A Computational Pipeline to Identify and Characterize Binding Sites and Interacting Chemotypes in SARS-CoV-2

Minimizing the human and economic costs of the COVID-19 pandemic and future pandemics requires the ability to develop and deploy effective treatments for novel pathogens as soon as possible after they emerge. To this end, we introduce a new computational pipeline for the rapid identification and characterization of binding sites in viral proteins along with the key chemical features, which we call chemotypes, of the compounds predicted to interact with those same sites. The composition of source organisms for the structural models associated with an individual binding site is used to assess the site's degree of structural conservation across different species, including other viruses and humans. We propose a search strategy for novel therapeutics that involves the selection of molecules preferentially containing the most structurally rich chemotypes identified by our algorithm. While we demonstrate the pipeline on SARS-CoV-2, it is generalizable to any new virus, as long as either experimentally solved structures for its proteins are available or sufficiently accurate predicted structures can be constructed.

Computationally restoring the potency of a clinical antibody against SARS-CoV-2 Omicron subvariants

The COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drugs1-3, but also revealed how quickly viral escape can curtail effective options4,5. With the emergence of the SARS-CoV-2 Omicron variant in late 2021, many clinically used antibody drug products lost potency, including Evusheld™ and its constituent, cilgavimab4,6. Cilgavimab, like its progenitor COV2-2130, is a class 3 antibody that is compatible with other antibodies in combination4 and is challenging to replace with existing approaches. Rapidly modifying such high-value antibodies with a known clinical profile to restore efficacy against emerging variants is a compelling mitigation strategy. We sought to redesign COV2-2130 to rescue in vivo efficacy against Omicron BA.1 and BA.1.1 strains while maintaining efficacy against the contemporaneously dominant Delta variant. Here we show that our computationally redesigned antibody, 2130-1-0114-112, achieves this objective, simultaneously increases neutralization potency against Delta and many variants of concern that subsequently emerged, and provides protection in vivo against the strains tested, WA1/2020, BA.1.1, and BA.5. Deep mutational scanning of tens of thousands pseudovirus variants reveals 2130-1-0114-112 improves broad potency without incurring additional escape liabilities. Our results suggest that computational approaches can optimize an antibody to target multiple escape variants, while simultaneously enriching potency. Because our approach is computationally driven, not requiring experimental iterations or pre-existing binding data, it could enable rapid response strategies to address escape variants or pre-emptively mitigate escape vulnerabilities.

PDBspheres: a method for finding 3D similarities in local regions in proteins

We present a structure-based method for finding and evaluating structural similarities in protein regions relevant to ligand binding. PDBspheres comprises an exhaustive library of protein structure regions ('spheres') adjacent to complexed ligands derived from the Protein Data Bank (PDB), along with methods to find and evaluate structural matches between a protein of interest and spheres in the library. PDBspheres uses the LGA (Local-Global Alignment) structure alignment algorithm as the main engine for detecting structural similarities between the protein of interest and template spheres from the library, which currently contains >2 million spheres. To assess confidence in structural matches, an all-atom-based similarity metric takes side chain placement into account. Here, we describe the PDBspheres method, demonstrate its ability to detect and characterize binding sites in protein structures, show how PDBspheres-a strictly structure-based method-performs on a curated dataset of 2528 ligand-bound and ligand-free crystal structures, and use PDBspheres to cluster pockets and assess structural similarities among protein binding sites of 4876 structures in the 'refined set' of the PDBbind 2019 dataset.

Large-scale application of free energy perturbation calculations for antibody design

Alchemical free energy perturbation (FEP) is a rigorous and powerful technique to calculate the free energy difference between distinct chemical systems. Here we report our implementation of automated large-scale FEP calculations, using the Amber software package, to facilitate antibody design and evaluation. In combination with Hamiltonian replica exchange, our FEP simulations aim to predict the effect of mutations on both the binding affinity and the structural stability. Importantly, we incorporate multiple strategies to faithfully estimate the statistical uncertainties in the FEP results. As a case study, we apply our protocols to systematically evaluate variants of the m396 antibody for their conformational stability and their binding affinity to the spike proteins of SARS-CoV-1 and SARS-CoV-2. By properly adjusting relevant parameters, the particle collapse problems in the FEP simulations are avoided. Furthermore, large statistical errors in a small fraction of the FEP calculations are effectively reduced by extending the sampling, such that acceptable statistical uncertainties are achieved for the vast majority of the cases with a modest total computational cost. Finally, our predicted conformational stability for the m396 variants is qualitatively consistent with the experimentally measured melting temperatures. Our work thus demonstrates the applicability of FEP in computational antibody design.

Multiscale analysis for patterns of Zika virus genotype emergence, spread, and consequence

The question of how Zika virus (ZIKV) changed from a seemingly mild virus to a human pathogen capable of microcephaly and sexual transmission remains unanswered. The unexpected emergence of ZIKV's pathogenicity and capacity for sexual transmission may be due to genetic changes, and future changes in phenotype may continue to occur as the virus expands its geographic range. Alternatively, the sheer size of the 2015-16 epidemic may have brought attention to a pre-existing virulent ZIKV phenotype in a highly susceptible population. Thus, it is important to identify patterns of genetic change that may yield a better understanding of ZIKV emergence and evolution. However, because ZIKV has an RNA genome and a polymerase incapable of proofreading, it undergoes rapid mutation which makes it difficult to identify combinations of mutations associated with viral emergence. As next generation sequencing technology has allowed whole genome consensus and variant sequence data to be generated for numerous virus samples, the task of analyzing these genomes for patterns of mutation has become more complex. However, understanding which combinations of mutations spread widely and become established in new geographic regions versus those that disappear relatively quickly is essential for defining the trajectory of an ongoing epidemic. In this study, multiscale analysis of the wealth of genomic data generated over the course of the epidemic combined with in vivo laboratory data allowed trends in mutations and outbreak trajectory to be assessed. Mutations were detected throughout the genome via deep sequencing, and many variants appeared in multiple samples and in some cases become consensus. Similarly, amino acids that were previously consensus in pre-outbreak samples were detected as low frequency variants in epidemic strains. Protein structural models indicate that most of the mutations associated with the epidemic transmission occur on the exposed surface of viral proteins. At the macroscale level, consensus data was organized into large and interactive databases to allow the spread of individual mutations and combinations of mutations to be visualized and assessed for temporal and geographical patterns. Thus, the use of multiscale modeling for identifying mutations or combinations of mutations that impact epidemic transmission and phenotypic impact can aid the formation of hypotheses which can then be tested using reverse genetics.

Sendai virus intra-host population dynamics and host immunocompetence influence viral virulence during in vivo passage

In vivo serial passage of non-pathogenic viruses has been shown to lead to increased viral virulence, and although the precise mechanism(s) are not clear, it is known that both host and viral factors are associated with increased pathogenicity. Under- or overnutrition leads to a decreased or dysregulated immune response and can increase viral mutant spectrum diversity and virulence. The objective of this study was to identify the role of viral mutant spectra dynamics and host immunocompetence in the development of pathogenicity during in vivo passage. Because the nutritional status of the host has been shown to affect the development of viral virulence, the diet of animal model reflected two extremes of diets which exist in the global population, malnutrition and obesity. Sendai virus was serially passaged in groups of mice with differing nutritional status followed by transmission of the passaged virus to a second host species, guinea pigs. Viral population dynamics were characterized using deep sequence analysis and computational modeling. Histopathology, viral titer and cytokine assays were used to characterize viral virulence. Viral virulence increased with passage and the virulent phenotype persisted upon passage to a second host species. Additionally, nutritional status of mice during passage influenced the phenotype. Sequencing revealed the presence of several non-synonymous changes in the consensus sequence associated with passage, a majority of which occurred in the hemagglutinin-neuraminidase and polymerase genes, as well as the presence of persistent high frequency variants in the viral population. In particular, an N1124D change in the consensus sequences of the polymerase gene was detected by passage 10 in a majority of the animals. In vivo comparison of an 1124D plaque isolate to a clone with 1124N genotype indicated that 1124D was associated with increased virulence.

Rapid countermeasure discovery against Francisella tularensis based on a metabolic network reconstruction

In the future, we may be faced with the need to provide treatment for an emergent biological threat against which existing vaccines and drugs have limited efficacy or availability. To prepare for this eventuality, our objective was to use a metabolic network-based approach to rapidly identify potential drug targets and prospectively screen and validate novel small-molecule antimicrobials. Our target organism was the fully virulent Francisella tularensis subspecies tularensis Schu S4 strain, a highly infectious intracellular pathogen that is the causative agent of tularemia and is classified as a category A biological agent by the Centers for Disease Control and Prevention. We proceeded with a staggered computational and experimental workflow that used a strain-specific metabolic network model, homology modeling and X-ray crystallography of protein targets, and ligand- and structure-based drug design. Selected compounds were subsequently filtered based on physiological-based pharmacokinetic modeling, and we selected a final set of 40 compounds for experimental validation of antimicrobial activity. We began screening these compounds in whole bacterial cell-based assays in biosafety level 3 facilities in the 20th week of the study and completed the screens within 12 weeks. Six compounds showed significant growth inhibition of F. tularensis, and we determined their respective minimum inhibitory concentrations and mammalian cell cytotoxicities. The most promising compound had a low molecular weight, was non-toxic, and abolished bacterial growth at 13 µM, with putative activity against pantetheine-phosphate adenylyltransferase, an enzyme involved in the biosynthesis of coenzyme A, encoded by gene coaD. The novel antimicrobial compounds identified in this study serve as starting points for lead optimization, animal testing, and drug development against tularemia. Our integrated in silico/in vitro approach had an overall 15% success rate in terms of active versus tested compounds over an elapsed time period of 32 weeks, from pathogen strain identification to selection and validation of novel antimicrobial compounds.

Highly similar structural frames link the template tunnel and NTP entry tunnel to the exterior surface in RNA-dependent RNA polymerases

RNA-dependent RNA polymerase (RdRp) is essential to viral replication and is therefore one of the primary targets of countermeasures against these dangerous infectious agents. Development of broad-spectrum therapeutics targeting polymerases has been hampered by the extreme sequence variability of these sequences. RdRps range in length from 400–800 residues, yet contain only ∼20 residues that are conserved in most species. In this study, we made structure-based comparisons that are independent of sequence composition using a recently developed algorithm. We identified residue-to-residue correspondences of multiple protein structures and created (two-dimensional) structure-based alignment maps of 37 polymerase structures that provide both sequence and structure details. Using these maps, we determined that ∼75% of each polymerase species consists of seven protein segments, each of which has high structural similarity to segments in other species, though they are widely divergent in sequence composition and order. We define each of these segments as a ‘homomorph’, and each includes (though most are much larger than) the well-known conserved polymerase motifs. All homomorphs contact the template tunnel or nucleoside triphosphate (NTP) entry tunnel and the exterior of the protein, suggesting they constitute a structural and functional skeleton common among the polymerases.

Computational analysis of pathogen-borne metallo β-lactamases reveals discriminating structural features between B1 types

Background

Genes conferring antibiotic resistance to groups of bacterial pathogens are cause for considerable concern, as many once-reliable antibiotics continue to see a reduction in efficacy. The recent discovery of the metallo β-lactamase blaNDM-1 gene, which appears to grant antibiotic resistance to a variety of Enterobacteriaceae via a mobile plasmid, is one example of this distressing trend. The following work describes a computational analysis of pathogen-borne MBLs that focuses on the structural aspects of characterized proteins.

Results

Using both sequence and structural analyses, we examine residues and structural features specific to various pathogen-borne MBL types. This analysis identifies a linker region within MBL-like folds that may act as a discriminating structural feature between these proteins, and specifically resistance-associated acquirable MBLs. Recently released crystal structures of the newly emerged NDM-1 protein were aligned against related MBL structures using a variety of global and local structural alignment methods, and the overall fold conformation is examined for structural conservation. Conservation appears to be present in most areas of the protein, yet is strikingly absent within a linker region, making NDM-1 unique with respect to a linker-based classification scheme. Variability analysis of the NDM-1 crystal structure highlights unique residues in key regions as well as identifying several characteristics shared with other transferable MBLs.

Conclusions

A discriminating linker region identified in MBL proteins is highlighted and examined in the context of NDM-1 and primarily three other MBL types: IMP-1, VIM-2 and ccrA. The presence of an unusual linker region variant and uncommon amino acid composition at specific structurally important sites may help to explain the unusually broad kinetic profile of NDM-1 and may aid in directing research attention to areas of this protein, and possibly other MBLs, that may be targeted for inactivation or attenuation of enzymatic activity.

StralSV: assessment of sequence variability within similar 3D structures and application to polio RNA-dependent RNA polymerase

Background

Most of the currently used methods for protein function prediction rely on sequence-based comparisons between a query protein and those for which a functional annotation is provided. A serious limitation of sequence similarity-based approaches for identifying residue conservation among proteins is the low confidence in assigning residue-residue correspondences among proteins when the level of sequence identity between the compared proteins is poor. Multiple sequence alignment methods are more satisfactory--still, they cannot provide reliable results at low levels of sequence identity. Our goal in the current work was to develop an algorithm that could help overcome these difficulties by facilitating the identification of structurally (and possibly functionally) relevant residue-residue correspondences between compared protein structures.

Results

Here we present StralSV (structure-alignment sequence variability), a new algorithm for detecting closely related structure fragments and quantifying residue frequency from tight local structure alignments. We apply StralSV in a study of the RNA-dependent RNA polymerase of poliovirus, and we demonstrate that the algorithm can be used to determine regions of the protein that are relatively unique, or that share structural similarity with proteins that would be considered distantly related. By quantifying residue frequencies among many residue-residue pairs extracted from local structural alignments, one can infer potential structural or functional importance of specific residues that are determined to be highly conserved or that deviate from a consensus. We further demonstrate that considerable detailed structural and phylogenetic information can be derived from StralSV analyses.

Conclusions

StralSV is a new structure-based algorithm for identifying and aligning structure fragments that have similarity to a reference protein. StralSV analysis can be used to quantify residue-residue correspondences and identify residues that may be of particular structural or functional importance, as well as unusual or unexpected residues at a given sequence position. StralSV is provided as a web service at http://proteinmodel.org/AS2TS/STRALSV/.

Structural Re-Alignment in an Immunogenic Surface Region of Ricin a Chain

We compared structure alignments generated by several protein structure comparison programs to determine whether existing methods would satisfactorily align residues at a highly conserved position within an immunogenic loop in ribosome inactivating proteins (RIPs). Using default settings, structure alignments generated by several programs (CE, DaliLite, FATCAT, LGA, MAMMOTH, MATRAS, SHEBA, SSM) failed to align the respective conserved residues, although LGA reported correct residue-residue (R-R) correspondences when the beta-carbon (Cb) position was used as the point of reference in the alignment calculations. Further tests using variable points of reference indicated that points distal from the beta carbon along a vector connecting the alpha and beta carbons yielded rigid structural alignments in which residues known to be highly conserved in RIPs were reported as corresponding residues in structural comparisons between ricin A chain, abrin-A, and other RIPs. Results suggest that approaches to structure alignment employing alternate point representations corresponding to side chain position may yield structure alignments that are more consistent with observed conservation of functional surface residues than do standard alignment programs, which apply uniform criteria for alignment (i.e. alpha carbon (Ca) as point of reference) along the entirety of the peptide chain. We present the results of tests that suggest the utility of allowing user-specified points of reference in generating alternate structural alignments, and we present a web server for automatically generating such alignments: http://as2ts.llnl.gov/AS2TS/LGA/lga_pdblist_plots.html.

MannDB - a microbial database of automated protein sequence analyses and evidence integration for protein characterization

Background

MannDB was created to meet a need for rapid, comprehensive automated protein sequence analyses to support selection of proteins suitable as targets for driving the development of reagents for pathogen or protein toxin detection. Because a large number of open-source tools were needed, it was necessary to produce a software system to scale the computations for whole-proteome analysis. Thus, we built a fully automated system for executing software tools and for storage, integration, and display of automated protein sequence analysis and annotation data.

Description

MannDB is a relational database that organizes data resulting from fully automated, high-throughput protein-sequence analyses using open-source tools. Types of analyses provided include predictions of cleavage, chemical properties, classification, features, functional assignment, post-translational modifications, motifs, antigenicity, and secondary structure. Proteomes (lists of hypothetical and known proteins) are downloaded and parsed from Genbank and then inserted into MannDB, and annotations from SwissProt are downloaded when identifiers are found in the Genbank entry or when identical sequences are identified. Currently 36 open-source tools are run against MannDB protein sequences either on local systems or by means of batch submission to external servers. In addition, BLAST against protein entries in MvirDB, our database of microbial virulence factors, is performed. A web client browser enables viewing of computational results and downloaded annotations, and a query tool enables structured and free-text search capabilities. When available, links to external databases, including MvirDB, are provided. MannDB contains whole-proteome analyses for at least one representative organism from each category of biological threat organism listed by APHIS, CDC, HHS, NIAID, USDA, USFDA, and WHO.

Conclusion

MannDB comprises a large number of genomes and comprehensive protein sequence analyses representing organisms listed as high-priority agents on the websites of several governmental organizations concerned with bio-terrorism. MannDB provides the user with a BLAST interface for comparison of native and non-native sequences and a query tool for conveniently selecting proteins of interest. In addition, the user has access to a web-based browser that compiles comprehensive and extensive reports. Access to MannDB is freely available at .

Computational approaches for identification of conserved/unique binding pockets in the A chain of ricin

MOTIVATION

Specific and sensitive ligand-based protein detection assays that employ antibodies or small molecules such as peptides, aptamers or other small molecules require that the corresponding surface region of the protein be accessible and that there be minimal cross-reactivity with non-target proteins. To reduce the time and cost of laboratory screening efforts for diagnostic reagents, we developed new methods for evaluating and selecting protein surface regions for ligand targeting.

RESULTS

We devised combined structure- and sequence-based methods for identifying 3D epitopes and binding pockets on the surface of the A chain of ricin that are conserved with respect to a set of ricin A chains and unique with respect to other proteins. We (1) used structure alignment software to detect structural deviations and extracted from this analysis the residue-residue correspondence, (2) devised a method to compare corresponding residues across sets of ricin structures and structures of closely related proteins, (3) devised a sequence-based approach to determine residue infrequency in local sequence context and (4) modified a pocket-finding algorithm to identify surface crevices in close proximity to residues determined to be conserved/unique based on our structure- and sequence-based methods. In applying this combined informatics approach to ricin A, we identified a conserved/unique pocket in close proximity (but not overlapping) the active site that is suitable for bi-dentate ligand development. These methods are generally applicable to identification of surface epitopes and binding pockets for development of diagnostic reagents, therapeutics and vaccines.