The binding of a DNA aptamer (5'-CCGTCTTCCAGACAAGAGTGCAGGG-3') to recombinant human vascular endothelial growth factor (VEGF(165)) was characterized using surface plasmon resonance (SPR), fluorescence anisotropy and isothermal titration calorimetry (ITC). Results from both fluorescence anisotropy and ITC indicated that a single aptamer molecule binds to each VEGF homodimer, unlike other VEGF inhibitors that exhibit 2(ligand):1(VEGF homodimer) stoichiometry. In addition, ITC revealed that the association of the aptamer to VEGF at 20 degrees C is enthalpically driven, with an unfavorable entropy contribution. SPR kinetic studies, with careful control of possible mass transfer effects, demonstrated that the aptamer binds to VEGF with an association rate constant k(on) = 4.79 +/- 0.03 x 10(4) M(-1) s(-1) and a dissociation rate constant k(off) = 5.21 +/- 0.02 x 10(-4) s(-1) at 25 degrees C. Key recognition hot-spots were determined by a combination of aptamer sequence substitutions, truncations, and extensions. Most single-nucleotide substitutions, particularly within an mfold-predicted stem, suppress binding, whereas those within a predicted loop have a minimal effect. The 5'-end of the aptamer plays a key role in VEGF recognition, as a single-nucleotide truncation abolished VEGF binding. Conversely, an 11-fold increase in the association rate (and affinity) is observed with a single cytosine nucleotide extension, due to pairing of the 3'-GGG with 5'-CCC in the extended aptamer. Our approach effectively maps the secondary structural elements in the free aptamer, which present the unpaired interface for high affinity VEGF recognition. These data demonstrate that a directed binding analysis can be used in concert with library screening to characterize and improve aptamer/ligand recognition.

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In previous work, Vibrio proteolyticus 5S rRNA was shown to stabilize 13-50 nucleotide "guest" RNA sequences for expression in Escherichia coli. The expressed chimeric RNAs accumulated to high levels in E. coli without being incorporated into ribosomes and without obvious effects on the host cells. In this work, we inserted sequences encoding known aptamers recognizing a protein and an organic dye into the 5S rRNA carrier and showed that aptamer function is preserved in the chimeras. A surface plasmon resonance competitive binding assay demonstrated that a vascular endothelial growth factor (VEGF) aptamer/5S rRNA chimera produced in vitro by transcriptional runoff could compete with a DNA aptamer for VEGF, implying binding of the growth factor by the VEGF "ribosomal RNA aptamer." Separately, a 5S rRNA chimera displaying an aptamer known to increase the fluorescence of malachite green (MG) also enhanced MG fluorescence. Closely related control rRNA molecules showed neither activity. The MG aptamer/5S rRNA chimera, like the original MG aptamer, also increased the fluorescence of other triphenyl methane (TPM) dyes such as crystal violet, methyl violet, and brilliant green, although less effectively than with MG. These results indicate that the molecular recognition properties of aptamers are not lost when they are expressed in the context of a stable 5S rRNA carrier. Inclusion of the aptamer in a carrier may facilitate production of large quantities of RNA aptamers, and may open an approach to screening aptamer libraries in vivo.

In this work, we examined the possibility of improving ion-exchange adsorbent performance by nanoscale structuring of ligands into clusters of fixed size rather than a random distribution of individual charges. The calcium-depleted form of the protein alpha-lactalbumin, which displays a cluster of acidic amino acid residues, showed enhanced adsorption affinity and capacity on clustered-charge pentalysinamide and pentaargininamide adsorbents as compared to single-charge lysinamide and argininamide adsorbents of matched total charge. Two differently charge-clustered mutants of rat microsomal cytochrome b(5), E11Q and E44Q, with the same total charge also were well differentiated by clustered-charge adsorbents. Thus, an organized rather than random distribution of charges may produce adsorbents with higher capacity and selectivity, especially for biomolecules with inherent charge clustering.

Background: Functional outcome scores provide valuable data, yet they can be burdensome to patients and require significant resources to administer. The Knee injury and Osteoarthritis Outcome Score (KOOS) is a knee-specific patient-reported outcome measure (PROM) and is validated for anterior cruciate ligament (ACL) reconstruction outcomes. The KOOS requires 42 questions in 5 subscales. We utilized a machine learning (ML) algorithm to determine whether the number of questions and the resultant burden to complete the survey can be lowered in a subset (activities of daily living; ADL) of KOOS, yet still provide identical data. Hypothesis: Fewer questions than the 17 currently provided are actually needed to predict KOOS ADL subscale scores with high accuracy. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: Pre- and postoperative patient-reported KOOS ADL scores were obtained from the Surgical Outcome System (SOS) data registry for patients who had ACL reconstruction. Categorical Boosting (CatBoost) ML models were built to analyze each question and its value in predicting the patient’s actual functional outcome (ie, KOOS ADL score). A streamlined set of minimal essential questions were then identified. Results: The SOS registry contained 6185 patients who underwent ACL reconstruction. A total of 2525 patients between the age of 16 and 50 years had completed KOOS ADL scores presurgically and 3 months postoperatively. The data set consisted of 51.84% male patients and 48.16% female patients, with a mean age of 29 years. The CatBoost model predicted KOOS ADL scores with high accuracy when only 6 questions were asked ( R2 = 0.95), similar to when all 17 questions of the subscale were asked ( R2 = 0.99). Conclusion: ML algorithms successfully identified the essential questions in the KOOS ADL questionnaire. Only 35% (6/17) of KOOS ADL questions (descending stairs, ascending stairs, standing, walking on flat surface, putting on socks/stockings, and getting on/off toilet) are needed to predict KOOS ADL scores with high accuracy after ACL reconstruction. ML can be utilized successfully to streamline the burden of patient data collection. This, in turn, can potentially lead to improved patient reporting, increased compliance, and increased utilization of PROMs while still providing quality data.