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Roman A Laskowski

Publications and source records attributed to Roman A Laskowski.

7 recordsLinked to original sources
PubMed2003

Estimation of weights and validation: a marginal likelihood approach.

The estimation of weights is quite an important aspect of the restrained refinement of macromolecular structures and related procedures such as the estimation of coordinate errors and structure validation using geometrical criteria. In principle, the method of maximum likelihood can be used for estimation of both atomic and weighting parameters. However, the low observation-to-parameter ratio in macromolecular refinement makes this kind of estimate of weighting parameters seriously biased; thus, the weighting parameters have traditionally been estimated separately from atomic parameters using a special technique, such as minimizing the free R factor. However, the variance of the latter estimate is large, as only a small portion of all data is used. In this work, an estimator of weights is proposed which is based on an approximation of a marginal likelihood function of the weighting parameters and which uses all the X-ray data. There is a known true value for the overall scaling coefficient for distance variances in restrained refinement and therefore the (maximum-likelihood) estimator for this coefficient may be used as a validation statistic.

Crystallography, X-Ray↗
PubMed2003

Integrating structure, bioinformatics, and enzymology to discover function: BioH, a new carboxylesterase from Escherichia coli.

Structural proteomics projects are generating three-dimensional structures of novel, uncharacterized proteins at an increasing rate. However, structure alone is often insufficient to deduce the specific biochemical function of a protein. Here we determined the function for a protein using a strategy that integrates structural and bioinformatics data with parallel experimental screening for enzymatic activity. BioH is involved in biotin biosynthesis in Escherichia coli and had no previously known biochemical function. The crystal structure of BioH was determined at 1.7 A resolution. An automated procedure was used to compare the structure of BioH with structural templates from a variety of different enzyme active sites. This screen identified a catalytic triad (Ser82, His235, and Asp207) with a configuration similar to that of the catalytic triad of hydrolases. Analysis of BioH with a panel of hydrolase assays revealed a carboxylesterase activity with a preference for short acyl chain substrates. The combined use of structural bioinformatics with experimental screens for detecting enzyme activity could greatly enhance the rate at which function is determined from structure.

Biotin↗
PubMed2002

Detecting cryptically simple protein sequences using the SIMPLE algorithm.

MOTIVATION: Low-complexity or cryptically simple sequences are widespread in protein sequences but their evolution and function are poorly understood. To date methods for the detection of low complexity in proteins have been directed towards the filtering of such regions prior to sequence homology searches but not to the analysis of the regions per se. However, many of these regions are encoded by non-repetitive DNA sequences and may therefore result from selection acting on protein structure and/or function. RESULTS: We have developed a new tool, based on the SIMPLE algorithm, that facilitates the quantification of the amount of simple sequence in proteins and determines the type of short motifs that show clustering above a certain threshold. By modifying the sensitivity of the program simple sequence content can be studied at various levels, from highly organised tandem structures to complex combinations of repeats. We compare the relative amount of simplicity in different functional groups of yeast proteins and determine the level of clustering of the different amino acids in these proteins. AVAILABILITY: The program is available on request or online at http://www.biochem.ucl.ac.uk/bsm/SIMPLE.

Algorithms↗
PubMed2002

Bioinformatics and protein design.

Traditionally, the development of drugs has focused on small molecule therapeutics. However, with recent advances in recombinant protein technology the potential of proteins as therapeutics is starting to be realized. Already there are protein drugs on the market, including naturally occurring proteins, engineered proteins and proteins introduced into the patient by way of gene therapy. The next generation of such drugs are likely to be designed molecules; proteins devised from scratch and specifically tailored to have a required 3-dimensional structure and biochemical function. The key to such de novo design is the extensive bioinformatics knowledge that has been obtained from experimentally determined protein structures during the past 40-50 years. The knowledge is far from complete; for example, the protein folding problem has not yet been completely solved. Despite this, bioinformatics plays a crucial role in protein design, as is outlined in this review, and a number of de novo protein structures have been successfully designed in recent years. Some examples of these successes, which are available in the Protein Data Bank, are presented. They suggest that carefully designed protein therapeutics are a genuine prospect for the future.

Animals↗
PubMedDate not supplied

Target selection and determination of function in structural genomics.

The first crucial step in any structural genomics project is the selection and prioritization of target proteins for structure determination. There may be a number of selection criteria to be satisfied, including that the proteins have novel folds, that they be representatives of large families for which no structure is known, and so on. The better the selection at this stage, the greater is the value of the structures obtained at the end of the experimental process. This value can be further enhanced once the protein structures have been solved if the functions of the given proteins can also be determined. Here we describe the methods used at either end of the experimental process: firstly, sensitive sequence comparison techniques for selecting a high-quality list of target proteins, and secondly the various computational methods that can be applied to the eventual 3D structures to determine the most likely biochemical function of the proteins in question.

Amino Acid Motifs↗