Marcin von Grotthuss

ORFeus: detection of distant homology using sequence profiles and predicted secondary structure

ORFeus is a fully automated, sensitive protein sequence similarity search server available to the academic community via the Structure Prediction Meta Server (http://BioInfo.PL/Meta/). The goal of the development of ORFeus was to increase the sensitivity of the detection of distantly related protein families. Predicted secondary structure information was added to the information about sequence conservation and variability, a technique known from hybrid threading approaches. The accuracy of the meta profiles created this way is compared with profiles containing only sequence information and with the standard approach of aligning a single sequence with a profile. Additionally, the alignment of meta profiles is more sensitive in detecting remote homology between protein families than if aligning two sequence-only profiles or if aligning a profile with a sequence. The specificity of the alignment score is improved in the lower specificity range compared with the robust sequence-only profiles.

Detecting distant homology with Meta-BASIC

Meta-BASIC (http://basic.bioinfo.pl) is a novel sensitive approach for recognition of distant similarity between proteins based on consensus alignments of meta profiles. Specifically, Meta-BASIC compares sequence profiles combined with predicted secondary structure by utilizing several scoring systems and alignment algorithms. In our benchmarking tests, Meta-BASIC outperforms many individual servers, including fold recognition servers, and it can compete with meta predictors that base their strength on the structural comparison of models. In addition, Meta-BASIC, which enables detection of very distant relationships even if the tertiary structure for the reference protein is not known, has a high-throughput capability. This new method is applied to 860 PfamA protein families with unknown function (DUF) and provides many novel structure-functional assignments available on-line at http://basic.bioinfo.pl/duf.pl. Detailed discussion is provided for two of the most interesting assignments. DUF271 and DUF431 are predicted to be a nucleotide-diphospho-sugar transferase and an alpha/beta-knot SAM-dependent RNA methyltransferase, respectively.

mRNA Cap-1 Methyltransferase in the SARS Genome

Abstract The 3D jury system has predicted the methyltransferase fold for the nsp13 protein of the SARS coronavirus. Based on the conservation of a characteristic tetrad of residues, the mRNA cap-1 methyltransferase function has been assigned to this protein, which has potential implications for antiviral therapy.

VoteDock: consensus docking method for prediction of protein-ligand interactions.

Molecular recognition plays a fundamental role in all biological processes, and that is why great efforts have been made to understand and predict protein–ligand interactions. Finding a molecule that can potentially bind to a target protein is particularly essential in drug discovery and still remains an expensive and time‐consuming task. In silico, tools are frequently used to screen molecular libraries to identify new lead compounds, and if protein structure is known, various protein–ligand docking programs can be used. The aim of docking procedure is to predict correct poses of ligand in the binding site of the protein as well as to score them according to the strength of interaction in a reasonable time frame. The purpose of our studies was to present the novel consensus approach to predict both protein–ligand complex structure and its corresponding binding affinity. Our method used as the input the results from seven docking programs (Surflex, LigandFit, Glide, GOLD, FlexX, eHiTS, and AutoDock) that are widely used for docking of ligands. We evaluated it on the extensive benchmark dataset of 1300 protein–ligands pairs from refined PDBbind database for which the structural and affinity data was available. We compared independently its ability of proper scoring and posing to the previously proposed methods. In most cases, our method is able to dock properly approximately 20% of pairs more than docking methods on average, and over 10% of pairs more than the best single program. The RMSD value of the predicted complex conformation versus its native one is reduced by a factor of 0.5 Å. Finally, we were able to increase the Pearson correlation of the predicted binding affinity in comparison with the experimental value up to 0.5.

Ligand.Info Small-Molecule Meta-Database

Ligand.Info is a compilation of various publicly available databases of small molecules. The total size of the Meta-Database is over 1 million entries. The compound records contain calculated three-dimensional coordinates and sometimes information about biological activity. Some molecules have information about FDA drug approving status or about anti-HIV activity. Meta-Database can be downloaded from the http://Ligand.Info web page. The database can also be screened using a Java-based tool. The tool can interactively cluster sets of molecules on the user side and automatically download similar molecules from the server. The application requires the Java Runtime Environment 1.4 or higher, which can be automatically downloaded from Sun Microsystems or Apple Computer and installed during the first use of Ligand.Info on desktop systems, which support Java (Ms Windows, Mac OS, Solaris, and Linux). The Ligand.Info Meta-Database can be used for virtual high-throughput screening of new potential drugs. Presented examples showed that using a known antiviral drug as query the system was able to find others antiviral drugs and inhibitors.

Application of 3D-Jury, GRDB, and Verify3D in fold recognition.

In CASP5, the BioInfo.PL group has used the structure prediction Meta Server and the associated newly developed flexible meta‐predictor, called 3D‐Jury, as the main structure prediction tools. The most important feature of the meta‐predictor is a high (86%) correlation between the reported confidence score and the quality of the selected model. The Gene Relational Database (GRDB) was used to confirm the fold recognition results by selecting distant homologues and subsequent structure prediction with the Meta Server. A fragment‐splicing procedure was performed as a final processing step with large fragments extracted from selected models using model quality control provided by Verify3D. The comparison of submitted models with the native structure conducted after the CASP meeting showed that the GRDB‐supported structure prediction led to a satisfactory template fold selection, whereas the fragment‐splicing procedure must be improved in the future. Proteins 2003;53:418–423.

Ligand-Info, searching for similar small compounds using index profiles

MOTIVATION The Ligand-Info system is based on the assumption that small molecules with similar structure have similar functional (binding) properties. The developed system enables a fast and sensitive index based search for similar compounds in large databases. Index profiles, constructed by averaging indexes of related molecules are used to increase the specificity of the search. The utilization of index profiles helps to focus on frequent, common features of a family of compounds. RESULTS A Java-based tool for clustering and scanning of small molecules has been created. The tool can interactively cluster sets of molecules and create index profiles on the user side and automatically download similar molecules from a databases of 250 000 compounds. The results of the application of index profiles demonstrate that the profile based search strategy can increase the quality of the selection process. AVAILABILITY The system is available at http://Ligand.Info. The application requires the Java Runtime Environment 1.4, which can be automatically installed during the first use on desktop systems, which support it. A standalone version of the program is available from the authors upon request.

Target Specific Compound Identification Using a Support Vector Machine

In many cases at the beginning of an HTS-campaign, some information about active molecules is already available. Often known active compounds (such as substrate analogues, natural products, inhibitors of a related protein or ligands published by a pharmaceutical company) are identified in low-throughput validation studies of the biochemical target. In this study we evaluate the effectiveness of a support vector machine applied for those compounds and used to classify a collection with unknown activity. This approach was aimed at reducing the number of compounds to be tested against the given target. Our method predicts the biological activity of chemical compounds based on only the atom pairs (AP) two dimensional topological descriptors. The supervised support vector machine (SVM) method herein is trained on compounds from the MDL drug data report (MDDR) known to be active for specific protein target. For detailed analysis, five different biological targets were selected including cyclooxygenase-2, dihydrofolate reductase, thrombin, HIV-reverse transcriptase and antagonists of the estrogen receptor. The accuracy of compound identification was estimated using the recall and precision values. The sensitivities for all protein targets exceeded 80% and the classification performance reached 100% for selected targets. In another application of the method, we addressed the absence of an initial set of active compounds for a selected protein target at the beginning of an HTS-campaign. In such a case, virtual high-throughput screening (vHTS) is usually applied by using a flexible docking procedure. However, the vHTS experiment typically contains a large percentage of false positives that should be verified by costly and time-consuming experimental follow-up assays. The subsequent use of our machine learning method was found to improve the speed (since the docking procedure was not required for all compounds from the database) and also the accuracy of the HTS hit lists (the enrichment factor).

Structure prediction, evolution and ligand interaction of CHASE domain

Cytokinins are plant hormones involved in the essential processes of plant growth and development. They bind with receptors known as CRE1/WOL/AHK4, AHK2, and AHK3, which possess histidine kinase activity. Recently, the sensor domain cyclases/histidine kinases associated sensory extracellular (CHASE) was identified in those proteins but little is known about its structure and interaction with ligands. Distant homology detection methods developed in our laboratory and molecular phylogeny enabled the prediction of the structure of the CHASE domain as similar to the photoactive yellow protein‐like sensor domain. We have identified the active site pocket and amino acids that are involved in receptor–ligand interactions. We also show that fold evolution of cytokinin receptors is very important for a full understanding of the signal transduction mechanism in plants.

Virtual high throughput screening using combined random forest and flexible docking.

We present here the random forest supervised machine learning algorithm applied to flexible docking results from five typical virtual high throughput screening (HTS) studies. Our approach is aimed at: i) reducing the number of compounds to be tested experimentally against the given protein target and ii) extending results of flexible docking experiments performed only on a subset of a chemical library in order to select promising inhibitors from the whole dataset. The random forest (RF) method is applied and tested here on compounds from the MDL drug data report (MDDR). The recall values for selected five diverse protein targets are over 90% and the performance reaches 100%. This machine learning method combined with flexible docking is capable to find 60% of the active compounds for most protein targets by docking only 10% of screened ligands. Therefore our in silico approach is able to scan very large databases rapidly in order to predict biological activity of small molecule inhibitors and provides an effective alternative for more computationally demanding methods in virtual HTS.