Generative Haplotype Prediction Outperforms Statistical Methods for Small Variant Detection in NGS Data.

MOTIVATION: Detection of germline variants in next-generation sequencing data is an essential component of modern genomics analysis. Variant detection tools typically rely on statistical algorithms such as de Bruijn graphs or Hidden Markov Models, and are often coupled with heuristic techniques and thresholds to maximize accuracy. Despite significant progress in recent years, current methods still generate thousands of false positive detections in a typical human whole genome, creating a significant manual review burden. RESULTS: We introduce a new approach that replaces the handcrafted statistical techniques of previous methods with a single deep generative model. Using a standard transformer-based encoder and double-decoder architecture, our model learns to construct diploid germline haplotypes in a generative fashion identical to modern Large Language Models (LLMs). We train our model on 37 Whole Genome Sequences (WGS) from Genome-in-a-Bottle samples, and demonstrate that our method learns to produce accurate haplotypes with correct phase and genotype for all classes of small variants. We compare our method, called Jenever, to FreeBayes, GATK HaplotypeCaller, Clair3 and DeepVariant, and demonstrate that our method has superior overall accuracy compared to other methods. At F1-maximizing quality thresholds, our model delivers the highest sensitivity, precision, and the fewest genotyping errors for insertion and deletion variants. For single nucleotide variants our model demonstrates the highest sensitivity but at somewhat lower precision, and achieves the highest overall F1 score among all callers we tested. AVAILABILITY AND IMPLEMENTATION: Jenever is implemented as a python-based command line tool. Source code is available at https://github.com/ARUP-NGS/jenever/.

Clinical utility of targeted next-generation sequencing panel in routine diagnosis of hereditary hemolytic anemia: A national reference laboratory experience.

INTRODUCTION: Hereditary hemolytic anemias (HHA) comprise a heterogeneous group of disorders resulting from defective red blood cell (RBC) cytoskeleton, RBC enzyme deficiencies, and hemoglobin (Hb) synthesis disorders such as thalassemia or sideroblastic anemia. MATERIALS AND METHODS: Our hemolytic anemia diagnostic next-generation sequencing (NGS) panel includes 28 genes encoding RBC cytoskeletal proteins, membrane transporter, RBC enzymes, and certain bilirubin metabolism genes. The panel covers the complete coding region of these genes, splice junctions, and, wherever appropriate, deep intronic or regulatory regions are also included. Four hundred fifty-six patients with unexplained hemolytic anemia were evaluated using our NGS panel between 2015 and 2019. RESULTS: We identified pathogenic/likely pathogenic variants in 111/456 (24%) patients that were responsible for the disease phenotype (e.g., moderate to severe hemolytic anemia and hyperbilirubinemia). Approximately 40% of the mutations were novel. As expected, 45/456 (10%) patients were homozygous for the promoter polymorphism in the UGT1A1 gene, A(TA)7 TAA (UGT1A1*28). 8/45 homozygous UGT1A1*28 cases were associated with additional pathogenic mutations causing hemolytic anemia, likely exacerbating hyperbilirubinemia. The most common mutated genes were membrane cytoskeleton genes SPTA1, and SPTB, followed by PKLR. Complex interactions between SPTA1 low expression alleles, alpha-LELY and alpha-LEPRA alleles, and intragenic SPTA1 variants were associated with hereditary pyropoikilocytosis and autosomal recessive hereditary spherocytosis in 23/111 patients. CONCLUSIONS: Our results demonstrate that hemolytic anemia is underscored by complex molecular interactions of previously known and novel mutations in RBC cytoskeleton/enzyme genes, and therefore, NGS should be considered in all patients with clinically unexplained hemolytic anemia and in neonates with hyperbilirubinemia. Moreover, low expression alleles alpha-LELY and alpha-LEPRA should be included in all targeted HHA panels.

Adaptive BP-Dock: An Induced Fit Docking Approach for Full Receptor Flexibility.

We present an induced fit docking approach called Adaptive BP-Dock that integrates perturbation response scanning (PRS) with the flexible docking protocol of RosettaLigand in an adaptive manner. We first perturb the binding pocket residues of a receptor and obtain a new conformation based on the residue response fluctuation profile using PRS. Next, we dock a ligand to this new conformation by RosettaLigand, where we repeat these steps for several iterations. We test this approach on several protein test sets including difficult unbound docking cases such as HIV-1 reverse transcriptase and HIV-1 protease. Adaptive BP-Dock results show better correlation with experimental binding affinities compared to other docking protocols. Overall, the results imply that Adaptive BP-Dock can easily capture binding induced conformational changes by simultaneous sampling of protein and ligand conformations. This can provide faster and efficient docking of novel targets for rational drug design.

A Rigid Hinge Region Is Necessary for High-Affinity Binding of Dimannose to Cyanovirin and Associated Constructs.

Mutations in the hinge region of cyanovirin-N (CVN) dictate its preferential oligomerization state. Constructs with the Pro51Gly mutation preferentially exist as monomers, whereas wild-type cyanovirin can form domain-swapped dimers under certain conditions. Because the hinge region is an integral part of the high-affinity binding site of CVN, we investigated whether this mutation affects the shape, flexibility, and binding affinity of domain B for dimannose. Our studies indicate that the capability of monomeric wild-type CVN to resist mechanical perturbations is enhanced when compared to that of constructs in which the hinge region is more flexible. Our computational results also show that enhanced flexibility leads to blocking of the binding site by allowing different rotational isomeric states of Asn53. Moreover, at higher temperatures, this observed flexibility leads to an interaction between Asn53 and Asn42, further hindering access to the binding site. On the basis of these results, we predicted that binding affinity for dimannose would be more favorable for cyanovirin constructs containing a wild-type hinge region, whereas affinity would be impaired in the case of mutants containing Pro51Gly. Experimental characterization by isothermal titration calorimetry of a set of cyanovirin mutants confirms this hypothesis. Those possessing the Pro51Gly mutation are consistently inferior binders.

A flexible docking scheme efficiently captures the energetics of glycan-cyanovirin binding.

Cyanovirin-N (CVN), a cyanobacterial lectin, exemplifies a class of antiviral agents that inhibit HIV by binding to the highly glycosylated envelope protein gp120. Here, we investigate the energetics of glycan recognition using a computationally inexpensive flexible docking approach, backbone perturbation docking (BP-Dock). We benchmarked our method using two mutants of CVN: P51G-m4-CVN, which binds dimannose with high affinity through domain B, and CVN((mutDB)), in which binding to domain B has been abolished through mutation of five polar residues to small nonpolar side chains. We investigated the energetic contribution of these polar residues along with the additional position 53 by docking dimannose to single-point CVN mutant models. Analysis of the docking simulations indicated that the E41A/G and T57A mutations led to a significant decrease in binding energy scores due to rearrangements of the hydrogen-bond network that reverberated throughout the binding cavity. N42A decreased the binding score to a level comparable to that of CVN((mutDB)) by affecting the integrity of the local protein structure. In contrast, N53S resulted in a high binding energy score, similar to P51G-m4-CVN. Experimental characterization of the five mutants by NMR spectroscopy confirmed the binding affinity pattern predicted by BP-Dock. Despite their mostly conserved fold and stability, E41A, E41G, and T57A displayed dissociation constants in the millimolar range. N53S showed a binding constant in the low micromolar range, similar to that observed for P51G-m4-CVN. No binding was observed for N42A. Our results show that BP-Dock is a useful tool for rapidly screening the relative binding affinity pattern of in silico-designed mutants compared with wild-type, supporting its use to design novel mutants with enhanced binding properties.

BP-Dock: a flexible docking scheme for exploring protein-ligand interactions based on unbound structures.

Molecular docking serves as an important tool in modeling protein-ligand interactions. However, it is still challenging to incorporate overall receptor flexibility, especially backbone flexibility, in docking due to the large conformational space that needs to be sampled. To overcome this problem, we developed a novel flexible docking approach, BP-Dock (Backbone Perturbation-Dock) that can integrate both backbone and side chain conformational changes induced by ligand binding through a multi-scale approach. In the BP-Dock method, we mimic the nature of binding-induced events as a first-order approximation by perturbing the residues along the protein chain with a small Brownian kick one at a time. The response fluctuation profile of the chain upon these perturbations is computed using the perturbation response scanning method. These response fluctuation profiles are then used to generate binding-induced multiple receptor conformations for ensemble docking. To evaluate the performance of BP-Dock, we applied our approach on a large and diverse data set using unbound structures as receptors. We also compared the BP-Dock results with bound and unbound docking, where overall receptor flexibility was not taken into account. Our results highlight the importance of modeling backbone flexibility in docking for recapitulating the experimental binding affinities, especially when an unbound structure is used. With BP-Dock, we can generate a wide range of binding site conformations realized in nature even in the absence of a ligand that can help us to improve the accuracy of unbound docking. We expect that our fast and efficient flexible docking approach may further aid in our understanding of protein-ligand interactions as well as virtual screening of novel targets for rational drug design.

The antiviral lectin cyanovirin-N: probing multivalency and glycan recognition through experimental and computational approaches.

CVN (cyanovirin-N), a small lectin isolated from cyanobacteria, exemplifies a novel class of anti-HIV agents that act by binding to the highly glycosylated envelope protein gp120 (glycoprotein 120), resulting in inhibition of the crucial viral entry step. In the present review, we summarize recent work in our laboratory and others towards determining the crucial role of multivalency in the antiviral activity, and we discuss features that contribute to the high specificity and affinity for the glycan ligand observed in CVN. An integrated approach that encompasses structural determination, mutagenesis analysis and computational work holds particular promise to clarify aspects of the interactions between CVN and glycans.

The binding affinities of proteins interacting with the PDZ domain of PICK1.

Protein interacting with C kinase (PICK1) is well conserved throughout evolution and plays a critical role in synaptic plasticity by regulating the trafficking and posttranslational modification of its interacting proteins. PICK1 contains a single PSD95/DlgA/Zo-1 (PDZ) protein-protein interaction domain, which is promiscuous and shown to interact with over 60 proteins, most of which play roles in neuronal function. Several reports have suggested the role of PICK1 in disorders such as epilepsy, pain, brain trauma and stroke, drug abuse and dependence, schizophrenia and psychosis. Importantly, lead compounds that block PICK1 interactions are also now becoming available. Here, a new modeling approach was developed to investigate binding affinities of PDZ interactions. Using these methods, the binding affinities of all major PICK1 interacting proteins are reported and the effects of PICK1 mutations on these interactions are described. These modeling methods have important implications in defining the binding properties of proteins interacting with PICK1 as well as the general structural requirements of PDZ interactions. The study also provides modeling methods to support in the drug design of ligands for PDZ domains, which may further aid in development of the family of PDZ domains as a drug target.

A novel modelling mechanism of PAEL receptor and GABARAPL2 interaction involved in Parkinson's disease.

Parkin associated endothelin like receptor (PAELR) is G-protein coupled and ubiquitinated by parkin, promoting its degradation. In autosomal recessive Parkinson's disease, mutations in parkin lead to PAELR aggregation in the endoplasmic reticulum (ER), ER stress, neurotoxicity and cell death. We have identified previously that the protein kinase C interacting protein (PICK1) interacts with and regulates the expression and cell toxicity of PAELR. Here, we experimentally identify and provide in-silico modelling of a novel interaction between PAELR and GABARAPL2 (γ-aminobutyrate type A receptor associated protein like 2), which is an autophagosome-specific Ub-like protein implicated in vesicle trafficking and autophagy. We show that the family of GABARAPs interact with the carboxy terminal (ct) of PAELR and find the cysteine rich region (-CCCCCC-EEC) of ct-PAELR interacts with the GABAA binding site of GABARAPL2. This interaction is modelled by in-slico analysis and confirmed using affinity chromatography, showing Myc-tagged GABARAPL2 is retained by a GST fusion of the ct-PAELR. We also demonstrate that transient transfection of GABARAPL2 in HEK293 cells reduces PAELR expression. This study supports the idea that protein levels of PAELR are likely regulated by a multitude of proteins including parkin, PICK1 and GABARAPL2 via mechanisms that include ubiquitination, proteasomal degradagtion and autophagy.

Author Correction: Plant-expressed cocaine hydrolase variants of butyrylcholinesterase exhibit altered allosteric effects of cholinesterase activity and increased inhibitor sensitivity.

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Plant-expressed cocaine hydrolase variants of butyrylcholinesterase exhibit altered allosteric effects of cholinesterase activity and increased inhibitor sensitivity.

Butyrylcholinesterase (BChE) is an enzyme with broad substrate and ligand specificities and may function as a generalized bioscavenger by binding and/or hydrolyzing various xenobiotic agents and toxicants, many of which target the central and peripheral nervous systems. Variants of BChE were rationally designed to increase the enzyme's ability to hydrolyze the psychoactive enantiomer of cocaine. These variants were cloned, and then expressed using the magnICON transient expression system in plants and their enzymatic properties were investigated. In particular, we explored the effects that these site-directed mutations have over the enzyme kinetics with various substrates of BChE. We further compared the affinity of various anticholinesterases including organophosphorous nerve agents and pesticides toward these BChE variants relative to the wild type enzyme. In addition to serving as a therapy for cocaine addiction-related diseases, enhanced bioscavenging against other harmful agents could add to the practicality and versatility of the plant-derived recombinant enzyme as a multivalent therapeutic.