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Despite substantial technological advances in antibody library and display platform development, the number of approved biotherapeutics from displayed libraries remains limited. In vivo , 20-50% of peripheral B cells undergo a process of receptor editing, which modifies the variable and junctional regions of light chains to delete auto-reactive clones. However, in vitro antibody evolution relies primarily on interaction with antigen, with no in-built checkpoints to ensure that the selected antibodies have not acquired additional specificities or biophysical liabilities during the optimization process. We had previously observed an enrichment of positive charge in the complementarity-determining regions of an anti-IL-21 R antibody during affinity optimization, which correlated with more potent IL-21 neutralization, but poor in vivo pharmacokinetics (PK). There is an emerging body of data that has correlated antibody nonspecificity with poor PK in vivo , and established a series of screening assays that are predictive of this behavior. In this study we revisit the challenge of developing an anti-IL-21 R antibody that can effectively compete with IL-21 for its highly negatively charged paratope while maintaining favorable biophysical properties. In vitro deselection methods that included an excess of negatively charged membrane preparations, or deoxyribonucleic acid, during phage selection of optimization libraries were unsuccessful in avoiding enrichment of highly charged, nonspecific antibody variants. However, a combination of structure-guided rational library design, next-generation sequencing of library outputs and application of linear regression models resulted in the identification of an antibody that maintained high affinity for IL-21 R and exhibited a desirable stability and biophysical profile.

Non-alcoholic fatty liver disease (NAFLD) is a progressive liver disease characterized by dysregulated lipid metabolism and chronic inflammation ultimately resulting in fibrosis. Untreated, NAFLD may progress to non-alcoholic steatohepatitis (NASH), cirrhosis and death. However, currently there are no FDA approved therapies that treat NAFLD/NASH. Thrombospondin-I (TSP-1) is a large glycoprotein in the extracellular matrix that regulates numerous cellular pathways including transforming growth factor beta 1 (TGF-β1) activation, angiogenesis, inflammation and cellular adhesion. Increased expression of TSP-1 has been reported in various liver diseases; however, its role in NAFLD/NASH is not well understood. We first examined TSP-1 modulation in hepatic stellate cell activation, a critical initiating step in hepatic fibrosis. Knockdown or inhibition of TSP-1 attenuated HSC activation measured by alpha smooth muscle actin (α-SMA) and Collagen I expression. To investigate the impact of TSP-1 modulation in context of NAFLD/NASH, we examined the effect of TSP-1 deficiency in the choline deficient L-amino acid defined high fat diet (CDAHFD) model of NASH in mice by assessing total body and liver weight, serum liver enzyme levels, serum lipid levels, liver steatosis, liver fibrosis and liver gene expression in wild type (WT) and TSP-1 null mice. CDAHFD fed mice, regardless of genotype, developed phenotypes of NASH, including significant increase in liver weight and liver enzymes, steatosis and fibrosis. However, in comparison to WT, CDAHFD-fed TSP-1 deficient mice were protected against numerous NASH phenotypes. TSP-1 null mice exhibited a decrease in serum lipid levels, inflammation markers and hepatic fibrosis. RNA-seq based transcriptomic profiles from the liver of CDAHFD fed mice determined that both WT and TSP-1 null mice exhibited similar gene expression signatures following CDAHFD, similar to biophysical and histological assessment comparison. Comparison of transcriptomic profiles based on genotype suggested that peroxisome proliferator activated receptor alpha (PPARα) pathway and amino acid metabolism pathways are differentially expressed in TSP-1 null mice. Activation of PPARα pathway was supported by observed decrease in serum lipid levels. Our findings provide important insights into the role of TSP-1 in context of NAFLD/NASH and TSP-1 may be a target of interest to develop anti-fibrotic therapeutics for NAFLD/NASH., Competing Interests: The authors have commercial affiliations to Pfizer Worldwide Research and Development, Biogen and Wave Life Sciences and these affiliations do not alter our adherence to PLOS ONE policies on sharing data and materials.

Background: The new National Cancer Institute (NCI) Community Oncology Research Program (NCORP) went live August 1, 2014; 34 sites were selected for the program, including 7 new sites that previously did not have a research grant from the NCI. This report describes the first year of a new program site., Methods: Accrual, investigator and site participation, and number of open studies by the program over the first 12 months of the grant were compared to performance at our institution over the prior 12 months., Results: During the pre-NCORP period, 84 patients were accrued to NCI-sponsored trials and 106 patients to non–NCI-sponsored trials. In year 1 of the new program, 140 were accrued to NCI-sponsored trials—a 66% improvement, and 109 patients to non–NCI-sponsored trials (P = 0.013 when comparing corresponding increases for NCI vs non-NCI trials). Success of the NCI-sponsored trials was associated with increased accrual to both treatment trials (P = 0.03) and Alliance for Clinical Trials in Oncology-sponsored trials (P = 0.0001)., Conclusions: NCORP implementation was associated with a significant (P = 0.013) improvement in accrual to NCI-sponsored trials that was immediate (1 year) and large (a 66% increase in accrual). In year 2, the intention is to increase cancer control studies; foster inclusion of radiation, surgical, gynecologic, and neurologic oncologists; and focus on minority outreach. Studies that accrue poorly will be assessed, and those accruing poorly on a national basis will be considered for closure. Studies accruing well nationally will be evaluated for barriers to local accrual.

The activation of CO2 and its hydrogenation to methanol are of much interest as a way to utilize captured CO2. Here, we investigate the use of size-selected Cu4 clusters supported on Al2O3 thin films for CO2 reduction in the presence of hydrogen. The catalytic activity was measured under near-atmospheric reaction conditions with a low CO2 partial pressure, and the oxidation state of the clusters was investigated by in situ grazing incidence X-ray absorption spectroscopy. The results indicate that size-selected Cu4 clusters are the most active low-pressure catalyst for catalytic CO2 conversion to CH3OH. Density functional theory calculations reveal that Cu4 clusters have a low activation barrier for conversion of CO2 to CH3OH. This study suggests that small Cu clusters may be excellent and efficient catalysts for the recycling of released CO2.

Size-selected subnanometer cobalt clusters with 4, 7, and 27 cobalt atoms supported on amorphous alumina and ultrananocrystalline diamond (UNCD) surfaces were oxidized after exposure to ambient air. Grazing incidence X-ray absorption near-edge spectroscopy (GIXANES) and near-edge X-ray absorption fine structure (NEXAFS) were used to characterize the clusters revealed a strong dependency of the oxidation state and structure of the clusters on the surface. A dominant Co(2+) phase was identified in all samples. However, XANES analysis of cobalt clusters on UNCD showed that ∼10% fraction of a Co(0) phase was identified for all three cluster sizes and about 30 and 12% fraction of a Co(3+) phase in 4, 7, and 27 atom clusters, respectively. In the alumina-supported clusters, the dominating Co(2+) component was attributed to a cobalt aluminate, indicative of a very strong binding to the support. NEXAFS showed that in addition to strong binding of the clusters to alumina, their structure to a great extent follows the tetrahedral morphology of the support. All supported clusters were found to be resistant to agglomeration when exposed to reactive gases at elevated temperatures and atmospheric pressure.

Programmed cell death is regulated by interactions between pro-apoptotic and prosurvival members of the Bcl-2 family. Pro-apoptotic family members contain a weakly conserved BH3 motif that can adopt an alpha-helical structure and bind to a groove on prosurvival partners Bcl-xL, Bcl-w, Bcl-2, Mcl-1 and Bfl-1. Peptides corresponding to roughly 13 reported BH3 motifs have been verified to bind in this manner. Due to their short lengths and low sequence conservation, BH3 motifs are not detected using standard sequence-based bioinformatics approaches. Thus, it is possible that many additional proteins harbor BH3-like sequences that can mediate interactions with the Bcl-2 family. In this work, we used structure-based and data-based Bcl-2 interaction models to find new BH3-like peptides in the human proteome. We used peptide SPOT arrays to test candidate peptides for interaction with one or more of the prosurvival proteins Bcl-xL, Bcl-w, Bcl-2, Mcl-1 and Bfl-1. For the 36 most promising array candidates, we quantified binding to all five human receptors using direct and competition binding assays in solution. All 36 peptides showed evidence of interaction with at least one prosurvival protein, and 22 peptides bound at least one prosurvival protein with a dissociation constant between 1 and 500 nM; many peptides had specificity profiles not previously observed. We also screened the full-length parent proteins of a subset of array-tested peptides for binding to Bcl-xL and Mcl-1. Finally, we used the peptide binding data, in conjunction with previously reported interactions, to assess the affinity and specificity prediction performance of different models.

To gain insight into the functional antibody repertoire of rabbits, the VH and VL repertoires of bone marrow (BM) and spleen (SP) of a naïve New Zealand White rabbit (NZW; Oryctolagus cuniculus) and that of lymphocytes collected from a NZW rabbit immunized (IM) with a 16-mer peptide were deep-sequenced. Two closely related genes, IGHV1S40 (VH1a3) and IGHV1S45 (VH4), were found to dominate (~90%) the VH repertoire of BM and SP, whereas, IGHV1S69 (VH1a1) contributed significantly (~40%) to IM. BM and SP antibodies recombined predominantly with IGHJ4. A significant proportion (~30%) of IM sequences recombined with IGHJ2. The VK repertoire was encoded by nine IGKV genes recombined with one IGKJ gene, IGKJ1. No significant bias in the VK repertoire of the BM, SP and IM samples was observed. The complementarity-determining region (CDR)-H3 and -L3 length distributions were similar in the three samples following a Gaussian curve with average length of 12.2 ± 2.4 and 11.1 ± 1.1 amino acids, respectively. The amino acid composition of the predominant CDR-H3 and -L3 loop lengths was similar to that of humans and mice, rich in Tyr, Gly, Ser and, in some specific positions, Asp. The average number of mutations along the IGHV/KV genes was similar in BM, SP and IM; close to 12 and 15 mutations for VH and VL, respectively. A monoclonal antibody specific for the peptide used as immunogen was obtained from the IM rabbit. The CDR-H3 sequence was found in 1,559 of 61,728 (2.5%) sequences, at position 10, in the rank order of the CDR-H3 frequencies. The CDR-L3 was found in 24 of 11,215 (0.2%) sequences, ranking 102. No match was found in the BM and SP samples, indicating positive selection for the hybridoma sequence. Altogether, these findings lay foundations for engineering of rabbit V regions to enhance their potential as therapeutics, i.e., design of strategies for selection of specific rabbit V regions from NGS data mining, humanization and design of libraries for affinity maturation campaigns.

Water oxidation is a key catalytic step for electrical fuel generation. Recently, significant progress has been made in synthesizing electrocatalytic materials with reduced overpotentials and increased turnover rates, both key parameters enabling commercial use in electrolysis or solar to fuels applications. The complexity of both the catalytic materials and the water oxidation reaction makes understanding the catalytic site critical to improving the process. Here we study water oxidation in alkaline conditions using size-selected clusters of Pd to probe the relationship between cluster size and the water oxidation reaction. We find that Pd4 shows no reaction, while Pd6 and Pd17 deposited clusters are among the most active (in terms of turnover rate per Pd atom) catalysts known. Theoretical calculations suggest that this striking difference may be a demonstration that bridging Pd-Pd sites (which are only present in three-dimensional clusters) are active for the oxygen evolution reaction in Pd6O6. The ability to experimentally synthesize size-specific clusters allows direct comparison to this theory. The support electrode for these investigations is ultrananocrystalline diamond (UNCD). This material is thin enough to be electrically conducting and is chemically/electrochemically very stable. Even under the harsh experimental conditions (basic, high potential) typically employed for water oxidation catalysts, UNCD demonstrates a very wide potential electrochemical working window and shows only minor evidence of reaction. The system (soft-landed Pd4, Pd6, or Pd17 clusters on a UNCD Si-coated electrode) shows stable electrochemical potentials over several cycles, and synchrotron studies of the electrodes show no evidence for evolution or dissolution of either the electrode material or the clusters.

Proteins of the Bcl-2 family either enhance or suppress programmed cell death and are centrally involved in cancer development and resistance to chemotherapy. BH3 (Bcl-2 homology 3)-only Bcl-2 proteins promote cell death by docking an α-helix into a hydrophobic groove on the surface of one or more of five pro-survival Bcl-2 receptor proteins. There is high structural homology within the pro-death and pro-survival families, yet a high degree of interaction specificity is nevertheless encoded, posing an interesting and important molecular recognition problem. Understanding protein features that dictate Bcl-2 interaction specificity is critical for designing peptide-based cancer therapeutics and diagnostics. In this study, we present peptide SPOT arrays and deep sequencing data from yeast display screening experiments that significantly expand the BH3 sequence space that has been experimentally tested for interaction with five human anti-apoptotic receptors. These data provide rich information about the determinants of Bcl-2 family specificity. To interpret and use the information, we constructed two simple data-based models that can predict affinity and specificity when evaluated on independent data sets within a limited sequence space. We also constructed a novel structure-based statistical potential, called STATIUM, which is remarkably good at predicting Bcl-2 affinity and specificity, especially considering it is not trained on experimental data. We compare the performance of our three models to each other and to alternative structure-based methods and discuss how such tools can guide prediction and design of new Bcl-2 family complexes., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Crystals of many important biological macromolecules diffract to limited resolution, rendering accurate model building and refinement difficult and time-consuming. We present a torsional optimization protocol that is applicable to many such situations and combines Protein Data Bank-based torsional optimization with real-space refinement against the electron density derived from crystallography or cryo-electron microscopy. Our method converts moderate- to low-resolution structures at initial (e.g., backbone trace only) or late stages of refinement to structures with increased numbers of hydrogen bonds, improved crystallographic R-factors, and superior backbone geometry. This automated method is applicable to DNA-binding and membrane proteins of any size and will aid studies of structural biology by improving model quality and saving considerable effort. The method can be extended to improve NMR and other structures. Our backbone score and its sequence profile provide an additional standard tool for evaluating structural quality., (Copyright © 2011 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

One of the major challenges with protein template-free modeling is an efficient sampling algorithm that can explore a huge conformation space quickly. The popular fragment assembly method constructs a conformation by stringing together short fragments extracted from the Protein Data Base (PDB). The discrete nature of this method may limit generated conformations to a subspace in which the native fold does not belong. Another worry is that a protein with really new fold may contain some fragments not in the PDB. This article presents a probabilistic model of protein conformational space to overcome the above two limitations. This probabilistic model employs directional statistics to model the distribution of backbone angles and 2(nd)-order Conditional Random Fields (CRFs) to describe sequence-angle relationship. Using this probabilistic model, we can sample protein conformations in a continuous space, as opposed to the widely used fragment assembly and lattice model methods that work in a discrete space. We show that when coupled with a simple energy function, this probabilistic method compares favorably with the fragment assembly method in the blind CASP8 evaluation, especially on alpha or small beta proteins. To our knowledge, this is the first probabilistic method that can search conformations in a continuous space and achieves favorable performance. Our method also generated three-dimensional (3D) models better than template-based methods for a couple of CASP8 hard targets. The method described in this article can also be applied to protein loop modeling, model refinement, and even RNA tertiary structure prediction.

For naturally occurring proteins, similar sequence implies similar structure. Consequently, multiple sequence alignments (MSAs) often are used in template-based modeling of protein structure and have been incorporated into fragment-based assembly methods. Our previous homology-free structure prediction study introduced an algorithm that mimics the folding pathway by coupling the formation of secondary and tertiary structure. Moves in the Monte Carlo procedure involve only a change in a single pair of phi,psi backbone dihedral angles that are obtained from a Protein Data Bank-based distribution appropriate for each amino acid, conditional on the type and conformation of the flanking residues. We improve this method by using MSAs to enrich the sampling distribution, but in a manner that does not require structural knowledge of any protein sequence (i.e., not homologous fragment insertion). In combination with other tools, including clustering and refinement, the accuracies of the predicted secondary and tertiary structures are substantially improved and a global and position-resolved measure of confidence is introduced for the accuracy of the predictions. Performance of the method in the Critical Assessment of Structure Prediction (CASP8) is discussed.

Since the demonstration that the sequence of a protein encodes its structure, the prediction of structure from sequence remains an outstanding problem that impacts numerous scientific disciplines, including many genome projects. By iteratively fixing secondary structure assignments of residues during Monte Carlo simulations of folding, our coarse-grained model without information concerning homology or explicit side chains can outperform current homology-based secondary structure prediction methods for many proteins. The computationally rapid algorithm using only single (phi,psi) dihedral angle moves also generates tertiary structures of accuracy comparable with existing all-atom methods for many small proteins, particularly those with low homology. Hence, given appropriate search strategies and scoring functions, reduced representations can be used for accurately predicting secondary structure and providing 3D structures, thereby increasing the size of proteins approachable by homology-free methods and the accuracy of template methods that depend on a high-quality input secondary structure.

Despite significant progress in recent years, ab initio folding is still one of the most challenging problems in structural biology. This paper presents a probabilistic graphical model for ab initio folding, which employs Conditional Random Fields (CRFs) and directional statistics to model the relationship between the primary sequence of a protein and its three-dimensional structure. Different from the widely-used fragment assembly method and the lattice model for protein folding, our graphical model can explore protein conformations in a continuous space according to their probability. The probability of a protein conformation reflects its stability and is estimated from PSI-BLAST sequence profile and predicted secondary structure. Experimental results indicate that this new method compares favorably with the fragment assembly method and the lattice model.