Biological function derived from predicted structures in CASP11.

In CASP11, the organizers sought to bring the biological inferences from predicted structures to the fore. To accomplish this, we assessed the models for their ability to perform quantifiable tasks related to biological function. First, for 10 targets that were probable homodimers, we measured the accuracy of docking the models into homodimers as a function of GDT-TS of the monomers, which produced characteristic L-shaped plots. At low GDT-TS, none of the models could be docked correctly as homodimers. Above GDT-TS of ∼60%, some models formed correct homodimers in one of the largest docked clusters, while many other models at the same values of GDT-TS did not. Docking was more successful when many of the templates shared the same homodimer. Second, we docked a ligand from an experimental structure into each of the models of one of the targets. Docking to the models with two different programs produced poor ligand RMSDs with the experimental structure. Measures that evaluated similarity of contacts were reasonable for some of the models, although there was not a significant correlation with model accuracy. Finally, we assessed whether models would be useful in predicting the phenotypes of missense mutations in three human targets by comparing features calculated from the models with those calculated from the experimental structures. The models were successful in reproducing accessible surface areas but there was little correlation of model accuracy with calculation of FoldX evaluation of the change in free energy between the wild-type and the mutant. Proteins 2016; 84(Suppl 1):370-391. © 2016 Wiley Periodicals, Inc.

High-throughput mutagenesis reveals functional determinants for DNA targeting by activation-induced deaminase.

Antibody maturation is a critical immune process governed by the enzyme activation-induced deaminase (AID), a member of the AID/APOBEC DNA deaminase family. AID/APOBEC deaminases preferentially target cytosine within distinct preferred sequence motifs in DNA, with specificity largely conferred by a small 9-11 residue protein loop that differs among family members. Here, we aimed to determine the key functional characteristics of this protein loop in AID and to thereby inform our understanding of the mode of DNA engagement. To this end, we developed a methodology (Sat-Sel-Seq) that couples saturation mutagenesis at each position across the targeting loop, with iterative functional selection and next-generation sequencing. This high-throughput mutational analysis revealed dominant characteristics for residues within the loop and additionally yielded enzymatic variants that enhance deaminase activity. To rationalize these functional requirements, we performed molecular dynamics simulations that suggest that AID and its hyperactive variants can engage DNA in multiple specific modes. These findings align with AID's competing requirements for specificity and flexibility to efficiently drive antibody maturation. Beyond insights into the AID-DNA interface, our Sat-Sel-Seq approach also serves to further expand the repertoire of techniques for deep positional scanning and may find general utility for high-throughput analysis of protein function.

A collaborative approach to improving representation in viral genomic surveillance.

The lack of routine viral genomic surveillance delayed the initial detection of SARS-CoV-2, allowing the virus to spread unfettered at the outset of the U.S. epidemic. Over subsequent months, poor surveillance enabled variants to emerge unnoticed. Against this backdrop, long-standing social and racial inequities have contributed to a greater burden of cases and deaths among minority groups. To begin to address these problems, we developed a new variant surveillance model geared toward building 'next generation' genome sequencing capacity at universities in or near rural areas and engaging the participation of their local communities. The resulting genomic surveillance network has generated more than 1,000 SARS-CoV-2 genomes to date, including the first confirmed case in northeast Louisiana of Omicron, and the first and sixth confirmed cases in Georgia of the emergent BA.2.75 and BQ.1.1 variants, respectively. In agreement with other studies, significantly higher viral gene copy numbers were observed in Delta variant samples compared to those from Omicron BA.1 variant infections, and lower copy numbers were seen in asymptomatic infections relative to symptomatic ones. Collectively, the results and outcomes from our collaborative work demonstrate that establishing genomic surveillance capacity at smaller academic institutions in rural areas and fostering relationships between academic teams and local health clinics represent a robust pathway to improve pandemic readiness.

A collaborative approach to improve representation in viral genomic surveillance.

The lack of routine viral genomic surveillance delayed the initial detection of SARS-CoV-2, allowing the virus to spread unfettered at the outset of the U.S. epidemic. Over subsequent months, poor surveillance enabled variants to emerge unnoticed. Against this backdrop, long-standing social and racial inequities have contributed to a greater burden of cases and deaths among minority groups. To begin to address these problems, we developed a new variant surveillance model geared toward building microbial genome sequencing capacity at universities in or near rural areas and engaging the participation of their local communities. The resulting genomic surveillance network has generated more than 1,000 SARS-CoV-2 genomes to date, including the first confirmed case in northeast Louisiana of Omicron, and the first and sixth confirmed cases in Georgia of the emergent BA.2.75 and BQ.1.1 variants, respectively. In agreement with other studies, significantly higher viral gene copy numbers were observed in Delta variant samples compared to those from Omicron BA.1 variant infections, and lower copy numbers were seen in asymptomatic infections relative to symptomatic ones. Collectively, the results and outcomes from our collaborative work demonstrate that establishing genomic surveillance capacity at smaller academic institutions in rural areas and fostering relationships between academic teams and local health clinics represent a robust pathway to improve pandemic readiness. Author summary: Genomic surveillance involves decoding a pathogen’s genetic code to track its spread and evolution. During the pandemic, genomic surveillance programs around the world provided valuable data to scientists, doctors, and public health officials. Knowing the complete SARS-CoV-2 genome has helped detect the emergence of new variants, including ones that are more transmissible or cause more severe disease, and has supported the development of diagnostics, vaccines, and therapeutics. The impact of genomic surveillance on public health depends on representative sampling that accurately reflects the diversity and distribution of populations, as well as rapid turnaround time from sampling to data sharing. After a slow start, SARS-CoV-2 genomic surveillance in the United States grew exponentially. Despite this, many rural regions and ethnic minorities remain poorly represented, leaving significant gaps in the data that informs public health responses. To address this problem, we formed a network of universities and clinics in Louisiana, Georgia, and Mississippi with the goal of increasing SARS-CoV-2 sequencing volume, representation, and equity. Our results demonstrate the advantages of rapidly sequencing pathogens in the same communities where the cases occur and present a model that leverages existing academic and clinical infrastructure for a powerful decentralized genomic surveillance system.

Functional analysis of rare variants in mismatch repair proteins augments results from computation-based predictive methods.

The cancer-predisposing Lynch Syndrome (LS) arises from germline mutations in DNA mismatch repair (MMR) genes, predominantly MLH1, MSH2, MSH6, and PMS2. A major challenge for clinical diagnosis of LS is the frequent identification of variants of uncertain significance (VUS) in these genes, as it is often difficult to determine variant pathogenicity, particularly for missense variants. Generic programs such as SIFT and PolyPhen-2, and MMR gene-specific programs such as PON-MMR and MAPP-MMR, are often used to predict deleterious or neutral effects of VUS in MMR genes. We evaluated the performance of multiple predictive programs in the context of functional biologic data for 15 VUS in MLH1, MSH2, and PMS2. Using cell line models, we characterized VUS predicted to range from neutral to pathogenic on mRNA and protein expression, basal cellular viability, viability following treatment with a panel of DNA-damaging agents, and functionality in DNA damage response (DDR) signaling, benchmarking to wild-type MMR proteins. Our results suggest that the MMR gene-specific classifiers do not always align with the experimental phenotypes related to DDR. Our study highlights the importance of complementary experimental and computational assessment to develop future predictors for the assessment of VUS.

ALK mutations confer differential oncogenic activation and sensitivity to ALK inhibition therapy in neuroblastoma.

Genetic studies have established anaplastic lymphoma kinase (ALK), a cell surface receptor tyrosine kinase, as a tractable molecular target in neuroblastoma. We describe comprehensive genomic, biochemical, and computational analyses of ALK mutations across 1,596 diagnostic neuroblastoma samples. ALK tyrosine kinase domain mutations occurred in 8% of samples--at three hot spots and 13 minor sites--and correlated significantly with poorer survival in high- and intermediate-risk neuroblastoma. Biochemical and computational studies distinguished oncogenic (constitutively activating) from nononcogenic mutations and allowed robust computational prediction of their effects. The mutated variants also showed differential in vitro crizotinib sensitivities. Our studies identify ALK genomic status as a clinically important therapeutic stratification tool in neuroblastoma and will allow tailoring of ALK-targeted therapy to specific mutations.

Quantum and all-atom molecular dynamics simulations of protonation and divalent ion binding to phosphatidylinositol 4,5-bisphosphate (PIP2).

Molecular dynamics calculations have been used to determine the structure of phosphatidylinositol 4,5 bisphosphate (PIP2) at the quantum level and to quantify the propensity for PIP2 to bind two physiologically relevant divalent cations, Mg(2+) and Ca(2+). We performed a geometry optimization at the Hartree-Fock 6-31+G(d) level of theory in vacuum and with a polarized continuum dielectric to determine the conformation of the phospholipid headgroup in the presence of water and its partial charge distribution. The angle between the headgroup and the acyl chains is nearly perpendicular, suggesting that in the absence of other interactions the inositol ring would lie flat along the cytoplasmic surface of the plasma membrane. Next, we employed hybrid quantum mechanics/molecular mechanics (QM/MM) simulations to investigate the protonation state of PIP2 and its interactions with magnesium or calcium. We test the hypothesis suggested by prior experiments that binding of magnesium to PIP2 is mediated by a water molecule that is absent when calcium binds. These results may explain the selective ability of calcium to induce the formation of PIP2 clusters and phase separation from other lipids.