Population-based incremental learning for the prediction of Homo sapiens’ protein secondary structure

prediction of the protein secondary structure of Homo sapiens is one of the more important domains. Many methods have been used to feed forward neural networks or SVMs combined with a sliding window. This method’s mechanisms are too complex to be able to extract clear and straightforward physical meanings from it. This paper explores population-based incremental learning (PBIL), which is a method that combines the mechanisms of a generational genetic algorithm with simple competitive learning. The result shows that its accuracies are particularly associated with the Homo species. This new perspective reveals a number of different possibilities for the purposes of performance improvements.     

Are prions a relic of an early stage of peptide evolution?

The rather unique properties of prions and their presence in very different kinds of living species suggest that this type of molecule was created at a very early stage of evolution and may even represent a relic from a time where peptide evolution was ongoing and RNA/DNA did not yet exist. A comparison of the most frequently occurring amino acid sequences in known prions with the sequences preferentially formed in the salt-induced peptide formation reaction, the most simple mechanism enabling the formation of peptides under primitive earth conditions, shows a remarkable coincidence that strongly supports this hypothesis.     

The 2000 Olympic Games of protein structure prediction; fully automated programs are being evaluated vis-à-vis human teams in the protein structure prediction experiment CAFASP2

In this commentary, we describe two new protein structure prediction experiments being run in parallel with the CASP experiment, which together may be regarded as the 2000 Olympic Games of structure prediction. The first new experiment is CAFASP, the Critical Assessment of Fully Automated Structure Prediction. In CAFASP, the participants are fully automated programs or Internet servers, and here the automated results of the programs are evaluated, without any human intervention. The second new experiment, named LiveBench, follows the CAFASP ideology in that it is aimed towards the evaluation of automatic servers only, while it runs on a large set of prediction targets and in a continuous fashion. Researchers will be watching the 2000 protein structure prediction Olympic Games, to be held in December, in order to learn about the advances in the classical 'human-plus-machine' CASP category, the fully automated CAFASP category, and the comparison between the two.     

PCS-based structure determination of protein–protein complexes

A simple and fast nuclear magnetic resonance method for docking proteins using pseudo-contact shift (PCS) and ¹H^N/¹⁵N chemical shift perturbation is presented. PCS is induced by a paramagnetic lanthanide ion that is attached to a target protein using a lanthanide binding peptide tag anchored at two points. PCS provides long-range (~40 Å) distance and angular restraints between the lanthanide ion and the observed nuclei, while the ¹H^N/¹⁵N chemical shift perturbation data provide loose contact-surface information. The usefulness of this method was demonstrated through the structure determination of the p62 PB1-PB1 complex, which forms a front-to-back 20 kDa homo-oligomer. As p62 PB1 does not intrinsically bind metal ions, the lanthanide binding peptide tag was attached to one subunit of the dimer at two anchoring points. Each monomer was treated as a rigid body and was docked based on the backbone PCS and backbone chemical shift perturbation data. Unlike NOE-based structural determination, this method only requires resonance assignments of the backbone ¹H^N/¹⁵N signals and the PCS data obtained from several sets of two-dimensional ¹⁵N-heteronuclear single quantum coherence spectra, thus facilitating rapid structure determination of the protein–protein complex.     

Automated feature engineering improves prediction of protein–protein interactions

Amino Acids - Over the last decade, various machine learning (ML) and statistical approaches for protein–protein interaction (PPI) predictions have been developed to help annotating...     

What is the best reference state for designing statistical atomic potentials in protein structure prediction?

Many statistical potentials were developed in last two decades for protein folding and protein structure recognition. The major difference of these potentials is on the selection of reference states to offset sampling bias. However, since these potentials used different databases and parameter cutoffs, it is difficult to judge what the best reference states are by examining the original programs. In this study, we aim to address this issue and evaluate the reference states by a unified database and programming environment. We constructed distance-specific atomic potentials using six widely-used reference states based on 1022 high-resolution protein structures, which are applied to rank modeling in six sets of structure decoys. The reference state on random-walk chain outperforms others in three decoy sets while those using ideal-gas, quasi-chemical approximation and averaging sample stand out in one set separately. Nevertheless, the performance of the potentials relies on the origin of decoy generations and no reference state can clearly outperform others in all decoy sets. Further analysis reveals that the statistical potentials have a contradiction between the universality and pertinence, and optimal reference states should be extracted based on specific application environments and decoy spaces.     

In silico prediction of the structure of membrane proteins: is it feasible?

In the 'omic' era, hundreds of genomes are available for protein sequence analysis, and some 30 per cent of all sequences are of membrane proteins. Unlike globular proteins, a 3D model for membrane proteins can hardly be computed starting from the sequence. Why is this so? What can we really compute and with what reliability? These and other matters are outlined.     

From protein structure to biochemical function?

Here we describe various methods currently under development aimed at identifying a proteins function from its three-dimensional structure. We are combining a number of these methods to create a pipeline of applications, called ProFunc, which will take a given 3D structure, run all the applications on it and compile and summarise the results obtained. The aim is to provide a best guess as to the proteins function from the evidence provided by the different methods. Here we present three examples, using structures solved by the Midwest Center for Structural Genomics consortium, illustrating the strengths and weaknesses of current approaches.     

How good is prediction of protein structural class by the component-coupled method?

Proteins of known structures are usually classified into four structural classes: all-alpha, all-beta, alpha+beta, and alpha/beta type of proteins. A number of methods to predicting the structural class of a protein based on its amino acid composition have been developed during the past few years. Recently, a component-coupled method was developed for predicting protein structural class according to amino acid composition. This method is based on the least Mahalanobis distance principle, and yields much better predicted results in comparison with the previous methods. However, the success rates reported for structural class prediction by different investigators are contradictory. The highest reported accuracies by this method are near 100%, but the lowest one is only about 60%. The goal of this study is to resolve this paradox and to determine the possible upper limit of prediction rate for structural classes. In this paper, based on the normality assumption and the Bayes decision rule for minimum error, a new method is proposed for predicting the structural class of a protein according to its amino acid composition. The detailed theoretical analysis indicates that if the four protein folding classes are governed by the normal distributions, the present method will yield the optimum predictive result in a statistical sense. A non-redundant data set of 1,189 protein domains is used to evaluate the performance of the new method. Our results demonstrate that 60% correctness is the upper limit for a 4-type class prediction from amino acid composition alone for an unknown query protein. The apparent relatively high accuracy level (more than 90%) attained in the previous studies was due to the preselection of test sets, which may not be adequately representative of all unrelated proteins.     

Use of tetrapeptide signals for protein secondary-structure prediction

This paper develops a novel sequence-based method, tetra-peptide-based increment of diversity with quadratic discriminant analysis (TPIDQD for short), for protein secondary-structure prediction. The proposed TPIDQD method is based on tetra-peptide signals and is used to predict the structure of the central residue of a sequence fragment. The three-state overall per-residue accuracy (Q ₃) is about 80% in the threefold cross-validated test for 21-residue fragments in the CB513 dataset. The accuracy can be further improved by  taking long-range sequence information (fragments of more than 21 residues) into account in prediction. The results show the tetra-peptide signals can indeed reflect some relationship between an amino acid’s sequence and its secondary structure, indicating the importance of  tetra-peptide signals as the protein folding code in the protein structure prediction.     

Can the fluorescence of green fluorescent protein chromophore be related directly to the nativity of protein structure?

In studies of green fluorescence protein (GFP) or other proteins with the use of GFP as a marker, the fluorescence of GFP is for the most part related directly to the nativity of its structure. Naturally, such a relation does exist since the chromophore of this protein is formed autocatalytically only just after GFP acquires its native structure. However, the fluorescence method may not yield reliable information on protein structure when studying renaturation and denaturation of this protein (with the formed chromophore). Using proteolysis, denaturant gradient gel electrophoresis and circular dichroism, we demonstrate herein that at major disturbances of the native structure of protein GFP-cycle3 the intensity of fluorescence of its chromophore can change insignificantly. In other words, the chromophore fluorescence does not reliably mirror alterations in protein structure. Since the main conclusions of this study are especially qualitative, it can be suggested that during renaturation/denaturation of wild-type GFP and its “multicolored” mutants their fluorescence is also not always associated with the changes in the structure of these proteins.     

How reliably can we predict the reliability of protein structure predictions?

Background  

Comparative methods have been the standard techniques for in silico protein structure prediction. The prediction is based on a multiple alignment that contains both reference sequences with known structures and the sequence whose unknown structure is predicted. Intensive research has been made to improve the quality of multiple alignments, since misaligned parts of the multiple alignment yield misleading predictions. However, sometimes all methods fail to predict the correct alignment, because the evolutionary signal is too weak to find the homologous parts due to the large number of mutations that separate the sequences.