modeling to nearly the degree seen in industries based on smaller molecules. To assess and advance progress in this area, spirited coopetition (mutually beneficial collaboration between competitors) was successfully used to motivate industrial scientists to develop, share, and compare data and methods which would normally have remained confidential. The first “Highland Games” competition was held in conjunction with the October 2018 Recovery of Biological Products Conference in Ashville, NC, with the goal of benchmarking and assessment of the ability to predict development‐related properties of six antibodies from their amino acid sequences alone. Predictions included purification‐influencing properties such as isoelectric point and protein A elution pH, and biophysical properties such as stability and viscosity at very high concentrations. Essential contributions were made by a large variety of individuals, including companies which consented to provide antibody amino acid sequences and test materials, volunteers who undertook the preparation and experimental characterization of these materials, and prediction teams who attempted to predict antibody properties from sequence alone. Best practices were identified and shared, and areas in which the community excels at making predictions were identified, as well as areas presenting opportunities for considerable improvement. Predictions of isoelectric point and protein A elution pH were especially good with all‐prediction average errors of 0.2 and 1.6 pH unit, respectively, while predictions of some other properties were notably less good. This manuscript presents the events, methods, and results of the competition, and can serve as a tutorial and as a reference for in‐house benchmarking by others. Organizations vary in their policies concerning disclosure of methods, but most managements were very cooperative with the Highland Games exercise, and considerable insight into common and best practices is available from the contributed methods. The accumulated data set will serve as a benchmarking tool for further development of in silico prediction tools.