Cancer-oocyte SAS1B protein is expressed at the cell surface of multiple solid tumors and targeted with antibody-drug conjugates.

BACKGROUND: Sperm acrosomal SLLP1 binding (SAS1B) protein is found in oocytes, which is necessary for sperm-oocyte interaction, and also in uterine and pancreatic cancers. Anti-SAS1B antibody-drug conjugates (ADCs) arrested growth in these cancers. However, SAS1B expression in cancers and normal tissues has not been characterized. We hypothesized that SAS1B is expressed on the surface of other common solid cancer cells, but not on normal tissue cells, and might be selectively targeted therapeutically. METHODS: SAS1B expression in human normal and cancer tissues was determined by immunohistochemistry, and complementary DNA (cDNA) libraries were employed to PCR amplify human SAS1B and its transcripts. Monoclonal antibodies (mAbs) to human SAS1B were generated using mouse hybridomas. SAS1B deletion constructs were developed to map SAS1B's epitope, enabling the creation of a blocking peptide. Indirect immunofluorescence (IIF) of human transfected normal and cancer cells was performed to assess SAS1B expression. SAS1B intracellular versus surface expression in normal and tumor tissues was evaluated by flow cytometry after staining with anti-SAS1B mAb, with specificity confirmed with the blocking peptide. Human cancer lines were treated with increasing mAb and ADC concentrations. ATP was quantitated as a measure of cell viability. RESULTS: SAS1B expression was identified in a subset of human cancers and the cytoplasm of pancreatic islet cells. Two new SAS1B splice variants were deduced. Monoclonal antibodies were generated to SAS1B splice variant A. The epitope for mAbs SB2 and SB5 is between SAS1B amino acids 32-39. IIF demonstrated intracellular SAS1B expression in transfected kidney cells and on the cell surface of squamous cell lung carcinoma. Flow cytometry demonstrated intracellular SAS1B expression in all tumors and some normal cells. However, surface expression of SAS1B was identified only on cancer cells. SB2 ADC mediated dose-dependent cytotoxic killing of multiple human cancer lines. CONCLUSION: SAS1B is a novel cancer-oocyte antigen with cell surface expression restricted to cancer cells. In vitro, it is an effective target for antibody-mediated cancer cell lysis. These findings support further exploration of SAS1B as a potential therapeutic cancer target in multiple human cancers, either with ADC or as a chimeric antigen receptor-T (CAR-T) cell target.

On the use of real-world datasets for reaction yield prediction.

The lack of publicly available, large, and unbiased datasets is a key bottleneck for the application of machine learning (ML) methods in synthetic chemistry. Data from electronic laboratory notebooks (ELNs) could provide less biased, large datasets, but no such datasets have been made publicly available. The first real-world dataset from the ELNs of a large pharmaceutical company is disclosed and its relationship to high-throughput experimentation (HTE) datasets is described. For chemical yield predictions, a key task in chemical synthesis, an attributed graph neural network (AGNN) performs as well as or better than the best previous models on two HTE datasets for the Suzuki-Miyaura and Buchwald-Hartwig reactions. However, training the AGNN on an ELN dataset does not lead to a predictive model. The implications of using ELN data for training ML-based models are discussed in the context of yield predictions.

SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials.

Machine learning potentials are an important tool for molecular simulation, but their development is held back by a shortage of high quality datasets to train them on. We describe the SPICE dataset, a new quantum chemistry dataset for training potentials relevant to simulating drug-like small molecules interacting with proteins. It contains over 1.1 million conformations for a diverse set of small molecules, dimers, dipeptides, and solvated amino acids. It includes 15 elements, charged and uncharged molecules, and a wide range of covalent and non-covalent interactions. It provides both forces and energies calculated at the ωB97M-D3(BJ)/def2-TZVPPD level of theory, along with other useful quantities such as multipole moments and bond orders. We train a set of machine learning potentials on it and demonstrate that they can achieve chemical accuracy across a broad region of chemical space. It can serve as a valuable resource for the creation of transferable, ready to use potential functions for use in molecular simulations.

End-to-end differentiable construction of molecular mechanics force fields.

Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules (atom types) for applying parameters to small molecules or biopolymers, making it difficult to optimize both types and parameters to fit quantum chemical or physical property data. Here, we propose an alternative approach that uses graph neural networks to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using invariance-preserving layers. Since all stages are built from smooth neural functions, the entire process-spanning chemical perception to parameter assignment-is modular and end-to-end differentiable with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. We show that this approach is not only sufficiently expressive to reproduce legacy atom types, but that it can learn to accurately reproduce and extend existing molecular mechanics force fields. Trained with arbitrary loss functions, it can construct entirely new force fields self-consistently applicable to both biopolymers and small molecules directly from quantum chemical calculations, with superior fidelity than traditional atom or parameter typing schemes. When adapted to simultaneously fit partial charge models, espaloma delivers high-quality partial atomic charges orders of magnitude faster than current best-practices with low inaccuracy. When trained on the same quantum chemical small molecule dataset used to parameterize the Open Force Field ("Parsley") openff-1.2.0 small molecule force field augmented with a peptide dataset, the resulting espaloma model shows superior accuracy vis-á-vis experiments in computing relative alchemical free energy calculations for a popular benchmark. This approach is implemented in the free and open source package espaloma, available at https://github.com/choderalab/espaloma.

Compressing physics with an autoencoder: Creating an atomic species representation to improve machine learning models in the chemical sciences.

We define a vector quantity which corresponds to atomic species identity by compressing a set of physical properties with an autoencoder. This vector, referred to here as the elemental modes, provides many advantages in downstream machine learning tasks. Using the elemental modes directly as the feature vector, we trained a neural network to predict formation energies of elpasolites with improved accuracy over previous works on the same task. Combining the elemental modes with geometric features used in high-dimensional neural network potentials (HD-NNPs) solves many problems of scaling and efficiency in the development of such neural network potentials. Whereas similar models in the past have been limited to typically four atomic species (H, C, N, and O), our implementation does not scale in cost by adding more atomic species and allows us to train an HD-NNP model which treats molecules containing H, C, N, O, F, P, S, Cl, Se, Br, and I. Finally, we establish that our implementation allows us to define feature vectors for alchemical intermediate states in the HD-NNP model, which opens up new possibilities for performing alchemical free energy calculations on systems where bond breaking/forming is important.

Metadynamics for training neural network model chemistries: A competitive assessment.

Neural network model chemistries (NNMCs) promise to facilitate the accurate exploration of chemical space and simulation of large reactive systems. One important path to improving these models is to add layers of physical detail, especially long-range forces. At short range, however, these models are data driven and data limited. Little is systematically known about how data should be sampled, and "test data" chosen randomly from some sampling techniques can provide poor information about generality. If the sampling method is narrow, "test error" can appear encouragingly tiny while the model fails catastrophically elsewhere. In this manuscript, we competitively evaluate two common sampling methods: molecular dynamics (MD), normal-mode sampling, and one uncommon alternative, Metadynamics (MetaMD), for preparing training geometries. We show that MD is an inefficient sampling method in the sense that additional samples do not improve generality. We also show that MetaMD is easily implemented in any NNMC software package with cost that scales linearly with the number of atoms in a sample molecule. MetaMD is a black-box way to ensure samples always reach out to new regions of chemical space, while remaining relevant to chemistry near kbT. It is a cheap tool to address the issue of generalization.

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics.

Traditional force fields cannot model chemical reactivity, and suffer from low generality without re-fitting. Neural network potentials promise to address these problems, offering energies and forces with near ab initio accuracy at low cost. However a data-driven approach is naturally inefficient for long-range interatomic forces that have simple physical formulas. In this manuscript we construct a hybrid model chemistry consisting of a nearsighted neural network potential with screened long-range electrostatic and van der Waals physics. This trained potential, simply dubbed "TensorMol-0.1", is offered in an open-source Python package capable of many of the simulation types commonly used to study chemistry: geometry optimizations, harmonic spectra, open or periodic molecular dynamics, Monte Carlo, and nudged elastic band calculations. We describe the robustness and speed of the package, demonstrating its millihartree accuracy and scalability to tens-of-thousands of atoms on ordinary laptops. We demonstrate the performance of the model by reproducing vibrational spectra, and simulating the molecular dynamics of a protein. Our comparisons with electronic structure theory and experimental data demonstrate that neural network molecular dynamics is poised to become an important tool for molecular simulation, lowering the resource barrier to simulating chemistry.

Evaluation of SAS1B as a target for antibody-drug conjugate therapy in the treatment of pancreatic cancer.

Successful therapeutic options remain elusive for pancreatic cancer. The exquisite sensitivity and specificity of humoral and cellular immunity may provide therapeutic approaches if antigens specific for pancreatic cancer cells can be identified. Here we characterize SAS1B (ovastacin, ASTL, astacin-like), a cancer-oocyte antigen, as an attractive immunotoxin target expressed at the surface of human pancreatic cancer cells, with limited expression among normal tissues. Immunohistochemistry shows that most pancreatic cancers are SAS1Bpos (68%), while normal pancreatic ductal epithelium is SAS1Bneg. Pancreatic cancer cell lines developed from patient-derived xenograft models display SAS1B cell surface localization, in addition to cytoplasmic expression, suggesting utility for SAS1B in multiple immunotherapeutic approaches. When pancreatic cancer cells were treated with an anti-SAS1B antibody-drug conjugate, significant cell death was observed at 0.01-0.1 µg/mL, while SAS1Bneg human keratinocytes were resistant. Cytotoxicity was correlated with SAS1B cell surface expression; substantial killing was observed for tumors with low steady state SAS1B expression, suggesting a substantial proportion of SAS1Bpos tumors can be targeted in this manner. These results demonstrate SAS1B is a surface target in pancreatic cancer cells capable of binding monoclonal antibodies, internalization, and delivering cytotoxic drug payloads, supporting further development of SAS1B as a novel target for pancreatic cancer.

Potent Pyrimidine and Pyrrolopyrimidine Inhibitors of Testis-Specific Serine/Threonine Kinase†2 (TSSK2).

Testis-specific serine/threonine kinase 2 (TSSK2) is an important target for reversible male contraception. A high-throughput screen of ≈17 000 compounds using a mobility shift assay identified two potent series of inhibitors having a pyrrolopyrimidine or pyrimidine core. The pyrrolopyrimidine 10 (IC50 22 nm; GSK2163632A) and the pyrimidine 17 (IC50 31 nm; ALK inhibitor 1) are the most potent TSSK2 inhibitors in these series, which contain the first sub-100 nanomolar inhibitors of any TSSK isoform reported, except for the broad kinase inhibitor staurosporine. The novel, potent pyrimidine TSSK2 inhibitor compound 19 (IC50 66 nm; 2-[[5-chloro-2-[2-methoxy-4-(1-methylpiperidin-4-yl)anilino]pyrimidin-4-yl]amino]-N-methylbenzenesulfonamide) lacks the potential for metabolic activation. Compound 19 had a potency rank order of TSSK1>TSSK2>TSSK3>TSSK6, indicating that potent dual inhibitors of TSSK1/2 can be identified, which may be required for a complete contraceptive effect. The future availability of a TSSK2 crystal structure will facilitate structure-based discovery of selective TSSK inhibitors from these pyrrolopyrimidine and pyrimidine scaffolds.

Origin of the Size-Dependent Stokes Shift in CsPbBr3 Perovskite Nanocrystals.

The origin of the size-dependent Stokes shift in CsPbBr3 nanocrystals (NCs) is explained for the first time. Stokes shifts range from 82 to 20 meV for NCs with effective edge lengths varying from ∼4 to 13 nm. We show that the Stokes shift is intrinsic to the NC electronic structure and does not arise from extrinsic effects such as residual ensemble size distributions, impurities, or solvent-related effects. The origin of the Stokes shift is elucidated via first-principles calculations. Corresponding theoretical modeling of the CsPbBr3 NC density of states and band structure reveals the existence of an intrinsic confined hole state 260 to 70 meV above the valence band edge state for NCs with edge lengths from ∼2 to 5 nm. A size-dependent Stokes shift is therefore predicted and is in quantitative agreement with the experimental data. Comparison between bulk and NC calculations shows that the confined hole state is exclusive to NCs. At a broader level, the distinction between absorbing and emitting states in CsPbBr3 is likely a general feature of other halide perovskite NCs and can be tuned via NC size to enhance applications involving these materials.

Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network.

Neural networks are being used to make new types of empirical chemical models as inexpensive as force fields, but with accuracy similar to the ab initio methods used to build them. In this work, we present a neural network that predicts the energies of molecules as a sum of intrinsic bond energies. The network learns the total energies of the popular GDB9 database to a competitive MAE of 0.94 kcal/mol on molecules outside of its training set, is naturally linearly scaling, and applicable to molecules consisting of thousands of bonds. More importantly, it gives chemical insight into the relative strengths of bonds as a function of their molecular environment, despite only being trained on total energy information. We show that the network makes predictions of relative bond strengths in good agreement with measured trends and human predictions. A Bonds-in-Molecules Neural Network (BIM-NN) learns heuristic relative bond strengths like expert synthetic chemists, and compares well with ab initio bond order measures such as NBO analysis.

The many-body expansion combined with neural networks.

Fragmentation methods such as the many-body expansion (MBE) are a common strategy to model large systems by partitioning energies into a hierarchy of decreasingly significant contributions. The number of calculations required for chemical accuracy is still prohibitively expensive for the ab initio MBE to compete with force field approximations for applications beyond single-point energies. Alongside the MBE, empirical models of ab initio potential energy surfaces have improved, especially non-linear models based on neural networks (NNs) which can reproduce ab initio potential energy surfaces rapidly and accurately. Although they are fast, NNs suffer from their own curse of dimensionality; they must be trained on a representative sample of chemical space. In this paper we examine the synergy of the MBE and NN's and explore their complementarity. The MBE offers a systematic way to treat systems of arbitrary size while reducing the scaling problem of large systems. NN's reduce, by a factor in excess of 106, the computational overhead of the MBE and reproduce the accuracy of ab initio calculations without specialized force fields. We show that for a small molecule extended system like methanol, accuracy can be achieved with drastically different chemical embeddings. To assess this we test a new chemical embedding which can be inverted to predict molecules with desired properties. We also provide our open-source code for the neural network many-body expansion, Tensormol.

Plasminogen Improves Mouse IVF by Interactions with Inner Acrosomal Membrane-Bound MMP2 and SAMP14.

Spermatozoa must penetrate the outer investments of the oocyte, the cumulus oophorus and the zona pellucida (ZP), in order for fertilization to occur. This may require exposure of enzymes on the sperm's inner acrosomal membrane (IAM), one of which is matrix metalloproteinase (MMP) 2, to factors in oviductal fluid. Plasminogen is present in oviductal fluid and activates MMP2 in somatic tissues. The objectives of this study were: 1) to examine possible interactions between plasminogen and IAM-bound plasminogen activator receptor (SAMP14) and -MMP2, 2) to demonstrate plasminogen's presence in the extracellular environment at the site of fertilization, and 3) to provide evidence that plasminogen plays a role in fertilization. Zymographs of sonicated bull and rat sperm extracts incubated with plasmin and/or plasminogen (plasmin/ogen) showed acceleration of initiation of MMP2 activity in concentrations as low as 1 µg/ml. Immunohistochemical and immunofluorescence analysis of plasmin/ogen revealed its presence in the cytoplasm of mouse ovarian and oviductal oocytes, oviductal epithelium, around the ZP, and amongst the cumulus cells. We modified the standard in vitro fertilization (IVF) approach to more closely mimic natural fertilization by reducing sperm concentration during insemination by ∼100× and also comparing cumulus-intact and denuded oocytes. In mice, addition of plasminogen in IVF medium significantly improved fertilization, while MMP2 antibody significantly inhibited sperm penetration in these conditions. IVF improvement by plasminogen was blocked by SAMP14 antibody. Furthermore, MMP2 antibody inhibition was coincident with a failure by spermatozoa to disperse the cumulus oophorus. We provide evidence that plasminogen on its own and through an MMP2-related mechanism improves the ability of oocytes to be fertilized, and demonstrate its effect in sperm penetration of oocyte investments.

Recombinant production of enzymatically active male contraceptive drug target hTSSK2 - Localization of the TSKS domain phosphorylated by TSSK2.

The testis-specific serine/threonine kinase 2 (TSSK2) has been proposed as a candidate male contraceptive target. Development of a selective inhibitor for this kinase first necessitates the production of highly purified, soluble human TSSK2 and its substrate, TSKS, with high yields and retention of biological activity for crystallography and compound screening. Strategies to produce full-length, soluble, biologically active hTSSK2 in baculovirus expression systems were tested and refined. Soluble preparations of TSSK2 were purified by immobilized-metal affinity chromatography (IMAC) followed by gel filtration chromatography. The biological activities of rec.hTSSK2 were verified by in vitro kinase and mobility shift assays using bacterially produced hTSKS (isoform 2), casein, glycogen synthase peptide (GS peptide) and various TSKS peptides as target substrates. Purified recombinant hTSSK2 showed robust kinase activity in the in vitro kinase assay by phosphorylating hTSKS isoform 2 and casein. The ATP Km values were similar for highly and partially purified fractions of hTSSK2 (2.2 and 2.7 µM, respectively). The broad spectrum kinase inhibitor staurosporine was a potent inhibitor of rec.hTSSK2 (IC50 = 20 nM). In vitro phosphorylation experiments carried out with TSKS (isoform 1) fragments revealed particularly strong phosphorylation of a recombinant N-terminal region representing aa 1-150 of TSKS, indicating that the N-terminus of human TSKS is phosphorylated by human TSSK2. Production of full-length enzymatically active recombinant TSSK2 kinase represents the achievement of a key benchmark for future discovery of TSSK inhibitors as male contraceptive agents.

Membrane associated cancer-oocyte neoantigen SAS1B/ovastacin is a candidate immunotherapeutic target for uterine tumors.

The metalloproteinase SAS1B [ovastacin, ASTL, astacin-like] was immunolocalized on the oolemma of ovulated human oocytes and in normal ovaries within the pool of growing oocytes where SAS1B protein was restricted to follicular stages spanning the primary-secondary follicle transition through ovulation. Gene-specific PCR and immunohistochemical studies revealed ASTL messages and SAS1B protein in both endometrioid [74%] and malignant mixed Mullerian tumors (MMMT) [87%] of the uterus. A MMMT-derived cell line, SNU539, expressed cell surface SAS1B that, after binding polyclonal antibodies, internalized into EEA1/LAMP1-positive early and late endosomes. Treatment of SNU539 cells with anti-SAS1B polyclonal antibodies caused growth arrest in the presence of active complement. A saporin-immunotoxin directed to SAS1B induced growth arrest and cell death. The oocyte restricted expression pattern of SAS1B among adult organs, cell-surface accessibility, internalization into the endocytic pathway, and tumor cell growth arrest induced by antibody-toxin conjugates suggest therapeutic approaches that would selectively target tumors while limiting adverse drug effects in healthy cells. The SAS1B metalloproteinase is proposed as a prototype cancer-oocyte tumor surface neoantigen for development of targeted immunotherapeutics with limited on-target/off tumor effects predicted to be restricted to the population of growing oocytes.

Dynamic Changes in Equatorial Segment Protein 1 (SPESP1) Glycosylation During Mouse Spermiogenesis.

ESP1/SPESP1 is a testis-specific, postmeiotic gene expressed in round spermatids that encodes equatorial segment protein 1, an intra-acrosomal protein found in the acrosomal matrix and on the luminal surface of the inner and outer acrosomal membranes within the equatorial segment domain of mature spermatozoa. A comparison of testicular protein extracts with caput, corpus, and caudal epididymal sperm proteins revealed striking differences in the apparent masses of SPESP1 isoforms. The predominant isoforms of SPESP1 in the testis were 77 and 67 kDa, with 47-kDa forms present to a minor degree. In contrast, SPESP1 isoforms of 47 and 43 kDa were found in caput, corpus, and caudal sperm, indicating that SPESP1 undergoes noticeable mass changes during spermiogenesis and/or subsequent transport to the epididymis. On two-dimensional (2D) SDS-PAGE, testicular SPESP1 isoforms resolved as a train of pI values from 4.9 to 5.2. Immunoprecipitated 77-kDa SPESP1 from testis reacted with the glycoprofile stain after one-dimensional and 2D gel electrophoresis, indicating that the 77-kDa testicular isoform was highly glycosylated. One charge variant of the 67-kDa isoform was also glycoprofile positive after 2D gel resolution. The 47- and 43-kDa isoforms of SPESP1 from epididymal sperm did not stain with glycoprofile, suggesting an absence of, or few, glycoprofile-sensitive glycoconjugates in epididymal SPESP1. Treatment of testicular extracts with a variety of glycosidases resulted in mass shifts in immunoreactive SPESP1, indicating that testicular SPESP1 was glycosylated and that terminal sialic acid, N- and O-glycans were present. A mixture of deglycosidase enzymes (including PNGase-F, neuraminidase, beta1-4 galactosidase, endo-alpha-N-acetylgalactosaminidase, and beta N-acetyl-glucosaminidase) completely eliminated the 77- and 67-kDa SPESP1 bands and resulted in the appearance of 75-, 60-, 55-, 50-, 47-, and 43-kDa forms, confirming that both the 77- and 67-kDa testicular forms of SPESP1 contain complex carbohydrate residues. Treatment of caudal epididymal sperm with PNGase-F enzymes showed a faint deglycosylated band at 30 kDa, but neuraminidase did not result in any molecular shift, indicating that epididymal sperm SPESP1 did not contain sialic acid/N-acetylglucosamine residues. These findings are consistent with the hypothesis that SPSPESP1 undergoes significant glycosylation in the testis and that the majority of these glycoconjugates are removed by the time sperm reach the caput epididymis. Studies of the fate of SPESP1 after the acrosome reaction localized SPESP1 to the equatorial segment region in both noncapacitated and capacitated, acrosome-reacted sperm. During capacitation, SPESP1 underwent proteolysis, resulting in a 27-kDa fragment. Zona-free oocytes incubated with recSPESP1 protein showed complementary binding sites on the microvillar oolemmal domain. Both recSPESP1 and anti-recSPESP1 antibody inhibited in vitro fertilization.

Field testing Diorhabda elongata (Coleoptera: Chrysomelidae) from Crete, Greece, to assess potential impact on nontarget native California plants in the genus Frankenia.

When laboratory host specificity tests on weed biological control agents produce ambiguous results or are suspected of producing false-positive findings, field cage or open field tests can be used to help determine the true ecological host range of the agent. The leaf beetle Diorhabda elongata (Brullé) from Crete, imported to the United States for the control of saltcedar (Tamarix spp., Tamaricaceae), showed a low but variable ovipositional response to nontarget Frankenia spp. (Frankeniaceae) in previous laboratory tests conducted in small cages, where up to 11.4% of eggs were laid on these native plants. Results from field tests presented in this article show that no eggs were laid on Frankenia palmeri S. Watson and significantly more eggs were always laid on Tamarix ramosissima Ledebour than Frankenia salina (Molina) I. M. Johnston. Furthermore, the ovipositional response to F. salina was substantially lower than that recorded in laboratory tests. The percent of eggs laid on F. salina in field tests was 3.7 in a paired choice cage test, 4.3 in a multiple choice cage test, and 2.5 in a multiple choice open field test, suggesting that the true acceptance rate of the nontarget by D. elongata in the field will be lower than laboratory tests predicted. However, some damage was caused to F. salina by adult and larval feeding in the field, although this occurred only at the very end of the open field test, when D. elongata densities were extremely high, and all of the surrounding saltcedar had been totally defoliated. Scientific representatives from various stakeholder organizations (state, county, university, and environmental groups) viewed the open field test when in progress and reviewed the final results before advising State regulatory agencies on beetle redistribution. These test results, and the open review process, led regulators to conclude that redistribution of D. elongata in California was warranted owing to its significant ability to defoliate saltcedar, and its low rate of feeding on nontarget Frankenia spp. The introduction of D. elongata provides an interesting case study for risk assessment of a potentially efficacious weed biocontrol agent that may also be capable of using nontarget native plants.

SAS1B protein [ovastacin] shows temporal and spatial restriction to oocytes in several eutherian orders and initiates translation at the primary to secondary follicle transition.

BACKGROUND: Sperm Acrosomal SLLP1 Binding (SAS1B) protein (ovastacin) is an oolemmal binding partner for the intra-acrosomal sperm protein SLLP1. RESULTS: Immunohistochemical localization revealed that SAS1B translation is restricted among adult tissues to the ovary and oocytes, SAS1B appearing first in follicles at the primary-secondary transition. Quiescent oocytes within primordial follicles and primary follicles did not stain for SAS1B. Examination of neonatal rat ovaries revealed SAS1B expression first as faint signals in postnatal day 3 oocytes, with SAS1B protein staining intensifying with oocyte growth. Irrespective of animal age or estrus stage, SAS1B was seen only in oocytes of follicles that initiated a second granulosa cell layer. The precise temporal and spatial onset of SAS1B expression was conserved in adult ovaries in seven eutherian species, including nonhuman primates. Immunoelectron micrographs localized SAS1B within cortical granules in MII oocytes. A population of SAS1B localized on the oolemma predominantly in the microvillar region anti-podal to the nucleus in ovulated MII rat oocytes and on the oolemma in macaque GV oocytes. CONCLUSIONS: The restricted expression of SAS1B protein in growing oocytes, absence in the ovarian reserve, and localization on the oolemma suggest this zinc metalloprotease deserves consideration as a candidate target for reversible female contraceptive strategies.

Identification of unknown protein function using metabolite cocktail screening.

Proteins of unknown function comprise a significant fraction of sequenced genomes. Defining the roles of these proteins is vital to understanding cellular processes. Here, we describe a method to determine a protein function based on the identification of its natural ligand(s) by the crystallographic screening of the binding of a metabolite library, followed by a focused search in the metabolic space. The method was applied to two protein families with unknown function, PF01256 and YjeF_N. The PF01256 proteins, represented by YxkO from Bacillus subtilis and the C-terminal domain of Tm0922 from Thermotoga maritima, were shown to catalyze ADP/ATP-dependent NAD(P)H-hydrate dehydratation, a previously described orphan activity. The YjeF_N proteins, represented by mouse apolipoprotein A-I binding protein and the N-terminal domain of Tm0922, were found to interact with an adenosine diphosphoribose-related substrate and likely serve as ADP-ribosyltransferases. Crystallographic screening of metabolites serves as an efficient tool in functional analyses of uncharacterized proteins.