Background

Identifying the catalytic residues in enzymes can aid in understanding the molecular basis of an enzyme's function and has significant implications for designing new drugs, identifying genetic disorders, and engineering proteins with novel functions. Since experimentally determining catalytic sites is expensive, better computational methods for identifying catalytic residues are needed.

Results

We propose ResBoost, a new computational method to learn characteristics of catalytic residues. The method effectively selects and combines rules of thumb into a simple, easily interpretable logical expression that can be used for prediction. We formally define the rules of thumb that are often used to narrow the list of candidate residues, including residue evolutionary conservation, 3D clustering, solvent accessibility, and hydrophilicity. ResBoost builds on two methods from machine learning, the AdaBoost algorithm and Alternating Decision Trees, and provides precise control over the inherent trade-off between sensitivity and specificity. We evaluated ResBoost using cross-validation on a dataset of 100 enzymes from the hand-curated Catalytic Site Atlas (CSA).

Conclusion

ResBoost achieved 85% sensitivity for a 9.8% false positive rate and 73% sensitivity for a 5.7% false positive rate. ResBoost reduces the number of false positives by up to 56% compared to the use of evolutionary conservation scoring alone. We also illustrate the ability of ResBoost to identify recently validated catalytic residues not listed in the CSA.

HIV infection is controlled immunologically in a small subset of infected individuals without antiretroviral therapy (ART), though the mechanism of control is unclear. CD8⁺ T cells are a critical component of HIV control in many immunological controllers. NK cells are also believed to have a role in controlling HIV infection, though their role is less well characterized. We used mass cytometry to simultaneously measure the levels of expression of 24 surface markers on peripheral NK cells from HIV-infected subjects with various degrees of HIV natural control; we then used machine learning to identify NK cell subpopulations that differentiate HIV controllers from noncontrollers. Using CITRUS (cluster identification, characterization, and regression), we identified 3 NK cell subpopulations that differentiated subjects with chronic HIV viremia (viremic noncontrollers [VNC]) from individuals with undetectable HIV viremia without ART (elite controllers [EC]). In a parallel approach, we identified 11 NK cell subpopulations that differentiated HIV-infected subject groups using k-means clustering after dimensionality reduction by t-neighbor stochastic neighbor embedding (tSNE) or linear discriminant analysis (LDA). Among these additional 11 subpopulations, the frequencies of 5 correlated with HIV DNA levels; importantly, significance was retained in 2 subpopulations in analyses that included only cohorts without detectable viremia. By comparing the surface marker expression patterns of all identified subpopulations, we revealed that the CD11b⁺ CD57^- CD161⁺ Siglec-7⁺ subpopulation of CD56^dim CD16⁺ NK cells are more abundant in EC and HIV-negative controls than in VNC and that the frequency of these cells correlated with HIV DNA levels. We hypothesize that this population may have a role in immunological control of HIV infection.IMPORTANCE HIV infection results in the establishment of a stable reservoir of latently infected cells; ART is usually required to keep viral replication under control and disease progression at bay, though a small subset of HIV-infected subjects can control HIV infection without ART through immunological mechanisms. In this study, we sought to identify subpopulations of NK cells that may be involved in the natural immunological control of HIV infection. We used mass cytometry to measure surface marker expression on peripheral NK cells. Using two distinct semisupervised machine learning approaches, we identified a CD11b⁺ CD57^- CD161⁺ Siglec-7⁺ subpopulation of CD56^dim CD16⁺ NK cells that differentiates HIV controllers from noncontrollers. These cells can be sorted out for future functional studies to assess their potential role in the immunological control of HIV infection.