Biophysical Techniques in Structural Biology.

Over the past six decades, steadily increasing progress in the application of the principles and techniques of the physical sciences to the study of biological systems has led to remarkable insights into the molecular basis of life. Of particular significance has been the way in which the determination of the structures and dynamical properties of proteins and nucleic acids has so often led directly to a profound understanding of the nature and mechanism of their functional roles. The increasing number and power of experimental and theoretical techniques that can be applied successfully to living systems is now ushering in a new era of structural biology that is leading to fundamentally new information about the maintenance of health, the origins of disease, and the development of effective strategies for therapeutic intervention. This article provides a brief overview of some of the most powerful biophysical methods in use today, along with references that provide more detailed information about recent applications of each of them. In addition, this article acts as an introduction to four authoritative reviews in this volume. The first shows the ways that a multiplicity of biophysical methods can be combined with computational techniques to define the architectures of complex biological systems, such as those involving weak interactions within ensembles of molecular components. The second illustrates one aspect of this general approach by describing how recent advances in mass spectrometry, particularly in combination with other techniques, can generate fundamentally new insights into the properties of membrane proteins and their functional interactions with lipid molecules. The third reviewdemonstrates the increasing power of rapidly evolving diffraction techniques, employing the very short bursts of X-rays of extremely high intensity that are now accessible as a result of the construction of free-electron lasers, in particular to carry out time-resolved studies of biochemical reactions. The fourth describes in detail the application of such approaches to probe the mechanism of the light-induced changes associated with bacteriorhodopsin's ability to convert light energy into chemical energy.

Opportunities and Challenges in Building a Spatiotemporal Multi-scale Model of the Human Pancreatic ß Cell.

The construction of a predictive model of an entire eukaryotic cell that describes its dynamic structure from atomic to cellular scales is a grand challenge at the intersection of biology, chemistry, physics, and computer science. Having such a model will open new dimensions in biological research and accelerate healthcare advancements. Developing the necessary experimental and modeling methods presents abundant opportunities for a community effort to realize this goal. Here, we present a vision for creation of a spatiotemporal multi-scale model of the pancreatic ß-cell, a relevant target for understanding and modulating the pathogenesis of diabetes.

Modeling Flexible Protein Structure With AlphaFold2 and Crosslinking Mass Spectrometry.

We propose a pipeline that combines AlphaFold2 (AF2) and crosslinking mass spectrometry (XL-MS) to model the structure of proteins with multiple conformations. The pipeline consists of two main steps: ensemble generation using AF2 and conformer selection using XL-MS data. For conformer selection, we developed two scores-the monolink probability score (MP) and the crosslink probability score (XLP)-both of which are based on residue depth from the protein surface. We benchmarked MP and XLP on a large dataset of decoy protein structures and showed that our scores outperform previously developed scores. We then tested our methodology on three proteins having an open and closed conformation in the Protein Data Bank: Complement component 3 (C3), luciferase, and glutamine-binding periplasmic protein, first generating ensembles using AF2, which were then screened for the open and closed conformations using experimental XL-MS data. In five out of six cases, the most accurate model within the AF2 ensembles-or a conformation within 1 Å of this model-was identified using crosslinks, as assessed through the XLP score. In the remaining case, only the monolinks (assessed through the MP score) successfully identified the open conformation of glutamine-binding periplasmic protein, and these results were further improved by including the "occupancy" of the monolinks. This serves as a compelling proof-of-concept for the effectiveness of monolinks. In contrast, the AF2 assessment score was only able to identify the most accurate conformation in two out of six cases. Our results highlight the complementarity of AF2 with experimental methods like XL-MS, with the MP and XLP scores providing reliable metrics to assess the quality of the predicted models. The MP and XLP scoring functions mentioned above are available at https://gitlab.com/topf-lab/xlms-tools.

Peptide-binding specificity prediction using fine-tuned protein structure prediction networks.

Peptide-binding proteins play key roles in biology, and predicting their binding specificity is a long-standing challenge. While considerable protein structural information is available, the most successful current methods use sequence information alone, in part because it has been a challenge to model the subtle structural changes accompanying sequence substitutions. Protein structure prediction networks such as AlphaFold model sequence-structure relationships very accurately, and we reasoned that if it were possible to specifically train such networks on binding data, more generalizable models could be created. We show that placing a classifier on top of the AlphaFold network and fine-tuning the combined network parameters for both classification and structure prediction accuracy leads to a model with strong generalizable performance on a wide range of Class I and Class II peptide-MHC interactions that approaches the overall performance of the state-of-the-art NetMHCpan sequence-based method. The peptide-MHC optimized model shows excellent performance in distinguishing binding and non-binding peptides to SH3 and PDZ domains. This ability to generalize well beyond the training set far exceeds that of sequence-only models and should be particularly powerful for systems where less experimental data are available.

Arsenate reductase of Rufibacter tibetensis is a metallophosphoesterase evolved to catalyze redox reactions.

An arsenate reductase (Car1) from the Bacteroidetes species Rufibacter tibetensis 1351T was isolated from the Tibetan Plateau. The strain exhibits resistance to arsenite [As(III)] and arsenate [As(V)] and reduces As(V) to As(III). Here we shed light on the mechanism of enzymatic reduction by Car1. AlphaFold2 structure prediction, active site energy minimization, and steady-state kinetics of wild-type and mutant enzymes give insight into the catalytic mechanism. Car1 is structurally related to calcineurin-like metallophosphoesterases (MPPs). It functions as a binuclear metal hydrolase with limited phosphatase activity, particularly relying on the divalent metal Ni2+. As an As(V) reductase, it displays metal promiscuity and is coupled to the thioredoxin redox cycle, requiring the participation of two cysteine residues, Cys74 and Cys76. These findings suggest that Car1 evolved from a common ancestor of extant phosphatases by incorporating a redox function into an existing MPP catalytic site. Its proposed mechanism of arsenate reduction involves Cys74 initiating a nucleophilic attack on arsenate, leading to the formation of a covalent intermediate. Next, a nucleophilic attack of Cys76 leads to the release of As(III) and the formation of a surface-exposed Cys74-Cys76 disulfide, ready for reduction by thioredoxin.

Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs.

Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques, such as RoseTTAFold and AlphaFold, we can predict the structure of proteins even in the absence of structural homologs. We modeled and extracted the domains from 553 disease-associated human proteins without known protein structures or close homologs in the Protein Databank. We noticed that the model quality was higher and the Root mean square deviation (RMSD) lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could only be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein-protein interfaces and conserved residues in these predicted structures. We then explored whether the disease-associated missense mutations were in the proximity of these predicted functional sites, whether they destabilized the protein structure based on ddG calculations or whether they were predicted to be pathogenic. We could explain 80% of these disease-associated mutations based on proximity to functional sites, structural destabilization or pathogenicity. When compared to polymorphisms, a larger percentage of disease-associated missense mutations were buried, closer to predicted functional sites, predicted as destabilizing and pathogenic. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models.

Clinical relevance of genetic variants in the von Willebrand factor according to in-silico methods.

Clinical interpretation of genetic variants in the context of the patient's phenotype is a time-consuming and costly process. In-silico analysis using in-silico prediction tools, and molecular modeling have been developed to predict the influence of genetic variants on the quality and/or quantity of the resulting translated protein, and in this way, to alert clinicians of disease likelihood in the absence of previous evidence. Our objectives were to evaluate the success rate of the in-silico analysis in predicting the disease-causing variants as pathogenic and the single-nucleotide variants as neutral, and to establish the reliability of in-silico analysis for determining pathogenicity or neutrality of von Willebrand factor gene-associated genetic variants. Using in-silico analysis, we studied pathogenicity in 31 disease-causing variants, and neutrality in 61 single-nucleotide variants from patients previously diagnosed as type 2 von Willebrand disease. Disease-causing variants and non-synonymous single-nucleotide variants were explored by in-silico tools that analyze the amino acidic sequence. Intronic and synonymous single-nucleotide variants were analyzed by in-silico methods that evaluate the nucleotidic sequence. We found a consistent agreement between predictions achieved by in-silico prediction tools and molecular modeling, both for defining the pathogenicity of disease-causing variants and the neutrality of single-nucleotide variants. Based on our results, the in-silico analysis would help to define the pathogenicity or neutrality in novel genetic variants observed in patients with clinical and laboratory phenotypes suggestive of von Willebrand disease.

Biochemical and in silico structural characterization of a cold-active arginase from the psychrophilic yeast, Glaciozyma antarctica PI12.

Glaciozyma antarctica PI12 is a psychrophilic yeast isolated from Antarctica. In this work, we describe the heterologous production, biochemical properties and in silico structure analysis of an arginase from this yeast (GaArg). GaArg is a metalloenzyme that catalyses the hydrolysis of L-arginine to L-ornithine and urea. The cDNA of GaArg was reversed transcribed, cloned, expressed and purified as a recombinant protein in Escherichia coli. The purified protein was active against L-arginine as its substrate in a reaction at 20 °C, pH 9. At 10-35 °C and pH 7-9, the catalytic activity of the protein was still present around 50%. Mn2+, Ni2+, Co2+ and K+ were able to enhance the enzyme activity more than two-fold, while GaArg is most sensitive to SDS, EDTA and DTT. The predicted structure model of GaArg showed a very similar overall fold with other known arginases. GaArg possesses predominantly smaller and uncharged amino acids, fewer salt bridges, hydrogen bonds and hydrophobic interactions compared to the other counterparts. GaArg is the first reported arginase that is cold-active, facilitated by unique structural characteristics for its adaptation of catalytic functions at low-temperature environments. The structure and function of cold-active GaArg provide insights into the potentiality of new applications in various biotechnology and pharmaceutical industries.

Structural modeling and gene expression analysis of phosvitinless vitellogenin (vgc) in the Indian freshwater murrel, Channa punctatus (Bloch, 1793).

Vitellogenin (Vg) is a female-specific egg-yolk precursor protein, synthesized in the liver of fish in response to estrogens. In the present study, complete gene of phosvitinless vitellogenin (vgc) was sequenced, its 3D structure was predicted and validated by web-based softwares. The complete nucleotide sequence of vgc was 4126 bp which encodes for 1272 amino acids and showed the presence of three conserved domains viz. LPD_N, DUF1943 and DUF1944. The retrieved amino acid sequence of VgC protein was subjected to in silico analysis for understanding the structural and functional properties of protein. mRNA levels of multiple vg genes have also been quantified during annual reproductive cycle employing qPCR. A correlation has been observed between seasonal changes in gonadosomatic index with estradiol levels and hepatic expression of three types of vg genes (vga, vgb, vgc) during ovarian cycle of murrel. During preparatory phase, when photoperiod and temperature are low; low titre of E2 in blood induces expression of vgc gene. A rapid increase in the levels of E2 favours induction of vgb and vga genes in liver of murrel during early pre-spawning phase when photoperiod is long and temperature is high in nature. These results suggest that among three vitellogenin proteins, VgC is synthesized earlier than VgA and VgB during oogenesis.

Improving AlphaFold Predicted Contacts for Alpha-Helical Transmembrane Proteins Using Structural Features.

Residue contact maps provide a condensed two-dimensional representation of three-dimensional protein structures, serving as a foundational framework in structural modeling but also as an effective tool in their own right in identifying inter-helical binding sites and drawing insights about protein function. Treating contact maps primarily as an intermediate step for 3D structure prediction, contact prediction methods have limited themselves exclusively to sequential features. Now that AlphaFold2 predicts 3D structures with good accuracy in general, we examine (1) how well predicted 3D structures can be directly used for deciding residue contacts, and (2) whether features from 3D structures can be leveraged to further improve residue contact prediction. With a well-known benchmark dataset, we tested predicting inter-helical residue contact based on AlphaFold2's predicted structures, which gave an 83% average precision, already outperforming a sequential features-based state-of-the-art model. We then developed a procedure to extract features from atomic structure in the neighborhood of a residue pair, hypothesizing that these features will be useful in determining if the residue pair is in contact, provided the structure is decently accurate, such as predicted by AlphaFold2. Training on features generated from experimentally determined structures, we leveraged knowledge from known structures to significantly improve residue contact prediction, when testing using the same set of features but derived using AlphaFold2 structures. Our results demonstrate a remarkable improvement over AlphaFold2, achieving over 91.9% average precision for a held-out subset and over 89.5% average precision in cross-validation experiments.

Modeling and Analysis of HIV-1 Pol Polyprotein as a Case Study for Predicting Large Polyprotein Structures.

Acquired immunodeficiency syndrome (AIDS) is caused by human immunodeficiency virus (HIV). HIV protease, reverse transcriptase, and integrase are targets of current drugs to treat the disease. However, anti-viral drug-resistant strains have emerged quickly due to the high mutation rate of the virus, leading to the demand for the development of new drugs. One attractive target is Gag-Pol polyprotein, which plays a key role in the life cycle of HIV. Recently, we found that a combination of M50I and V151I mutations in HIV-1 integrase can suppress virus release and inhibit the initiation of Gag-Pol autoprocessing and maturation without interfering with the dimerization of Gag-Pol. Additional mutations in integrase or RNase H domain in reverse transcriptase can compensate for the defect. However, the molecular mechanism is unknown. There is no tertiary structure of the full-length HIV-1 Pol protein available for further study. Therefore, we developed a workflow to predict the tertiary structure of HIV-1 NL4.3 Pol polyprotein. The modeled structure has comparable quality compared with the recently published partial HIV-1 Pol structure (PDB ID: 7SJX). Our HIV-1 NL4.3 Pol dimer model is the first full-length Pol tertiary structure. It can provide a structural platform for studying the autoprocessing mechanism of HIV-1 Pol and for developing new potent drugs. Moreover, the workflow can be used to predict other large protein structures that cannot be resolved via conventional experimental methods.

Low concentrations of tricyclic antidepressants stimulate TRPC4 channel activity by acting as an opioid receptor ligand.

Traditionally prescribed for mood disorders, tricyclic antidepressants (TCAs) have shown promising therapeutic effects on chronic neuralgia and irritable bowel syndrome. However, the mechanism by which these atypical effects manifest is unclear. Among the proposed mechanisms is the well-known pain-related inhibitory G-protein coupled receptor, namely the opioid receptor (OR). Here, we confirmed that TCA indeed stimulates OR and regulates the gating of TRPC4, a downstream signaling of the Gi-pathway. In an ELISA to quantify the amount of intracellular cAMP, a downstream product of OR/Gi-pathway, treatment with amitriptyline (AMI) showed a decrease in [cAMP]i similar to that of the µOR agonist. Next, we explored the binding site of TCA by modeling the previously revealed ligand-bound structure of µOR. A conserved aspartate residue of ORs was predicted to participate in salt bridge interaction with the amine group of TCAs, and in aspartate-to-arginine mutation, AMI did not decrease the FRET-based binding efficiency between the ORs and Gαi2. As an alternative way to monitor the downstream signaling of Gi-pathway, we evaluated the functional activity of TRPC4 channel, as it is well known to be activated by Gαi. TCAs increased the TRPC4 current through ORs, and TCA-evoked TRPC4 activation was abolished by an inhibitor of Gαi2 or its dominant-negative mutant. As expected, TCA-evoked activation of TRPC4 was not observed in the aspartate mutants of OR. Taken together, OR could be proclaimed as a promising target among numerous binding partners of TCA, and TCA-evoked TRPC4 activation may help to explain the nonopioid analgesic effect of TCA.NEW & NOTEWORTHY Endogenous opioid systems modulate pain perception, but concerns about opioid-related substance misuse limit their use. This study has raised TRPC4 channel as a candidate target for alternative analgesics, tricyclic antidepressants (TCAs). TCAs have been shown to bind to and activate opioid receptors (ORs), leading to downstream signaling pathways involving TRPC4. The functional selectivity and biased agonism of TCA towards TRPC4 in dependence on OR may provide a better understanding of its efficacy or side effects.

Application of conformational space annealing to the protein structure modeling using cryo-EM maps.

Conformational space annealing (CSA), a global optimization method, has been applied to various protein structure modeling tasks. In this paper, we applied CSA to the cryo-EM structure modeling task by combining the python subroutine of CSA (PyCSA) and the fast relax (FastRelax) protocol of PyRosetta. Refinement of initial structures generated from two methods, rigid fitting of predicted structures to the Cryo-EM map and de novo protein modeling by tracing the Cryo-EM map, was performed by CSA. In the refinement of the rigid-fitted structures, the final models showed that CSA can generate reliable atomic structures of proteins, even when large movements of protein domains were required. In the de novo modeling case, although the overall structural qualities of the final models were rather dependent on the initial models, the final models generated by CSA showed improved MolProbity scores and cross-correlation coefficients to the maps. These results suggest that CSA can accomplish flexible fitting and refinement together by sampling diverse conformations effectively and thus can be utilized for cryo-EM structure modeling.

Neglected but Efficient Electron Utilization Driven by Biochar-Coactivated Phenols and Peroxydisulfate: Polyphenol Accumulation Rather than Mineralization.

We report an unrecognized but efficient nonradical mechanism in biochar-activated peroxydisulfate (PDS) systems. Combining a newly developed fluorescence trapper of reactive oxygen species with steady-state concentration calculations, we showed that raising pyrolysis temperatures of biochar (BC) from 400 to 800 °C remarkably enhanced trichlorophenol degradation but inhibited the catalytic production of radicals (SO4•- and •OH) in water and soil, thereby switching a radical-based activation into an electron-transfer-dominated nonradical pathway (contribution increased from 12.9 to 76.9%). Distinct from previously reported PDS* complex-determined oxidation, in situ Raman and electrochemical results of this study demonstrated that the simultaneous activation of phenols and PDS on the biochar surface triggers the potential difference-driven electron transfer. The formed phenoxy radicals subsequently undergo coupling and polymerization reactions to generate dimeric and oligomeric intermediates, which are eventually accumulated on the biochar surface and removed. Such a unique nonmineralizing oxidation achieved an ultrahigh electron utilization efficiency (ephenols/ePDS) of 182%. Through biochar molecular modeling and theoretical calculations, we highlighted the critical role of graphitic domains rather than redox-active moieties in lowering band-gap energy to facilitate electron transfer. Our work provides insights into outstanding contradictions and controversies related to nonradical oxidation and inspiration for more oxidant-saving remediation technologies.

Characterization of an A3G-VifHIV-1-CRL5-CBFß Structure Using a Cross-linking Mass Spectrometry Pipeline for Integrative Modeling of Host-Pathogen Complexes.

Structural analysis of host-pathogen protein complexes remains challenging, largely due to their structural heterogeneity. Here, we describe a pipeline for the structural characterization of these complexes using integrative structure modeling based on chemical cross-links and residue-protein contacts inferred from mutagenesis studies. We used this approach on the HIV-1 Vif protein bound to restriction factor APOBEC3G (A3G), the Cullin-5 E3 ring ligase (CRL5), and the cellular transcription factor Core Binding Factor Beta (CBFß) to determine the structure of the (A3G-Vif-CRL5-CBFß) complex. Using the MS-cleavable DSSO cross-linker to obtain a set of 132 cross-links within this reconstituted complex along with the atomic structures of the subunits and mutagenesis data, we computed an integrative structure model of the heptameric A3G-Vif-CRL5-CBFß complex. The structure, which was validated using a series of tests, reveals that A3G is bound to Vif mostly through its N-terminal domain. Moreover, the model ensemble quantifies the dynamic heterogeneity of the A3G C-terminal domain and Cul5 positions. Finally, the model was used to rationalize previous structural, mutagenesis and functional data not used for modeling, including information related to the A3G-bound and unbound structures as well as mapping functional mutations to the A3G-Vif interface. The experimental and computational approach described here is generally applicable to other challenging host-pathogen protein complexes.

Bayesian inference of chromatin structure ensembles from population-averaged contact data.

Mounting experimental evidence suggests a role for the spatial organization of chromatin in crucial processes of the cell nucleus such as transcription regulation. Chromosome conformation capture techniques allow us to characterize chromatin structure by mapping contacts between chromosomal loci on a genome-wide scale. The most widespread modality is to measure contact frequencies averaged over a population of cells. Single-cell variants exist, but suffer from low contact numbers and have not yet gained the same resolution as population methods. While intriguing biological insights have already been garnered from ensemble-averaged data, information about three-dimensional (3D) genome organization in the underlying individual cells remains largely obscured because the contact maps show only an average over a huge population of cells. Moreover, computational methods for structure modeling of chromatin have mostly focused on fitting a single consensus structure, thereby ignoring any cell-to-cell variability in the model itself. Here, we propose a fully Bayesian method to infer ensembles of chromatin structures and to determine the optimal number of states in a principled, objective way. We illustrate our approach on simulated data and compute multistate models of chromatin from chromosome conformation capture carbon copy (5C) data. Comparison with independent data suggests that the inferred ensembles represent the underlying sample population faithfully. Harnessing the rich information contained in multistate models, we investigate cell-to-cell variability of chromatin organization into topologically associating domains, thus highlighting the ability of our approach to deliver insights into chromatin organization of great biological relevance.

Structural dynamics of the human COP9 signalosome revealed by cross-linking mass spectrometry and integrative modeling.

The COP9 signalosome (CSN) is an evolutionarily conserved eight-subunit (CSN1-8) protein complex that controls protein ubiquitination by deneddylating Cullin-RING E3 ligases (CRLs). The activation and function of CSN hinges on its structural dynamics, which has been challenging to decipher by conventional tools. Here, we have developed a multichemistry cross-linking mass spectrometry approach enabled by three mass spectometry-cleavable cross-linkers to generate highly reliable cross-link data. We applied this approach with integrative structure modeling to determine the interaction and structural dynamics of CSN with the recently discovered ninth subunit, CSN9, in solution. Our results determined the localization of CSN9 binding sites and revealed CSN9-dependent structural changes of CSN. Together with biochemical analysis, we propose a structural model in which CSN9 binding triggers CSN to adopt a configuration that facilitates CSN-CRL interactions, thereby augmenting CSN deneddylase activity. Our integrative structure analysis workflow can be generalized to define in-solution architectures of dynamic protein complexes that remain inaccessible to other approaches.

Environment-by-PGS Interaction in the Classical Twin Design: An Application to Childhood Anxiety and Negative Affect.

One type of genotype-environment interaction occurs when genetic effects on a phenotype are moderated by an environment; or when environmental effects on a phenotype are moderated by genes. Here we outline these types of genotype-environment interaction models, and propose a test of genotype-environment interaction based on the classical twin design, which includes observed genetic variables (polygenic scores: PGSs) that account for part of the genetic variance of the phenotype. We introduce environment-by-PGS interaction and the results of a simulation study to address statistical power and parameter recovery. Next, we apply the model to empirical data on anxiety and negative affect in children. The power to detect environment-by-PGS interaction depends on the heritability of the phenotype, and the strength of the PGS. The simulation results indicate that under realistic conditions of sample size, heritability and strength of the interaction, the environment-by-PGS model is a viable approach to detect genotype-environment interaction. In 7-year-old children, we defined two PGS based on the largest genetic association studies for 2 traits that are genetically correlated to childhood anxiety and negative affect, namely major depression (MDD) and intelligence (IQ). We find that common environmental influences on negative affect are amplified for children with a lower IQ-PGS.

Region Met225 to Ile412 of Bacillus cereus Hemolysin II Is Capable to Agglutinate Red Blood Cells.

Hemolysin II (HlyII) is one of the virulence factors of the opportunistic bacterium Bacillus cereus belonging to the group of ß-pore-forming toxins. This work created a genetic construct encoding a large C-terminal fragment of the toxin (HlyIILCTD, M225-I412 according to the numbering of amino acid residues in HlyII). A soluble form of HlyIILCTD was obtained using the SlyD chaperone protein. HlyIILCTD was first shown to be capable of agglutinating rabbit erythrocytes. Monoclonal antibodies against HlyIILCTD were obtained by hybridoma technology. We also proposed a mode of rabbit erythrocyte agglutination by HlyIILCTD and selected three anti-HlyIILCTD monoclonal antibodies that inhibited the agglutination.

Characterization of Treponema denticola Major Surface Protein (Msp) by Deletion Analysis and Advanced Molecular Modeling.

Treponema denticola, a keystone pathogen in periodontitis, is a model organism for studying Treponema physiology and host-microbe interactions. Its major surface protein Msp forms an oligomeric outer membrane complex that binds fibronectin, has cytotoxic pore-forming activity, and disrupts several intracellular processes in host cells. T. denticola msp is an ortholog of the Treponema pallidum tprA to -K gene family that includes tprK, whose remarkable in vivo hypervariability is proposed to contribute to T. pallidum immune evasion. We recently identified the primary Msp surface-exposed epitope and proposed a model of the Msp protein as a ß-barrel protein similar to Gram-negative bacterial porins. Here, we report fine-scale Msp mutagenesis demonstrating that both the N and C termini as well as the centrally located Msp surface epitope are required for native Msp oligomer expression. Removal of as few as three C-terminal amino acids abrogated Msp detection on the T. denticola cell surface, and deletion of four residues resulted in complete loss of detectable Msp. Substitution of a FLAG tag for either residues 6 to 13 of mature Msp or an 8-residue portion of the central Msp surface epitope resulted in expression of full-length Msp but absence of the oligomer, suggesting roles for both domains in oligomer formation. Consistent with previously reported Msp N-glycosylation, proteinase K treatment of intact cells released a 25 kDa polypeptide containing the Msp surface epitope into culture supernatants. Molecular modeling of Msp using novel metagenome-derived multiple sequence alignment (MSA) algorithms supports the hypothesis that Msp is a large-diameter, trimeric outer membrane porin-like protein whose potential transport substrate remains to be identified. IMPORTANCE The Treponema denticola gene encoding its major surface protein (Msp) is an ortholog of the T. pallidum tprA to -K gene family that includes tprK, whose remarkable in vivo hypervariability is proposed to contribute to T. pallidum immune evasion. Using a combined strategy of fine-scale mutagenesis and advanced predictive molecular modeling, we characterized the Msp protein and present a high-confidence model of its structure as an oligomer embedded in the outer membrane. This work adds to knowledge of Msp-like proteins in oral treponemes and may contribute to understanding the evolutionary and potential functional relationships between T. denticola Msp and the orthologous T. pallidum Tpr proteins.