The prediction of protein structures is an important problem in molecular biology.In spite of the large efforts from the research community, and of the recent development of artificial intelligence tools specifically designed for this problem, a complete and definitive solution to the problem has not been found yet.This work is based on the observation that many tools for the prediction of protein conformations rely on both local and non-local geometrical information, even though the non-local information can be very hard to identify within the desired precision in some particular situations.For this reason, we explore in this work the effect of local geometry on methods capable of constructing protein conformations.This initial study has the final aim of devising new alternative methods where the predictions may be guided mainly by the local geometry of proteins.

The Distance Geometry Problem (DGP) consists of finding the coordinates of a given set of points where the distances between some pairs of points are known. The DGP has several applications and one of the most relevant ones arises in the context of structural biology, where NMR experiments are performed to estimate distances between some atom pairs in a given molecule, and the possible conformations for the molecule are calculated through the formulation and the solution of a DGP. We focus our attention on DGP instances for which some special assumptions allow us to discretize the DGP search space and to potentially perform the complete enumeration of the solution set. We refer to the subclass of DGP instances satisfying such discretizability assumptions as the Discretizable DGP (DDGP). In this context, we propose a new procedure for the generation of DDGP instances where real data and simulated data (from known molecular models) can coexist. Our procedure can give rise to peculiar DDGP instances that we use for studying the impact of every distance type, involved in NMR protein structure determination, on the quality of the found solutions. Surprisingly, our experiments suggest that the distance types implying a larger effect on the solution quality are not the ones related to NMR data, but rather the more abundant, but much less informative, van der Waals distance type.

Nuclear Magnetic Resonance (NMR) experiments provide distances between nearby atoms of a protein molecule. The corresponding structure determination problem is to determine the 3D protein structure by exploiting such distances. We present a new order on the atoms of the protein, based on information from the chemistry of proteins and NMR experiments, which allows us to formulate the problem as a combinatorial search. Additionally, this order tells us what kind of NMR distance information is crucial to understand the cardinality of the solution set of the problem and its computational complexity.

MOTIVATION: Recent large-scale omics initiatives have catalogued the somatic alterations of cancer cell line panels along with their pharmacological response to hundreds of compounds. In this study, we have explored these data to advance computational approaches that enable more effective and targeted use of current and future anticancer therapeutics. RESULTS: We modelled the 50% growth inhibition bioassay end-point (GI50) of 17,142 compounds screened against 59 cancer cell lines from the NCI60 panel (941,831 data-points, matrix 93.08% complete) by integrating the chemical and biological (cell line) information. We determine that the protein, gene transcript and miRNA abundance provide the highest predictive signal when modelling the GI50 endpoint, which significantly outperformed the DNA copy-number variation or exome sequencing data (Tukey's Honestly Significant Difference, P <0.05). We demonstrate that, within the limits of the data, our approach exhibits the ability to both interpolate and extrapolate compound bioactivities to new cell lines and tissues and, although to a lesser extent, to dissimilar compounds. Moreover, our approach outperforms previous models generated on the GDSC dataset. Finally, we determine that in the cases investigated in more detail, the predicted drug-pathway associations and growth inhibition patterns are mostly consistent with the experimental data, which also suggests the possibility of identifying genomic markers of drug sensitivity for novel compounds on novel cell lines. CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The determination of protein structures satisfying distance constraints is an important problem in structural biology. Whereas the most common method currently employed is simulated annealing, there have been other methods previously proposed in the literature. Most of them, however, are designed to find one solution only. RESULTS: In order to explore exhaustively the feasible conformational space, we propose here an interval Branch-and-Prune algorithm (iBP) to solve the Distance Geometry Problem (DGP) associated to protein structure determination. This algorithm is based on a discretization of the problem obtained by recursively constructing a search space having the structure of a tree, and by verifying whether the generated atomic positions are feasible or not by making use of pruning devices. The pruning devices used here are directly related to features of protein conformations. CONCLUSIONS: We described the new algorithm iBP to generate protein conformations satisfying distance constraints, that would potentially allows a systematic exploration of the conformational space. The algorithm iBP has been applied on three α-helical peptides.

Proteochemometrics (PCM) is a predictive bioactivity modelling method which simultaneously models the bioactivity of multiple ligands against multiple targets. PCM permits exploration of the selectivity and promiscuity of ligands on biomolecular systems of different complexity. This includes proteins and even cell-line models [ 1 , 2 ]. The suitability of PCM to predict compound polypharmacology has been validated both retrospectively and in prospective experimental validation [ 1 , 2 ]. In practice, each ligand-target interaction is encoded by the concatenation of ligand and target descriptor vectors used to train a single machine learning model. The inclusion of both chemical and target information enables the extra- and interpolation on the chemical and on the biological space. Therefore, PCM permits to predict compound bioactivities on targets not present in the training phase [ 3 ]. In this contribution, we show a methodological advancement in the field [ 4 ], namely how Bayesian inference (Gaussian Processes) can be successfully applied in the context of PCM for (i) the prediction of compound bioactivity along with the error estimation of the prediction; (ii) the determination of the applicability domain of a PCM model; and (iii) the inclusion of experimental uncertainty of bioactivity measurements. We illustrate how the application of PCM can be useful in medicinal chemistry to concomitantly optimize compounds selectivity and potency, in the context of two application scenarios: (a) modelling isoform-selective cyclooxygenase inhibition; and (b) large-scale cancer cell line drug sensitivity prediction, where we benchmark the predictive signal of basal gene expression, gene copy-number variation, exome sequencing, and protein abundance data. We present the R package C hemically A ware M odel B uilder ( camb ) [ 5 ], which is able to perform the above mentioned modelling tasks. camb is an open source platform for the generation of Structure-Activity and Structure-Property models. The functionalities of camb include: (i) standardisation of chemical structure representation, (ii) calculation of 905 one-dimensional descriptors and 14 fingerprints for small molecules, (iii) 8 types of amino acid descriptors, (iv) 13 whole protein sequence descriptors, and (iv) training, validation and visualization of predictive models. Overall, the application of PCM in these two case scenarios let us conclude that PCM is a suitable technique, on this data, to model the activity of ligands exhibiting diverse bioactivity profiles across a panel of targets, which can range from protein binding sites (a), to cancer cell-lines (b). The camb package constitutes a platform encompassing all steps for the generation of predictive models from chemical structures and their associated bioactivities/properties, which will provide reproducibility and simplify the generation of predictive bioactivity/property models.

BACKGROUND: Identifying druggable cavities on a protein surface is a crucial step in structure based drug design. The cavities have to present suitable size and shape, as well as appropriate chemical complementarity with ligands. RESULTS: We present a novel cavity prediction method that analyzes results of virtual screening of specific ligands or fragment libraries by means of Self-Organizing Maps. We demonstrate the method with two thoroughly studied proteins where it successfully identified their active sites (AS) and relevant secondary binding sites (BS). Moreover, known active ligands mapped the AS better than inactive ones. Interestingly, docking a naive fragment library brought even more insight. We then systematically applied the method to the 102 targets from the DUD-E database, where it showed a 90% identification rate of the AS among the first three consensual clusters of the SOM, and in 82% of the cases as the first one. Further analysis by chemical decomposition of the fragments improved BS prediction. Chemical substructures that are representative of the active ligands preferentially mapped in the AS. CONCLUSION: The new approach provides valuable information both on relevant BSs and on chemical features promoting bioactivity.

MOTIVATION: Methods for automatic nuclear magnetic resonance (NMR) structure determination need to face a high level of ambiguity encountered in NMR spectra recorded by solid-state NMR and by solution NMR of partially unfolded proteins, leading to time-consuming calculations. The software package Ambiguous Restraints for Iterative Assignment (ARIA) allows for straightforward parallelization of the calculation, as the conformers can be generated in parallel on many nodes. RESULTS: Due to its architecture, the adaptation of ARIA to grid computing can be easily achieved by using the middleware glite and JDL (Job Description Language) scripts. This adaptation makes it possible to address highly ambiguous datasets, because of the much larger conformational sampling that can be generated by use of the grid computational power. AVAILABILITY: The version 2.3.1 of ARIA implemented on the grid is freely available from the ARIA web site: aria.pasteur.fr/downloads.

The function of bio-macromolecules is determined by both their 3D structure and conformational dynamics. These molecules are inherently flexible systems displaying a broad range of dynamics on time-scales from picoseconds to seconds. Nuclear Magnetic Resonance (NMR) spectroscopy has emerged as the method of choice for studying both protein structure and dynamics in solution. Typically, NMR experiments are sensitive both to structural features and to dynamics, and hence the measured data contain information on both. Despite major progress in both experimental approaches and computational methods, obtaining a consistent view of structure and dynamics from experimental NMR data remains a challenge. Molecular dynamics simulations have emerged as an indispensable tool in the analysis of NMR data.

UNLABELLED: Modern structural genomics projects demand for integrated methods for the interpretation and storage of nuclear magnetic resonance (NMR) data. Here we present version 2.1 of our program ARIA (Ambiguous Restraints for Iterative Assignment) for automated assignment of nuclear Overhauser enhancement (NOE) data and NMR structure calculation. We report on recent developments, most notably a graphical user interface, and the incorporation of the object-oriented data model of the Collaborative Computing Project for NMR (CCPN). The CCPN data model defines a storage model for NMR data, which greatly facilitates the transfer of data between different NMR software packages. AVAILABILITY: A distribution with the source code of ARIA 2.1 is freely available at http://www.pasteur.fr/recherche/unites/Binfs/aria2.

UNLABELLED: The development of NMR in structural proteomics requires the availability of automatic structure determination methods. Many researchers are commonly confronted with the lack of raw datasets during the validation step of such methods. In order to increase test possibilities, the NMRb web-site offers a database of NMR raw datasets, ordered by spectral characteristics. AVAILABILITY: NMRb is available from: http://nmrb.cbs.cnrs.fr. SUPPLEMENTARY INFORMATION: General organization of NMRb figure, relational model organization, and XML structure files are available from http://nmrb.cbs.cnrs.fr/nmrb-doc.html.

MOTIVATION: Peptide and protein structures are determined daily using NMR spectroscopy. Assignment of the NMR spectra is an important step within the procedure and is usually the limiting one. Computer-aided assignment tools should be user friendly with open architecture to communicate with other programs involved in the structure determination. RESULTS: Here we present an interactive NMR assignment module which provides numerous graphic tools for the user. The module is composed of a database management system-handling peaks, spins and spin-systems. The assignment information is maintained as a set of interrelated associative arrays, which serve as generic high-level data structures. The module is developed in the macro language embedded in the Gifa NMR processing program (Pons et al. , J. Biomol. NMR, 8 , 445-452, 1996). This provides the user with a consistent interface, a set of sophisticated tools, and an easily extendible and customizable environment. AVAILABILITY: The program is available on request from the authors. The Gifa package can be accessed at: ((http://www.cbs. univ-montp1.fr/GIFA)) CONTACT: [email protected]