The local information content of the protein structural database

The future of protein secondary structure prediction accuracy

BACKGROUND

The accuracy of secondary structure prediction for a protein from knowledge of its sequence has been significantly improved by about 7% to the 70-75% range by inclusion of information residing in sequences similar to the query sequence. The scientific literature has been inconsistent, if not negative, regarding chances for further improvement from the vast knowledge to be provided by genome sequencing efforts.

RESULTS

By applying a prediction technique that is particularly sensitive to added sequence information to a standard set of query sequences with related primary structures taken from chronologically successive releases of the SWISS-PROT database, it is shown that prediction accuracy can be expected to reach 80-85% with a large 10-fold increase in present sequence knowledge.

CONCLUSIONS

Even with present prediction approaches, improvement in prediction accuracy can still be expected, albeit limited to no more than 10%.

The blind watchmaker and rational protein engineering

In the present review some scientific areas of key importance for protein engineering are discussed, such as problems involved in deducting protein sequence from DNA sequence (due to posttranscriptional editing, splicing and posttranslational modifications), modelling of protein structures by homology, NMR of large proteins (including probing the molecular surface with relaxation agents), simulation of protein structures by molecular dynamics and simulation of electrostatic effects in proteins (including pH-dependent effects). It is argued that all of these areas could be of key importance in most protein engineering projects, because they give access to increased and often unique information. In the last part of the review some potential areas for future applications of protein engineering approaches are discussed, such as non-conventional media, de novo design and nanotechnology.

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.

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.