Protein homology reveals new targets for bioactive small molecules

Motivation: The functional impact of small molecules is increasingly being assessed in different eukaryotic species through large-scale phenotypic screening initiatives. Identifying the targets of these molecules is crucial to mechanistically understand their function and uncover new therapeutically relevant modes of action. However, despite extensive work carried out in model organisms and human, it is still unclear to what extent one can use information obtained in one species to make predictions in other species. Results: Here, for the first time, we explore and validate at a large scale the use of protein homology relationships to predict the targets of small molecules across different species. Our results show that exploiting target homology can significantly improve the predictions, especially for molecules experimentally tested in other species. Interestingly, when considering separately orthology and paralogy relationships, we observe that mapping small molecule interactions among orthologs improves prediction accuracy, while including paralogs does not improve and even sometimes worsens the prediction accuracy. Overall, our results provide a novel approach to integrate chemical screening results across multiple species and highlight the promises and remaining challenges of using protein homology for small molecule target identification. Availability and implementation: Homology-based predictions can be tested on our website http://www.swisstargetprediction.ch. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics onlin

SwissSidechain: a molecular and structural database of non-natural sidechains

Amino acids form the building blocks of all proteins. Naturally occurring amino acids are restricted to a few tens of sidechains, even when considering post-translational modifications and rare amino acids such as selenocysteine and pyrrolysine. However, the potential chemical diversity of amino acid sidechains is nearly infinite. Exploiting this diversity by using non-natural sidechains to expand the building blocks of proteins and peptides has recently found widespread applications in biochemistry, protein engineering and drug design. Despite these applications, there is currently no unified online bioinformatics resource for non-natural sidechains. With the SwissSidechain database (http://www.swisssidechain.ch), we offer a central and curated platform about non-natural sidechains for researchers in biochemistry, medicinal chemistry, protein engineering and molecular modeling. SwissSidechain provides biophysical, structural and molecular data for hundreds of commercially available non-natural amino acid sidechains, both in l- and d-configurations. The database can be easily browsed by sidechain names, families or physico-chemical properties. We also provide plugins to seamlessly insert non-natural sidechains into peptides and proteins using molecular visualization software, as well as topologies and parameters compatible with molecular mechanics softwar

SwissDock, a protein-small molecule docking web service based on EADock DSS

Most life science processes involve, at the atomic scale, recognition between two molecules. The prediction of such interactions at the molecular level, by so-called docking software, is a non-trivial task. Docking programs have a wide range of applications ranging from protein engineering to drug design. This article presents SwissDock, a web server dedicated to the docking of small molecules on target proteins. It is based on the EADock DSS engine, combined with setup scripts for curating common problems and for preparing both the target protein and the ligand input files. An efficient Ajax/HTML interface was designed and implemented so that scientists can easily submit dockings and retrieve the predicted complexes. For automated docking tasks, a programmatic SOAP interface has been set up and template programs can be downloaded in Perl, Python and PHP. The web site also provides an access to a database of manually curated complexes, based on the Ligand Protein Database. A wiki and a forum are available to the community to promote interactions between users. The SwissDock web site is available online at http://www.swissdock.ch. We believe it constitutes a step toward generalizing the use of docking tools beyond the traditional molecular modeling communit

Shaping the interaction landscape of bioactive molecules

Motivation: Most bioactive molecules perform their action by interacting with proteins or other macromolecules. However, for a significant fraction of them, the primary target remains unknown. In addition, the majority of bioactive molecules have more than one target, many of which are poorly characterized. Computational predictions of bioactive molecule targets based on similarity with known ligands are powerful to narrow down the number of potential targets and to rationalize side effects of known molecules. Results: Using a reference set of 224 412 molecules active on 1700 human proteins, we show that accurate target prediction can be achieved by combining different measures of chemical similarity based on both chemical structure and molecular shape. Our results indicate that the combined approach is especially efficient when no ligand with the same scaffold or from the same chemical series has yet been discovered. We also observe that different combinations of similarity measures are optimal for different molecular properties, such as the number of heavy atoms. This further highlights the importance of considering different classes of similarity measures between new molecules and known ligands to accurately predict their targets. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

SwissDock, a protein-small molecule docking web service based on EADock DSS

Most life science processes involve, at the atomic scale, recognition between two molecules. The prediction of such interactions at the molecular level, by so-called docking software, is a non-trivial task. Docking programs have a wide range of applications ranging from protein engineering to drug design. This article presents SwissDock, a web server dedicated to the docking of small molecules on target proteins. It is based on the EADock DSS engine, combined with setup scripts for curating common problems and for preparing both the target protein and the ligand input files. An efficient Ajax/HTML interface was designed and implemented so that scientists can easily submit dockings and retrieve the predicted complexes. For automated docking tasks, a programmatic SOAP interface has been set up and template programs can be downloaded in Perl, Python and PHP. The web site also provides an access to a database of manually curated complexes, based on the Ligand Protein Database. A wiki and a forum are available to the community to promote interactions between users. The SwissDock web site is available online at http://www.swissdock.ch. We believe it constitutes a step toward generalizing the use of docking tools beyond the traditional molecular modeling community

SwissBioisostere: a database of molecular replacements for ligand design

The SwissBioisostere database (http://www.swissbioisostere.ch) contains information on molecular replacements and their performance in biochemical assays. It is meant to provide researchers in drug discovery projects with ideas for bioisosteric modifications of their current lead molecule, as well as to give interested scientists access to the details on particular molecular replacements. As of August 2012, the database contains 21 293 355 datapoints corresponding to 5 586 462 unique replacements that have been measured in 35 039 assays against 1948 molecular targets representing 30 target classes. The accessible data were created through detection of matched molecular pairs and mining bioactivity data in the ChEMBL database. The SwissBioisostere database is hosted by the Swiss Institute of Bioinformatics and available via a web-based interfac

SwissTargetPrediction: a web server for target prediction of bioactive small molecules

Bioactive small molecules, such as drugs or metabolites, bind to proteins or other macro-molecular targets to modulate their activity, which in turn results in the observed phenotypic effects. For this reason, mapping the targets of bioactive small molecules is a key step toward unraveling the molecular mechanisms underlying their bioactivity and predicting potential side effects or cross-reactivity. Recently, large datasets of protein-small molecule interactions have become available, providing a unique source of information for the development of knowledge-based approaches to computationally identify new targets for uncharacterized molecules or secondary targets for known molecules. Here, we introduce SwissTargetPrediction, a web server to accurately predict the targets of bioactive molecules based on a combination of 2D and 3D similarity measures with known ligands. Predictions can be carried out in five different organisms, and mapping predictions by homology within and between different species is enabled for close paralogs and orthologs. SwissTargetPrediction is accessible free of charge and without login requirement at http://www.swisstargetprediction.c

Protein pocket and ligand shape comparison and its application in virtual screening

Understanding molecular recognition is one major requirement for drug discovery and design. Physicochemical and shape complementarity between two binding partners is the driving force during complex formation. In this study, the impact of shape within this process is analyzed. Protein binding pockets and co-crystallized ligands are represented by normalized principal moments of inertia ratios (NPRs). The corresponding descriptor space is triangular, with its corners occupied by spherical, discoid, and elongated shapes. An analysis of a selected set of sc-PDB complexes suggests that pockets and bound ligands avoid spherical shapes, which are, however, prevalent in small unoccupied pockets. Furthermore, a direct shape comparison confirms previous studies that on average only one third of a pocket is filled by its bound ligand, supplemented by a 50 % subpocket coverage. In this study, we found that shape complementary is expressed by low pairwise shape distances in NPR space, short distances between the centers-of-mass, and small deviations in the angle between the first principal ellipsoid axes. Furthermore, it is assessed how different binding pocket parameters are related to bioactivity and binding efficiency of the co-crystallized ligand. In addition, the performance of different shape and size parameters of pockets and ligands is evaluated in a virtual screening scenario performed on four representative target

Proteolysis of HCF-1 by Ser/Thr glycosylation-incompetent O-GlcNAc transferase:UDP-GlcNAc complexes

In complex with the cosubstrate UDP-N-acetylglucosamine (UDP-GlcNAc),O-linked-GlcNAc transferase (OGT) catalyzes Ser/ThrO-GlcNAcylation of many cellular proteins and proteolysis of the transcriptional coregulator HCF-1. Such a dual glycosyltransferase-protease activity, which occurs in the same active site, is unprecedented and integrates both reversible and irreversible forms of protein post-translational modification within one enzyme. Although occurring within the same active site, we show here that glycosylation and proteolysis occur through separable mechanisms. OGT consists of tetratricopeptide repeat (TPR) and catalytic domains, which, together with UDP-GlcNAc, are required for both glycosylation and proteolysis. Nevertheless, a specific TPR domain contact with the HCF-1 substrate is critical for proteolysis but not Ser/Thr glycosylation. In contrast, key catalytic domain residues and even a UDP-GlcNAc oxygen important for Ser/Thr glycosylation are irrelevant for proteolysis. Thus, from a dual glycosyltransferase-protease, essentially single-activity enzymes can be engineered both in vitro and in vivo. Curiously, whereas OGT-mediated HCF-1 proteolysis is limited to vertebrate species, invertebrate OGTs can cleave human HCF-1. We present a model for the evolution of HCF-1 proteolysis by OGT

Structure-based prediction of BRAF mutation classes using machine-learning approaches.

The BRAF kinase is attracting a lot of attention in oncology as alterations of its amino acid sequence can constitutively activate the MAP kinase signaling pathway, potentially contributing to the malignant transformation of the cell but at the same time rendering it sensitive to targeted therapy. Several pathologic BRAF variants were grouped in three different classes (I, II and III) based on their effects on the protein activity and pathway. Discerning the class of a BRAF mutation permits to adapt the treatment proposed to the patient. However, this information is lacking new and experimentally uncharacterized BRAF mutations detected in a patient biopsy. To overcome this issue, we developed a new in silico tool based on machine learning approaches to predict the potential class of a BRAF missense variant. As class I only involves missense mutations of Val600, we focused on the mutations of classes II and III, which are more diverse and challenging to predict. Using a logistic regression model and features including structural information, we were able to predict the classes of known mutations with an accuracy of 90%. This new and fast predictive tool will help oncologists to tackle potential pathogenic BRAF mutations and to propose the most appropriate treatment for their patients