https://www.cell.com/developmental-cell/pdf/S1534-5807(04)00099-1.pdf

Development by Self-Digestion: Molecular Mechanisms and Biological Functions of Autophagy

Functional characterization of a protein sequence is a common goal in biology, and is usually facilitated by having an accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.

/pdf/comparative-protein-structure-modeling-using-modeller-262v4shee8.pdf

Comparative Protein Structure Modeling Using MODELLER

The process of autophagy, or bulk degradation of cellular proteins through an autophagosomic-lysosomal pathway, is important in normal growth control and may be defective in tumour cells. However, little is known about the genetic mediators of autophagy in mammalian cells or their role in tumour development. The mammalian gene encoding Beclin 1, a novel Bcl-2-interacting, coiled-coil protein, has structural similarity to the yeast autophagy gene, apg6/vps30, and is mono-allelically deleted in 40-75% of sporadic human breast cancers and ovarian cancers. Here we show, using gene-transfer techniques, that beclin 1 promotes autophagy in autophagy-defective yeast with a targeted disruption of agp6/vps30, and in human MCF7 breast carcinoma cells. The autophagy-promoting activity of beclin 1 in MCF7 cells is associated with inhibition of MCF7 cellular proliferation, in vitro clonigenicity and tumorigenesis in nude mice. Furthermore, endogenous Beclin 1 protein expression is frequently low in human breast epithelial carcinoma cell lines and tissue, but is expressed ubiquitously at high levels in normal breast epithelia. Thus, beclin 1 is a mammalian autophagy gene that can inhibit tumorigenesis and is expressed at decreased levels in human breast carcinoma. These findings suggest that decreased expression of autophagy proteins may contribute to the development or progression of breast and other human malignancies.

Induction of autophagy and inhibition of tumorigenesis by beclin 1.

Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.

/pdf/comparative-protein-structure-modeling-using-modeller-1ye86wsiv7.pdf

Comparative protein structure modeling using Modeller.

Malignant cells often display defects in autophagy, an evolutionarily conserved pathway for degrading long-lived proteins and cytoplasmic organelles. However, as yet, there is no genetic evidence for a role of autophagy genes in tumor suppression. The beclin 1 autophagy gene is monoallelically deleted in 40-75% of cases of human sporadic breast, ovarian, and prostate cancer. Therefore, we used a targeted mutant mouse model to test the hypothesis that monoallelic deletion of beclin 1 promotes tumorigenesis. Here we show that heterozygous disruption of beclin 1 increases the frequency of spontaneous malignancies and accelerates the development of hepatitis B virus-induced premalignant lesions. Molecular analyses of tumors in beclin 1 heterozygous mice show that the remaining wild-type allele is neither mutated nor silenced. Furthermore, beclin 1 heterozygous disruption results in increased cellular proliferation and reduced autophagy in vivo. These findings demonstrate that beclin 1 is a haplo-insufficient tumor-suppressor gene and provide genetic evidence that autophagy is a novel mechanism of cell-growth control and tumor suppression. Thus, mutation of beclin 1 or other autophagy genes may contribute to the pathogenesis of human cancers.

/pdf/promotion-of-tumorigenesis-by-heterozygous-disruption-of-the-24z4y3aa4w.pdf

Promotion of tumorigenesis by heterozygous disruption of the beclin 1 autophagy gene

The application of all-atom force fields (and explicit or implicit solvent models) to protein homology-modeling tasks such as side-chain and loop prediction remains challenging both because of the expense of the individual energy calculations and because of the difficulty of sampling the rugged all-atom energy surface. Here we address this challenge for the problem of loop prediction through the development of numerous new algorithms, with an emphasis on multiscale and hierarchical techniques. As a first step in evaluating the performance of our loop prediction algorithm, we have applied it to the problem of reconstructing loops in native structures; we also explicitly include crystal packing to provide a fair comparison with crystal structures. In brief, large numbers of loops are generated by using a dihedral angle-based buildup procedure followed by iterative cycles of clustering, side-chain optimization, and complete energy minimization of selected loop structures. We evaluate this method by using the largest test set yet used for validation of a loop prediction method, with a total of 833 loops ranging from 4 to 12 residues in length. Average/median backbone root-mean-square deviations (RMSDs) to the native structures (superimposing the body of the protein, not the loop itself) are 0.42/0.24 A for 5 residue loops, 1.00/0.44 A for 8 residue loops, and 2.47/1.83 A for 11 residue loops. Median RMSDs are substantially lower than the averages because of a small number of outliers; the causes of these failures are examined in some detail, and many can be attributed to errors in assignment of protonation states of titratable residues, omission of ligands from the simulation, and, in a few cases, probable errors in the experimentally determined structures. When these obvious problems in the data sets are filtered out, average RMSDs to the native structures improve to 0.43 A for 5 residue loops, 0.84 A for 8 residue loops, and 1.63 A for 11 residue loops. In the vast majority of cases, the method locates energy minima that are lower than or equal to that of the minimized native loop, thus indicating that sampling rarely limits prediction accuracy. The overall results are, to our knowledge, the best reported to date, and we attribute this success to the combination of an accurate all-atom energy function, efficient methods for loop buildup and side-chain optimization, and, especially for the longer loops, the hierarchical refinement protocol.

A hierarchical approach to all-atom protein loop prediction.

Cloning and Genomic Organization of Beclin 1, a Candidate Tumor Suppressor Gene on Chromosome 17q21

We have developed an improved sampling algorithm and energy model for protein loop prediction, the combination of which has yielded the first methodology capable of achieving good results for the prediction of loop backbone conformations of 11 residue length or greater. Applied to our newly constructed test suite of 104 loops ranging from 11 to 13 residues, our method obtains average/median global backbone root-mean-square deviations (RMSDs) to the native structure (superimposing the body of the protein, not the loop itself) of 1.00/0.62 A for 11 residue loops, 1.15/0.60 A for 12 residue loops, and 1.25/0.76 A for 13 residue loops. Sampling errors are virtually eliminated, while energy errors leading to large backbone RMSDs are very infrequent compared to any previously reported efforts, including our own previous study. We attribute this success to both an improved sampling algorithm and, more critically, the inclusion of a hydrophobic term, which appears to approximately fix a major flaw in SGB solvation model that we have been employing. A discussion of these results in the context of the general question of the accuracy of continuum solvation models is presented.

Long loop prediction using the protein local optimization program.

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.

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

We adapt a technique for searching for ideal classes of arbitrary order and apply it to three families of number fields. We show that a family of cyclic sextic number fields has infinitely many fields in it that contain a relative ideal class of order r, where r is a positive integer relatively prime to the degree of the extension. We then show that the same holds true for a family of cyclic quartic number fields. Though the technique is traditionally applied to Galois extensions, we show how it may be adapted to handle a family of non-Galois cubic number fields and prove that this family contains infinitely many fields with an ideal class of arbitrary order relatively prime to three. 

/pdf/relative-ideal-classes-of-arbitrary-order-mrcy60h6.pdf

David L. Pincus

Papers

A hierarchical approach to all-atom protein loop prediction.

Cloning and Genomic Organization of Beclin 1, a Candidate Tumor Suppressor Gene on Chromosome 17q21

Long loop prediction using the protein local optimization program.

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

Relative ideal classes of arbitrary order