Progression to type 1 diabetes in the DPT-1 and TN07 clinical trials is critically associated with specific residues in HLA-DQA1-B1 heterodimers.

AIMS/HYPOTHESIS: The aim of this work was to explore molecular amino acids (AAs) and related structures of HLA-DQA1-DQB1 that underlie its contribution to the progression from stages 1 or 2 to stage 3 type 1 diabetes. METHODS: Using high-resolution DQA1 and DQB1 genotypes from 1216 participants in the Diabetes Prevention Trial-Type 1 and the Diabetes Prevention Trial, we applied hierarchically organised haplotype association analysis (HOH) to decipher which AAs contributed to the associations of DQ with disease and their structural properties. HOH relied on the Cox regression to quantify the association of DQ with time-to-onset of type 1 diabetes. RESULTS: By numerating all possible DQ heterodimers of α- and ß-chains, we showed that the heterodimerisation increases genetic diversity at the cellular level from 43 empirically observed haplotypes to 186 possible heterodimers. Heterodimerisation turned several neutral haplotypes (DQ2.2, DQ2.3 and DQ4.4) to risk haplotypes (DQ2.2/2.3-DQ4.4 and DQ4.4-DQ2.2). HOH uncovered eight AAs on the α-chain (-16α, -13α, -6α, α22, α23, α44, α72, α157) and six AAs on the ß-chain (-18ß, ß9, ß13, ß26, ß57, ß135) that contributed to the association of DQ with progression of type 1 diabetes. The specific AAs concerned the signal peptide (minus sign, possible linkage to expression levels), pockets 1, 4 and 9 in the antigen-binding groove of the α1ß1 domain, and the putative homodimerisation of the αß heterodimers. CONCLUSIONS/INTERPRETATION: These results unveil the contribution made by DQ to type 1 diabetes progression at individual residues and related protein structures, shedding light on its immunological mechanisms and providing new leads for developing treatment strategies. DATA AVAILABILITY: Clinical trial data and biospecimen samples are available through the National Institute of Diabetes and Digestive and Kidney Diseases Central Repository portal ( https://repository.niddk.nih.gov/studies ).

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.

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.

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.

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.

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.

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.

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.

TagDock: an efficient rigid body docking algorithm for oligomeric protein complex model construction and experiment planning.

We report here new computational tools and strategies to efficiently generate three-dimensional models for oligomeric biomolecular complexes in cases where there is limited experimental restraint data to guide the docking calculations. Our computational tools are designed to rapidly and exhaustively enumerate all geometrically possible docking poses for an oligomeric complex, rather than generate detailed, atomic-resolution models. Experimental data, such as interatomic distance measurements, are then used to select and refine docking poses that are consistent with the experimental restraints. Our computational toolkit is designed for use with sparse data sets to generate intermediate-resolution docking models, and utilizes distance difference matrix analysis to identify further restraint measurements that will provide maximum additional structural refinement. Thus, these tools can be used to help plan optimal residue positions for probe incorporation in labor-intensive biophysical experiments such as chemical cross-linking, electron paramagnetic resonance, or Förster resonance energy transfer spectroscopy studies. We present benchmark results for docking the collection of all 176 heterodimer protein complexes from the ZDOCK database, as well as a protein homodimer with recently collected experimental distance restraints, to illustrate the toolkit's capabilities and performance, and to demonstrate how distance difference matrix analysis can automatically identify and prioritize additional restraint measurements that allow us to rapidly optimize docking poses.

Structural consequences of cutting a binding loop: two circularly permuted variants of streptavidin.

Circular permutation of streptavidin was carried out in order to investigate the role of a main-chain amide in stabilizing the high-affinity complex of the protein and biotin. Mutant proteins CP49/48 and CP50/49 were constructed to place new N-termini at residues 49 and 50 in a flexible loop involved in stabilizing the biotin complex. Crystal structures of the two mutants show that half of each loop closes over the binding site, as observed in wild-type streptavidin, while the other half adopts the open conformation found in the unliganded state. The structures are consistent with kinetic and thermodynamic data and indicate that the loop plays a role in enthalpic stabilization of the bound state via the Asn49 amide-biotin hydrogen bond. In wild-type streptavidin, the entropic penalties of immobilizing a flexible portion of the protein to enhance binding are kept to a manageable level by using a contiguous loop of medium length (six residues) which is already constrained by its anchorage to strands of the ß-barrel protein. A molecular-dynamics simulation for CP50/49 shows that cleavage of the binding loop results in increased structural fluctuations for Ser45 and that these fluctuations destabilize the streptavidin-biotin complex.

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.

Second-contact shell mutation diminishes streptavidin-biotin binding affinity through transmitted effects on equilibrium dynamics.

We report a point mutation in the second contact shell of the high-affinity streptavidin-biotin complex that appears to reduce binding affinity through transmitted effects on equilibrium dynamics. The Y54F streptavidin mutation causes a 75-fold loss of binding affinity with 73-fold faster dissociation, a large loss of binding enthalpy (ΔΔH = 3.4 kcal/mol at 37 °C), and a small gain in binding entropy (TΔΔS = 0.7 kcal/mol). The removed Y54 hydroxyl is replaced by a water molecule in the bound structure, but there are no observable changes in structure in the first contact shell and no additional changes surrounding the mutation. Molecular dynamics simulations reveal a large increase in the atomic fluctuation amplitudes for W79, a key biotin contact residue, compared to the fluctuation amplitudes in the wild-type. The increased W79 atomic fluctuation amplitudes are caused by loss of water-mediated hydrogen bonds between the Y54 hydroxyl group and peptide backbone atoms in and near W79. We propose that the increased atomic fluctuation amplitudes diminish the integrity of the W79-biotin interaction and represents a loosening of the "tryptophan collar" that is critical to the slow dissociation and high affinity of streptavidin-biotin binding. These results illustrate how changes in protein dynamics distal to the ligand binding pocket can have a profound impact on ligand binding, even when equilibrium structure is unperturbed.

Determination of structural models of the complex between the cytoplasmic domain of erythrocyte band 3 and ankyrin-R repeats 13-24.

The adaptor protein ankyrin-R interacts via its membrane binding domain with the cytoplasmic domain of the anion exchange protein (AE1) and via its spectrin binding domain with the spectrin-based membrane skeleton in human erythrocytes. This set of interactions provides a bridge between the lipid bilayer and the membrane skeleton, thereby stabilizing the membrane. Crystal structures for the dimeric cytoplasmic domain of AE1 (cdb3) and for a 12-ankyrin repeat segment (repeats 13-24) from the membrane binding domain of ankyrin-R (AnkD34) have been reported. However, structural data on how these proteins assemble to form a stable complex have not been reported. In the current studies, site-directed spin labeling, in combination with electron paramagnetic resonance (EPR) and double electron-electron resonance, has been utilized to map the binding interfaces of the two proteins in the complex and to obtain inter-protein distance constraints. These data have been utilized to construct a family of structural models that are consistent with the full range of experimental data. These models indicate that an extensive area on the peripheral domain of cdb3 binds to ankyrin repeats 18-20 on the top loop surface of AnkD34 primarily through hydrophobic interactions. This is a previously uncharacterized surface for binding of cdb3 to AnkD34. Because a second dimer of cdb3 is known to bind to ankyrin repeats 7-12 of the membrane binding domain of ankyrin-R, the current models have significant implications regarding the structural nature of a tetrameric form of AE1 that is hypothesized to be involved in binding to full-length ankyrin-R in the erythrocyte membrane.

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.

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.

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.