Evaluating protein cross-linking as a therapeutic strategy to stabilize SOD1 variants in a mouse model of familial ALS.

Mutations in the gene encoding Cu-Zn superoxide dismutase 1 (SOD1) cause a subset of familial amyotrophic lateral sclerosis (fALS) cases. A shared effect of these mutations is that SOD1, which is normally a stable dimer, dissociates into toxic monomers that seed toxic aggregates. Considerable research effort has been devoted to developing compounds that stabilize the dimer of fALS SOD1 variants, but unfortunately, this has not yet resulted in a treatment. We hypothesized that cyclic thiosulfinate cross-linkers, which selectively target a rare, 2 cysteine-containing motif, can stabilize fALS-causing SOD1 variants in vivo. We created a library of chemically diverse cyclic thiosulfinates and determined structure-cross-linking-activity relationships. A pre-lead compound, "S-XL6," was selected based upon its cross-linking rate and drug-like properties. Co-crystallographic structure clearly establishes the binding of S-XL6 at Cys 111 bridging the monomers and stabilizing the SOD1 dimer. Biophysical studies reveal that the degree of stabilization afforded by S-XL6 (up to 24°C) is unprecedented for fALS, and to our knowledge, for any protein target of any kinetic stabilizer. Gene silencing and protein degrading therapeutic approaches require careful dose titration to balance the benefit of diminished fALS SOD1 expression with the toxic loss-of-enzymatic function. We show that S-XL6 does not share this liability because it rescues the activity of fALS SOD1 variants. No pharmacological agent has been proven to bind to SOD1 in vivo. Here, using a fALS mouse model, we demonstrate oral bioavailability; rapid engagement of SOD1G93A by S-XL6 that increases SOD1G93A's in vivo half-life; and that S-XL6 crosses the blood-brain barrier. S-XL6 demonstrated a degree of selectivity by avoiding off-target binding to plasma proteins. Taken together, our results indicate that cyclic thiosulfinate-mediated SOD1 stabilization should receive further attention as a potential therapeutic approach for fALS.

Biochemical Activity of 17 Cancer-Associated Variants of DNA Polymerase Kappa Predicted by Electrostatic Properties.

DNA damage and repair have been widely studied in relation to cancer and therapeutics. Y-family DNA polymerases can bypass DNA lesions, which may result from external or internal DNA damaging agents, including some chemotherapy agents. Overexpression of the Y-family polymerase human pol kappa can result in tumorigenesis and drug resistance in cancer. This report describes the use of computational tools to predict the effects of single nucleotide polymorphism variants on pol kappa activity. Partial Order Optimum Likelihood (POOL), a machine learning method that uses input features from Theoretical Microscopic Titration Curve Shapes (THEMATICS), was used to identify amino acid residues most likely involved in catalytic activity. The µ4 value, a metric obtained from POOL and THEMATICS that serves as a measure of the degree of coupling between one ionizable amino acid and its neighbors, was then used to identify which protein mutations are likely to impact the biochemical activity. Bioinformatic tools SIFT, PolyPhen-2, and FATHMM predicted most of these variants to be deleterious to function. Along with computational and bioinformatic predictions, we characterized the catalytic activity and stability of 17 cancer-associated DNA pol kappa variants. We identified pol kappa variants R48I, H105Y, G147D, G154E, V177L, R298C, E362V, and R470C as having lower activity relative to wild-type pol kappa; the pol kappa variants T102A, H142Y, R175Q, E210K, Y221C, N330D, N338S, K353T, and L383F were identified as being similar in catalytic efficiency to WT pol kappa. We observed that POOL predictions can be used to predict which variants have decreased activity. Predictions from bioinformatic tools like SIFT, PolyPhen-2, and FATHMM are based on sequence comparisons and therefore are complementary to POOL but are less capable of predicting biochemical activity. These bioinformatic and computational tools can be used to identify SNP variants with deleterious effects and altered biochemical activity from a large data set.

Identification and characterization of alternative sites and molecular probes for SARS-CoV-2 target proteins.

Three protein targets from SARS-CoV-2, the viral pathogen that causes COVID-19, are studied: the main protease, the 2'-O-RNA methyltransferase, and the nucleocapsid (N) protein. For the main protease, the nucleophilicity of the catalytic cysteine C145 is enabled by coupling to three histidine residues, H163 and H164 and catalytic dyad partner H41. These electrostatic couplings enable significant population of the deprotonated state of C145. For the RNA methyltransferase, the catalytic lysine K6968 that serves as a Brønsted base has significant population of its deprotonated state via strong coupling with K6844 and Y6845. For the main protease, Partial Order Optimum Likelihood (POOL) predicts two clusters of biochemically active residues; one includes the catalytic H41 and C145 and neighboring residues. The other surrounds a second pocket adjacent to the catalytic site and includes S1 residues F140, L141, H163, E166, and H172 and also S2 residue D187. This secondary recognition site could serve as an alternative target for the design of molecular probes. From in silico screening of library compounds, ligands with predicted affinity for the secondary site are reported. For the NSP16-NSP10 complex that comprises the RNA methyltransferase, three different sites are predicted. One is the catalytic core at the conserved K-D-K-E motif that includes catalytic residues D6928, K6968, and E7001 plus K6844. The second site surrounds the catalytic core and consists of Y6845, C6849, I6866, H6867, F6868, V6894, D6895, D6897, I6926, S6927, Y6930, and K6935. The third is located at the heterodimer interface. Ligands predicted to have high affinity for the first or second sites are reported. Three sites are also predicted for the nucleocapsid protein. This work uncovers key interactions that contribute to the function of the three viral proteins and also suggests alternative sites for ligand design.

Electrostatic fingerprints of catalytically active amino acids in enzymes.

The computed electrostatic and proton transfer properties are studied for 20 enzymes that represent all six major enzyme commission classes and a variety of different folds. The properties of aspartate, glutamate, and lysine residues that have been previously experimentally determined to be catalytically active are reported. The catalytic aspartate and glutamate residues studied here are strongly coupled to at least one other aspartate or glutamate residue and often to multiple other carboxylate residues with intrinsic pKa differences less than 1 pH unit. Sometimes these catalytic acidic residues are also coupled to a histidine residue, such that the intrinsic pKa of the acidic residue is higher than that of the histidine. All catalytic lysine residues studied here are strongly coupled to tyrosine or cysteine residues, wherein the intrinsic pKa of the anion-forming residue is higher than that of the lysine. Some catalytic lysines are also coupled to other lysines with intrinsic pKa differences within 1 pH unit. Some evidence of the possible types of interactions that facilitate nucleophilicity is discussed. The interactions reported here provide important clues about how side chain functional groups that are weak Brønsted acids or bases for the free amino acid in solution can achieve catalytic potency and become strong acids, bases or nucleophiles in the enzymatic environment.

Amino acid interactions that facilitate enzyme catalysis.

Interactions in enzymes between catalytic and neighboring amino acids and how these interactions facilitate catalysis are examined. In examples from both natural and designed enzymes, it is shown that increases in catalytic rates may be achieved through elongation of the buffer range of the catalytic residues; such perturbations in the protonation equilibria are, in turn, achieved through enhanced coupling of the protonation equilibria of the active ionizable residues with those of other ionizable residues. The strongest coupling between protonation states for a pair of residues that deprotonate to form an anion (or a pair that accept a proton to form a cation) is achieved when the difference in the intrinsic pKas of the two residues is approximately within 1 pH unit. Thus, catalytic aspartates and glutamates are often coupled to nearby acidic residues. For an anion-forming residue coupled to a cation-forming residue, the elongated buffer range is achieved when the intrinsic pKa of the anion-forming residue is higher than the intrinsic pKa of the (conjugate acid of the) cation-forming residue. Therefore, the high pKa, anion-forming residues tyrosine and cysteine make good coupling partners for catalytic lysine residues. For the anion-cation pairs, the optimum difference in intrinsic pKas is a function of the energy of interaction between the residues. For the energy of interaction ε expressed in units of (ln 10)RT, the optimum difference in intrinsic pKas is within ∼1 pH unit of ε.

Adapting Undergraduate Research to Remote Work to Increase Engagement.

Demand for undergraduate research experiences typically outstrips the available laboratory positions, which could have been exacerbated during the remote work conditions imposed by the SARS-CoV-2/COVID-19 pandemic. This report presents a collection of examples of how undergraduates have been engaged in research under pandemic work restrictions. Examples include a range of projects related to fluid dynamics, cancer biology, nanomedicine, circadian clocks, metabolic disease, catalysis, and environmental remediation. Adaptations were made that included partnerships between remote and in-person research students and students taking on more data analysis and literature surveys, as well as data mining, computational, and informatics projects. In many cases, these projects engaged students who otherwise would have worked in traditional bench research, as some previously had. Several examples of beneficial experiences are reported, such as the additional time spent studying the literature, which gave students a heightened sense of project ownership, and more opportunities to integrate feedback into writing and research. Additionally, the more intentional and regular communication necessitated by remote work proved beneficial for all team members. Finally, online seminars and conferences have made participation possible for many more students, especially those at predominantly undergraduate institutions. Participants aim to adopt these beneficial practices in our research groups even after pandemic restrictions end.

Synthesis of benzoylbenzamide derivatives of 17α-E-vinyl estradiol and evaluation as ligands for the estrogen receptor-α ligand binding domain.

A series consisting of substituted benzoylbenzamide derivatives of 17α-E-vinyl estradiol 6a-i and 7a-d was prepared in good overall yields from the corresponding novel iodinated benzoylbenzamide precursors using Pd(0)-catalyzed Stille coupling. Biological evaluation using competitive binding assays indicated that all compounds were effective ligands for the ERα- and ERß-LBD (RBA = 0.5-10.0% of estradiol). Most of the compounds expressed lower stimulatory (agonist) potency (RSA <0.2-0.5%) compared to their binding affinity, however, the meta-substituted isomer 6h demonstrated a level of efficacy (RSA = 5.7%) comparable to its affinity (RBA = 9.5%). Docking studies of 6b, 6h, and 6i with the 2YAT crystal structure suggested that higher affinity and efficacy of 6h are due to an effective set of interactions with exposed receptor sidechains not observed with the ortho- and para- isomers. In this binding model, the terminal ring of the ligand is exposed to the solvent space, which would explain both the small variation in RBA values and the narrow SAR for the diverse structural features.

Tri-arginine exosite patch of caspase-6 recruits substrates for hydrolysis.

Caspases are cysteine-aspartic proteases involved in the regulation of programmed cell death (apoptosis) and a number of other biological processes. Despite overall similarities in structure and active-site composition, caspases show striking selectivity for particular protein substrates. Exosites are emerging as one of the mechanisms by which caspases can recruit, engage, and orient these substrates for proper hydrolysis. Following computational analyses and database searches for candidate exosites, we utilized site-directed mutagenesis to identify a new exosite in caspase-6 at the hinge between the disordered N-terminal domain (NTD), residues 23-45, and core of the caspase-6 structure. We observed that substitutions of the tri-arginine patch Arg-42-Arg-44 or the R44K cancer-associated mutation in caspase-6 markedly alter its rates of protein substrate hydrolysis. Notably, turnover of protein substrates but not of short peptide substrates was affected by these exosite alterations, underscoring the importance of this region for protein substrate recruitment. Hydrogen-deuterium exchange MS-mediated interrogation of the intrinsic dynamics of these enzymes suggested the presence of a substrate-binding platform encompassed by the NTD and the 240's region (containing residues 236-246), which serves as a general exosite for caspase-6-specific substrate recruitment. In summary, we have identified an exosite on caspase-6 that is critical for protein substrate recognition and turnover and therefore highly relevant for diseases such as cancer in which caspase-6-mediated apoptosis is often disrupted, and in neurodegeneration in which caspase-6 plays a central role.

Fluorinated Adenosine A2A Receptor Antagonists Inspired by Preladenant as Potential Cancer Immunotherapeutics.

Antagonism of the adenosine A2A receptor on T cells blocks the hypoxia-adenosinergic pathway to promote tumor rejection. Using an in vivo immunoassay based on the Concanavalin A mouse model, a series of A2A antagonists were studied and identified preladenant as a potent lead compound for development. Molecular modeling was employed to assist drug design and subsequent synthesis of analogs and those of tozadenant, including fluorinated polyethylene glycol PEGylated derivatives. The efficacy of the analogs was evaluated using two in vitro functional bioassays, and compound 29, a fluorinated triethylene glycol derivative of preladenant, was confirmed as a potential immunotherapeutic agent.

Synthesis and evaluation of 2-halogenated-1,1-bis(4-hydroxyphenyl)-2-(3-hydroxyphenyl)-ethylenes as potential estrogen receptor-targeted radiodiagnostic and radiotherapeutic agents.

A series of three 1,1-bis(4-hydroxyphenyl)-2-(3-hydroxyphenyl)-ethylene derivatives was prepared and evaluated as potential estrogen receptor imaging agents. The compounds display high binding affinity compared to estradiol, with the 2-iodo and 2-bromo-derivatives expressing higher affinity than the parent 2-nonhalogenated derivative. Evaluation in immature female rats also indicate that the compounds were all full estrogenic agonists with potencies in the same order of activity (I∼Br>H). Computational analysis of the interactions between the ligands and ERα-LBD demonstrated positive contribution of halide to binding properties. In preparation for studies using the radiohalogenated analogs, the corresponding protected 2-(tributylstannyl) derivative was prepared and converted to the corresponding 2-iodo-product.

Prediction of distal residue participation in enzyme catalysis.

A scoring method for the prediction of catalytically important residues in enzyme structures is presented and used to examine the participation of distal residues in enzyme catalysis. Scores are based on the Partial Order Optimum Likelihood (POOL) machine learning method, using computed electrostatic properties, surface geometric features, and information obtained from the phylogenetic tree as input features. Predictions of distal residue participation in catalysis are compared with experimental kinetics data from the literature on variants of the featured enzymes; some additional kinetics measurements are reported for variants of Pseudomonas putida nitrile hydratase (ppNH) and for Escherichia coli alkaline phosphatase (AP). The multilayer active sites of P. putida nitrile hydratase and of human phosphoglucose isomerase are predicted by the POOL log ZP scores, as is the single-layer active site of P. putida ketosteroid isomerase. The log ZP score cutoff utilized here results in over-prediction of distal residue involvement in E. coli alkaline phosphatase. While fewer experimental data points are available for P. putida mandelate racemase and for human carbonic anhydrase II, the POOL log ZP scores properly predict the previously reported participation of distal residues.

Design and evaluation of xanthine based adenosine receptor antagonists: potential hypoxia targeted immunotherapies.

Molecular modeling techniques were applied to the design, synthesis and optimization of a new series of xanthine based adenosine A(2A) receptor antagonists. The optimized lead compound was converted to a PEG derivative and a functional in vitro bioassay used to confirm efficacy. Additionally, the PEGylated version showed enhanced aqueous solubility and was inert to photoisomerization, a known limitation of existing antagonists of this class.

A tale of two isomerases: compact versus extended active sites in ketosteroid isomerase and phosphoglucose isomerase.

Understanding the catalytic efficiency and specificity of enzymes is a fundamental question of major practical and conceptual importance in biochemistry. Although progress in biochemical and structural studies has enriched our knowledge of enzymes, the role in enzyme catalysis of residues that are not nearest neighbors of the reacting substrate molecule is largely unexplored experimentally. Here computational active site predictors, THEMATICS and POOL, were employed to identify functionally important residues that are not in direct contact with the reacting substrate molecule. These predictions then guided experiments to explore the active sites of two isomerases, Pseudomonas putida ketosteroid isomerase (KSI) and human phosphoglucose isomerase (PGI), as prototypes for very different types of predicted active sites. Both KSI and PGI are members of EC 5.3 and catalyze similar reactions, but they represent significantly different degrees of remote residue participation, as predicted by THEMATICS and POOL. For KSI, a compact active site of mostly first-shell residues is predicted, but for PGI, an extended active site in which residues in the first, second, and third layers around the reacting substrate are predicted. Predicted residues that have not been previously tested experimentally were investigated by site-directed mutagenesis and kinetic analysis. In human PGI, single-point mutations of the predicted second- and third-shell residues K362, H100, E495, D511, H396, and Q388 show significant decreases in catalytic activity relative to that of the wild type. The results of these experiments demonstrate that, as predicted, remote residues are very important in PGI catalysis but make only small contributions to catalysis in KSI.

Crystal structure of a metal-dependent phosphoesterase (YP_910028.1) from Bifidobacterium adolescentis: Computational prediction and experimental validation of phosphoesterase activity.

The crystal structures of an unliganded and adenosine 5'-monophosphate (AMP) bound, metal-dependent phosphoesterase (YP_910028.1) from Bifidobacterium adolescentis are reported at 2.4 and 1.94 Å, respectively. Functional characterization of this enzyme was guided by computational analysis and then confirmed by experiment. The structure consists of a polymerase and histidinol phosphatase (PHP, Pfam: PF02811) domain with a second domain (residues 105-178) inserted in the middle of the PHP sequence. The insert domain functions in binding AMP, but the precise function and substrate specificity of this domain are unknown. Initial bioinformatics analyses yielded multiple potential functional leads, with most of them suggesting DNA polymerase or DNA replication activity. Phylogenetic analysis indicated a potential DNA polymerase function that was somewhat supported by global structural comparisons identifying the closest structural match to the alpha subunit of DNA polymerase III. However, several other functional predictions, including phosphoesterase, could not be excluded. Theoretical microscopic anomalous titration curve shapes, a computational method for the prediction of active sites from protein 3D structures, identified potential reactive residues in YP_910028.1. Further analysis of the predicted active site and local comparison with its closest structure matches strongly suggested phosphoesterase activity, which was confirmed experimentally. Primer extension assays on both normal and mismatched DNA show neither extension nor degradation and provide evidence that YP_910028.1 has neither DNA polymerase activity nor DNA-proofreading activity. These results suggest that many of the sequence neighbors previously annotated as having DNA polymerase activity may actually be misannotated.

Identification of functional subclasses in the DJ-1 superfamily proteins.

Genomics has posed the challenge of determination of protein function from sequence and/or 3-D structure. Functional assignment from sequence relationships can be misleading, and structural similarity does not necessarily imply functional similarity. Proteins in the DJ-1 family, many of which are of unknown function, are examples of proteins with both sequence and fold similarity that span multiple functional classes. THEMATICS (theoretical microscopic titration curves), an electrostatics-based computational approach to functional site prediction, is used to sort proteins in the DJ-1 family into different functional classes. Active site residues are predicted for the eight distinct DJ-1 proteins with available 3-D structures. Placement of the predicted residues onto a structural alignment for six of these proteins reveals three distinct types of active sites. Each type overlaps only partially with the others, with only one residue in common across all six sets of predicted residues. Human DJ-1 and YajL from Escherichia coli have very similar predicted active sites and belong to the same probable functional group. Protease I, a known cysteine protease from Pyrococcus horikoshii, and PfpI/YhbO from E. coli, a hypothetical protein of unknown function, belong to a separate class. THEMATICS predicts a set of residues that is typical of a cysteine protease for Protease I; the prediction for PfpI/YhbO bears some similarity. YDR533Cp from Saccharomyces cerevisiae, of unknown function, and the known chaperone Hsp31 from E. coli constitute a third group with nearly identical predicted active sites. While the first four proteins have predicted active sites at dimer interfaces, YDR533Cp and Hsp31 both have predicted sites contained within each subunit. Although YDR533Cp and Hsp31 form different dimers with different orientations between the subunits, the predicted active sites are superimposable within the monomer structures. Thus, the three predicted functional classes form four different types of quaternary structures. The computational prediction of the functional sites for protein structures of unknown function provides valuable clues for functional classification.

Statistical criteria for the identification of protein active sites using Theoretical Microscopic Titration Curves.

Theoretical Microscopic Titration Curves (THEMATICS) may be used to identify chemically important residues in active sites of enzymes by characteristic deviations from the normal, sigmoidal Henderson-Hasselbalch titration behavior. Clusters of such deviant residues in physical proximity constitute reliable predictors of the location of the active site. Originally the residues with deviant predicted behavior were identified by human observation of the computed titration curves. However, it is preferable to select the unusual residues by mathematically well-defined criteria, in order to reduce the chance of error, eliminate any possible biases, and substantially speed up the selection process. Here we present some simple statistical tests that constitute such selection criteria. The first derivatives of the predicted titration curves resemble distribution functions and are normalized. The moments of these first derivative functions are computed. It is shown that the third and fourth moments, measures of asymmetry and kurtosis, respectively, are good measures of the deviations from normal behavior. Results are presented for 44 different enzymes. Detailed results are given for 4 enzymes with 4 different types of chemistry: arginine kinase from Limulus polyphemus (horseshoe crab); beta-lactamase from Escherichia coli; glutamate racemase from Aquifex pyrophilus; and 3-isopropylmalate dehydrogenase from Thiobacillus ferrooxidans. The relationship between the statistical measures of nonsigmoidal behavior in the predicted titration curves and the catalytic activity of the residue is discussed.

Future directions in protein function prediction.

New directions in computational methods for the prediction of protein function are discussed. THEMATICS, a method for the location and characterization of the active sites of enzymes, is featured. THEMATICS, for Theoretical Microscopic Titration Curves, is based on well-established finite-difference Poisson-Boltzmann methods for computing the electric field function of a protein. THEMATICS requires only the structure of the subject protein and thus may be applied to proteins that bear no similarity in structure or sequence to any previously characterized protein. The unique features of catalytic sites in proteins are discussed. Discussion of the chemical basis for the predictive powers of THEMATICS is featured in this paper. Some results are given for three illustrative examples: HIV-1 protease, human apurinic/apyrimidinic endonuclease, and human adenosine kinase.