Intratumoral delivery of shRNA targeting cyclin D1 attenuates pancreatic cancer growth.

The aim of this study was to assess the biological consequences of cyclin D1 silencing in pancreatic cancer cells. A replication-defective lentivirus based small hairpin RNA (shRNA) system targeting cyclin D1 caused a marked reduction in cyclin D1 protein levels in ASPC-1 and BxPC3 pancreatic cancer cell lines in conjunction with decreased cell growth and invasiveness in vitro. Moreover, a single intratumoral injection of the recombinant lentivirus targeting cyclin D1 attenuated the growth of pre-existing tumors arising from two distinct cell lines. This attenuated growth correlated with decreased proliferation and angiogenesis, as well as attenuated vascular endothelial growth factor expression. It is concluded that lentivirus-delivered shRNA targeting cyclin D1 suppresses the growth, invasiveness, tumorigenicity and pro-angiogenic potential of human pancreatic cancer cells, thereby raising the possibility that intratumoral injections of viruses targeting cyclin D1 could provide a new therapeutic approach in pancreatic ductal adenocarcinoma.

Protein structure prediction using a combination of sequence-based alignment, constrained energy minimization, and structural alignment.

We present a novel approach to protein structure prediction in which fold recognition techniques are combined with ab initio folding methods. Based on the predicted secondary structure, one of two different protocols is followed. For mostly alpha proteins, global optimization and sampling of a statistical energy function is used to generate many low-energy structures; these structures are then screened against a fold library. Any structural matches are then selected for further refinement. For proteins predicted to have significant beta-content, sequence and secondary structure-based alignment is used to identify candidate templates; spatial constraints are then extracted from these templates and used, along with the statistical energy function, in the global sampling and optimization program. Successes and failures of both protocols are discussed.

Hierarchical minimization with distance and angle constraints.

The incorporation of experimentally-determined constraints into structure-prediction methods based on energy minimization leads to both improved selectivity with empirical potential functions and structure determination with far fewer constraints than are required for distance-geometry calculations. Some methods will be described for using both distance and angle constraints with the hierarchical minimization algorithm. The simulation is based on a combination of Monte Carlo Simulated Annealing and Genetic Algorithm techniques which are integrated into a single framework. The selection cycle of the genetic algorithm is carried out at the same temperature as the mutations, or alternatively the crossover cycle can be considered as a type of Monte Carlo trial move, such that each temperature annealing step corresponds to a new generation. The sequence is divided up into segments, and the mutation step consists of replacing an entire segment with a choice from a pre-selected list. This list is in turn constructed from a list of smaller segments, and the number of overall conformations can thus be pruned at each level of selection. Results will be shown for test cases using a small number of flexible distance constraints used as an additional term in the potential, and for restrictions placed on backbone dihedral angles used as an additional screening criterion for constructing trial moves.

Tertiary structure prediction of mixed alpha/beta proteins via energy minimization.

We describe an improved algorithm for protein structure prediction, assuming that the location of secondary structural elements is known, with particular focus on prediction for proteins containing beta-strands. Hydrogen bonding terms are incorporated into the potential function, supplementing our previously developed residue-residue potential which is based on a combination of database statistics and an excluded volume term. Two small mixed alpha/beta proteins, 1-CTF and BPTI, are studied. In order to obtain native-like structures, it is necessary to allow the beta-strands in BPTI to distort substantially from an ideal geometry, and an automated algorithm to carry this out efficiently is presented. Simulated annealing Monte Carlo methods, which contain a genetic algorithm component as well, are used to produce an ensemble of low-energy structures. For both proteins, a cluster of structures with low RMS deviation from the native structure is generated and the energetic ranking of this cluster is in the top 2 or 3 clusters obtained from simulations. These results are encouraging with regard to the possibility of constructing a robust procedure for tertiary folding which is applicable to beta-strand containing proteins.

Dendritic cells require maturation via CD40 to generate protective antitumor immunity.

A critical role for CD40/CD154 interactions in the generation of protective cell-mediated tumor immunity has been demonstrated previously. Herein, we show that the failure to generate systemic tumor immunity in the absence of CD40/CD154 interactions correlates with an inhibition of Th1-type cytokine production following tumor vaccination. Furthermore, protective antitumor responses can be restored in CD40-deficient mice by the coadministration of CD40+/+ but not CD40-/- dendritic cells (DCs) with tumor Ag, suggesting that CD40 is critical for the maturation and function of DCs in vivo. Finally, we demonstrate that an IL-12-transduced but not a mock-transduced tumor vaccine induces systemic tumor immunity in anti-CD154-treated and CD154-deficient mice. These data suggest that impaired antitumor responses in the absence of CD40/CD154 interactions are the result of a lesion in APC function, namely IL-12 production, and that CD40 plays a critical role in the maturation of DCs in vivo.

Protective immunity induced by tumor vaccines requires interaction between CD40 and its ligand, CD154.

Interactions between CD40 and its ligand, CD154 (CD40L, gp39), have been shown to play a central role in the regulation of humoral immunity. Recent evidence suggests that this ligand-receptor pair also plays an important role in the induction of cell-mediated immune responses, including those directed against viral pathogens, intracellular parasites, and alloantigens. The contribution of this ligand-receptor pair to the development of protective immunity against syngeneic tumors was evaluated by blocking the in vivo function of CD154 or by studying tumor resistance in mice genetically deficient in CD40 expression (CD40-/-). In the former case, anti-CD154 monoclonal antibody treatment inhibited the generation of protective immune responses after the administration of three potent tumor vaccines: irradiated MCA 105, MCA 105 admixed with Corynebacterium parvum adjuvant, and irradiated B16 melanoma cells transduced with the gene for granulocyte macrophage colony-stimulating factor. Confirmation of the role of CD40/CD154 interactions in tumor immunity was provided by the overt tumor susceptibility in CD40-deficient mice as compared to that in CD40+/+ mice. In this case, wild-type but not CD40-deficient mice could be readily protected against live TS/A tumor challenge by preimmunization with TS/A admixed with C. parvum. These findings suggest a critical role for CD40/CD154 interactions in the induction of cellular immunity by tumor vaccines and may have important implications for future approaches to cell-based cancer therapies.

Computational studies of protein folding.

This review describes computational approaches to the determination of protein structure from sequence. The emphasis is on reduced protein models that are sufficiently accurate to represent protein structure at low resolution, yet are computationally efficient enough to allow the extensive search of phase space required to locate the global minimum from an unfolded state. A discussion of both potential functions and algorithmic simulation strategies for such models are presented, along with a number of specific models that have been developed and successfully applied to proteins as large as myoglobin. The results indicate that significant progress is being made in understanding the requirements for computational prediction of protein structure.

Computer modeling of protein folding: conformational and energetic analysis of reduced and detailed protein models.

Recently we developed methods to generate low-resolution protein tertiary structures using a reduced model of the protein where secondary structure is specified and a simple potential based on a statistical analysis of the Protein Data Bank is employed. Here we present the results of an extensive analysis of a large number of detailed, all-atom structures generated from these reduced model structures. Following side-chain addition, minimization and simulated annealing simulations are carried out with a molecular mechanics potential including an approximate continuum solvent treatment. By combining reduced model simulations with molecular modeling calculations we generate energetically competitive, plausible misfolded structures which provide a more significant test of the potential function than current misfolded models based on superimposing the native sequence on the folded structures of completely different proteins. The various contributions to the total energy and their interdependence are analyzed in detail for many conformations of three proteins (myoglobin, the C-terminal fragment of the L7/L12 ribosomal protein, and the N-terminal domain of phage 434 repressor). Our analysis indicates that the all-atom potential performs reasonably well in distinguishing the native structure. It also reveals inadequacies in the reduced model potential, which suggests how this potential can be improved to yield greater accuracy. Preliminary results with an improved potential are presented.