HLA Class II (DR, DQ, DP) Genes Were Separately Associated With the Progression From Seroconversion to Onset of Type 1 Diabetes Among Participants in Two Diabetes Prevention Trials (DPT-1 and TN07).

OBJECTIVE: To explore associations of HLA class II genes (HLAII) with the progression of islet autoimmunity from asymptomatic to symptomatic type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: Next-generation targeted sequencing was used to genotype eight HLAII genes (DQA1, DQB1, DRB1, DRB3, DRB4, DRB5, DPA1, DPB1) in 1,216 participants from the Diabetes Prevention Trial-1 and Randomized Diabetes Prevention Trial with Oral Insulin sponsored by TrialNet. By the linkage disequilibrium, DQA1 and DQB1 are haplotyped to form DQ haplotypes; DP and DR haplotypes are similarly constructed. Together with available clinical covariables, we applied the Cox regression model to assess HLAII immunogenic associations with the disease progression. RESULTS: First, the current investigation updated the previously reported genetic associations of DQA1*03:01-DQB1*03:02 (hazard ratio [HR] = 1.25, P = 3.50*10-3) and DQA1*03:03-DQB1*03:01 (HR = 0.56, P = 1.16*10-3), and also uncovered a risk association with DQA1*05:01-DQB1*02:01 (HR = 1.19, P = 0.041). Second, after adjusting for DQ, DPA1*02:01-DPB1*11:01 and DPA1*01:03-DPB1*03:01 were found to have opposite associations with progression (HR = 1.98 and 0.70, P = 0.021 and 6.16*10-3, respectively). Third, DRB1*03:01-DRB3*01:01 and DRB1*03:01-DRB3*02:02, sharing the DRB1*03:01, had opposite associations (HR = 0.73 and 1.44, P = 0.04 and 0.019, respectively), indicating a role of DRB3. Meanwhile, DRB1*12:01-DRB3*02:02 and DRB1*01:03 alone were found to associate with progression (HR = 2.6 and 2.32, P = 0.018 and 0.039, respectively). Fourth, through enumerating all heterodimers, it was found that both DQ and DP could exhibit associations with disease progression. CONCLUSIONS: These results suggest that HLAII polymorphisms influence progression from islet autoimmunity to T1D among at-risk subjects with islet autoantibodies.

Conformational Morphing by a DNA Analogue Featuring 7-Deazapurines and 5-Halogenpyrimidines and the Origins of Adenine-Tract Geometry.

Several efforts are currently directed at the creation and cellular implementation of alternative genetic systems composed of pairing components that are orthogonal to the natural dA/dT and dG/dC base pairs. In an alternative approach, Watson-Crick-type pairing is conserved, but one or all of the four letters of the A, C, G, and T alphabet are substituted by modified components. Thus, all four nucleobases were altered to create halogenated deazanucleic acid (DZA): dA was replaced by 7-deaza-2'-deoxyadenosine (dzA), dG by 7-deaza-2'-deoxyguanosine (dzG), dC by 5-fluoro-2'-deoxycytidine (FdC), and dT by 5-chloro-2'-deoxyuridine (CldU). This base-pairing system was previously shown to retain function in Escherichia coli. Here, we analyze the stability, hydration, structure, and dynamics of a DZA Dickerson-Drew Dodecamer (DDD) of sequence 5'-FdC-dzG-FdC-dzG-dzA-dzA-CldU-CldU-FdC-dzG-FdC-dzG-3'. Contrary to similar stabilities of DDD and DZA-DDD, osmotic stressing revealed a dramatic loss of hydration for the DZA-DDD relative to that for the DDD. The parent DDD 5'-d(CGCGAATTCGCG)-3' features an A-tract, a run of adenosines uninterrupted by a TpA step, and exhibits a hallmark narrow minor groove. Crystal structures─in the presence of RNase H─and MD simulations show increased conformational plasticity ("morphing") of DZA-DDD relative to that of the DDD. The narrow dzA-tract minor groove in one structure widens to resemble that in canonical B-DNA in a second structure. These changes reflect an indirect consequence of altered DZA major groove electrostatics (less negatively polarized compared to that in DNA) and hydration (reduced compared to that in DNA). Therefore, chemical modifications outside the minor groove that lead to collapse of major groove electrostatics and hydration can modulate A-tract geometry.

Using Haplotype-Based Artificial Intelligence to Evaluate SARS-CoV-2 Novel Variants and Mutations.

Importance: Earlier detection of emerging novel SARS-COV-2 variants is important for public health surveillance of potential viral threats and for earlier prevention research. Artificial intelligence may facilitate early detection of SARS-CoV2 emerging novel variants based on variant-specific mutation haplotypes and, in turn, be associated with enhanced implementation of risk-stratified public health prevention strategies. Objective: To develop a haplotype-based artificial intelligence (HAI) model for identifying novel variants, including mixture variants (MVs) of known variants and new variants with novel mutations. Design, Setting, and Participants: This cross-sectional study used serially observed viral genomic sequences globally (prior to March 14, 2022) to train and validate the HAI model and used it to identify variants arising from a prospective set of viruses from March 15 to May 18, 2022. Main Outcomes and Measures: Viral sequences, collection dates, and locations were subjected to statistical learning analysis to estimate variant-specific core mutations and haplotype frequencies, which were then used to construct an HAI model to identify novel variants. Results: Through training on more than 5 million viral sequences, an HAI model was built, and its identification performance was validated on an independent validation set of more than 5 million viruses. Its identification performance was assessed on a prospective set of 344 901 viruses. In addition to achieving an accuracy of 92.8% (95% CI within 0.1%), the HAI model identified 4 Omicron MVs (Omicron-Alpha, Omicron-Delta, Omicron-Epsilon, and Omicron-Zeta), 2 Delta MVs (Delta-Kappa and Delta-Zeta), and 1 Alpha-Epsilon MV, among which Omicron-Epsilon MVs were most frequent (609/657 MVs [92.7%]). Furthermore, the HAI model found that 1699 Omicron viruses had unidentifiable variants given that these variants acquired novel mutations. Lastly, 524 variant-unassigned and variant-unidentifiable viruses carried 16 novel mutations, 8 of which were increasing in prevalence percentages as of May 2022. Conclusions and Relevance: In this cross-sectional study, an HAI model found SARS-COV-2 viruses with MV or novel mutations in the global population, which may require closer examination and monitoring. These results suggest that HAI may complement phylogenic variant assignment, providing additional insights into emerging novel variants in the population.

Rapidly identifying new coronavirus mutations of potential concern in the Omicron variant using an unsupervised learning strategy.

Extensive mutations in the Omicron spike protein appear to accelerate the transmission of SARS-CoV-2, and rapid infections increase the odds that additional mutants will emerge. To build an investigative framework, we have applied an unsupervised machine learning approach to 4296 Omicron viral genomes collected and deposited to GISAID as of December 14, 2021, and have identified a core haplotype of 28 polymutants (A67V, T95I, G339D, R346K, S371L, S373P, S375F, K417N, N440K, G446S, S477N, T478K, E484A, Q493R, G496S, Q498R, N501Y, Y505H, T547K, D614G, H655Y, N679K, P681H, N764K, K796Y, N856K, Q954H, N69K, L981F) in the spike protein and a separate core haplotype of 17 polymutants in non-spike genes: (K38, A1892) in nsp3, T492 in nsp4, (P132, V247, T280, S284) in 3C-like proteinase, I189 in nsp6, P323 in RNA-dependent RNA polymerase, I42 in Exonuclease, T9 in envelope protein, (D3, Q19, A63) in membrane glycoprotein, and (P13, R203, G204) in nucleocapsid phosphoprotein. Using these core haplotypes as reference, we have identified four newly emerging polymutants (R346, A701, I1081, N1192) in the spike protein (p value = 9.37*10-4, 1.0*10-15, 4.76*10-7 and 1.56*10-4, respectively), and five additional polymutants in non-spike genes (D343G in nucleocapsid phosphoprotein, V1069I in nsp3, V94A in nsp4, F694Y in the RNA-dependent RNA polymerase and L106L/F of ORF3a) that exhibit significant increasing trajectories (all p values < 1.0*10-15). In the absence of relevant clinical data for these newly emerging mutations, it is important to monitor them closely. Two emerging mutations may be of particular concern: the N1192S mutation in spike protein locates in an extremely highly conserved region of all human coronaviruses that is integral to the viral fusion process, and the F694Y mutation in the RNA polymerase may induce conformational changes that could impact remdesivir binding.

Application of Statistical Learning to Identify Omicron Mutations in SARS-CoV-2 Viral Genome Sequence Data From Populations in Africa and the United States.

Importance: With timely collection of SARS-CoV-2 viral genome sequences, it is important to apply efficient data analytics to detect emerging variants at the earliest time. Objective: To evaluate the application of a statistical learning strategy (SLS) to improve early detection of novel SARS-CoV-2 variants using viral sequence data from global surveillance. Design, Setting, and Participants: This case series applied an SLS to viral genomic sequence data collected from 63 686 individuals in Africa and 531 827 individuals in the United States with SARS-CoV-2. Data were collected from January 1, 2020, to December 28, 2021. Main Outcomes and Measures: The outcome was an indicator of Omicron variant derived from viral sequences. Centering on a temporally collected outcome, the SLS used the generalized additive model to estimate locally averaged Omicron caseload percentages (OCPs) over time to characterize Omicron expansion and to estimate when OCP exceeded 10%, 25%, 50%, and 75% of the caseload. Additionally, an unsupervised learning technique was applied to visualize Omicron expansions, and temporal and spatial distributions of Omicron cases were investigated. Results: In total, there were 2698 cases of Omicron in Africa and 12 141 in the United States. The SLS found that Omicron was detectable in South Africa as early as December 31, 2020. With 10% OCP as a threshold, it may have been possible to declare Omicron a variant of concern as early as November 4, 2021, in South Africa. In the United States, the application of SLS suggested that the first case was detectable on November 21, 2021. Conclusions and Relevance: The application of SLS demonstrates how the Omicron variant may have emerged and expanded in Africa and the United States. Earlier detection could help the global effort in disease prevention and control. To optimize early detection, efficient data analytics, such as SLS, could assist in the rapid identification of new variants as soon as they emerge, with or without lineages designated, using viral sequence data from global surveillance.

Cryo neutron crystallography demonstrates influence of RNA 2'-OH orientation on conformation, sugar pucker and water structure.

The ribose 2'-hydroxyl is the key chemical difference between RNA and DNA and primary source of their divergent structural and functional characteristics. Macromolecular X-ray diffraction experiments typically do not reveal the positions of hydrogen atoms. Thus, standard crystallography cannot determine 2'-OH orientation (H2'-C2'-O2'-HO2' torsion angle) and its potential roles in sculpting the RNA backbone and the expansive fold space. Here, we report the first neutron crystal structure of an RNA, the Escherichia coli rRNA Sarcin-Ricin Loop (SRL). 2'-OD orientations were established for all 27 residues and revealed O-D bonds pointing toward backbone (O3', 13 observations), nucleobase (11) or sugar (3). Most riboses in the SRL stem region show a 2'-OD backbone-orientation. GAGA-tetraloop riboses display a 2'-OD base-orientation. An atypical C2'-endo sugar pucker is strictly correlated with a 2'-OD sugar-orientation. Neutrons reveal the strong preference of the 2'-OH to donate in H-bonds and that 2'-OH orientation affects both backbone geometry and ribose pucker. We discuss 2'-OH and water molecule orientations in the SRL neutron structure and compare with results from a solution phase 10 µs MD simulation. We demonstrate that joint cryo-neutron/X-ray crystallography offers an all-in-one approach to determine the complete structural properties of RNA, i.e. geometry, conformation, protonation state and hydration structure.

Mutations in viral nucleocapsid protein and endoRNase are discovered to associate with COVID19 hospitalization risk.

SARS-CoV-2 is spreading worldwide with continuously evolving variants, some of which occur in the Spike protein and appear to increase viral transmissibility. However, variants that cause severe COVID-19 or lead to other breakthroughs have not been well characterized. To discover such viral variants, we assembled a cohort of 683 COVID-19 patients; 388 inpatients ("cases") and 295 outpatients ("controls") from April to August 2020 using electronically captured COVID test request forms and sequenced their viral genomes. To improve the analytical power, we accessed 7137 viral sequences in Washington State to filter out viral single nucleotide variants (SNVs) that did not have significant expansions over the collection period. Applying this filter led to the identification of 53 SNVs that were statistically significant, of which 13 SNVs each had 3 or more variant copies in the discovery cohort. Correlating these selected SNVs with case/control status, eight SNVs were found to significantly associate with inpatient status (q-values < 0.01). Using temporal synchrony, we identified a four SNV-haplotype (t19839-g28881-g28882-g28883) that was significantly associated with case/control status (Fisher's exact p = 2.84 × 10-11). This haplotype appeared in April 2020, peaked in June, and persisted into January 2021. The association was replicated (OR = 5.46, p-value = 4.71 × 10-12) in an independent cohort of 964 COVID-19 patients (June 1, 2020 to March 31, 2021). The haplotype included a synonymous change N73N in endoRNase, and three non-synonymous changes coding residues R203K, R203S and G204R in the nucleocapsid protein. This discovery points to the potential functional role of the nucleocapsid protein in triggering "cytokine storms" and severe COVID-19 that led to hospitalization. The study further emphasizes a need for tracking and analyzing viral sequences in correlations with clinical status.

Tracking SARS-CoV-2 Spike Protein Mutations in the United States (2020/01 - 2021/03) Using a Statistical Learning Strategy.

The emergence and establishment of SARS-CoV-2 variants of interest (VOI) and variants of concern (VOC) highlight the importance of genomic surveillance. We propose a statistical learning strategy (SLS) for identifying and spatiotemporally tracking potentially relevant Spike protein mutations. We analyzed 167,893 Spike protein sequences from US COVID-19 cases (excluding 21,391 sequences from VOI/VOC strains) deposited at GISAID from January 19, 2020 to March 15, 2021. Alignment against the reference Spike protein sequence led to the identification of viral residue variants (VRVs), i.e., residues harboring a substitution compared to the reference strain. Next, generalized additive models were applied to model VRV temporal dynamics, to identify VRVs with significant and substantial dynamics (false discovery rate q-value <0.01; maximum VRV proportion > 10% on at least one day). Unsupervised learning was then applied to hierarchically organize VRVs by spatiotemporal patterns and identify VRV-haplotypes. Finally, homology modelling was performed to gain insight into potential impact of VRVs on Spike protein structure. We identified 90 VRVs, 71 of which have not previously been observed in a VOI/VOC, and 35 of which have emerged recently and are durably present. Our analysis identifies 17 VRVs ∼91 days earlier than their first corresponding VOI/VOC publication. Unsupervised learning revealed eight VRV-haplotypes of 4 VRVs or more, suggesting two emerging strains (B1.1.222 and B.1.234). Structural modeling supported potential functional impact of the D1118H and L452R mutations. The SLS approach equally monitors all Spike residues over time, independently of existing phylogenic classifications, and is complementary to existing genomic surveillance methods.

The KAG motif of HLA-DRB1 (ß71, ß74, ß86) predicts seroconversion and development of type 1 diabetes.

BACKGROUND: HLA-DR4, a common antigen of HLA-DRB1, has multiple subtypes that are strongly associated with risk of type 1 diabetes (T1D); however, some are risk neutral or resistant. The pathobiological mechanism of HLA-DR4 subtypes remains to be elucidated. METHODS: We used a population-based case-control study of T1D (962 patients and 636 controls) to decipher genetic associations of HLA-DR4 subtypes and specific residues with susceptibility to T1D. Using a birth cohort of 7865 children with periodically measured islet autoantibodies (GADA, IAA or IA-2A), we proposed to validate discovered genetic associations with a totally different study design and time-to-seroconversions prior to clinical onset of T1D. A novel analytic strategy hierarchically organized the HLA-DRB1 alleles by sequence similarity and identified critical amino acid residues by minimizing local genomic architecture and higher-order interactions. FINDINGS: Three amino acid residues of HLA-DRB1 (ß71, ß74, ß86) were found to be predictive of T1D risk in the population-based study. The "KAG" motif, corresponding to HLA-DRB1×04:01, was most strongly associated with T1D risk ([O]dds [R]atio=3.64, p = 3.19 × 10-64). Three less frequent motifs ("EAV", OR = 2.55, p = 0.025; "RAG", OR = 1.93, p = 0.043; and "RAV", OR = 1.56, p = 0.003) were associated with T1D risk, while two motifs ("REG" and "REV") were equally protective (OR = 0.11, p = 4.23 × 10-4). In an independent birth cohort of HLA-DR3 and HLA-DR4 subjects, those having the "KAG" motif had increased risk for time-to-seroconversion (Hazard Ratio = 1.74, p = 6.51 × 10-14) after adjusting potential confounders. INTERPRETATIONS: DNA sequence variation in HLA-DRB1 at positions ß71, ß74, and ß86 are non-conservative (ß74 A→E, ß71 E vs K vs R and ß86 G vs V). They result in substantial differences in peptide antigen anchor pocket preferences at p1, p4 and potentially neighboring regions such as pocket p7. Differential peptide antigen binding is likely to be affected. These sequence substitutions may account for most of the HLA-DR4 contribution to T1D risk as illustrated in two HLA-peptide model complexes of the T1D autoantigens preproinsulin and GAD65. FUNDING: National Institute of Diabetes and Digestive and Kidney Diseases and the Swedish Child Diabetes Foundation and the Swedish Research Council.

Tracking SARS-CoV-2 Spike Protein Mutations in the United States (January 2020-March 2021) Using a Statistical Learning Strategy.

The emergence and establishment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of interest (VOIs) and variants of concern (VOCs) highlight the importance of genomic surveillance. We propose a statistical learning strategy (SLS) for identifying and spatiotemporally tracking potentially relevant Spike protein mutations. We analyzed 167,893 Spike protein sequences from coronavirus disease 2019 (COVID-19) cases in the United States (excluding 21,391 sequences from VOI/VOC strains) deposited at GISAID from 19 January 2020 to 15 March 2021. Alignment against the reference Spike protein sequence led to the identification of viral residue variants (VRVs), i.e., residues harboring a substitution compared to the reference strain. Next, generalized additive models were applied to model VRV temporal dynamics and to identify VRVs with significant and substantial dynamics (false discovery rate q-value < 0.01; maximum VRV proportion >10% on at least one day). Unsupervised learning was then applied to hierarchically organize VRVs by spatiotemporal patterns and identify VRV-haplotypes. Finally, homology modeling was performed to gain insight into the potential impact of VRVs on Spike protein structure. We identified 90 VRVs, 71 of which had not previously been observed in a VOI/VOC, and 35 of which have emerged recently and are durably present. Our analysis identified 17 VRVs ~91 days earlier than their first corresponding VOI/VOC publication. Unsupervised learning revealed eight VRV-haplotypes of four VRVs or more, suggesting two emerging strains (B1.1.222 and B.1.234). Structural modeling supported a potential functional impact of the D1118H and L452R mutations. The SLS approach equally monitors all Spike residues over time, independently of existing phylogenic classifications, and is complementary to existing genomic surveillance methods.

Incorporating a Thiophosphate Modification into a Common RNA Tetraloop Motif Causes an Unanticipated Stability Boost.

GNRA (N = A, C, G, or U; R = A or G) tetraloops are common RNA secondary structural motifs and feature a phosphate stacked atop a nucleobase. The rRNA sarcin/ricin loop (SRL) is capped by GApGA, and the phosphate p stacks on G. We recently found that regiospecific incorporation of a single dithiophosphate (PS2) but not a monothiophosphate (PSO) instead of phosphate in the backbone of RNA aptamers dramatically increases the binding affinity for their targets. In the RNA:thrombin complex, the key contribution to the 1000-fold tighter binding stems from an edge-on contact between PS2 and a phenylalanine ring. Here we investigated the consequences of replacing the SRL phosphate engaged in a face-on interaction with guanine with either PS2 or PSO for stability. We found that PS2···G and Rp-PSO···G contacts stabilize modified SRLs compared to the parent loop to unexpected levels: up to 6.3 °C in melting temperature Tm and -4.7 kcal/mol in ΔΔG°. Crystal structures demonstrate that the vertical distance to guanine for the closest sulfur is just 0.05 Å longer on average compared to that of oxygen despite the larger van der Waals radius of the former (1.80 Å for S vs 1.52 Å for O). The higher stability is enthalpy-based, and the negative charge as assessed by a neutral methylphosphonate modification plays only a minor role. Quantum mechanical/molecular mechanical calculations are supportive of favorable dispersion attraction interactions by sulfur making the dominant contribution. A stacking interaction between phosphate and guanine (SRL) or uracil (U-turn) is also found in newly classified RNA tetraloop families besides GNRA.

Enhanced Dispersion and Polarization Interactions Achieved through Dithiophosphate Group Incorporation Yield a Dramatic Binding Affinity Increase for an RNA Aptamer-Thrombin Complex.

Regiospecific replacement of a single phosphate (PO2) by a dithiophosphate (PS2) group in an RNA can dramatically increase its binding affinity for a target protein. Thus, complexes between antithrombin and anti-VEGF RNA aptamers with single dithiophosphate moieties and thrombin and VEGF, respectively, display equilibrium dissociation constants KD of ca. 1 pM, 1000-fold tighter than the native RNA complexes (ca. 1 nM). Inspection of crystal structures of the native and PS2-RNA aptamer:thrombin complexes reveals an RNA-induced fit in the latter. This leads to a close approach between PS2 and the phenyl ring edge of Phe-232 that is surrounded by pairs of lysines and arginines. To better understand the origins of the tighter binding and individual contributions to the interaction energy, we carried out QM calculations with phosphate- and dithiophosphate-benzene and dimethyl phosphate- and dimethyl dithiophosphate-benzene model systems. These calculations demonstrate that the dithiophosphate-benzene interaction is much stronger than the corresponding interaction with phosphate. QM/MM calculations with the full complexes confirmed this finding and support the hypothesis that the electric field generated by basic residues surrounding Phe-232 is key to the polarization of the PS2 moiety. Thus, disparate polarization and dispersion energies between the PO2 and PS2 complexes contribute critically to the difference in binding affinity. By comparison, easier desolvation of the dithiophosphate group compared to phosphate does not contribute decisively to the observed difference in binding affinity. Favorable polarization and dispersion energies may be a general feature of the dramatic affinity gains seen for complexes between RNAs carrying dithiophosphate groups and their binding proteins.

Molecular Dynamics Simulation of Cyclooxygenase-2 Complexes with Indomethacin closo-Carborane Analogs.

Molecular dynamics simulation of carborane-containing ligands in complex with target enzymes is a challenging task due to the unique structure and properties of the carborane substituents and relative lack of appropriate experimental data to help assess the quality of carborane force field parameters. Here, we report results from energy minimization calculations for a series of carborane-amino acid complexes using carborane force field parameters published previously in the literature and adapted for use with the AMBER ff99SB and ff14SB potential functions. These molecular mechanics results agree well with quantum mechanical geometry optimization calculations obtained using dispersion-corrected density functional theory methods, suggesting that the carborane force field parameters should be suitable for more detailed calculations. We then performed molecular dynamics simulations for the 1,2-, 1,7-, and 1,12-dicarba- closo-dodecaborane(12) derivatives of indomethacin methyl ester bound with cyclooxygenase-2. The simulation results suggest that only the ortho-carborane derivative forms a stable complex, in agreement with experimental findings, and provide insight into the possible molecular basis for isomer binding selectivity.

Molecular Modeling of the Interactions between Carborane-Containing Analogs of Indomethacin and Cyclooxygenase-2.

Molecular modeling studies were performed in order to gain insight into the binding mode and interaction of carborane-containing derivatives of indomethacin methyl ester with the cyclooxygenase-2 (COX-2) isoform, and to assess the predictive capability of the computational tools available for studying carboranes, a unique class of pharmacophores. Docking simulations were able to identify the correct binding mode and reproduced the experimental binding affinity trends with encouraging quality. Nevertheless, the docking results needed to be verified through extensive and resource-intensive quantum chemical calculations, and the interpretation of the theoretical results would not have been straightforward without the supporting experimental data. The inclusion of full receptor and ligand flexibility into the molecular modeling of carborane-containing drug molecules may yield more definitive results, but is currently hindered by the lack of appropriate carborane force field parameters.

A Streptavidin Binding Site Mutation Yields an Unexpected Result: An Ionized Asp128 Residue Is Not Essential for Strong Biotin Binding.

We report a detailed study of a point mutation of the crucial binding site residue, D128, in the biotin-streptavidin complex. The conservative substitution, D128N, preserves the detailed structure observed for the wild-type complex but has an only minimal impact on biotin binding, even though previous experimental and computational studies suggested that a charged D128 residue was crucial for high-affinity binding. These results show clearly that the fundamental basis for streptavidin's extremely strong biotin binding affinity is more complex than assumed and illustrate some of the challenges that may arise when analyzing extremely strong ligand-protein binding interactions.

Evoking picomolar binding in RNA by a single phosphorodithioate linkage.

RNA aptamers are synthetic oligonucleotide-based affinity molecules that utilize unique three-dimensional structures for their affinity and specificity to a target such as a protein. They hold the promise of numerous advantages over biologically produced antibodies; however, the binding affinity and specificity of RNA aptamers are often insufficient for successful implementation in diagnostic assays or as therapeutic agents. Strong binding affinity is important to improve the downstream applications. We report here the use of the phosphorodithioate (PS2) substitution on a single nucleotide of RNA aptamers to dramatically improve target binding affinity by ∼1000-fold (from nanomolar to picomolar). An X-ray co-crystal structure of the α-thrombin:PS2-aptamer complex reveals a localized induced-fit rearrangement of the PS2-containing nucleotide which leads to enhanced target interaction. High-level quantum mechanical calculations for model systems that mimic the PS2 moiety and phenylalanine demonstrate that an edge-on interaction between sulfur and the aromatic ring is quite favorable, and also confirm that the sulfur analogs are much more polarizable than the corresponding phosphates. This favorable interaction involving the sulfur atom is likely even more significant in the full aptamer-protein complexes than in the model systems.

Conservative Secondary Shell Substitution In Cyclooxygenase-2 Reduces Inhibition by Indomethacin Amides and Esters via Altered Enzyme Dynamics.

The cyclooxygenase enzymes (COX-1 and COX-2) are the therapeutic targets of nonsteroidal anti-inflammatory drugs (NSAIDs). Neutralization of the carboxylic acid moiety of the NSAID indomethacin to an ester or amide functionality confers COX-2 selectivity, but the molecular basis for this selectivity has not been completely revealed through mutagenesis studies and/or X-ray crystallographic attempts. We expressed and assayed a number of divergent secondary shell COX-2 active site mutants and found that a COX-2 to COX-1 change at position 472 (Leu in COX-2, Met in COX-1) reduced the potency of enzyme inhibition by a series of COX-2-selective indomethacin amides and esters. In contrast, the potencies of indomethacin, arylacetic acid, propionic acid, and COX-2-selective diarylheterocycle inhibitors were either unaffected or only mildly affected by this mutation. Molecular dynamics simulations revealed identical equilibrium enzyme structures around residue 472; however, calculations indicated that the L472M mutation impacted local low-frequency dynamical COX constriction site motions by stabilizing the active site entrance and slowing constriction site dynamics. Kinetic analysis of inhibitor binding is consistent with the computational findings.

Characterization of the Lipid-Binding Site of Equinatoxin II by NMR and Molecular Dynamics Simulation.

Equinatoxin II (EqtII) is a soluble, 20 kDa pore-forming protein toxin isolated from the sea anemone Actinia equina. Although pore formation has long been known to occur in distinct stages, including monomeric attachment to phospholipid membranes followed by detachment of the N-terminal helical domain and oligomerization into the final pore assembly, atomistic-level detail of the protein-lipid interactions underlying these events remains elusive. Using high-resolution solution state NMR of uniformly-(15)N-labeled EqtII at the critical micelle concentration of dodecylphosphocholine, we have mapped the lipid-binding site through chemical shift perturbations. Subsequent docking of an EqtII monomer onto a dodecylphosphocholine micelle, followed by 400 ns of all-atom molecular dynamics simulation, saw several high-occupancy lipid-binding pockets stabilized by cation-π, hydrogen bonding, and hydrophobic interactions; and stabilization of the loop housing the conserved arginine-glycine-aspartate motif. Additional simulation of EqtII with an N-acetyl sphingomyelin micelle, for which high-resolution NMR data cannot be obtained due to aggregate formation, revealed that sphingomyelin specificity might occur via hydrogen bonding to the 3-OH and 2-NH groups unique to the ceramide backbone by side chains of D109 and Y113; and main chains of P81 and W112. Furthermore, a binding pocket formed by K30, K77, and P81, proximate to the hinge region of the N-terminal helix, was identified and may be implicated in triggering pore formation.

Distance geometry protocol to generate conformations of natural products to structurally interpret ion mobility-mass spectrometry collision cross sections.

Ion mobility-mass spectrometry (IM-MS) allows the separation of ionized molecules based on their charge-to-surface area (IM) and mass-to-charge ratio (MS), respectively. The IM drift time data that is obtained is used to calculate the ion-neutral collision cross section (CCS) of the ionized molecule with the neutral drift gas, which is directly related to the ion conformation and hence molecular size and shape. Studying the conformational landscape of these ionized molecules computationally provides interpretation to delineate the potential structures that these CCS values could represent, or conversely, structural motifs not consistent with the IM data. A challenge in the IM-MS community is the ability to rapidly compute conformations to interpret natural product data, a class of molecules exhibiting a broad range of biological activity. The diversity of biological activity is, in part, related to the unique structural characteristics often observed for natural products. Contemporary approaches to structurally interpret IM-MS data for peptides and proteins typically utilize molecular dynamics (MD) simulations to sample conformational space. However, MD calculations are computationally expensive, they require a force field that accurately describes the molecule of interest, and there is no simple metric that indicates when sufficient conformational sampling has been achieved. Distance geometry is a computationally inexpensive approach that creates conformations based on sampling different pairwise distances between the atoms within the molecule and therefore does not require a force field. Progressively larger distance bounds can be used in distance geometry calculations, providing in principle a strategy to assess when all plausible conformations have been sampled. Our results suggest that distance geometry is a computationally efficient and potentially superior strategy for conformational analysis of natural products to interpret gas-phase CCS data.