AnAnaS: Software for Analytical Analysis of Symmetries in Protein Structures.

Symmetry is very common among proteins found in structural databases such as the Protein Data Bank (PDB). We present novel software, called AnAnaS, that finds positions and orientations of the symmetry axes in all types of symmetrical protein assemblies. It deals with five symmetry groups: cyclic, dihedral, tetrahedral, octahedral, and icosahedral. The software also assesses the quality of symmetry and can detect symmetries in incomplete cyclic assemblies. Internally, AnAnaS comprises discrete and continuous optimization steps and is applicable to assemblies with multiple chains in the asymmetric subunits or to those with pseudosymmetry. The method is very fast as most of the steps are performed analytically.

Modelling time variations of root diameter and elongation rate as related to assimilate supply and demand.

In a given root system, individual roots usually exhibit a rather homogeneous tip structure although highly different diameters and growth patterns, and this diversity is of prime importance in the definition of the whole root system architecture and foraging characteristics. In order to represent and predict this diversity, we built a simple and generic model at root tip level combining structural and functional knowledge on root elongation. The tip diameter, reflecting meristem size, is used as a driving variable of elongation. It varies, in response to the fluctuations of photo-assimilate availability, between two limits (minimal and maximal diameter). The elongation rate is assumed to be dependent on the transient value of the diameter. Elongation stops when the tip reaches the minimal diameter. The model could satisfactorily reproduce patterns of root elongation and tip diameter changes observed in various species at different scales. Although continuous, the model could generate divergent root classes as classically observed within populations of lateral roots. This model should help interpret the large plasticity of root elongation patterns which can be obtained in response to different combinations of endogenous and exogenous factors. The parameters could be used in phenotyping the root system.

Assessment of chemical-crosslink-assisted protein structure modeling in CASP13.

With the advance of experimental procedures obtaining chemical crosslinking information is becoming a fast and routine practice. Information on crosslinks can greatly enhance the accuracy of protein structure modeling. Here, we review the current state of the art in modeling protein structures with the assistance of experimentally determined chemical crosslinks within the framework of the 13th meeting of Critical Assessment of Structure Prediction approaches. This largest-to-date blind assessment reveals benefits of using data assistance in difficult to model protein structure prediction cases. However, in a broader context, it also suggests that with the unprecedented advance in accuracy to predict contacts in recent years, experimental crosslinks will be useful only if their specificity and accuracy further improved and they are better integrated into computational workflows.

Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment.

We present the results for CAPRI Round 46, the third joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo-oligomers and 6 heterocomplexes. Eight of the homo-oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher-order assemblies. These were more difficult to model, as their prediction mainly involved "ab-initio" docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance "gap" was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template-based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.

DeepSymmetry: using 3D convolutional networks for identification of tandem repeats and internal symmetries in protein structures.

MOTIVATION: Thanks to the recent advances in structural biology, nowadays 3D structures of various proteins are solved on a routine basis. A large portion of these structures contain structural repetitions or internal symmetries. To understand the evolution mechanisms of these proteins and how structural repetitions affect the protein function, we need to be able to detect such proteins very robustly. As deep learning is particularly suited to deal with spatially organized data, we applied it to the detection of proteins with structural repetitions. RESULTS: We present DeepSymmetry, a versatile method based on 3D convolutional networks that detects structural repetitions in proteins and their density maps. Our method is designed to identify tandem repeat proteins, proteins with internal symmetries, symmetries in the raw density maps, their symmetry order and also the corresponding symmetry axes. Detection of symmetry axes is based on learning 6D Veronese mappings of 3D vectors, and the median angular error of axis determination is less than one degree. We demonstrate the capabilities of our method on benchmarks with tandem-repeated proteins and also with symmetrical assemblies. For example, we have discovered about 7800 putative tandem repeat proteins in the PDB. AVAILABILITY AND IMPLEMENTATION: The method is available at https://team.inria.fr/nano-d/software/deepsymmetry. It consists of a C++ executable that transforms molecular structures into volumetric density maps, and a Python code based on the TensorFlow framework for applying the DeepSymmetry model to these maps. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Protein model quality assessment using 3D oriented convolutional neural networks.

MOTIVATION: Protein model quality assessment (QA) is a crucial and yet open problem in structural bioinformatics. The current best methods for single-model QA typically combine results from different approaches, each based on different input features constructed by experts in the field. Then, the prediction model is trained using a machine-learning algorithm. Recently, with the development of convolutional neural networks (CNN), the training paradigm has changed. In computer vision, the expert-developed features have been significantly overpassed by automatically trained convolutional filters. This motivated us to apply a three-dimensional (3D) CNN to the problem of protein model QA. RESULTS: We developed Ornate (Oriented Routed Neural network with Automatic Typing)-a novel method for single-model QA. Ornate is a residue-wise scoring function that takes as input 3D density maps. It predicts the local (residue-wise) and the global model quality through a deep 3D CNN. Specifically, Ornate aligns the input density map, corresponding to each residue and its neighborhood, with the backbone topology of this residue. This circumvents the problem of ambiguous orientations of the initial models. Also, Ornate includes automatic identification of atom types and dynamic routing of the data in the network. Established benchmarks (CASP 11 and CASP 12) demonstrate the state-of-the-art performance of our approach among single-model QA methods. AVAILABILITY AND IMPLEMENTATION: The method is available at https://team.inria.fr/nano-d/software/Ornate/. It consists of a C++ executable that transforms molecular structures into volumetric density maps, and a Python code based on the TensorFlow framework for applying the Ornate model to these maps. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Smooth orientation-dependent scoring function for coarse-grained protein quality assessment.

MOTIVATION: Protein quality assessment (QA) is a crucial element of protein structure prediction, a fundamental and yet open problem in structural bioinformatics. QA aims at ranking predicted protein models to select the best candidates. The assessment can be performed based either on a single model or on a consensus derived from an ensemble of models. The latter strategy can yield very high performance but substantially depends on the pool of available candidate models, which limits its applicability. Hence, single-model QA methods remain an important research target, also because they can assist the sampling of candidate models. RESULTS: We present a novel single-model QA method called SBROD. The SBROD (Smooth Backbone-Reliant Orientation-Dependent) method uses only the backbone protein conformation, and hence it can be applied to scoring coarse-grained protein models. The proposed method deduces its scoring function from a training set of protein models. The SBROD scoring function is composed of four terms related to different structural features: residue-residue orientations, contacts between backbone atoms, hydrogen bonding and solvent-solute interactions. It is smooth with respect to atomic coordinates and thus is potentially applicable to continuous gradient-based optimization of protein conformations. Furthermore, it can also be used for coarse-grained protein modeling and computational protein design. SBROD proved to achieve similar performance to state-of-the-art single-model QA methods on diverse datasets (CASP11, CASP12 and MOULDER). AVAILABILITY AND IMPLEMENTATION: The standalone application implemented in C++ and Python is freely available at https://gitlab.inria.fr/grudinin/sbrod and supported on Linux, MacOS and Windows. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Analytical symmetry detection in protein assemblies. II. Dihedral and cubic symmetries.

Protein assemblies are often symmetric, as this organization has many advantages compared to individual proteins. Complex protein structures thus very often possess high-order symmetries. Detection and analysis of these symmetries has been a challenging problem and no efficient algorithms have been developed so far. This paper presents the extension of our cyclic symmetry detection method for higher-order symmetries with multiple symmetry axes. These include dihedral and cubic, i.e., tetrahedral, octahedral, and icosahedral, groups. Our method assesses the quality of a particular symmetry group and also determines all of its symmetry axes with a machine precision. The method comprises discrete and continuous optimization steps and is applicable to assemblies with multiple chains in the asymmetric subunits or to those with pseudo-symmetry. We implemented the method in C++ and exhaustively tested it on all 51,358 symmetric assemblies from the Protein Data Bank (PDB). It allowed us to study structural organization of symmetric assemblies solved by X-ray crystallography, and also to assess the symmetry annotation in the PDB. For example, in 1.6% of the cases we detected a higher symmetry group compared to the PDB annotation, and we also detected several cases with incorrect annotation. The method is available at http://team.inria.fr/nano-d/software/ananas. The graphical user interface of the method built for the SAMSON platform is available at http://samson-connect.net.

Analytical symmetry detection in protein assemblies. I. Cyclic symmetries.

Symmetry in protein, and, more generally, in macromolecular assemblies is a key point to understand their structure, stability and function. Many symmetrical assemblies are currently present in the Protein Data Bank (PDB) and some of them are among the largest solved structures, thus an efficient computational method is needed for the exhaustive analysis of these. The cyclic symmetry groups represent the most common assemblies in the PDB. These are also the building blocks for higher-order symmetries. This paper presents a mathematical formulation to find the position and the orientation of the symmetry axis in a cyclic symmetrical protein assembly, and also to assess the quality of this symmetry. Our method can also detect symmetries in partial assemblies. We provide an efficient C++ implementation of the method and demonstrate its efficiency on several examples including partial assemblies and pseudo symmetries. We also compare the method with two other published techniques and show that it is significantly faster on all the tested examples. Our method produces results with a machine precision, its cost function is solely based on 3D Euclidean geometry, and most of the operations are performed analytically. The method is available athttp://team.inria.fr/nano-d/software/ananas. The graphical user interface of the method built for the SAMSON platform is available athttp://samson-connect.net.

The impact of geophagy on ranging behaviour in Phayre's leaf monkeys (Trachypithecus phayrei).

Resource use is known to influence the ranging behaviour of primates, and this effect may be pronounced when the resource is uncommon in the environment. Here we investigate the effect of geophagy on the ranging behaviour of Phayre's leaf monkeys (Trachypithecus phayrei) in the Phu Khieo Wildlife Sanctuary, Thailand. UTM coordinates were collected via GPS from the group centre every 30 min during all-day follows of one study group over a 4-month period. On days during which the group visited a distant saltlick (n = 3), the monkeys travelled significantly further (p < 0.001), significantly faster (p < 0.001) and with fewer stops compared to non-saltlick days (n = 61). Saltlick visits also increased the home range size from 73.7 ha to 93.4 ha. Increased daily path length, increased travel speed and consistent pace are necessary because of the distance from the saltlick to the regularly used portion of the home range, and these reflect the import- ance of soil consumption to the leaf monkeys.