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The analysis of biomolecular interactions is important in characterizing and understanding many fundamental processes that occur in the body and biological systems. A variety of methods are available for studying the extent and rate of binding of these interactions. Some of these techniques are homogeneous methods, with all interacting components being present in the solution-phase, while others are heterogeneous, such as involving both solution-phase and solid-phase components. LC and HPLC have often been used to study biomolecular processes. Although these chromatographic methods make use of both a liquid phase (i.e., the mobile phase and applied samples) and a solid phase (the stationary phase and support), they can be used to study solution-phase interactions. This review examines several strategies that have been developed and employed to use LC and HPLC for this purpose. These strategies include the Hummel-Dreyer method, solution-phase frontal analysis, and the use of physical entrapment for a soluble component of a biomolecular interaction. Other strategies that are discussed are those in which the stationary phase of the column is used as a secondary component or capture agent when studying a solution-phase interaction, as occurs in normal-role affinity chromatography and ultrafast affinity extraction. The general principles for each of these strategies will be considered, along with their advantages, potential limitations, and applications. 

Background: DJ-1 is a protein whose mutation causes rare heritable forms of Parkinson’s disease (PD) and is of interest as a target for treating PD and other disorders. This work used high performance affinity microcolumns to screen and examine the binding of small molecules to DJ-1, as could be used to develop new therapeutics or to study the role of DJ-1 in PD. Non-covalent entrapment was used to place microgram quantities of DJ-1 in an unmodified form within microcolumns, which were then used in multiple studies to analyze binding by model compounds and possible drug candidates to DJ-1. Results: Several factors were examined in optimizing the entrapment method, including the addition of a reducing agent to maintain a reduced active site cysteine residue in DJ-1, the concentration of DJ-1 employed, and the entrapment times. Isatin was used as a known binding agent (dissociation constant, ~2.0 µM) and probe for DJ-1 activity. This compound gave good retention on 2.0 cm × 2.1 mm inner diameter DJ-1 microcolumns made under the final entrapment conditions, with a typical retention factor of 14 and elution in ~8 min at 0.50 mL/min. These DJ-1 microcolumns were used to evaluate the binding of small molecules that were selected in silico to bind or not to bind DJ-1. A compound predicted to have good binding with DJ-1 gave a retention factor of 122, an elution time of ~15 min at 0.50 mL/min, and an estimated dissociation constant for this protein of 0.5 µM. Significance: These chromatographic tools can be used in future work to screen additional possible binding agents for DJ-1 or adapted for examining drug candidates for other proteins. This work represents the first time protein entrapment has been deployed with DJ-1, and it is the first experimental confirmation of binding to DJ-1 by a small lead compound selected in silico. 

Information Retrieval (IR) plays a pivotal role indiverse Software Engineering (SE) tasks, e.g., bug localization and triaging, bug report routing, code retrieval, requirements analysis, etc. SE tasks operate on diverse types of documents including code, text, stack-traces, and structured, semi-structured and unstructured meta-data that often contain specialized vocabularies. As the performance of any IR-based tool critically depends on the underlying document types, and given the diversity of SE corpora, it is essential to understand which models work best for which types of SE documents and tasks.We empirically investigate the interaction between IR models and document types for two representative SE tasks (bug localization and relevant project search), carefully chosen as they require a diverse set of SE artifacts (mixtures of code and text),and confirm that the models’ performance varies significantly with mix of document types. Leveraging this insight, we propose a generalized framework, SRCH, to automatically select the most favorable IR model(s) for a given SE task. We evaluate SRCH w.r.t. these two tasks and confirm its effectiveness. Our preliminary user study shows that SRCH’s intelligent adaption of the IR model(s) to the task at hand not only improves precision and recall for SE tasks but may also improve users’ satisfaction.