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Roman A Laskowski

Publications and source records attributed to Roman A Laskowski.

13 recordsLinked to original sources
PubMed2006

A method for localizing ligand binding pockets in protein structures.

The accurate identification of ligand binding sites in protein structures can be valuable in determining protein function. Once the binding site is known, it becomes easier to perform in silico and experimental procedures that may allow the ligand type and the protein function to be determined. For example, binding pocket shape analysis relies heavily on the correct localization of the ligand binding site. We have developed SURFNET-ConSurf, a modular, two-stage method for identifying the location and shape of potential ligand binding pockets in protein structures. In the first stage, the SURFNET program identifies clefts in the protein surface that are potential binding sites. In the second stage, these clefts are trimmed in size by cutting away regions distant from highly conserved residues, as defined by the ConSurf-HSSP database. The largest clefts that remain tend to be those where ligands bind. To test the approach, we analyzed a nonredundant set of 244 protein structures from the PDB and found that SURFNET-ConSurf identifies a ligand binding pocket in 75% of them. The trimming procedure reduces the original cleft volumes by 30% on average, while still encompassing an average 87% of the ligand volume. From the analysis of the results we conclude that for those cases in which the ligands are found in large, highly conserved clefts, the combined SURFNET-ConSurf method gives pockets that are a better match to the ligand shape and location. We also show that this approach works better for enzymes than for nonenzyme proteins.

Algorithms↗
PubMed2005

Protein function prediction using local 3D templates.

The prediction of a protein's function from its 3D structure is becoming more and more important as the worldwide structural genomics initiatives gather pace and continue to solve 3D structures, many of which are of proteins of unknown function. Here, we present a methodology for predicting function from structure that shows great promise. It is based on 3D templates that are defined as specific 3D conformations of small numbers of residues. We use four types of template, covering enzyme active sites, ligand-binding residues, DNA-binding residues and reverse templates. The latter are templates generated from the target structure itself and scanned against a representative subset of all known protein structures. Together, the templates provide a fairly thorough coverage of the known structures and ensure that if there is a match to a known structure it is unlikely to be missed. A new scoring scheme provides a highly sensitive means of discriminating between true positive and false positive template matches. In all, the methodology provides a powerful new tool for function prediction to complement those already in use.

Models, Molecular↗
PubMed2005

ProFunc: a server for predicting protein function from 3D structure.

ProFunc (http://www.ebi.ac.uk/thornton-srv/databases/ProFunc) is a web server for predicting the likely function of proteins whose 3D structure is known but whose function is not. Users submit the coordinates of their structure to the server in PDB format. ProFunc makes use of both existing and novel methods to analyse the protein's sequence and structure identifying functional motifs or close relationships to functionally characterized proteins. A summary of the analyses provides an at-a-glance view of what each of the different methods has found. More detailed results are available on separate pages. Often where one method has failed to find anything useful another may be more forthcoming. The server is likely to be of most use in structural genomics where a large proportion of the proteins whose structures are solved are of hypothetical proteins of unknown function. However, it may also find use in a comparative analysis of members of large protein families. It provides a convenient compendium of sequence and structural information that often hold vital functional clues to be followed up experimentally.

Binding Sites↗
PubMed2005

PDBsum more: new summaries and analyses of the known 3D structures of proteins and nucleic acids.

PDBsum is a database of mainly pictorial summaries of the 3D structures of proteins and nucleic acids in the Protein Data Bank. Its pages aim to provide an at-a-glance view of the contents of every 3D structure, plus detailed structural analyses of each protein chain, DNA-RNA chain and any bound ligands and metals. In the past year, the database has been significantly improved, in terms of both appearance and new content. Moreover, it has moved to its new address at http://www.ebi.ac.uk/thornton-srv/databases/pdbsum.

Databases, Protein↗
PubMed2005

Predicting protein function from sequence and structural data.

When a protein's function cannot be experimentally determined, it can often be inferred from sequence similarity. Should this process fail, analysis of the protein structure can provide functional clues or confirm tentative functional assignments inferred from the sequence. Many structure-based approaches exist (e.g. fold similarity, three-dimensional templates), but as no single method can be expected to be successful in all cases, a more prudent approach involves combining multiple methods. Several automated servers that integrate evidence from multiple sources have been released this year and particular improvements have been seen with methods utilizing the Gene Ontology functional annotation schema.

Binding Sites↗
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↗
PubMed2003

From protein structure to biochemical function?

Here we describe various methods currently under development aimed at identifying a protein's 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 protein's 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.

Amino Acid Sequence↗
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↗