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B Rost

Publications and source records attributed to B Rost.

At least 55 records · Page 3Linked to original sources
PubMed1996

Refining neural network predictions for helical transmembrane proteins by dynamic programming.

For transmembrane proteins experimental determination of three-dimensional structure is problematic. However, membrane proteins have important impact for molecular biology in general, and for drug design in particular. Thus, prediction method are needed. Here we introduce a method that started from the output of the profile-based neural network system PHDhtm (Rost, et al. 1995). Instead of choosing the neural network output unit with maximal value as prediction, we implemented a dynamic programming-like refinement procedure that aimed at producing the best model for all transmembrane helices compatible with the neural network output. The refined prediction was used successfully to predict transmembrane topology based on an empirical rule for the charge difference between extra- and intra-cytoplasmic regions (positive-inside rule). Preliminary results suggest that the refinement was clearly superior to the initial neural network system; and that the method predicted all transmembrane helices correctly for more proteins than a previously applied empirical filter. The resulting accuracy in predicting topology was better than 80%. Although a more thorough evaluation of the method on a larger data set will be required, the results compared favourably with alternative methods. The results reflected the strength of the refinement procedure which was the successful incorporation of global information: whereas the residue preferences output by the neural network were derived from stretches of 17 adjacent residues, the refinement procedure involved constraints on the level of the entire protein.

Algorithms↗
PubMed1995

Transmembrane helices predicted at 95% accuracy.

We describe a neural network system that predicts the locations of transmembrane helices in integral membrane proteins. By using evolutionary information as input to the network system, the method significantly improved on a previously published neural network prediction method that had been based on single sequence information. The input data were derived from multiple alignments for each position in a window of 13 adjacent residues: amino acid frequency, conservation weights, number of insertions and deletions, and position of the window with respect to the ends of the protein chain. Additional input was the amino acid composition and length of the whole protein. A rigorous cross-validation test on 69 proteins with experimentally determined locations of transmembrane segments yielded an overall two-state per-residue accuracy of 95%. About 94% of all segments were predicted correctly. When applied to known globular proteins as a negative control, the network system incorrectly predicted fewer than 5% of globular proteins as having transmembrane helices. The method was applied to all 269 open reading frames from the complete yeast VIII chromosome. For 59 of these, at least two transmembrane helices were predicted. Thus, the prediction is that about one-fourth of all proteins from yeast VIII contain one transmembrane helix, and some 20%, more than one.

Amino Acid Sequence↗
PubMed1995

Progress of 1D protein structure prediction at last.

Accuracy of predicting protein secondary structure and solvent accessibility from sequence information has been improved significantly by using information contained in multiple sequence alignments as input to a neural network system. For the Asilomar meeting, predictions for 13 proteins were generated automatically using the publicly available prediction method PHD. The results confirm the estimate of 72% three-state prediction accuracy. The fairly accurate predictions of secondary structure segments made the tool useful as a starting point for modeling of higher dimensional aspects of protein structure.

Amino Acid Sequence↗
PubMed1995

TOPITS: threading one-dimensional predictions into three-dimensional structures.

Homology modelling, currently, is the only theoretical tool which can successfully predict protein 3D structure. As 3D structure is conserved in sequence families, homology modelling allows to predict 3D structure for 20% of SWISSPROT. 20% of the proteins in PDB are remote homologues to another PDB protein. Threading techniques attempt to predict such remote homologues based on sequence information. Here, a new threading method is presented. First, for a list of PDB proteins, 3D structure was projected onto 1D strings of secondary structure and relative solvent accessibility. Then, secondary structure and accessibility were predicted by neural network systems (PHD). Finally, the predicted and observed 1D strings were aligned by dynamic programming. The resulting alignment was used to detect remote 3D homologues. Four results stand out. Firstly, even for an optimal prediction (assignment based on known structure), only about half the hits that ranked above a given threshold were correctly identified as remote homologues; only about 25% of the first hits were correct. Secondly, real predictions (PHD) were not much worse: about 20% of the first hits were correct. Thirdly, a simple filtering procedure improved prediction performance to about 30% correct first hits. The correct hit ranked among the first three for more than 23 out of 46 cases. Fourthly, the combination of the 1D threading and sequence alignments markedly improved the performance of the threading method TOPITS for some selected cases.

Amino Acid Sequence↗
PubMed1994

Redefining the goals of protein secondary structure prediction.

Secondary structure prediction recently has surpassed the 70% level of average accuracy, evaluated on the single residue states helix, strand and loop (Q3). But the ultimate goal is reliable prediction of tertiary (three-dimensional, 3D) structure, not 100% single residue accuracy for secondary structure. A comparison of pairs of structurally homologous proteins with divergent sequences reveals that considerable variation in the position and length of secondary structure segments can be accommodated within the same 3D fold. It is therefore sufficient to predict the approximate location of helix, strand, turn and loop segments, provided they are compatible with the formation of 3D structure. Accordingly, we define here a measure of segment overlap (Sov) that is somewhat insensitive to small variations in secondary structure assignments. The new segment overlap measure ranges from an ignorance level of 37% (random protein pairs) via a current level of 72% for a prediction method based on sequence profile input to neural networks (PHD) to an average 90% level for homologous protein pairs. We conclude that the highest scores one can reasonably expect for secondary structure prediction are a single residue accuracy of Q3 > 85% and a fractional segment overlap of Sov > 90%.

Amino Acid Sequence↗
PubMed1994

Combining evolutionary information and neural networks to predict protein secondary structure.

Using evolutionary information contained in multiple sequence alignments as input to neural networks, secondary structure can be predicted at significantly increased accuracy. Here, we extend our previous three-level system of neural networks by using additional input information derived from multiple alignments. Using a position-specific conservation weight as part of the input increases performance. Using the number of insertions and deletions reduces the tendency for overprediction and increases overall accuracy. Addition of the global amino acid content yields a further improvement, mainly in predicting structural class. The final network system has sustained overall accuracy of 71.6% in a multiple cross-validation test on 126 unique protein chains. A test on a new set of 124 recently solved protein structures that have no significant sequence similarity to the learning set confirms the high level of accuracy. The average cross-validated accuracy for all 250 sequence-unique chains is above 72%. Using various data sets, the method is compared to alternative prediction methods, some of which also use multiple alignments: the performance advantage of the network system is at least 6 percentage points in three-state accuracy. In addition, the network estimates secondary structure content from multiple sequence alignments about as well as circular dichroism spectroscopy on a single protein and classifies 75% of the 250 proteins correctly into one of four protein structural classes. Of particular practical importance is the definition of a position-specific reliability index. For 40% of all residues the method has a sustained three-state accuracy of 88%, as high as the overall average for homology modelling. A further strength of the method is greatly increased accuracy in predicting the placement of secondary structure segments.

Amino Acid Sequence↗
PubMed1994

Conservation and prediction of solvent accessibility in protein families.

Currently, the prediction of three-dimensional (3D) protein structure from sequence alone is an exceedingly difficult task. As an intermediate step, a much simpler task has been pursued extensively: predicting 1D strings of secondary structure. Here, we present an analysis of another 1D projection from 3D structure: the relative solvent accessibility of each residue. We show that solvent accessibility is less conserved in 3D homologues than is secondary structure, and hence is predicted less accurately from automatic homology modeling; the correlation coefficient of relative solvent accessibility between 3D homologues is only 0.77, and the average accuracy of predictions based on sequence alignments is only 0.68. The latter number provides an effective upper limit on the accuracy of predicting accessibility from sequence when homology modeling is not possible. We introduce a neural network system that predicts relative solvent accessibility (projected onto ten discrete states) using evolutionary profiles of amino acid substitutions derived from multiple sequence alignments. Evaluated in a cross-validation test on 238 unique proteins, the correlation between predicted and observed relative accessibility is 0.54. Interpreted in terms of a three-state (buried, intermediate, exposed) description of relative accessibility, the fraction of correctly predicted residue states is about 58%. In absolute terms this accuracy appears poor, but given the relatively low conservation of accessibility in 3D families, the network system is not far from its likely optimal performance. The most reliably predicted fraction of the residues (50%) is predicted as accurately as by automatic homology modeling. Prediction is best for buried residues, e.g., 86% of the completely buried sites are correctly predicted as having 0% relative accessibility.

Biological Evolution↗
PubMed1994

Suitability of the Vero cell method for titration of diphtheria antitoxin in the United States potency test for diphtheria toxoid.

The in vitro Vero cell method for titration of diphtheria antitoxin in immunized guinea pig sera was standardized to obtain comparable results to the in vivo toxin neutralization (TN) test in guinea pigs used to test potency of adsorbed diphtheria toxoid according to the United States Minimum Requirements. In the Vero cell method, the antitoxin titers of the guinea pig sera, obtained 4 weeks after immunization, were markedly dependent on the toxin dose level used in the assay. A toxin dose level termed the Lcd/1 dose (limit of cytopathic dose at 1 IU/ml) for Vero cell method gave comparable estimates of antitoxin activity to the in vivo TN method performed at L+/1 dose of toxin. The Lcd/1 toxin dose level for the Vero cell method is defined as the minimum concentration of diphtheria toxin required to produce a cytopathic effect on Vero cells in 4 days when the solution of diphtheria toxin is mixed with an equal volume of standard diphtheria antitoxin whose concentration is 1 IU/ml. When lower dose levels of toxin were used in the Vero cell assay (Lcd/10 to Lcd/1000), the concentrations of antitoxin in 4 weeks guinea pig sera were 2 to 11.7 times lower than with the Lcd/1 dose level and similarly lower than those measured by the in vivo TN test at L+/1 level. In contrast, the concentration of antitoxin measured in sera from guinea pigs who have been boosted with diphtheria toxoid increased approximately two-fold with the Lcd/1000 dose level.(ABSTRACT TRUNCATED AT 250 WORDS)

Animals↗
PubMed1994

Structure prediction of proteins--where are we now?

Although the 'structure from sequence' prediction problem remains fundamentally unsolved, new and promising methods in one, two and three dimensions have reopened the field. Significantly improved one-dimensional prediction of secondary structure from multiple sequence alignments is now in routine use. In the two-dimensional approach, inter-residue contacts can be detected by analysis of correlated mutations, albeit with low accuracy. Finally, three-dimensional methods, in which pseudopotentials or information values are derived from the databases, are proving their value for distinguishing between correct and incorrect models.

Amino Acid Sequence↗
PubMed1994

PHD--an automatic mail server for protein secondary structure prediction.

By the middle of 1993, > 30,000 protein sequences has been listed. For 1000 of these, the three-dimensional (tertiary) structure has been experimentally solved. Another 7000 can be modelled by homology. For the remaining 21,000 sequences, secondary structure prediction provides a rough estimate of structural features. Predictions in three states range between 35% (random) and 88% (homology modelling) overall accuracy. Using information about evolutionary conservation as contained in multiple sequence alignments, the secondary structure of 4700 protein sequences was predicted by the automatic e-mail server PHD. For proteins with at least one known homologue, the method has an expected overall three-state accuracy of 71.4% for proteins with at least one known homologue (evaluated on 126 unique protein chains).

Algorithms↗
PubMed1993

Improved prediction of protein secondary structure by use of sequence profiles and neural networks.

The explosive accumulation of protein sequences in the wake of large-scale sequencing projects is in stark contrast to the much slower experimental determination of protein structures. Improved methods of structure prediction from the gene sequence alone are therefore needed. Here, we report a substantial increase in both the accuracy and quality of secondary-structure predictions, using a neural-network algorithm. The main improvements come from the use of multiple sequence alignments (better overall accuracy), from "balanced training" (better prediction of beta-strands), and from "structure context training" (better prediction of helix and strand lengths). This method, cross-validated on seven different test sets purged of sequence similarity to learning sets, achieves a three-state prediction accuracy of 69.7%, significantly better than previous methods. In addition, the predicted structures have a more realistic distribution of helix and strand segments. The predictions may be suitable for use in practice as a first estimate of the structural type of newly sequenced proteins.

Amino Acid Sequence↗
PubMed1993

Prediction of protein secondary structure at better than 70% accuracy.

We have trained a two-layered feed-forward neural network on a non-redundant data base of 130 protein chains to predict the secondary structure of water-soluble proteins. A new key aspect is the use of evolutionary information in the form of multiple sequence alignments that are used as input in place of single sequences. The inclusion of protein family information in this form increases the prediction accuracy by six to eight percentage points. A combination of three levels of networks results in an overall three-state accuracy of 70.8% for globular proteins (sustained performance). If four membrane protein chains are included in the evaluation, the overall accuracy drops to 70.2%. The prediction is well balanced between alpha-helix, beta-strand and loop: 65% of the observed strand residues are predicted correctly. The accuracy in predicting the content of three secondary structure types is comparable to that of circular dichroism spectroscopy. The performance accuracy is verified by a sevenfold cross-validation test, and an additional test on 26 recently solved proteins. Of particular practical importance is the definition of a position-specific reliability index. For half of the residues predicted with a high level of reliability the overall accuracy increases to better than 82%. A further strength of the method is the more realistic prediction of segment length. The protein family prediction method is available for testing by academic researchers via an electronic mail server.

Mathematical Computing↗
PubMed1993

Progress in protein structure prediction?

Prediction of protein secondary structure is an old problem and progress has been slow. Recently, spectacular success has been claimed in the blind prediction of the catalytic subunit of the cAMP-dependent protein kinase. When predictions in this and other test cases are assessed critically, some claims of prediction success turn out to be exaggerated, but a kernel of real progress remains: protein structure prediction can be improved substantially when a family of related sequences is available. Enough so that molecular biologists equipped with a new amino acid sequence and a multiple sequence alignment in hand may be tempted to test the new prediction methods.

Amino Acid Sequence↗
PubMed1993

Molecular modelling of the Norrie disease protein predicts a cystine knot growth factor tertiary structure.

The X-lined gene for Norrie disease, which is characterized by blindness, deafness and mental retardation has been cloned recently. This gene has been thought to code for a putative extracellular factor; its predicted amino acid sequence is homologous to the C-terminal domain of diverse extracellular proteins. Sequence pattern searches and three-dimensional modelling now suggest that the Norrie disease protein (NDP) has a tertiary structure similar to that of transforming growth factor beta (TGF beta). Our model identifies NDP as a member of an emerging family of growth factors containing a cystine knot motif, with direct implications for the physiological role of NDP. The model also sheds light on sequence related domains such as the C-terminal domain of mucins and of von Willebrand factor.

Amino Acid Sequence↗
PubMed1993

[Locoregional intra-arterial chemotherapy of primary incurable local recurrence of breast cancer].

Approximately one third of all local recurrences of breast cancer are incurable at the time of diagnosis. Locoregional intraarterial chemotherapy is one of the new therapy modalities besides laser therapy and combined radiotherapy/hyperthermia. The results of a phase I-II study, in which 15 patients with advanced, partly pretreated local recurrences as well as 2 patients with T4N2/N3 tumours were included, are reported as follows. All in all, 39 superselective intraarterial chemotherapy courses were carried out. Mitomycin (10 mg) and Mitoxantrone (25 mg) were infused over 90 min. The side effects due to the catheter system were two haematomas and one thrombosis attributed to an insufficient heparin dose. Locally, the chemotherapy was well tolerated. One severe systemic side effect, a leucopenia WHO 4 degrees was observed. Nausea, thrombocytopenia and alopecia rates were low (7 x nausea 1 degree, 5 x thrombocytopenia 1 degree, 2 x thrombocytopenia 2 degrees, 2 x alopecia 1 degree). 6 Complete remissions (3 x pCR, 3 x cCR) as well as 6 partial remissions and 5 no changes were found. We believe that this method, because of the low side-effect profile and the temporary good results, represents a good alternative in otherwise incurable locoregional recurrence of breast carcinoma and in specified cases of locally advanced disease. 1 degree and 2 x thrombocytopenia 2 degrees, 2 x alopecia 1 degree). Altogether, 6 complete remissions were found (3 x cCR, 3 x pCR), 6 partial remissions with 4 x no change occurred.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗
PubMed1993

Secondary structure prediction of all-helical proteins in two states.

Can secondary structure prediction be improved by prediction rules that focus on a particular structural class of proteins? To help answer this question, we have assessed the accuracy of prediction for all-helical proteins, using two conceptually different methods and two levels of description. An overall two-state single-residue accuracy of approximately 80% can be obtained by a neural network, no matter whether it is trained on two states (helix and non-helix) or first trained on three states (helix, strand and loop) and then evaluated on two states. For four test proteins, this is similar to the accuracy obtained with inductive logic programming. We conclude that on the level of secondary structure, there is no practical advantage in training on two states, especially given the added margin of error in identifying the structural class of a protein. In the further development of these methods, it is increasingly important to focus on aspects of secondary structure that aid in the construction of a correct 3-D model, such as the correct placement of segments.

Amino Acid Sequence↗
PubMed1992

[Need for control and anxiety of losing it--reflections on the treatment of anorexia nervosa].

In connection with the symptoms of anorexia nervosa, which are usually based on a fear of losing control over food intake, we describe a therapeutic approach with the following important aims: To reduce patient's fear of loss of control, to free parents from controlling the food intake of their daughter and to establish a framework that provides a good climate for effective family-oriented or individual psychotherapy or both. In patients with anorexia that began early, we recommend starting with family therapy. This can be followed by individual psychotherapy, which is in fact often requested by the patients themselves.

Adolescent↗