New simulation insights on the structural transition mechanism of bovine rhodopsin activation.

Inactive rhodopsin can absorb photons, which induces different structural transitions that finally activate rhodopsin. We have examined the change in spatial configurations and physicochemical factors that result during the transition mechanism from the inactive to the active rhodopsin state via intermediates. During the activation process, many existing atomic contacts are disrupted, and new ones are formed. This is related to the movement of Helix 5, which tilts away from Helix 3 in the intermediate state in lumirhodopsin and moves closer to Helix 3 again in the active state. Similar patterns of changing atomic contacts are observed between Helices 3 and 5 of the adenosine and neurotensin receptors. In addition, residues 220-238 of rhodopsin, which are disordered in the inactive state, fold in the active state before binding to the Gα, where it catalyzes GDP/GTP exchange on the Gα subunit. Finally, molecular dynamics simulations in the membrane environment revealed that the arrestin binding region adopts a more flexible extended conformation upon phosphorylation, likely promoting arrestin binding and inactivation. In summary, our results provide additional structural understanding of specific rhodopsin activation which might be relevant to other Class A G protein-coupled receptor proteins.

Exploring the limitations of biophysical propensity scales coupled with machine learning for protein sequence analysis.

Machine learning (ML) is ubiquitous in bioinformatics, due to its versatility. One of the most crucial aspects to consider while training a ML model is to carefully select the optimal feature encoding for the problem at hand. Biophysical propensity scales are widely adopted in structural bioinformatics because they describe amino acids properties that are intuitively relevant for many structural and functional aspects of proteins, and are thus commonly used as input features for ML methods. In this paper we reproduce three classical structural bioinformatics prediction tasks to investigate the main assumptions about the use of propensity scales as input features for ML methods. We investigate their usefulness with different randomization experiments and we show that their effectiveness varies among the ML methods used and the tasks. We show that while linear methods are more dependent on the feature encoding, the specific biophysical meaning of the features is less relevant for non-linear methods. Moreover, we show that even among linear ML methods, the simpler one-hot encoding can surprisingly outperform the "biologically meaningful" scales. We also show that feature selection performed with non-linear ML methods may not be able to distinguish between randomized and "real" propensity scales by properly prioritizing to the latter. Finally, we show that learning problem-specific embeddings could be a simple, assumptions-free and optimal way to perform feature learning/engineering for structural bioinformatics tasks.

Investigating the Molecular Mechanisms Behind Uncharacterized Cysteine Losses from Prediction of Their Oxidation State.

Cysteines are among the rarest amino acids in nature, and are both functionally and structurally very important for proteins. The ability of cysteines to form disulfide bonds is especially relevant, both for constraining the folded state of the protein and for performing enzymatic duties. But how does the variation record of human proteins reflect their functional importance and structural role, especially with regard to deleterious mutations? We created HUMCYS, a manually curated dataset of single amino acid variants that (1) have a known disease/neutral phenotypic outcome and (2) cause the loss of a cysteine, in order to investigate how mutated cysteines relate to structural aspects such as surface accessibility and cysteine oxidation state. We also have developed a sequence-based in silico cysteine oxidation predictor to overcome the scarcity of experimentally derived oxidation annotations, and applied it to extend our analysis to classes of proteins for which the experimental determination of their structure is technically challenging, such as transmembrane proteins. Our investigation shows that we can gain insights into the reason behind the outcome of cysteine losses in otherwise uncharacterized proteins, and we discuss the possible molecular mechanisms leading to deleterious phenotypes, such as the involvement of the mutated cysteine in a structurally or enzymatically relevant disulfide bond.

An Evolutionary View on Disulfide Bond Connectivities Prediction Using Phylogenetic Trees and a Simple Cysteine Mutation Model.

Disulfide bonds are crucial for many structural and functional aspects of proteins. They have a stabilizing role during folding, can regulate enzymatic activity and can trigger allosteric changes in the protein structure. Moreover, knowledge of the topology of the disulfide connectivity can be relevant in genomic annotation tasks and can provide long range constraints for ab-initio protein structure predictors. In this paper we describe PhyloCys, a novel unsupervised predictor of disulfide bond connectivity from known cysteine oxidation states. For each query protein, PhyloCys retrieves and aligns homologs with HHblits and builds a phylogenetic tree using ClustalW. A simplified model of cysteine co-evolution is then applied to the tree in order to hypothesize the presence of oxidized cysteines in the inner nodes of the tree, which represent ancestral protein sequences. The tree is then traversed from the leaves to the root and the putative disulfide connectivity is inferred by observing repeated patterns of tandem mutations between a sequence and its ancestors. A final correction is applied using the Edmonds-Gabow maximum weight perfect matching algorithm. The evolutionary approach applied in PhyloCys results in disulfide bond predictions equivalent to Sephiroth, another approach that takes whole sequence information into account, and is 26-29% better than state of the art methods based on cysteine covariance patterns in multiple sequence alignments, while requiring one order of magnitude fewer homologous sequences (10(3) instead of 10(4)), thus extending its range of applicability. The software described in this article and the datasets used are available at http://ibsquare.be/phylocys.

Clustering-based model of cysteine co-evolution improves disulfide bond connectivity prediction and reduces homologous sequence requirements.

MOTIVATION: Cysteine residues have particular structural and functional relevance in proteins because of their ability to form covalent disulfide bonds. Bioinformatics tools that can accurately predict cysteine bonding states are already available, whereas it remains challenging to infer the disulfide connectivity pattern of unknown protein sequences. Improving accuracy in this area is highly relevant for the structural and functional annotation of proteins. RESULTS: We predict the intra-chain disulfide bond connectivity patterns starting from known cysteine bonding states with an evolutionary-based unsupervised approach called Sephiroth that relies on high-quality alignments obtained with HHblits and is based on a coarse-grained cluster-based modelization of tandem cysteine mutations within a protein family. We compared our method with state-of-the-art unsupervised predictors and achieve a performance improvement of 25-27% while requiring an order of magnitude less of aligned homologous sequences (∼10(3) instead of ∼10(4)). AVAILABILITY AND IMPLEMENTATION: The software described in this article and the datasets used are available at http://ibsquare.be/sephiroth. CONTACT: wvranken@vub.ac.be SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

From protein sequence to dynamics and disorder with DynaMine.

Protein function and dynamics are closely related; however, accurate dynamics information is difficult to obtain. Here based on a carefully assembled data set derived from experimental data for proteins in solution, we quantify backbone dynamics properties on the amino-acid level and develop DynaMine--a fast, high-quality predictor of protein backbone dynamics. DynaMine uses only protein sequence information as input and shows great potential in distinguishing regions of different structural organization, such as folded domains, disordered linkers, molten globules and pre-structured binding motifs of different sizes. It also identifies disordered regions within proteins with an accuracy comparable to the most sophisticated existing predictors, without depending on prior disorder knowledge or three-dimensional structural information. DynaMine provides molecular biologists with an important new method that grasps the dynamical characteristics of any protein of interest, as we show here for human p53 and E1A from human adenovirus 5.

Determination of secondary structure populations in disordered states of proteins using nuclear magnetic resonance chemical shifts.

One of the major open challenges in structural biology is to achieve effective descriptions of disordered states of proteins. This problem is difficult because these states are conformationally highly heterogeneous and cannot be represented as single structures, and therefore it is necessary to characterize their conformational properties in terms of probability distributions. Here we show that it is possible to obtain highly quantitative information about particularly important types of probability distributions, the populations of secondary structure elements (α-helix, ß-strand, random coil, and polyproline II), by using the information provided by backbone chemical shifts. The application of this approach to mammalian prions indicates that for these proteins a key role in molecular recognition is played by disordered regions characterized by highly conserved polyproline II populations. We also determine the secondary structure populations of a range of other disordered proteins that are medically relevant, including p53, α-synuclein, and the Aß peptide, as well as an oligomeric form of αB-crystallin. Because chemical shifts are the nuclear magnetic resonance parameters that can be measured under the widest variety of conditions, our approach can be used to obtain detailed information about secondary structure populations for a vast range of different protein states.

Pescador: the PEptides in Solution ConformAtion Database: Online Resource.

In recent years a large body of data has been obtained from Nuclear Magnetic Resonance and Circular Dichroism experiments on the influence of the amino acid sequence and various other parameters on the conformational state of peptides in solution. Interpreting the experimental data in terms of the conformational populations of the peptides remains a key problem, for which current solutions leave appreciable room for improvement. Considering that making this body of data available for surveys and analysis should be instrumental in tackling the problem, we undertook the development of Pescador: The 'PEptides in Solution ConformAtion Database: Online Resource'. Pescador contains data from NMR and CD spectroscopy on peptides in solution as well as information on the structural parameters derived from these data. It also features specialized Web-based tools for data deposition, and means for readily accessing the stored information for analysis purposes. To illustrate the use of the database in deriving information for the conformational analysis of peptides, we show how the alpha proton delta-values stored in Pescador and measured by NMR for different peptides in different laboratories can be used to derive a new set of 'random coil' chemical shift values. Firstly, we show these values to be very similar to those obtained experimentally for model peptides in water, and their variation with increasing Tri-Fluoro-Ethanol (TFE) concentration is similar to that reported for model peptides. We show, furthermore, that the chemical shift data in Pescador can be used to derive correction factors that take into account effects of neighboring residues. These correction factors compare favorably with those recently derived from a series of model GGXGG peptides (Schwarzinger et al., 2001). These encouraging results suggest that, as the quantity of NMR data on peptide deposited in Pescador increases, surveys of these data should be a valuable means of deriving key parameters for the analysis of peptide conformation.